

import copy
import os
from typing import Union

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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"git_vision_model"


						def __init__(       self						,					A_=768						,					A_=3072						,					A_=12						,					A_=12						,					A_=3						,					A_=224						,					A_=16						,					A_="quick_gelu"						,					A_=1e-5						,					A_=0.0						,					A_=0.02						,					**A_						,					):







										'''simple docstring'''


										super().__init__(**A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    hidden_size
										_UpperCAmelCase       :		Tuple 		=    intermediate_size
										_UpperCAmelCase       :		int 		=    num_hidden_layers
										_UpperCAmelCase       :		str 		=    num_attention_heads
										_UpperCAmelCase       :		str 		=    num_channels
										_UpperCAmelCase       :		Any 		=    patch_size
										_UpperCAmelCase       :		Any 		=    image_size
										_UpperCAmelCase       :		Optional[Any] 		=    initializer_range
										_UpperCAmelCase       :		Optional[Any] 		=    attention_dropout
										_UpperCAmelCase       :		Optional[Any] 		=    layer_norm_eps
										_UpperCAmelCase       :		Dict 		=    hidden_act





						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_						,					**A_	):







										'''simple docstring'''


										cls._set_token_in_kwargs(A_	)

										_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    cls.get_config_dict(A_						,					**A_	)

										# get the vision config dict if we are loading from GITConfig
										if config_dict.get("model_type"	) == "git":
														_UpperCAmelCase       :		List[Any] 		=    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  (	UpperCAmelCase				):
						_lowercase    						=			"git"


						def __init__(       self						,					A_=None						,					A_=30522						,					A_=768						,					A_=6						,					A_=12						,					A_=3072						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=1024						,					A_=0.02						,					A_=1e-12						,					A_=0						,					A_="absolute"						,					A_=True						,					A_=False						,					A_=101						,					A_=102						,					A_=None						,					**A_						,					):







										'''simple docstring'''


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

										if vision_config is None:
														_UpperCAmelCase       :		Any 		=    {}
														logger.info("vision_config is None. initializing the GitVisionConfig with default values."	)

										_UpperCAmelCase       :		str 		=    GitVisionConfig(**A_	)
										_UpperCAmelCase       :		Any 		=    vocab_size
										_UpperCAmelCase       :		List[Any] 		=    hidden_size
										_UpperCAmelCase       :		List[str] 		=    num_hidden_layers
										_UpperCAmelCase       :		Optional[Any] 		=    num_attention_heads
										_UpperCAmelCase       :		Tuple 		=    hidden_act
										_UpperCAmelCase       :		List[Any] 		=    intermediate_size
										_UpperCAmelCase       :		int 		=    hidden_dropout_prob
										_UpperCAmelCase       :		str 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		Tuple 		=    max_position_embeddings
										_UpperCAmelCase       :		Union[str, Any] 		=    initializer_range
										_UpperCAmelCase       :		Tuple 		=    layer_norm_eps
										_UpperCAmelCase       :		Dict 		=    position_embedding_type
										_UpperCAmelCase       :		Any 		=    use_cache
										_UpperCAmelCase       :		Optional[int] 		=    tie_word_embeddings
										_UpperCAmelCase       :		List[str] 		=    num_image_with_embedding

										_UpperCAmelCase       :		Optional[int] 		=    bos_token_id
										_UpperCAmelCase       :		List[Any] 		=    eos_token_id





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    copy.deepcopy(self.__dict__	)
										_UpperCAmelCase       :		Union[str, Any] 		=    self.vision_config.to_dict()
										_UpperCAmelCase       :		Union[str, Any] 		=    self.__class__.model_type
										return output





import logging
import os

from .state import PartialState

class 				a  (	logging.LoggerAdapter				):


						@staticmethod
						def        _UpperCAmelCase				(       A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    PartialState()
										return not main_process_only or (main_process_only and state.is_main_process)





						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					*A_						,					**A_	):







										'''simple docstring'''


										if PartialState._shared_state == {}:
														raise RuntimeError(
														    "You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."	)
										_UpperCAmelCase       :		Tuple 		=    kwargs.pop("main_process_only"						,					A_	)
										_UpperCAmelCase       :		int 		=    kwargs.pop("in_order"						,					A_	)

										if self.isEnabledFor(A_	):
														if self._should_log(A_	):
																		_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[int] 		=    self.process(A_						,					A_	)
																		self.logger.log(A_						,					A_						,					*A_						,					**A_	)

														elif in_order:
																		_UpperCAmelCase       :		Dict 		=    PartialState()
																		for i in range(state.num_processes	):
																						if i == state.process_index:
																										_UpperCAmelCase		,							_UpperCAmelCase       :		Union[str, Any] 		=    self.process(A_						,					A_	)
																										self.logger.log(A_						,					A_						,					*A_						,					**A_	)
																						state.wait_for_everyone()







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  str = None )   ->       List[Any]:
				if log_level is None:
								_UpperCAmelCase       :		List[str] 		=    os.environ.get("ACCELERATE_LOG_LEVEL"  ,      lowerCAmelCase )
				_UpperCAmelCase       :		str 		=    logging.getLogger(lowerCAmelCase )
				if log_level is not None:
								logger.setLevel(log_level.upper() )
								logger.root.setLevel(log_level.upper() )
				return MultiProcessAdapter(lowerCAmelCase  ,      {} )


