textflint.generation_layer.generator.generator

Generator base Class

class textflint.generation_layer.generator.generator.Generator(task='UT', max_trans=1, random_seed=1, fields='x', trans_methods=None, trans_config=None, return_unk=True, sub_methods=None, sub_config=None, attack_methods=None, validate_methods=None, **kwargs)

Bases: abc.ABC

Transformation controller which applies multi transformations to each data sample.

__init__(task='UT', max_trans=1, random_seed=1, fields='x', trans_methods=None, trans_config=None, return_unk=True, sub_methods=None, sub_config=None, attack_methods=None, validate_methods=None, **kwargs)

Parameters

• task (str) – Indicate which task of your transformation data.

• max_trans (int) – Maximum transformed samples generate by one original sample pre Transformation.

• random_seed (int) – random number seed to reproduce generation.

• fields (str|list) – Indicate which fields to apply transformations. Multi fields transform just for some special task, like: SM、NLI.

• trans_methods (list) – list of transformations’ name.

• trans_config (dict) – transformation class configs, useful to control the behavior of transformations.

• return_unk (bool) – Some transformation may generate unk labels, s.t. insert a word to a sequence in NER task. If set False, would skip these transformations.

• sub_methods (list) – list of subpopulations’ name.

• sub_config (dict) – subpopulation class configs, useful to control the behavior of subpopulation.

• attack_methods (str) – path to the python file containing the Attack instances.

• validate_methods (list) – confidence calculate functions.

prepare(dataset)

Check dataset

Parameters

dataset (textflint.Dataset) – the input dataset

generate(dataset, model=None)

Returns a list of possible generated samples for dataset.

Parameters

• dataset (textflint.Dataset) – the input dataset

• model (textflint.FlintModel) – the model to attack if given.

Returns

yield (original samples, new samples, generated function string).

generate_by_transformations(dataset, **kwargs)

Generate samples by a list of transformation methods.

Parameters

dataset – the input dataset

Returns

(original samples, new samples, generated function string)

generate_by_subpopulations(dataset, **kwargs)

Generate samples by a list of subpopulation methods.

Parameters

dataset – the input dataset

Returns

the transformed dataset

generate_by_attacks(dataset, model=None, **kwargs)

Generate samples by a list of attack methods.

Parameters

• dataset – the input dataset

• model – the model to attack if given.

Returns

the transformed dataset

class textflint.generation_layer.generator.generator.ABC

Bases: object

Helper class that provides a standard way to create an ABC using inheritance.

class textflint.generation_layer.generator.generator.Dataset(task='UT')

Bases: object

Any iterable of (label, text_input) pairs qualifies as a Dataset.

__init__(task='UT')

Parameters

task (str) – indicate data sample format.

free()

Fully clear dataset.

dump()

Return dataset in json object format.

load(dataset)

Loads json object and prepares it as a Dataset.

Support two formats input, Example:

1. {‘x’: [

   ‘The robustness of deep neural networks has received much attention recently’, ‘We focus on certified robustness of smoothed classifiers in this work’, …, ‘our approach exceeds the state-of-the-art.’ ],

   ‘y’: [

   ‘neural’, ‘positive’, …, ‘positive’ ]}

   2. [

      {‘x’: ‘The robustness of deep neural networks has received much attention recently’, ‘y’: ‘neural’}, {‘x’: ‘We focus on certified robustness of smoothed classifiers in this work’, ‘y’: ‘positive’}, …, {‘x’: ‘our approach exceeds the state-of-the-art.’, ‘y’: ‘positive’} ]

Parameters

dataset (list|dict) –

Returns

load_json(json_path, encoding='utf-8', fields=None, dropna=True)

Loads json file, each line of the file is a json string.

Parameters

• json_path – file path

• encoding – file’s encoding, default: utf-8

• fields – json object’s fields that needed, if None, all fields are needed. default: None

• dropna – weather to ignore and drop invalid data, :if False, raise ValueError when reading invalid data. default: True

Returns

load_csv(csv_path, encoding='utf-8', headers=None, sep=',', dropna=True)

Loads csv file, one line correspond one sample.

Parameters

• csv_path – file path

• encoding – file’s encoding, default: utf-8

• headers – file’s headers, if None, make file’s first line as headers. default: None

• sep – separator for each column. default: ‘,’

• dropna – weather to ignore and drop invalid data, :if False, raise ValueError when reading invalid data. default: True

Returns

load_hugging_face(name, subset='train')

Loads a dataset from HuggingFace datasets and prepares it as a Dataset.

