

import argparse
from collections import defaultdict

import yaml


_lowerCAmelCase					:			str  				=			"docs/source/en/_toctree.yml"






def        UpperCamelCase_(							_snake_case   :  Union[str, Any]	):




			"""simple docstring"""






			__a        =defaultdict(_snake_case	)
			__a        =[]
			__a        =[]
			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(_snake_case	)

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

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

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

			overview_doc.extend(_snake_case	)

			# Sort
			return overview_doc






def        UpperCamelCase_(							_snake_case   :  Optional[int]=False	):




			"""simple docstring"""






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

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

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

			__a        =api_doc[scheduler_idx]['sections']
			__a        =clean_doc_toc(_snake_case	)

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

			if diff:
						if overwrite:
									__a        =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.'	)






def        UpperCamelCase_(							_snake_case   :  Optional[Any]=False	):




			"""simple docstring"""






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

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

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

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

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

			# sort overall pipeline doc
			__a        =clean_doc_toc(_snake_case	)

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

			if diff:
						if overwrite:
									__a        =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__":
						_lowerCAmelCase					:			Tuple  				=			argparse.ArgumentParser()
						parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
						_lowerCAmelCase					:			Tuple  				=			parser.parse_args()

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









import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCAmelCase					:			List[str]  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			Union[str, Any]  				=			{
    "Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'instructblip_vision_model'





			def __init__(		self					,  __snake_case=1408					,  __snake_case=6144					,  __snake_case=39					,  __snake_case=16					,  __snake_case=224					,  __snake_case=14					,  __snake_case="gelu"					,  __snake_case=1e-6					,  __snake_case=0.0					,  __snake_case=1e-10					,  __snake_case=True					,  **__snake_case					,  )   -> str:




						'''simple docstring'''
						super().__init__(**__snake_case					)

						__a        =hidden_size
						__a        =intermediate_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =patch_size
						__a        =image_size
						__a        =initializer_range
						__a        =attention_dropout
						__a        =layer_norm_eps
						__a        =hidden_act
						__a        =qkv_bias





			@classmethod
			def 		__magic_name__    (		cls					,  __snake_case					,  **__snake_case					)   -> "PretrainedConfig":




						'''simple docstring'''
						cls._set_token_in_kwargs(__snake_case					)

						__a      ,    __a        =cls.get_config_dict(__snake_case					,  **__snake_case					)

						# get the vision config dict if we are loading from InstructBlipConfig
						if config_dict.get('model_type'					) == "instructblip":
									__a        =config_dict['vision_config']

						if "model_type" in config_dict and hasattr(cls					,  'model_type'					) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
									    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'					)

						return cls.from_dict(__snake_case					,  **__snake_case					)






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'instructblip_qformer'





			def __init__(		self					,  __snake_case=3_0522					,  __snake_case=768					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=512					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0					,  __snake_case="absolute"					,  __snake_case=2					,  __snake_case=1408					,  **__snake_case					,  )   -> List[str]:




						'''simple docstring'''
						super().__init__(pad_token_id=__snake_case					,  **__snake_case					)

						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =hidden_act
						__a        =intermediate_size
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =initializer_range
						__a        =layer_norm_eps
						__a        =position_embedding_type
						__a        =cross_attention_frequency
						__a        =encoder_hidden_size





			@classmethod
			def 		__magic_name__    (		cls					,  __snake_case					,  **__snake_case					)   -> "PretrainedConfig":




						'''simple docstring'''
						cls._set_token_in_kwargs(__snake_case					)

						__a      ,    __a        =cls.get_config_dict(__snake_case					,  **__snake_case					)

						# get the qformer config dict if we are loading from InstructBlipConfig
						if config_dict.get('model_type'					) == "instructblip":
									__a        =config_dict['qformer_config']

						if "model_type" in config_dict and hasattr(cls					,  'model_type'					) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
									    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'					)

						return cls.from_dict(__snake_case					,  **__snake_case					)






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'instructblip'
			SCREAMING_SNAKE_CASE							   =						True





			def __init__(		self					,  __snake_case=None					,  __snake_case=None					,  __snake_case=None					,  __snake_case=32					,  **__snake_case					)   -> str:




						'''simple docstring'''
						super().__init__(**__snake_case					)

						if vision_config is None:
									__a        ={}
									logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'					)

						if qformer_config is None:
									__a        ={}
									logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'					)

						if text_config is None:
									__a        ={}
									logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'					)

						__a        =InstructBlipVisionConfig(**__snake_case					)
						__a        =InstructBlipQFormerConfig(**__snake_case					)
						__a        =text_config['model_type'] if 'model_type' in text_config else 'opt'
						__a        =CONFIG_MAPPING[text_model_type](**__snake_case					)

						__a        =self.text_config.tie_word_embeddings
						__a        =self.text_config.is_encoder_decoder

						__a        =num_query_tokens
						__a        =self.vision_config.hidden_size
						__a        =self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
						__a        =1.0
						__a        =0.02





			@classmethod
			def 		__magic_name__    (		cls					,  __snake_case					,  __snake_case					,  __snake_case					,  **__snake_case					,  )   -> Optional[Any]:




						'''simple docstring'''
						return cls(
						    vision_config=vision_config.to_dict()					,  qformer_config=qformer_config.to_dict()					,  text_config=text_config.to_dict()					,  **__snake_case					,  )





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						__a        =copy.deepcopy(self.__dict__					)
						__a        =self.vision_config.to_dict()
						__a        =self.qformer_config.to_dict()
						__a        =self.text_config.to_dict()
						__a        =self.__class__.model_type
						return output






