
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


SCREAMING_SNAKE_CASE							:Optional[int]										=		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Union[str, Any]										=		{
    'salesforce/blip2-opt-2.7b': 'https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json',
}





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"blip_2_vision_model"






	def __init__(	self :			str							,A :			Dict=14_08							,A :			Union[str, Any]=61_44							,A :			List[Any]=39							,A :			Optional[int]=16							,A :			int=2_24							,A :			str=14							,A :			Tuple="gelu"							,A :			Optional[int]=0.0_00_01							,A :			int=0.0							,A :			str=1E-10							,A :			List[Any]=True							,**A :			Union[str, Any]							,):
					super().__init__(**__a			)

					__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     UpperCamelCase_  (	cls :			Dict							,A :			Optional[Any]							,**A :			List[str]			):
					cls._set_token_in_kwargs(__a			)

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

					# get the vision config dict if we are loading from Blip2Config
					if config_dict.get("model_type"			) == "blip-2":
									__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(__a							,**__a			)





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"blip_2_qformer"






	def __init__(	self :			Union[str, Any]							,A :			Tuple=3_05_22							,A :			Tuple=7_68							,A :			Any=12							,A :			Any=12							,A :			str=30_72							,A :			Dict="gelu"							,A :			Optional[Any]=0.1							,A :			Tuple=0.1							,A :			str=5_12							,A :			Any=0.02							,A :			Optional[int]=1E-12							,A :			str=0							,A :			Any="absolute"							,A :			Dict=2							,A :			Optional[Any]=14_08							,**A :			Tuple							,):
					super().__init__(pad_token_id=__a							,**__a			)

					__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     UpperCamelCase_  (	cls :			Any							,A :			int							,**A :			List[str]			):
					cls._set_token_in_kwargs(__a			)

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

					# get the qformer config dict if we are loading from Blip2Config
					if config_dict.get("model_type"			) == "blip-2":
									__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(__a							,**__a			)





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"blip-2"
	snake_case_    =					True






	def __init__(	self :			Dict							,A :			Tuple=None							,A :			List[str]=None							,A :			Dict=None							,A :			Dict=32							,**A :			Optional[int]			):
					super().__init__(**__a			)

					if vision_config is None:
									__A             =			{}
									logger.info("vision_config is None. initializing the Blip2VisionConfig with default values."			)

					if qformer_config is None:
									__A             =			{}
									logger.info("qformer_config is None. Initializing the Blip2QFormerConfig with default values."			)

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

					__A             =			BlipaVisionConfig(**__a			)
					__A             =			BlipaQFormerConfig(**__a			)
					__A             =			text_config["model_type"] if "model_type" in text_config else "opt"
					__A             =			CONFIG_MAPPING[text_model_type](**__a			)

					__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     UpperCamelCase_  (	cls :			List[str]							,A :			Any							,A :			List[str]							,A :			Dict							,**A :			Optional[Any]							,):
					return cls(
					    vision_config=vision_config.to_dict()							,qformer_config=qformer_config.to_dict()							,text_config=text_config.to_dict()							,**__a							,)







	def     UpperCamelCase_  (	self :			Any			):
					__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


import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE							:str										=    'docs/source/en/_toctree.yml'


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



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

				__A             =			[]
				for duplicate_key in duplicates:
								__A             =			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						)		->     List[Any]:







				"""simple docstring"""



				with open(a_		,       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[model_idx]["title"] != "Models":
								model_idx += 1

				__A             =			api_doc[model_idx]["sections"]

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

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

				if diff:
								if overwrite:
												__A             =			model_doc
												__A             =			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__":
					SCREAMING_SNAKE_CASE							:Tuple										=    argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					SCREAMING_SNAKE_CASE							:List[str]										=    parser.parse_args()

					check_model_doc(args.fix_and_overwrite)