Parameters

• name – the dataset name

• subset – the subset of the main dataset.

Returns

append(data_sample, sample_id=- 1)

Load single data sample and append to dataset.

Parameters

• data_sample (dict|sample) –

• sample_id (int) – useful to identify sample, default -1

Returns

True / False indicate whether append action successful.

extend(data_samples)

Load multi data samples and extend to dataset.

Parameters

data_samples (list|dict|Sample) –

Returns

static norm_input(data_samples)

Convert various data input to list of dict. Example:

 {'x': [
          'The robustness of deep neural networks has received
          much attention recently',
          'We focus on certified robustness of smoothed classifiers
          in this work',
          ...,
          'our approach exceeds the state-of-the-art.'
      ],
 'y': [
          'neural',
          'positive',
          ...,
          'positive'
      ]
}
convert to
[
    {'x': 'The robustness of deep neural networks has received
    much attention recently', 'y': 'neural'},
    {'x': 'We focus on certified robustness of smoothed classifiers
    in this work', 'y': 'positive'},
    ...,
    {'x': 'our approach exceeds the state-of-the-art.',
    'y': 'positive'}
]

Parameters

data_samples (list|dict|Sample) –

Returns

Normalized data.

save_csv(out_path, encoding='utf-8', headers=None, sep=',')

Save dataset to csv file.

Parameters

• out_path – file path

• encoding – file’s encoding, default: utf-8

• headers – file’s headers, if None, make file’s first line as headers. default: None

• sep – separator for each column. default: ‘,’

Returns

save_json(out_path, encoding='utf-8', fields=None)

Save dataset to json file which contains json object in each line.

Parameters

• out_path – file path

• encoding – file’s encoding, default: utf-8

• fields – json object’s fields that needed, if None, all fields are needed. default: None

Returns

class textflint.generation_layer.generator.generator.EnProcessor(*args, **kwargs)

Bases: object

Text Processor class implement NER, POS tag, lexical tree parsing. EnProcessor is designed by single instance mode.

sentence_tokenize(text)

Split text to sentences.

Parameters

text (str) – text string

Returns

list[str]

tokenize_one_sent(text, split_by_space=False)

Tokenize one sentence.

Parameters

• text (str) –

• split_by_space (bool) – whether tokenize sentence by split space

Returns

tokens

tokenize(text, is_one_sent=False, split_by_space=False)

Split a text into tokens (words, morphemes we can separate such as “n’t”, and punctuation).

Parameters

• text (str) –

• is_one_sent (bool) –

• split_by_space (bool) –

Returns

list of tokens

static inverse_tokenize(tokens)

Convert tokens to sentence.

Untokenizing a text undoes the tokenizing operation, restoring punctuation and spaces to the places that people expect them to be. Ideally, untokenize(tokenize(text)) should be identical to text, except for line breaks.

Watch out! Default punctuation add to the word before its index, it may raise inconsistency bug.

Parameters

tokens (list[str]r) – target token list

Returns

str

get_pos(sentence)

POS tagging function.

Example:

EnProcessor().get_pos(
    'All things in their being are good for something.'
)

>> [('All', 'DT'),
    ('things', 'NNS'),
    ('in', 'IN'),
    ('their', 'PRP$'),
    ('being', 'VBG'),
    ('are', 'VBP'),
    ('good', 'JJ'),
    ('for', 'IN'),
    ('something', 'NN'),
    ('.', '.')]

Parameters

sentence (str|list) – A sentence which needs to be tokenized.

Returns

Tokenized tokens with their POS tags.

get_ner(sentence, return_char_idx=True)

NER function. This method uses implemented based on spacy model.

Example:

EnProcessor().get_ner(
    'Lionel Messi is a football player from Argentina.'
)

if return_word_index is False
>>[('Lionel Messi', 0, 12, 'PERSON'),
   ('Argentina', 39, 48, 'LOCATION')]

if return_word_index is True
>>[('Lionel Messi', 0, 2, 'PERSON'),
   ('Argentina', 7, 8, 'LOCATION')]

Parameters

• sentence (str|list) – text string or token list

• return_char_idx (bool) – if set True, return character start to end index, else return char start to end index.

Returns

A list of tuples, (entity, start, end, label)

get_parser(sentence)

Lexical tree parsing function based on NLTK toolkit.

Example:

EnProcessor().get_parser('Messi is a football player.')

>>'(ROOT\n  (S\n    (NP (NNP Messi))\n    (VP (VBZ is) (NP (DT a)
(NN football) (NN player)))\n    (. .)))'

