

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							(							__A      ):
			'''simple docstring'''







			lowerCamelCase__							='git_vision_model'
			def __init__(	self							:       Dict					,					a							:       Optional[Any]=768					,					a							:       Union[str, Any]=3072					,					a							:       Tuple=12					,					a							:       int=12					,					a							:       List[str]=3					,					a							:       str=224					,					a							:       List[str]=16					,					a							:       List[Any]="quick_gelu"					,					a							:       Any=1e-5					,					a							:       str=0.0					,					a							:       Optional[int]=0.02					,					**a							:       Optional[int]					,					)							->				Any:

				"""simple docstring"""

				super().__init__(**a		)

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






			@classmethod
			def 							__UpperCamelCase       (	cls							:       int					,					a							:       Union[str, os.PathLike]					,					**a							:       Union[str, Any]		)							->				"PretrainedConfig":

				"""simple docstring"""

				cls._set_token_in_kwargs(a		)

				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					int    =				cls.get_config_dict(a					,					**a		)

				# get the vision config dict if we are loading from GITConfig
				if config_dict.get("model_type"		) == "git":
					SCREAMING_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 _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			lowerCamelCase__							='git'
			def __init__(	self							:       Optional[Any]					,					a							:       Optional[Any]=None					,					a							:       str=3_0522					,					a							:       Dict=768					,					a							:       int=6					,					a							:       Optional[int]=12					,					a							:       List[str]=3072					,					a							:       int="gelu"					,					a							:       List[str]=0.1					,					a							:       int=0.1					,					a							:       List[Any]=1024					,					a							:       Union[str, Any]=0.02					,					a							:       Dict=1e-12					,					a							:       Any=0					,					a							:       Any="absolute"					,					a							:       List[Any]=True					,					a							:       Optional[Any]=False					,					a							:       Any=101					,					a							:       Union[str, Any]=102					,					a							:       Optional[Any]=None					,					**a							:       Any					,					)							->				str:

				"""simple docstring"""

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

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

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

				SCREAMING_SNAKE_CASE      :					int    =				bos_token_id
				SCREAMING_SNAKE_CASE      :					Any    =				eos_token_id






			def 							__UpperCamelCase       (	self							:       Dict		)							->				List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Tuple    =				copy.deepcopy(self.__dict__		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				self.vision_config.to_dict()
				SCREAMING_SNAKE_CASE      :					Tuple    =				self.__class__.model_type
				return output
import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size'''		,  [None, 400 * 2**20, 600 * 2**20]						)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,  ['''default''', 0, 100 * 2**20, 900 * 2**20]						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						if input_in_memory_max_size != "default":
												monkeypatch.setattr(datasets.config		,  '''IN_MEMORY_MAX_SIZE'''		,  _lowercase						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							datasets.config.IN_MEMORY_MAX_SIZE
						if input_in_memory_max_size == "default":
												assert in_memory_max_size == 0
						else:
												assert in_memory_max_size == input_in_memory_max_size
						if dataset_size and in_memory_max_size:
												SCREAMING_SNAKE_CASE							:    str							=							dataset_size < in_memory_max_size
						else:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							False
						SCREAMING_SNAKE_CASE							:    Any							=							is_small_dataset(_lowercase						)
						assert result == expected





