



import argparse
from collections import defaultdict

import yaml


__SCREAMING_SNAKE_CASE           =  """docs/source/en/_toctree.yml"""




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					Union[str, Any]              =						defaultdict(_lowerCamelCase    )
			A							:					Dict              =						[]
			A							:					List[Any]              =						[]
			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(_lowerCamelCase    )

			A							:					str              =						new_doc_list
			A							:					Optional[int]              =						[key for key, value in counts.items() if value > 1]

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

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

			overview_doc.extend(_lowerCamelCase    )

			# Sort
			return overview_doc




def 	UpperCAmelCase   (						_lowerCamelCase=False    ):
			with open(_lowerCamelCase ,    encoding="utf-8"    ) as f:
						A							:					List[Any]              =						yaml.safe_load(f.read()    )

			# Get to the API doc
			A							:					Optional[int]              =						0
			while content[api_idx]["title"] != "API":
						api_idx += 1
			A							:					List[Any]              =						content[api_idx]["sections"]

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

			A							:					Tuple              =						api_doc[scheduler_idx]["sections"]
			A							:					int              =						clean_doc_toc(_lowerCamelCase    )

			A							:					List[Any]              =						False
			if new_scheduler_doc != scheduler_doc:
						A							:					List[Any]              =						True
						if overwrite:
									A							:					Tuple              =						new_scheduler_doc

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




def 	UpperCAmelCase   (						_lowerCamelCase=False    ):
			with open(_lowerCamelCase ,    encoding="utf-8"    ) as f:
						A							:					Optional[Any]              =						yaml.safe_load(f.read()    )

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

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

			A							:					Any              =						False
			A							:					int              =						api_doc[pipeline_idx]["sections"]
			A							:					Tuple              =						[]

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

			# sort overall pipeline doc
			A							:					Union[str, Any]              =						clean_doc_toc(_lowerCamelCase    )

			if new_pipeline_docs != pipeline_docs:
						A							:					Tuple              =						True
						if overwrite:
									A							:					Union[str, Any]              =						new_pipeline_docs

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

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




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
							import torch

							from transformers import XLMRobertaModel

@require_sentencepiece
@require_tokenizers
@require_torch
class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Union[str, Any]:
									A							:					Union[str, Any]              =						XLMRobertaModel.from_pretrained("xlm-roberta-base" )
									A							:					Tuple              =						torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]] )
									# The dog is cute and lives in the garden house

									A							:					Tuple              =						torch.Size((1, 12, 7_68) )  # batch_size, sequence_length, embedding_vector_dim
									A							:					List[str]              =						torch.tensor(
									    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
									#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
									#  xlmr.eval()
									#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
									with torch.no_grad():
												A							:					Tuple              =						model(__lowerCamelCase )["last_hidden_state"].detach()
									self.assertEqual(output.shape	,	__lowerCamelCase )
									# compare the actual values for a slice of last dim
									self.assertTrue(torch.allclose(output[:, :, -1]	,	__lowerCamelCase	,	atol=1e-3 ) )







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				int:
									A							:					str              =						XLMRobertaModel.from_pretrained("xlm-roberta-large" )
									A							:					List[Any]              =						torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]] )
									# The dog is cute and lives in the garden house

									A							:					Optional[Any]              =						torch.Size((1, 12, 10_24) )  # batch_size, sequence_length, embedding_vector_dim
									A							:					List[Any]              =						torch.tensor(
									    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]] )
									#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
									#  xlmr.eval()
									#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
									with torch.no_grad():
												A							:					Optional[int]              =						model(__lowerCamelCase )["last_hidden_state"].detach()
									self.assertEqual(output.shape	,	__lowerCamelCase )
									# compare the actual values for a slice of last dim
									self.assertTrue(torch.allclose(output[:, :, -1]	,	__lowerCamelCase	,	atol=1e-3 ) )