Parameters

sentence (str|list) – A sentence needs to be parsed.

:return:The result tree of lexicalized parser in string format.

get_dep_parser(sentence, is_one_sent=True, split_by_space=False)

Dependency parsing based on spacy model.

Example:

EnProcessor().get_dep_parser(
'The quick brown fox jumps over the lazy dog.'
)

>>
    The     DT      4       det
    quick   JJ      4       amod
    brown   JJ      4       amod
    fox     NN      5       nsubj
    jumps   VBZ     0       root
    over    IN      9       case
    the     DT      9       det
    lazy    JJ      9       amod
    dog     NN      5       obl

Parameters

• sentence (str|list) – input text string

• is_one_sent (bool) – whether do sentence tokenzie

• split_by_space (bool) – whether tokenize sentence by split with ” “

Returns

dp tags.

get_lemmas(token_and_pos)

Lemmatize function. This method uses nltk.WordNetLemmatier to lemmatize tokens.

Parameters

token_and_pos (list) – (token, POS).

Returns

A lemma or a list of lemmas depends on your input.

get_all_lemmas(pos)

Lemmatize function for all words in WordNet.

Parameters

pos – POS tag pr a list of POS tag.

Returns

A list of lemmas that have the given pos tag.

get_delemmas(lemma_and_pos)

Delemmatize function.

This method uses a pre-processed dict which maps (lemma, pos) to original token for delemmatizing.

Parameters

lemma_and_pos (tuple|list) – A tuple or a list of (lemma, POS).

Returns

A word or a list of words, each word represents the specific form of input lemma.

get_synsets(tokens_and_pos, lang='eng')

Get synsets from WordNet.

Parameters

• tokens_and_pos (list) – A list of tuples, (token, POS).

• lang (str) – language name

Returns

A list of str, represents the sense of each input token.

get_antonyms(tokens_and_pos, lang='eng')

Get antonyms from WordNet.

This method uses NTLK WordNet to generate antonyms, and uses “lesk” algorithm which is proposed by Michael E. Lesk in 1986, to screen the sense out.

Parameters

• tokens_and_pos (list) – A list of tuples, (token, POS).

• lang (str) – language name.

Returns

A list of str, represents the sense of each input token.

filter_candidates_by_pos(token_and_pos, candidates)

Filter synonyms not contain the same pos tag with given token.

Parameters

• token_and_pos (list|tuple) – (token, pos)

• candidates (list) – strings to verify

Returns

filtered candidates list.

feature_extract(sent)

Generate linguistic tags for tokens.

Parameters

sent (str) – input sentence

Returns

list of dict

class textflint.generation_layer.generator.generator.Pipeline(transform_objs)

Bases: textflint.generation_layer.transformation.transformation.Transformation, list

Apply sequential transformations to input sample. Default generate transformed samples of combination number of contained transformations.

get_transformations()

Returns

List of transformation string.

class textflint.generation_layer.generator.generator.SubPopulation(intervals=None, **kwargs)

Bases: abc.ABC

An abstract class for extracting subset of examples.

text_processor = <textflint.common.preprocess.en_processor.EnProcessor object>

score(sample, field, **kwargs)

Score the sample

Parameters

• sample – data sample

• field (str|list) – field str

• kwargs –

Return int

score for sample

get_slice(scores, dataset)

Pick up samples based on scores

Parameters

• scores (list) – list of int

• dataset – Dataset

Returns

subset samples

slice_population(dataset, fields, **kwargs)

Extract a subset of samples.

Parameters

• dataset – Dataset

• fields (list) – field str list

• kwargs –

Returns

Subset Dataset

static normalize_bound(limit, size)

Normalize the bound of slice

Parameters

• limit (str|float|int) – left_bound or right_bound for intervals can be percentile like 10%, 20% can be float between 0 and 1 like 0.3 can be int index like 50

• size – the size of samples

:return int : bound

class textflint.generation_layer.generator.generator.Transformation(**kwargs)

Bases: abc.ABC

An abstract class for transforming a sequence of text to produce a list of potential adversarial example.

processor = <textflint.common.preprocess.en_processor.EnProcessor object>

transform(sample, n=1, field='x', **kwargs)

Transform data sample to a list of Sample.

Parameters

• sample (Sample) – Data sample for augmentation.

• n (int) – Max number of unique augmented output, default is 5.

• field (str|list) – Indicate which fields to apply transformations.

• **kwargs (dict) –

  other auxiliary params.

