




"""simple docstring"""







import copy
import os
from typing import Union

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


A    =			logging.get_logger(__name__)

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






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Dict 							=     """git_vision_model"""



		def __init__( self  :    List[str]      ,							snake_case  :    Union[str, Any]=768      ,							snake_case  :    List[Any]=3_072      ,							snake_case  :    Any=12      ,							snake_case  :    Tuple=12      ,							snake_case  :    Dict=3      ,							snake_case  :    Union[str, Any]=224      ,							snake_case  :    str=16      ,							snake_case  :    List[str]="quick_gelu"      ,							snake_case  :    str=1E-5      ,							snake_case  :    int=0.0      ,							snake_case  :    Optional[int]=0.02      ,							**snake_case  :    Optional[Any]      ,							)			->						List[str]:






									'''simple docstring'''

									super().__init__(**snake_case				)

									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 					A_		( cls  :    int      ,							snake_case  :    Union[str, os.PathLike]      ,							**snake_case  :    Optional[int]				)			->						"PretrainedConfig":






									'''simple docstring'''

									cls._set_token_in_kwargs(snake_case				)

									A      ,  A         =					cls.get_config_dict(snake_case      ,							**snake_case				)

									# 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(snake_case      ,							**snake_case				)






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	List[Any] 							=     """git"""



		def __init__( self  :    Optional[Any]      ,							snake_case  :    Optional[int]=None      ,							snake_case  :    Dict=30_522      ,							snake_case  :    int=768      ,							snake_case  :    str=6      ,							snake_case  :    Tuple=12      ,							snake_case  :    List[str]=3_072      ,							snake_case  :    str="gelu"      ,							snake_case  :    Dict=0.1      ,							snake_case  :    Tuple=0.1      ,							snake_case  :    Dict=1_024      ,							snake_case  :    Any=0.02      ,							snake_case  :    List[str]=1E-1_2      ,							snake_case  :    List[Any]=0      ,							snake_case  :    Tuple="absolute"      ,							snake_case  :    Tuple=True      ,							snake_case  :    Dict=False      ,							snake_case  :    int=101      ,							snake_case  :    int=102      ,							snake_case  :    List[Any]=None      ,							**snake_case  :    Union[str, Any]      ,							)			->						Tuple:






									'''simple docstring'''

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

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

									A         =					GitVisionConfig(**snake_case				)
									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 					A_		( self  :    int				)			->						Dict:






									'''simple docstring'''

									A         =					copy.deepcopy(self.__dict__				)
									A         =					self.vision_config.to_dict()
									A         =					self.__class__.model_type
									return output








"""simple docstring"""







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A    =			{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MraForMaskedLM',
							    'MraForMultipleChoice',
							    'MraForQuestionAnswering',
							    'MraForSequenceClassification',
							    'MraForTokenClassification',
							    'MraLayer',
							    'MraModel',
							    'MraPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							A    =			_LazyModule(__name__, globals()['__file__'], _import_structure)



