






import argparse
from collections import defaultdict

import yaml


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





def 						__UpperCAmelCase			(     a_):
							snake_case_				=							defaultdict(a_)
							for doc in model_doc:
														counts[doc["local"]] += 1
							snake_case_				=							[key for key, value in counts.items() if value > 1]

							snake_case_				=							[]
							for duplicate_key in duplicates:
														snake_case_				=							list({doc['title'] for doc in model_doc 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 model_doc if counts[doc['local']] == 1])

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





def 						__UpperCAmelCase			(     a_=False):
							with open(a_							,     encoding='utf-8') as f:
														snake_case_				=							yaml.safe_load(f.read())

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

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

							snake_case_				=							api_doc[model_idx]['sections']

							snake_case_				=							[(idx, section) for idx, section in enumerate(a_) if 'sections' in section]
							snake_case_				=							False
							for idx, modality_doc in modalities_docs:
														snake_case_				=							modality_doc['sections']
														snake_case_				=							clean_model_doc_toc(a_)

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

							if diff:
														if overwrite:
																					snake_case_				=							model_doc
																					snake_case_				=							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_model_doc(args.fix_and_overwrite)













from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowercase  							=					{"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					["GPTNeoXTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					[
	    "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "GPTNeoXForCausalLM",
	    "GPTNeoXForQuestionAnswering",
	    "GPTNeoXForSequenceClassification",
	    "GPTNeoXForTokenClassification",
	    "GPTNeoXLayer",
	    "GPTNeoXModel",
	    "GPTNeoXPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	lowercase  							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