Returns

list of Sample

classmethod sample_num(x, num)

Get ‘num’ samples from x.

Parameters

• x (list) – list to sample

• num (int) – sample number

Returns

max ‘num’ unique samples.

textflint.generation_layer.generator.generator.load_module_from_file(module_name, file_path)

Uses importlib to dynamically open a file and load an object from it.

class textflint.generation_layer.generator.generator.product

Bases: object

product(*iterables, repeat=1) –> product object

Cartesian product of input iterables. Equivalent to nested for-loops.

For example, product(A, B) returns the same as: ((x,y) for x in A for y in B). The leftmost iterators are in the outermost for-loop, so the output tuples cycle in a manner similar to an odometer (with the rightmost element changing on every iteration).

To compute the product of an iterable with itself, specify the number of repetitions with the optional repeat keyword argument. For example, product(A, repeat=4) means the same as product(A, A, A, A).

product(‘ab’, range(3)) –> (‘a’,0) (‘a’,1) (‘a’,2) (‘b’,0) (‘b’,1) (‘b’,2) product((0,1), (0,1), (0,1)) –> (0,0,0) (0,0,1) (0,1,0) (0,1,1) (1,0,0) …

class textflint.generation_layer.generator.generator.tqdm(*args, **kwargs)

Bases: tqdm.utils.Comparable

Decorate an iterable object, returning an iterator which acts exactly like the original iterable, but prints a dynamically updating progressbar every time a value is requested.

monitor_interval = 10

static format_sizeof(num, suffix='', divisor=1000)

Formats a number (greater than unity) with SI Order of Magnitude prefixes.

numfloat

Number ( >= 1) to format.

suffixstr, optional

Post-postfix [default: ‘’].

divisorfloat, optional

Divisor between prefixes [default: 1000].

outstr

Number with Order of Magnitude SI unit postfix.

static format_interval(t)

Formats a number of seconds as a clock time, [H:]MM:SS

tint

Number of seconds.

outstr

[H:]MM:SS

static format_num(n)

Intelligent scientific notation (.3g).

nint or float or Numeric

A Number.

outstr

Formatted number.

static ema(x, mu=None, alpha=0.3)

Exponential moving average: smoothing to give progressively lower weights to older values.

xfloat

New value to include in EMA.

mufloat, optional

Previous EMA value.

alphafloat, optional

Smoothing factor in range [0, 1], [default: 0.3]. Increase to give more weight to recent values. Ranges from 0 (yields mu) to 1 (yields x).

static status_printer(file)

Manage the printing and in-place updating of a line of characters. Note that if the string is longer than a line, then in-place updating may not work (it will print a new line at each refresh).

static format_meter(n, total, elapsed, ncols=None, prefix='', ascii=False, unit='it', unit_scale=False, rate=None, bar_format=None, postfix=None, unit_divisor=1000, initial=0, **extra_kwargs)

Return a string-based progress bar given some parameters

nint or float

Number of finished iterations.

totalint or float

The expected total number of iterations. If meaningless (None), only basic progress statistics are displayed (no ETA).

elapsedfloat

Number of seconds passed since start.

ncolsint, optional

The width of the entire output message. If specified, dynamically resizes {bar} to stay within this bound [default: None]. If 0, will not print any bar (only stats). The fallback is {bar:10}.

prefixstr, optional

Prefix message (included in total width) [default: ‘’]. Use as {desc} in bar_format string.

asciibool, optional or str, optional

If not set, use unicode (smooth blocks) to fill the meter [default: False]. The fallback is to use ASCII characters ” 123456789#”.

unitstr, optional

The iteration unit [default: ‘it’].

unit_scalebool or int or float, optional

If 1 or True, the number of iterations will be printed with an appropriate SI metric prefix (k = 10^3, M = 10^6, etc.) [default: False]. If any other non-zero number, will scale total and n.

ratefloat, optional

Manual override for iteration rate. If [default: None], uses n/elapsed.

bar_formatstr, optional

Specify a custom bar string formatting. May impact performance. [default: ‘{l_bar}{bar}{r_bar}’], where l_bar=’{desc}: {percentage:3.0f}%|’ and r_bar=’| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, ‘

‘{rate_fmt}{postfix}]’

Possible vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt,

percentage, elapsed, elapsed_s, ncols, nrows, desc, unit, rate, rate_fmt, rate_noinv, rate_noinv_fmt, rate_inv, rate_inv_fmt, postfix, unit_divisor, remaining, remaining_s.

