
'''simple docstring'''







import copy
import os
from typing import Union

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


_lowercase :  Union[str, Any]    							=       logging.get_logger(__name__)

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






class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     '''git_vision_model'''






					def __init__(   self				,							__SCREAMING_SNAKE_CASE=7_68				,							__SCREAMING_SNAKE_CASE=30_72				,							__SCREAMING_SNAKE_CASE=12				,							__SCREAMING_SNAKE_CASE=12				,							__SCREAMING_SNAKE_CASE=3				,							__SCREAMING_SNAKE_CASE=2_24				,							__SCREAMING_SNAKE_CASE=16				,							__SCREAMING_SNAKE_CASE="quick_gelu"				,							__SCREAMING_SNAKE_CASE=1E-5				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=0.02				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											super().__init__(**__SCREAMING_SNAKE_CASE		)

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






					@classmethod
					def 	_snake_case    (   cls				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE		)

											lowercase_						, lowercase_	:     Union[str, Any]													=       cls.get_config_dict(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)

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







class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     '''git'''






					def __init__(   self				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=3_05_22				,							__SCREAMING_SNAKE_CASE=7_68				,							__SCREAMING_SNAKE_CASE=6				,							__SCREAMING_SNAKE_CASE=12				,							__SCREAMING_SNAKE_CASE=30_72				,							__SCREAMING_SNAKE_CASE="gelu"				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE=10_24				,							__SCREAMING_SNAKE_CASE=0.02				,							__SCREAMING_SNAKE_CASE=1E-1_2				,							__SCREAMING_SNAKE_CASE=0				,							__SCREAMING_SNAKE_CASE="absolute"				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=1_01				,							__SCREAMING_SNAKE_CASE=1_02				,							__SCREAMING_SNAKE_CASE=None				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





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

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

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

											lowercase_	:     int													=       bos_token_id
											lowercase_	:     int													=       eos_token_id






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     str													=       copy.deepcopy(self.__dict__		)
											lowercase_	:     Optional[int]													=       self.vision_config.to_dict()
											lowercase_	:     List[str]													=       self.__class__.model_type
											return output




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCamelCase  							=						logging.get_logger(__name__)


if is_vision_available():
      import PIL



class   snake_case_       (		__A       ):
   __A	:		str         =				["pixel_values"]






   def __init__(      self				:     int	,						lowercase_				:     bool = True	,						lowercase_				:     Dict[str, int] = None	,						lowercase_				:     PILImageResampling = PILImageResampling.BICUBIC	,						lowercase_				:     bool = True	,						lowercase_				:     Dict[str, int] = None	,						lowercase_				:     bool = True	,						lowercase_				:     Union[int, float] = 1 / 2_55	,						lowercase_				:     bool = True	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     bool = True	,						**lowercase_				:     Union[str, Any]	,						)					->						None:
       super().__init__(**lowercase_  )
       lowercase__ :   Tuple    					=				size if size is not None else {"shortest_edge": 2_24}
       lowercase__ :   Tuple    					=				get_size_dict(lowercase_	,						default_to_square=lowercase_  )
       lowercase__ :   List[str]    					=				crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
       lowercase__ :   Tuple    					=				get_size_dict(lowercase_	,						default_to_square=lowercase_	,						param_name="crop_size"  )

       lowercase__ :   Dict    					=				do_resize
       lowercase__ :   List[Any]    					=				size
       lowercase__ :   int    					=				resample
       lowercase__ :   Union[str, Any]    					=				do_center_crop
       lowercase__ :   Optional[int]    					=				crop_size
       lowercase__ :   List[str]    					=				do_rescale
       lowercase__ :   int    					=				rescale_factor
       lowercase__ :   List[Any]    					=				do_normalize
       lowercase__ :   Union[str, Any]    					=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       lowercase__ :   str    					=				image_std if image_std is not None else OPENAI_CLIP_STD
       lowercase__ :   Dict    					=				do_convert_rgb






