






import argparse
from collections import defaultdict

import yaml


A__			:    Dict         =				'docs/source/en/_toctree.yml'






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    defaultdict(SCREAMING_SNAKE_CASE_       )
				for doc in model_doc:
								counts[doc["local"]] += 1
				lowercase__     				=    [key for key, value in counts.items() if value > 1]

				lowercase__     				=    []
				for duplicate_key in duplicates:
								lowercase__     				=    list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}       )
								if len(SCREAMING_SNAKE_CASE_       ) > 1:
												raise ValueError(
												    F"""{duplicate_key} is present several times in the documentation table of content at """
												    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
												    '''others.'''       )
								# Only add this once
								new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}       )

				# Add none duplicate-keys
				new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]       )

				# Sort
				return sorted(SCREAMING_SNAKE_CASE_	,     key=lambda lowerCamelCase_       : s["title"].lower()       )






def 						a    (		lowerCamelCase_=False       ):






				'''simple docstring'''

				with open(SCREAMING_SNAKE_CASE_	,     encoding='''utf-8'''       ) as f:
								lowercase__     				=    yaml.safe_load(f.read()       )

				# Get to the API doc
				lowercase__     				=    0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				lowercase__     				=    content[api_idx]["sections"]

				# Then to the model doc
				lowercase__     				=    0
				while api_doc[model_idx]["title"] != "Models":
								model_idx += 1

				lowercase__     				=    api_doc[model_idx]["sections"]

				lowercase__     				=    [(idx, section) for idx, section in enumerate(SCREAMING_SNAKE_CASE_       ) if "sections" in section]
				lowercase__     				=    False
				for idx, modality_doc in modalities_docs:
								lowercase__     				=    modality_doc["sections"]
								lowercase__     				=    clean_model_doc_toc(SCREAMING_SNAKE_CASE_       )

								if old_modality_doc != new_modality_doc:
												lowercase__     				=    True
												if overwrite:
																lowercase__     				=    new_modality_doc

				if diff:
								if overwrite:
												lowercase__     				=    model_doc
												lowercase__     				=    api_doc
												with open(SCREAMING_SNAKE_CASE_	,     '''w'''	,     encoding='''utf-8'''       ) as f:
																f.write(yaml.dump(SCREAMING_SNAKE_CASE_	,     allow_unicode=SCREAMING_SNAKE_CASE_       )       )
								else:
												raise ValueError(
												    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''       )


if __name__ == "__main__":
							A__			:    Dict         =				argparse.ArgumentParser()
							parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
							A__			:    Union[str, Any]         =				parser.parse_args()

							check_model_doc(args.fix_and_overwrite)












import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


A__			:    Dict         =				50_00_00

A__     ,     A__			:    str         =				os.path.split(__file__)
A__			:    Optional[Any]         =				os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






@get_duration
def 						a    (		lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    dataset.map(**lowerCamelCase_       )






@get_duration
def 						a    (		lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    dataset.filter(**lowerCamelCase_       )






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    {'''num examples''': SPEED_TEST_N_EXAMPLES}
				with tempfile.TemporaryDirectory() as tmp_dir:
								lowercase__     				=    datasets.Features({'''text''': datasets.Value('''string'''       ), '''numbers''': datasets.Value('''float32'''       )}       )
								lowercase__     				=    generate_example_dataset(
								    os.path.join(lowerCamelCase_	,     '''dataset.arrow'''       )	,     lowerCamelCase_	,     num_examples=lowerCamelCase_       )

								lowercase__     				=    transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''	,     use_fast=lowerCamelCase_       )

								def tokenize(lowerCamelCase_       ):
												return tokenizer(examples['''text''']       )

								lowercase__     				=    map(lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     batched=lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''numpy'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''pandas'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''torch'''	,     columns='''numbers'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''tensorflow'''	,     columns='''numbers'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     function=lowerCamelCase_	,     batched=lowerCamelCase_       )

								lowercase__     				=    filter(lowerCamelCase_       )

								# Activate later when tokenizer support batched inputs
								# with dataset.formatted_as(type='numpy'):
								#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

				with open(lowerCamelCase_	,     '''wb'''       ) as f:
								f.write(json.dumps(lowerCamelCase_       ).encode('''utf-8'''       )       )


if __name__ == "__main__":  # useful to run the profiler
							benchmark_map_filter()