Note that a trailing “: ” is automatically removed after {desc} if the latter is empty.

postfix*, optional

Similar to prefix, but placed at the end (e.g. for additional stats). Note: postfix is usually a string (not a dict) for this method, and will if possible be set to postfix = ‘, ‘ + postfix. However other types are supported (#382).

unit_divisorfloat, optional

[default: 1000], ignored unless unit_scale is True.

initialint or float, optional

The initial counter value [default: 0].

out : Formatted meter and stats, ready to display.

classmethod write(s, file=None, end='\n', nolock=False)

Print a message via tqdm (without overlap with bars).

classmethod external_write_mode(file=None, nolock=False)

Disable tqdm within context and refresh tqdm when exits. Useful when writing to standard output stream

classmethod set_lock(lock)

Set the global lock.

classmethod get_lock()

Get the global lock. Construct it if it does not exist.

classmethod pandas(**tqdm_kwargs)

Registers the current tqdm class with

pandas.core. ( frame.DataFrame | series.Series | groupby.(generic.)DataFrameGroupBy | groupby.(generic.)SeriesGroupBy ).progress_apply

A new instance will be create every time progress_apply is called, and each instance will automatically close() upon completion.

tqdm_kwargs : arguments for the tqdm instance

>>> import pandas as pd
>>> import numpy as np
>>> from tqdm import tqdm
>>> from tqdm.gui import tqdm as tqdm_gui
>>>
>>> df = pd.DataFrame(np.random.randint(0, 100, (100000, 6)))
>>> tqdm.pandas(ncols=50)  # can use tqdm_gui, optional kwargs, etc
>>> # Now you can use `progress_apply` instead of `apply`
>>> df.groupby(0).progress_apply(lambda x: x**2)

<https://stackoverflow.com/questions/18603270/ progress-indicator-during-pandas-operations-python>

__init__(iterable=None, desc=None, total=None, leave=True, file=None, ncols=None, mininterval=0.1, maxinterval=10.0, miniters=None, ascii=None, disable=False, unit='it', unit_scale=False, dynamic_ncols=False, smoothing=0.3, bar_format=None, initial=0, position=None, postfix=None, unit_divisor=1000, write_bytes=None, lock_args=None, nrows=None, gui=False, **kwargs)

iterableiterable, optional

Iterable to decorate with a progressbar. Leave blank to manually manage the updates.

descstr, optional

Prefix for the progressbar.

totalint or float, optional

The number of expected iterations. If unspecified, len(iterable) is used if possible. If float(“inf”) or as a last resort, only basic progress statistics are displayed (no ETA, no progressbar). If gui is True and this parameter needs subsequent updating, specify an initial arbitrary large positive number, e.g. 9e9.

leavebool, optional

If [default: True], keeps all traces of the progressbar upon termination of iteration. If None, will leave only if position is 0.

fileio.TextIOWrapper or io.StringIO, optional

Specifies where to output the progress messages (default: sys.stderr). Uses file.write(str) and file.flush() methods. For encoding, see write_bytes.

ncolsint, optional

The width of the entire output message. If specified, dynamically resizes the progressbar to stay within this bound. If unspecified, attempts to use environment width. The fallback is a meter width of 10 and no limit for the counter and statistics. If 0, will not print any meter (only stats).

minintervalfloat, optional

Minimum progress display update interval [default: 0.1] seconds.

maxintervalfloat, optional

Maximum progress display update interval [default: 10] seconds. Automatically adjusts miniters to correspond to mininterval after long display update lag. Only works if dynamic_miniters or monitor thread is enabled.

minitersint or float, optional

Minimum progress display update interval, in iterations. If 0 and dynamic_miniters, will automatically adjust to equal mininterval (more CPU efficient, good for tight loops). If > 0, will skip display of specified number of iterations. Tweak this and mininterval to get very efficient loops. If your progress is erratic with both fast and slow iterations (network, skipping items, etc) you should set miniters=1.

asciibool or str, optional

If unspecified or False, use unicode (smooth blocks) to fill the meter. The fallback is to use ASCII characters ” 123456789#”.

disablebool, optional

Whether to disable the entire progressbar wrapper [default: False]. If set to None, disable on non-TTY.

unitstr, optional

String that will be used to define the unit of each iteration [default: it].

unit_scalebool or int or float, optional

If 1 or True, the number of iterations will be reduced/scaled automatically and a metric prefix following the International System of Units standard will be added (kilo, mega, etc.) [default: False]. If any other non-zero number, will scale total and n.

