



"""simple docstring"""







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


A		=						logging.get_logger(__name__)

A		=						{
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """instructblip_vision_model"""



			def __init__(			self							:				Optional[Any]   ,	_lowercase							:				List[Any]=1408   ,	_lowercase							:				List[Any]=6144   ,	_lowercase							:				Optional[Any]=39   ,	_lowercase							:				Optional[int]=16   ,	_lowercase							:				Union[str, Any]=224   ,	_lowercase							:				Optional[Any]=14   ,	_lowercase							:				List[str]="gelu"   ,	_lowercase							:				List[str]=1E-6   ,	_lowercase							:				List[str]=0.0   ,	_lowercase							:				int=1E-10   ,	_lowercase							:				Optional[int]=True   ,	**_lowercase							:				Union[str, Any]   ,	)							->					Tuple:
									super().__init__(**A_		)

									snake_case   :			List[str]						    = hidden_size
									snake_case   :			Tuple						    = intermediate_size
									snake_case   :			List[str]						    = num_hidden_layers
									snake_case   :			Union[str, Any]						    = num_attention_heads
									snake_case   :			str						    = patch_size
									snake_case   :			Union[str, Any]						    = image_size
									snake_case   :			int						    = initializer_range
									snake_case   :			List[str]						    = attention_dropout
									snake_case   :			Tuple						    = layer_norm_eps
									snake_case   :			Tuple						    = hidden_act
									snake_case   :			Optional[int]						    = qkv_bias



			@classmethod
			def 				__lowercase					(			cls							:				Tuple   ,	_lowercase							:				Optional[Any]   ,	**_lowercase							:				Optional[Any]		)							->					"PretrainedConfig":
									cls._set_token_in_kwargs(A_		)

									snake_case   :			str						    = cls.get_config_dict(A_   ,	**A_		)

									# get the vision config dict if we are loading from InstructBlipConfig
									if config_dict.get("model_type"		) == "instructblip":
															snake_case   :			Dict						    = 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       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """instructblip_qformer"""



			def __init__(			self							:				Dict   ,	_lowercase							:				Any=30522   ,	_lowercase							:				Tuple=768   ,	_lowercase							:				str=12   ,	_lowercase							:				List[Any]=12   ,	_lowercase							:				Optional[int]=3072   ,	_lowercase							:				Union[str, Any]="gelu"   ,	_lowercase							:				List[Any]=0.1   ,	_lowercase							:				Union[str, Any]=0.1   ,	_lowercase							:				int=512   ,	_lowercase							:				Tuple=0.02   ,	_lowercase							:				List[Any]=1E-12   ,	_lowercase							:				Dict=0   ,	_lowercase							:				Optional[Any]="absolute"   ,	_lowercase							:				Tuple=2   ,	_lowercase							:				str=1408   ,	**_lowercase							:				Any   ,	)							->					Optional[int]:
									super().__init__(pad_token_id=A_   ,	**A_		)

									snake_case   :			Optional[Any]						    = vocab_size
									snake_case   :			Optional[int]						    = hidden_size
									snake_case   :			Dict						    = num_hidden_layers
									snake_case   :			Union[str, Any]						    = num_attention_heads
									snake_case   :			List[Any]						    = hidden_act
									snake_case   :			Union[str, Any]						    = intermediate_size
									snake_case   :			Union[str, Any]						    = hidden_dropout_prob
									snake_case   :			Tuple						    = attention_probs_dropout_prob
									snake_case   :			Optional[int]						    = max_position_embeddings
									snake_case   :			Optional[int]						    = initializer_range
									snake_case   :			Optional[Any]						    = layer_norm_eps
									snake_case   :			Optional[Any]						    = position_embedding_type
									snake_case   :			Tuple						    = cross_attention_frequency
									snake_case   :			str						    = encoder_hidden_size



			@classmethod
			def 				__lowercase					(			cls							:				Optional[Any]   ,	_lowercase							:				Union[str, Any]   ,	**_lowercase							:				Optional[Any]		)							->					"PretrainedConfig":
									cls._set_token_in_kwargs(A_		)

									snake_case   :			Tuple						    = cls.get_config_dict(A_   ,	**A_		)

									# get the qformer config dict if we are loading from InstructBlipConfig
									if config_dict.get("model_type"		) == "instructblip":
															snake_case   :			Tuple						    = 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       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """instructblip"""
			__magic_name__     					=     True



			def __init__(			self							:				Tuple   ,	_lowercase							:				Dict=None   ,	_lowercase							:				Optional[int]=None   ,	_lowercase							:				List[Any]=None   ,	_lowercase							:				Optional[int]=32   ,	**_lowercase							:				Union[str, Any]		)							->					str:
									super().__init__(**A_		)

									if vision_config is None:
															snake_case   :			str						    = {}
															logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values."		)

									if qformer_config is None:
															snake_case   :			List[str]						    = {}
															logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."		)

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

									snake_case   :			int						    = InstructBlipVisionConfig(**A_		)
									snake_case   :			Optional[Any]						    = InstructBlipQFormerConfig(**A_		)
									snake_case   :			Optional[Any]						    = text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
									snake_case   :			Optional[Any]						    = CONFIG_MAPPING[text_model_type](**A_		)

									snake_case   :			Tuple						    = self.text_config.tie_word_embeddings
									snake_case   :			Dict						    = self.text_config.is_encoder_decoder

									snake_case   :			List[Any]						    = num_query_tokens
									snake_case   :			Any						    = self.vision_config.hidden_size
									snake_case   :			List[Any]						    = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
									snake_case   :			Union[str, Any]						    = 1.0
									snake_case   :			Dict						    = 0.02



			@classmethod
			def 				__lowercase					(			cls							:				Union[str, Any]   ,	_lowercase							:				str   ,	_lowercase							:				Dict   ,	_lowercase							:				str   ,	**_lowercase							:				Optional[int]   ,	)							->					str:
									return cls(
									    vision_config=vision_config.to_dict()   ,	qformer_config=qformer_config.to_dict()   ,	text_config=text_config.to_dict()   ,	**A_   ,	)



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Union[str, Any]:
									snake_case   :			List[str]						    = copy.deepcopy(self.__dict__		)
									snake_case   :			Tuple						    = self.vision_config.to_dict()
									snake_case   :			Tuple						    = self.qformer_config.to_dict()
									snake_case   :			int						    = self.text_config.to_dict()
									snake_case   :			Any						    = self.__class__.model_type
									return output





"""simple docstring"""




import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
A__ :       Optional[int]				= [
    'kernels/rwkv/wkv_cuda.cu',
    'kernels/rwkv/wkv_op.cpp',
    'kernels/deformable_detr/ms_deform_attn.h',
    'kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh',
    'models/graphormer/algos_graphormer.pyx',
]




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  for file in FILES_TO_FIND:
    if not (transformers_path / file).exists():
      return False
  return True


if __name__ == "__main__":
  A__ :       List[Any]				= argparse.ArgumentParser()
  parser.add_argument('--check_lib', action='store_true', help='Whether to check the build or the actual package.')
  A__ :       Optional[int]				= parser.parse_args()
  if args.check_lib:
    A__ :       Union[str, Any]				= importlib.import_module('transformers')
    A__ :       Optional[Any]				= Path(transformers_module.__file__).parent
  else:
    A__ :       str				= Path.cwd() / 'build/lib/transformers'
  if not test_custom_files_are_present(transformers_path):
    raise ValueError('The built release does not contain the custom files. Fix this before going further!')
