"""simple docstring"""







import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_a  :  Union[str, Any]=				500_000

_a  :  Tuple=				os.path.split(__file__)
_a  :  str=				os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))
@get_duration
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       datasets.Dataset    ,							**UpperCAmelCase_					:       Optional[Any]						)							->    Tuple:

	'''simple docstring'''





	__snake_case    :							str   	=				dataset.map(**UpperCAmelCase_						)
@get_duration
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       datasets.Dataset    ,							**UpperCAmelCase_					:       Optional[Any]						)							->    List[str]:

	'''simple docstring'''





	__snake_case    :							List[str]   	=				dataset.filter(**UpperCAmelCase_						)
def 						__UpperCAmelCase       (					)							->    Dict:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				{'num examples': SPEED_TEST_N_EXAMPLES}
	with tempfile.TemporaryDirectory() as tmp_dir:
		__snake_case    :							Tuple   	=				datasets.Features({'text': datasets.Value('string'						), 'numbers': datasets.Value('float32'						)}						)
		__snake_case    :							Any   	=				generate_example_dataset(
		    os.path.join(UpperCAmelCase_    ,							'dataset.arrow'						)    ,							UpperCAmelCase_    ,							num_examples=UpperCAmelCase_						)

		__snake_case    :							List[Any]   	=				transformers.AutoTokenizer.from_pretrained('bert-base-cased'    ,							use_fast=UpperCAmelCase_						)

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

		__snake_case    :							str   	=				map(UpperCAmelCase_						)

		__snake_case    :							List[str]   	=				map(UpperCAmelCase_    ,							batched=UpperCAmelCase_						)

		__snake_case    :							Dict   	=				map(UpperCAmelCase_    ,							function=lambda UpperCAmelCase_						: None    ,							batched=UpperCAmelCase_						)

		with dataset.formatted_as(type='numpy'						):
			__snake_case    :							Dict   	=				map(UpperCAmelCase_    ,							function=lambda UpperCAmelCase_						: None    ,							batched=UpperCAmelCase_						)

		with dataset.formatted_as(type='pandas'						):
			__snake_case    :							Any   	=				map(UpperCAmelCase_    ,							function=lambda UpperCAmelCase_						: None    ,							batched=UpperCAmelCase_						)

		with dataset.formatted_as(type='torch'    ,							columns='numbers'						):
			__snake_case    :							Tuple   	=				map(UpperCAmelCase_    ,							function=lambda UpperCAmelCase_						: None    ,							batched=UpperCAmelCase_						)

		with dataset.formatted_as(type='tensorflow'    ,							columns='numbers'						):
			__snake_case    :							Tuple   	=				map(UpperCAmelCase_    ,							function=lambda UpperCAmelCase_						: None    ,							batched=UpperCAmelCase_						)

		__snake_case    :							str   	=				map(UpperCAmelCase_    ,							function=UpperCAmelCase_    ,							batched=UpperCAmelCase_						)

		__snake_case    :							Tuple   	=				filter(UpperCAmelCase_						)

		# 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(UpperCAmelCase_    ,							'wb'						) as f:
		f.write(json.dumps(UpperCAmelCase_						).encode('utf-8'						)						)


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


"""simple docstring"""







import argparse
from collections import defaultdict

import yaml


_a  :  Tuple=				"docs/source/en/_toctree.yml"
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[Any]						)							->    Dict:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				defaultdict(UpperCAmelCase_						)
	__snake_case    :							List[str]   	=				[]
	__snake_case    :							Tuple   	=				[]
	for doc in doc_list:
		if "local" in doc:
			counts[doc["local"]] += 1

		if doc["title"].lower() == "overview":
			overview_doc.append({'local': doc['local'], 'title': doc['title']}						)
		else:
			new_doc_list.append(UpperCAmelCase_						)

	__snake_case    :							Any   	=				new_doc_list
	__snake_case    :							Dict   	=				[key for key, value in counts.items() if value > 1]

