



"""simple docstring"""





import argparse
from collections import defaultdict

import yaml


lowercase  							=					'''docs/source/en/_toctree.yml'''




def 	UpperCAmelCase     (		A	:						Optional[int]       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		defaultdict(A       )
		_UpperCAmelCase							 =		[]
		_UpperCAmelCase							 =		[]
		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(A       )

		_UpperCAmelCase							 =		new_doc_list
		_UpperCAmelCase							 =		[key for key, value in counts.items() if value > 1]

		_UpperCAmelCase							 =		[]
		for duplicate_key in duplicates:
				_UpperCAmelCase							 =		list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}       )
				if len(A       ) > 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]       )
		_UpperCAmelCase							 =		sorted(A     ,				key=lambda A       : s["title"].lower()       )

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

		overview_doc.extend(A       )

		# Sort
		return overview_doc




def 	UpperCAmelCase     (		A	:						int=False       ):

		'''simple docstring'''


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

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

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

		_UpperCAmelCase							 =		api_doc[scheduler_idx]['sections']
		_UpperCAmelCase							 =		clean_doc_toc(A       )

		_UpperCAmelCase							 =		False
		if new_scheduler_doc != scheduler_doc:
				_UpperCAmelCase							 =		True
				if overwrite:
						_UpperCAmelCase							 =		new_scheduler_doc

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




def 	UpperCAmelCase     (		A	:						str=False       ):

		'''simple docstring'''


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

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

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

		_UpperCAmelCase							 =		False
		_UpperCAmelCase							 =		api_doc[pipeline_idx]['sections']
		_UpperCAmelCase							 =		[]

		# sort sub pipeline docs
		for pipeline_doc in pipeline_docs:
				if "section" in pipeline_doc:
						_UpperCAmelCase							 =		pipeline_doc['section']
						_UpperCAmelCase							 =		clean_doc_toc(A       )
						if overwrite:
								_UpperCAmelCase							 =		new_sub_pipeline_doc
				new_pipeline_docs.append(A       )

		# sort overall pipeline doc
		_UpperCAmelCase							 =		clean_doc_toc(A       )

		if new_pipeline_docs != pipeline_docs:
				_UpperCAmelCase							 =		True
				if overwrite:
						_UpperCAmelCase							 =		new_pipeline_docs

		if diff:
				if overwrite:
						_UpperCAmelCase							 =		api_doc
						with open(A     ,				'w'     ,				encoding='utf-8'       ) as f:
								f.write(yaml.dump(A     ,				allow_unicode=A       )       )
				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__":
					lowercase  							=					argparse.ArgumentParser()
					parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
					lowercase  							=					parser.parse_args()

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







"""simple docstring"""





import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
lowercase  							=					logging.getLogger()




def 	UpperCAmelCase     (		A	:						Path     ,				A	:						list       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		'\n'.join(A       )
		Path(A       ).open('w'       ).writelines(A       )


lowercase  							=					'''patrickvonplaten/t5-tiny-random'''
lowercase  							=					'''sshleifer/bart-tiny-random'''
lowercase  							=					'''sshleifer/tiny-mbart'''

lowercase  							=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class 			lowercase__			(      A    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self	,       snake_case						)			->							str:
									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						) / 'utest_input.source'
									_UpperCAmelCase							 =		input_file_name.parent / 'utest_output.txt'
									assert not output_file_name.exists()
									_UpperCAmelCase							 =		[' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
									_dump_articles(snake_case	,       snake_case						)

									_UpperCAmelCase							 =		str(Path(self.get_auto_remove_tmp_dir()						) / 'scores.json'						)
									_UpperCAmelCase							 =		'translation_en_to_de' if model == T5_TINY else 'summarization'
									_UpperCAmelCase							 =		f'\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '.split()

									with patch.object(snake_case	,       'argv'	,       snake_case						):
											run_generate()
											assert Path(snake_case						).exists()
											# os.remove(Path(output_file_name))






							def        lowerCamelCase_   (  self						)			->							str:
									self.run_eval_tester(snake_case						)






							@parameterized.expand([BART_TINY, MBART_TINY]						)
							@slow
							def        lowerCamelCase_   (  self	,       snake_case						)			->							List[Any]:
									self.run_eval_tester(snake_case						)






							@parameterized.expand([T5_TINY, MBART_TINY]						)
							@slow
							def        lowerCamelCase_   (  self	,       snake_case						)			->							Dict:
									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						) / 'utest_input.source'
									_UpperCAmelCase							 =		input_file_name.parent / 'utest_output.txt'
									assert not output_file_name.exists()

									_UpperCAmelCase							 =		{
									    'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
									    'de': [
									        'Maschinelles Lernen ist großartig, oder?',
									        'Ich esse gerne Bananen',
									        'Morgen ist wieder ein toller Tag!',
									    ],
									}

									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						)
									_UpperCAmelCase							 =		str(tmp_dir / 'scores.json'						)
									_UpperCAmelCase							 =		str(tmp_dir / 'val.target'						)
									_dump_articles(snake_case	,       text['en']						)
									_dump_articles(snake_case	,       text['de']						)
									_UpperCAmelCase							 =		'translation_en_to_de' if model == T5_TINY else 'summarization'
									_UpperCAmelCase							 =		f'\n            run_eval_search.py\n            {model}\n            {str(snake_case						)}\n            {str(snake_case						)}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '.split()
									testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']						)

									with patch.object(snake_case	,       'argv'	,       snake_case						):
											with CaptureStdout() as cs:
													run_search()
											_UpperCAmelCase							 =		[' num_beams | length_penalty', model, 'Best score args']
											_UpperCAmelCase							 =		['Info']
											if "translation" in task:
													expected_strings.append('bleu'						)
											else:
													expected_strings.extend(snake_case						)
											for w in expected_strings:
													assert w in cs.out
											for w in un_expected_strings:
													assert w not in cs.out
											assert Path(snake_case						).exists()
											os.remove(Path(snake_case						)						)


