
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
						from ... import PreTrainedTokenizerBase, TensorType

__a  			=					logging.get_logger(__name__)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Optional[int]         =	'vision-encoder-decoder'
	a			:Any         =	True
	def __init__(       self  :			Optional[int]       ,							**SCREAMING_SNAKE_CASE_  :			Optional[int]       )       ->     List[Any]:
					super().__init__(**SCREAMING_SNAKE_CASE_       )
					if "encoder" not in kwargs or "decoder" not in kwargs:
									raise ValueError(
									    f'''A configuraton of type {self.model_type} cannot be instantiated because '''
									    f'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''       )

					lowercase_       =					kwargs.pop('''encoder'''       )
					lowercase_       =					encoder_config.pop('''model_type'''       )
					lowercase_       =					kwargs.pop('''decoder'''       )
					lowercase_       =					decoder_config.pop('''model_type'''       )

					lowercase_       =					AutoConfig.for_model(SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )
					lowercase_       =					AutoConfig.for_model(SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )
					lowercase_       =					True
	@classmethod
	def 						_lowercase  (       cls  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			PretrainedConfig       ,							SCREAMING_SNAKE_CASE_  :			PretrainedConfig       ,							**SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     PretrainedConfig:
					logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''       )
					lowercase_       =					True
					lowercase_       =					True

					return cls(encoder=encoder_config.to_dict()       ,							decoder=decoder_config.to_dict()       ,							**SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Optional[Any]       )       ->     Any:
					lowercase_       =					copy.deepcopy(self.__dict__       )
					lowercase_       =					self.encoder.to_dict()
					lowercase_       =					self.decoder.to_dict()
					lowercase_       =					self.__class__.model_type
					return output

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Any         =	version.parse('1.11'							)
	@property
	def 						_lowercase  (       self  :			str       )       ->     Mapping[str, Mapping[int, str]]:
					return OrderedDict(
					    [
					        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
					    ]       )
	@property
	def 						_lowercase  (       self  :			Dict       )       ->     float:
					return 1e-4




	@property
	def 						_lowercase  (       self  :			List[Any]       )       ->     Mapping[str, Mapping[int, str]]:
					return OrderedDict({'''last_hidden_state''': {0: '''batch''', 1: '''encoder_sequence'''}}       )

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	@property
	def 						_lowercase  (       self  :			Any       )       ->     Mapping[str, Mapping[int, str]]:
					lowercase_       =					OrderedDict()
					lowercase_       =					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
					lowercase_       =					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
					lowercase_       =					{0: '''batch''', 1: '''encoder_sequence'''}

					return common_inputs




	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			"PreTrainedTokenizerBase"       ,							SCREAMING_SNAKE_CASE_  :			int = -1       ,							SCREAMING_SNAKE_CASE_  :			int = -1       ,							SCREAMING_SNAKE_CASE_  :			bool = False       ,							SCREAMING_SNAKE_CASE_  :			Optional["TensorType"] = None       ,							)       ->     Mapping[str, Any]:
					import torch

					lowercase_       =					OrderedDict()

					lowercase_       =					super().generate_dummy_inputs(
					    SCREAMING_SNAKE_CASE_       ,							batch_size=SCREAMING_SNAKE_CASE_       ,							seq_length=SCREAMING_SNAKE_CASE_       ,							is_pair=SCREAMING_SNAKE_CASE_       ,							framework=SCREAMING_SNAKE_CASE_       )

					lowercase_  ,	lowercase_       =					dummy_input['''input_ids'''].shape
					lowercase_       =					(batch, encoder_sequence, self._config.encoder_hidden_size)
					lowercase_       =					dummy_input.pop('''input_ids'''       )
					lowercase_       =					dummy_input.pop('''attention_mask'''       )
					lowercase_       =					torch.zeros(SCREAMING_SNAKE_CASE_       )

					return common_inputs

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	@property
	def 						_lowercase  (       self  :			Dict       )       ->     None:
					pass
	def 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			PretrainedConfig       )       ->     OnnxConfig:
					return VisionEncoderDecoderEncoderOnnxConfig(SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			PretrainedConfig       ,							SCREAMING_SNAKE_CASE_  :			PretrainedConfig       ,							SCREAMING_SNAKE_CASE_  :			str = "default"       )       ->     OnnxConfig:
					lowercase_       =					encoder_config.hidden_size
					return VisionEncoderDecoderDecoderOnnxConfig(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )






import argparse
from collections import defaultdict

import yaml


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




def 				a						(							snake_case__:					Dict					):


				'''simple docstring'''
				lowercase_       =					defaultdict(snake_case__					)
				for doc in model_doc:
								counts[doc["local"]] += 1
				lowercase_       =					[key for key, value in counts.items() if value > 1]

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

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




def 				a						(							snake_case__:					List[Any]=False					):


				'''simple docstring'''
				with open(snake_case__						,   encoding='''utf-8'''					) as f:
								lowercase_       =					yaml.safe_load(f.read()					)

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

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

				lowercase_       =					api_doc[model_idx]['''sections''']

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

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

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

						check_model_doc(args.fix_and_overwrite)