	__snake_case    :							Optional[int]   	=				[]
	for duplicate_key in duplicates:
		__snake_case    :							Tuple   	=				list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}						)
		if len(UpperCAmelCase_						) > 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 doc_list if 'local' not in counts or counts[doc['local']] == 1]						)
	__snake_case    :							List[Any]   	=				sorted(UpperCAmelCase_    ,							key=lambda UpperCAmelCase_						: s["title"].lower()						)

	# "overview" gets special treatment and is always first
	if len(UpperCAmelCase_						) > 1:
		raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'						)

	overview_doc.extend(UpperCAmelCase_						)

	# Sort
	return overview_doc
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[int]=False						)							->    str:

	'''simple docstring'''





	with open(UpperCAmelCase_    ,							encoding='utf-8'						) as f:
		__snake_case    :							List[Any]   	=				yaml.safe_load(f.read()						)

	# Get to the API doc
	__snake_case    :							str   	=				0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	__snake_case    :							Union[str, Any]   	=				content[api_idx]['sections']

	# Then to the model doc
	__snake_case    :							Dict   	=				0
	while api_doc[scheduler_idx]["title"] != "Schedulers":
		scheduler_idx += 1

	__snake_case    :							Optional[Any]   	=				api_doc[scheduler_idx]['sections']
	__snake_case    :							Tuple   	=				clean_doc_toc(UpperCAmelCase_						)

	__snake_case    :							Tuple   	=				False
	if new_scheduler_doc != scheduler_doc:
		__snake_case    :							Optional[int]   	=				True
		if overwrite:
			__snake_case    :							Tuple   	=				new_scheduler_doc

	if diff:
		if overwrite:
			__snake_case    :							Optional[int]   	=				api_doc
			with open(UpperCAmelCase_    ,							'w'    ,							encoding='utf-8'						) as f:
				f.write(yaml.dump(UpperCAmelCase_    ,							allow_unicode=UpperCAmelCase_						)						)
		else:
			raise ValueError(
			    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Any=False						)							->    Union[str, Any]:

	'''simple docstring'''





	with open(UpperCAmelCase_    ,							encoding='utf-8'						) as f:
		__snake_case    :							Dict   	=				yaml.safe_load(f.read()						)

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

	# Then to the model doc
	__snake_case    :							Dict   	=				0
	while api_doc[pipeline_idx]["title"] != "Pipelines":
		pipeline_idx += 1

	__snake_case    :							Optional[int]   	=				False
	__snake_case    :							Optional[Any]   	=				api_doc[pipeline_idx]['sections']
	__snake_case    :							Tuple   	=				[]

	# sort sub pipeline docs
	for pipeline_doc in pipeline_docs:
		if "section" in pipeline_doc:
			__snake_case    :							List[str]   	=				pipeline_doc['section']
			__snake_case    :							Optional[int]   	=				clean_doc_toc(UpperCAmelCase_						)
			if overwrite:
				__snake_case    :							List[Any]   	=				new_sub_pipeline_doc
		new_pipeline_docs.append(UpperCAmelCase_						)

	# sort overall pipeline doc
	__snake_case    :							List[Any]   	=				clean_doc_toc(UpperCAmelCase_						)

	if new_pipeline_docs != pipeline_docs:
		__snake_case    :							Dict   	=				True
		if overwrite:
			__snake_case    :							Tuple   	=				new_pipeline_docs

	if diff:
		if overwrite:
			__snake_case    :							Union[str, Any]   	=				api_doc
			with open(UpperCAmelCase_    ,							'w'    ,							encoding='utf-8'						) as f:
				f.write(yaml.dump(UpperCAmelCase_    ,							allow_unicode=UpperCAmelCase_						)						)
		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  :  Optional[int]=				argparse.ArgumentParser()
	parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
	_a  :  List[str]=				parser.parse_args()

	check_scheduler_doc(args.fix_and_overwrite)
	check_pipeline_doc(args.fix_and_overwrite)





