


'''simple docstring'''






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_   :      List[Any]				    =      {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[str]				    =      ["GPTNeoXTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[Any]				    =      [
     "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTNeoXForCausalLM",
     "GPTNeoXForQuestionAnswering",
     "GPTNeoXForSequenceClassification",
     "GPTNeoXForTokenClassification",
     "GPTNeoXLayer",
     "GPTNeoXModel",
     "GPTNeoXPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox import (
      GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXForCausalLM,
      GPTNeoXForQuestionAnswering,
      GPTNeoXForSequenceClassification,
      GPTNeoXForTokenClassification,
      GPTNeoXLayer,
      GPTNeoXModel,
      GPTNeoXPreTrainedModel,
  )


else:
 import sys

 a_   :      Optional[Any]				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





'''simple docstring'''






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

a_   :      str				    =      {
    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		"""git_vision_model"""
 def __init__(	self ,			__magic_name__=7_68 ,			__magic_name__=30_72 ,			__magic_name__=12 ,			__magic_name__=12 ,			__magic_name__=3 ,			__magic_name__=2_24 ,			__magic_name__=16 ,			__magic_name__="quick_gelu" ,			__magic_name__=1e-5 ,			__magic_name__=0.0 ,			__magic_name__=0.0_2 ,			**__magic_name__ ,			)   ->							Union[str, Any]:
      super().__init__(**__magic_name__  )

      _a      =							hidden_size
      _a      =							intermediate_size
      _a      =							num_hidden_layers
      _a      =							num_attention_heads
      _a      =							num_channels
      _a      =							patch_size
      _a      =							image_size
      _a      =							initializer_range
      _a      =							attention_dropout
      _a      =							layer_norm_eps
      _a      =							hidden_act




 @classmethod
 def 							__UpperCAmelCase						(	cls ,			__magic_name__ ,			**__magic_name__  )   ->							"PretrainedConfig":
      cls._set_token_in_kwargs(__magic_name__  )

      _a ,				_a      =							cls.get_config_dict(__magic_name__ ,			**__magic_name__  )

      # get the vision config dict if we are loading from GITConfig
      if config_dict.get('model_type'  ) == "git":
           _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(__magic_name__ ,			**__magic_name__  )





class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		"""git"""
 def __init__(	self ,			__magic_name__=None ,			__magic_name__=3_05_22 ,			__magic_name__=7_68 ,			__magic_name__=6 ,			__magic_name__=12 ,			__magic_name__=30_72 ,			__magic_name__="gelu" ,			__magic_name__=0.1 ,			__magic_name__=0.1 ,			__magic_name__=10_24 ,			__magic_name__=0.0_2 ,			__magic_name__=1e-12 ,			__magic_name__=0 ,			__magic_name__="absolute" ,			__magic_name__=True ,			__magic_name__=False ,			__magic_name__=1_01 ,			__magic_name__=1_02 ,			__magic_name__=None ,			**__magic_name__ ,			)   ->							Optional[int]:
      super().__init__(bos_token_id=__magic_name__ ,			eos_token_id=__magic_name__ ,			pad_token_id=__magic_name__ ,			**__magic_name__  )

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

      _a      =							GitVisionConfig(**__magic_name__  )
      _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      =							use_cache
      _a      =							tie_word_embeddings
      _a      =							num_image_with_embedding

      _a      =							bos_token_id
      _a      =							eos_token_id




 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							copy.deepcopy(self.__dict__  )
      _a      =							self.vision_config.to_dict()
      _a      =							self.__class__.model_type
      return output

