
"""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!')


"""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__ :       List[Any]				= logging.get_logger(__name__)

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


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''instructblip_vision_model'''
       def __init__(  self			,					A_=1408			,					A_=6144			,					A_=39			,					A_=16			,					A_=224			,					A_=14			,					A_="gelu"			,					A_=1E-6			,					A_=0.0			,					A_=1E-10			,					A_=True			,					**A_			,					)  ->						Tuple:





         """simple docstring"""




         super().__init__(**A_							)

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



       @classmethod
       def 				lowercase_				(  cls			,					A_			,					**A_							)  ->						"PretrainedConfig":





         """simple docstring"""




         cls._set_token_in_kwargs(A_							)

         _lowercase			,					_lowercase:      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":
           _lowercase:      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       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''instructblip_qformer'''
       def __init__(  self			,					A_=3_0522			,					A_=768			,					A_=12			,					A_=12			,					A_=3072			,					A_="gelu"			,					A_=0.1			,					A_=0.1			,					A_=512			,					A_=0.02			,					A_=1E-12			,					A_=0			,					A_="absolute"			,					A_=2			,					A_=1408			,					**A_			,					)  ->						Optional[int]:





         """simple docstring"""




         super().__init__(pad_token_id=A_			,					**A_							)

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



       @classmethod
       def 				lowercase_				(  cls			,					A_			,					**A_							)  ->						"PretrainedConfig":





         """simple docstring"""




         cls._set_token_in_kwargs(A_							)

         _lowercase			,					_lowercase:      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":
           _lowercase:      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       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''instructblip'''
       UpperCamelCase_ =		True
       def __init__(  self			,					A_=None			,					A_=None			,					A_=None			,					A_=32			,					**A_							)  ->						str:





         """simple docstring"""




         super().__init__(**A_							)

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

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

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

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

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

         _lowercase:      List[Any]   			=  num_query_tokens
         _lowercase:      Any   			=  self.vision_config.hidden_size
         _lowercase:      List[Any]   			=  self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
         _lowercase:      Union[str, Any]   			=  1.0
         _lowercase:      Dict   			=  0.02
       @classmethod
       def 				lowercase_				(  cls			,					A_			,					A_			,					A_			,					**A_			,					)  ->						str:





         """simple docstring"""




         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]:





         """simple docstring"""




         _lowercase:      List[str]   			=  copy.deepcopy(self.__dict__							)
         _lowercase:      Tuple   			=  self.vision_config.to_dict()
         _lowercase:      Tuple   			=  self.qformer_config.to_dict()
         _lowercase:      int   			=  self.text_config.to_dict()
         _lowercase:      Any   			=  self.__class__.model_type
         return output