dynamic_ncolsbool, optional

If set, constantly alters ncols and nrows to the environment (allowing for window resizes) [default: False].

smoothingfloat, optional

Exponential moving average smoothing factor for speed estimates (ignored in GUI mode). Ranges from 0 (average speed) to 1 (current/instantaneous speed) [default: 0.3].

bar_formatstr, optional

Specify a custom bar string formatting. May impact performance. [default: ‘{l_bar}{bar}{r_bar}’], where l_bar=’{desc}: {percentage:3.0f}%|’ and r_bar=’| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, ‘

‘{rate_fmt}{postfix}]’

Possible vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt,

percentage, elapsed, elapsed_s, ncols, nrows, desc, unit, rate, rate_fmt, rate_noinv, rate_noinv_fmt, rate_inv, rate_inv_fmt, postfix, unit_divisor, remaining, remaining_s.

Note that a trailing “: ” is automatically removed after {desc} if the latter is empty.

initialint or float, optional

The initial counter value. Useful when restarting a progress bar [default: 0]. If using float, consider specifying {n:.3f} or similar in bar_format, or specifying unit_scale.

positionint, optional

Specify the line offset to print this bar (starting from 0) Automatic if unspecified. Useful to manage multiple bars at once (eg, from threads).

postfixdict or *, optional

Specify additional stats to display at the end of the bar. Calls set_postfix(**postfix) if possible (dict).

unit_divisorfloat, optional

[default: 1000], ignored unless unit_scale is True.

write_bytesbool, optional

If (default: None) and file is unspecified, bytes will be written in Python 2. If True will also write bytes. In all other cases will default to unicode.

lock_argstuple, optional

Passed to refresh for intermediate output (initialisation, iterating, and updating).

nrowsint, optional

The screen height. If specified, hides nested bars outside this bound. If unspecified, attempts to use environment height. The fallback is 20.

guibool, optional

WARNING: internal parameter - do not use. Use tqdm.gui.tqdm(…) instead. If set, will attempt to use matplotlib animations for a graphical output [default: False].

out : decorated iterator.

update(n=1)

Manually update the progress bar, useful for streams such as reading files. E.g.: >>> t = tqdm(total=filesize) # Initialise >>> for current_buffer in stream: … … … t.update(len(current_buffer)) >>> t.close() The last line is highly recommended, but possibly not necessary if t.update() will be called in such a way that filesize will be exactly reached and printed.

nint or float, optional

Increment to add to the internal counter of iterations [default: 1]. If using float, consider specifying {n:.3f} or similar in bar_format, or specifying unit_scale.

outbool or None

True if a display() was triggered.

close()

Cleanup and (if leave=False) close the progressbar.

clear(nolock=False)

Clear current bar display.

refresh(nolock=False, lock_args=None)

Force refresh the display of this bar.

nolockbool, optional

If True, does not lock. If [default: False]: calls acquire() on internal lock.

lock_argstuple, optional

Passed to internal lock’s acquire(). If specified, will only display() if acquire() returns True.

unpause()

Restart tqdm timer from last print time.

reset(total=None)

Resets to 0 iterations for repeated use.

Consider combining with leave=True.

total : int or float, optional. Total to use for the new bar.

set_description(desc=None, refresh=True)

Set/modify description of the progress bar.

desc : str, optional refresh : bool, optional

Forces refresh [default: True].

set_description_str(desc=None, refresh=True)

Set/modify description without ‘: ‘ appended.

set_postfix(ordered_dict=None, refresh=True, **kwargs)

Set/modify postfix (additional stats) with automatic formatting based on datatype.

ordered_dict : dict or OrderedDict, optional refresh : bool, optional

Forces refresh [default: True].

kwargs : dict, optional

set_postfix_str(s='', refresh=True)

Postfix without dictionary expansion, similar to prefix handling.

property format_dict

Public API for read-only member access.

display(msg=None, pos=None)

Use self.sp to display msg in the specified pos.

Consider overloading this function when inheriting to use e.g.: self.some_frontend(**self.format_dict) instead of self.sp.

msg : str, optional. What to display (default: repr(self)). pos : int, optional. Position to moveto

(default: abs(self.pos)).

classmethod wrapattr(stream, method, total=None, bytes=True, **tqdm_kwargs)

stream : file-like object. method : str, “read” or “write”. The result of read() and

the first argument of write() should have a len().

>>> with tqdm.wrapattr(file_obj, "read", total=file_obj.size) as fobj:
...     while True:
...         chunk = fobj.read(chunk_size)
...         if not chunk:
...             break