




import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE__     :     Any		    =       "docs/source/en/_toctree.yml"

def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[str]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						defaultdict(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						[]
			SCREAMING_SNAKE_CASE_								=						[]
			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(SCREAMING_SNAKE_CASE )

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

			SCREAMING_SNAKE_CASE_								=						[]
			for duplicate_key in duplicates:
						SCREAMING_SNAKE_CASE_								=						list({doc['title'] for doc in doc_list 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 doc_list if 'local' not in counts or counts[doc['local']] == 1] )
			SCREAMING_SNAKE_CASE_								=						sorted(SCREAMING_SNAKE_CASE       ,    key=lambda SCREAMING_SNAKE_CASE : s["title"].lower() )

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

			overview_doc.extend(SCREAMING_SNAKE_CASE )

			# Sort
			return overview_doc

def lowercase    (  SCREAMING_SNAKE_CASE=False )			->							Optional[int]:


			'''simple docstring'''







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

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

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

			SCREAMING_SNAKE_CASE_								=						api_doc[scheduler_idx]['sections']
			SCREAMING_SNAKE_CASE_								=						clean_doc_toc(SCREAMING_SNAKE_CASE )

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

			if diff:
						if overwrite:
									SCREAMING_SNAKE_CASE_								=						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.' )

def lowercase    (  SCREAMING_SNAKE_CASE=False )			->							List[Any]:


			'''simple docstring'''







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

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

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

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

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

			# sort overall pipeline doc
			SCREAMING_SNAKE_CASE_								=						clean_doc_toc(SCREAMING_SNAKE_CASE )

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

			if diff:
						if overwrite:
									SCREAMING_SNAKE_CASE_								=						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__":
				SCREAMING_SNAKE_CASE__     :     List[str]		    =       argparse.ArgumentParser()
				parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
				SCREAMING_SNAKE_CASE__     :     Any		    =       parser.parse_args()

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






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
				import tensorflow as tf

				from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class       a_       (     unittest.TestCase     ):





	@slow
	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'      )
				SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained('google/mt5-small'      )

				SCREAMING_SNAKE_CASE_								=						tokenizer('Hello there'      ,     return_tensors='tf'      ).input_ids
				SCREAMING_SNAKE_CASE_								=						tokenizer('Hi I am'      ,     return_tensors='tf'      ).input_ids

				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     labels=SCREAMING_SNAKE_CASE      ).loss
				SCREAMING_SNAKE_CASE_								=						-tf.math.reduce_mean(SCREAMING_SNAKE_CASE      ).numpy()

				SCREAMING_SNAKE_CASE_								=						-2_1.2_2_8_1_6_8
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 2e-4      )