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     np.ndarray	,						lowercase_				:     Dict[str, int]	,						lowercase_				:     PILImageResampling = PILImageResampling.BICUBIC	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     Union[str, Any]	,						)					->						np.ndarray:
       lowercase__ :   str    					=				get_size_dict(lowercase_	,						default_to_square=lowercase_  )
       if "shortest_edge" not in size:
           raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''  )
       lowercase__ :   Dict    					=				get_resize_output_image_size(lowercase_	,						size=size["shortest_edge"]	,						default_to_square=lowercase_  )
       return resize(lowercase_	,						size=lowercase_	,						resample=lowercase_	,						data_format=lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     int	,						lowercase_				:     np.ndarray	,						lowercase_				:     Dict[str, int]	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     int	,						)					->						np.ndarray:
       lowercase__ :   Optional[Any]    					=				get_size_dict(lowercase_  )
       if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''  )
       return center_crop(lowercase_	,						size=(size["height"], size["width"])	,						data_format=lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     np.ndarray	,						lowercase_				:     Union[int, float]	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     Optional[Any]	,						)					->						Any:
       return rescale(lowercase_	,						scale=lowercase_	,						data_format=lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     str	,						lowercase_				:     np.ndarray	,						lowercase_				:     Union[float, List[float]]	,						lowercase_				:     Union[float, List[float]]	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     str	,						)					->						np.ndarray:
       return normalize(lowercase_	,						mean=lowercase_	,						std=lowercase_	,						data_format=lowercase_	,						**lowercase_  )







   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     ImageInput	,						lowercase_				:     bool = None	,						lowercase_				:     Dict[str, int] = None	,						lowercase_				:     PILImageResampling = None	,						lowercase_				:     bool = None	,						lowercase_				:     int = None	,						lowercase_				:     bool = None	,						lowercase_				:     float = None	,						lowercase_				:     bool = None	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     bool = None	,						lowercase_				:     Optional[Union[str, TensorType]] = None	,						lowercase_				:     Optional[ChannelDimension] = ChannelDimension.FIRST	,						**lowercase_				:     Union[str, Any]	,						)					->						PIL.Image.Image:
       lowercase__ :   int    					=				do_resize if do_resize is not None else self.do_resize
       lowercase__ :   Dict    					=				size if size is not None else self.size
       lowercase__ :   List[Any]    					=				get_size_dict(lowercase_	,						param_name="size"	,						default_to_square=lowercase_  )
       lowercase__ :   Dict    					=				resample if resample is not None else self.resample
       lowercase__ :   int    					=				do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase__ :   Dict    					=				crop_size if crop_size is not None else self.crop_size
       lowercase__ :   List[str]    					=				get_size_dict(lowercase_	,						param_name="crop_size"	,						default_to_square=lowercase_  )
       lowercase__ :   int    					=				do_rescale if do_rescale is not None else self.do_rescale
       lowercase__ :   Dict    					=				rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase__ :   Union[str, Any]    					=				do_normalize if do_normalize is not None else self.do_normalize
       lowercase__ :   int    					=				image_mean if image_mean is not None else self.image_mean
       lowercase__ :   List[str]    					=				image_std if image_std is not None else self.image_std
       lowercase__ :   Union[str, Any]    					=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       lowercase__ :   Union[str, Any]    					=				make_list_of_images(lowercase_  )

       if not valid_images(lowercase_  ):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."  )

       if do_resize and size is None:
           raise ValueError("Size must be specified if do_resize is True."  )

       if do_center_crop and crop_size is None:
           raise ValueError("Crop size must be specified if do_center_crop is True."  )

       if do_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."  )

       if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("Image mean and std must be specified if do_normalize is True."  )

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
           lowercase__ :   Dict    					=				[convert_to_rgb(lowercase_  ) for image in images]

       # All transformations expect numpy arrays.
       lowercase__ :   Optional[Any]    					=				[to_numpy_array(lowercase_  ) for image in images]

       if do_resize:
           lowercase__ :   List[Any]    					=				[self.resize(image=lowercase_	,						size=lowercase_	,						resample=lowercase_  ) for image in images]

       if do_center_crop:
           lowercase__ :   int    					=				[self.center_crop(image=lowercase_	,						size=lowercase_  ) for image in images]

       if do_rescale:
           lowercase__ :   str    					=				[self.rescale(image=lowercase_	,						scale=lowercase_  ) for image in images]

       if do_normalize:
           lowercase__ :   Optional[int]    					=				[self.normalize(image=lowercase_	,						mean=lowercase_	,						std=lowercase_  ) for image in images]

       lowercase__ :   Optional[Any]    					=				[to_channel_dimension_format(lowercase_	,						lowercase_  ) for image in images]

       lowercase__ :   List[str]    					=				{"pixel_values": images}
       return BatchFeature(data=lowercase_	,						tensor_type=lowercase_  )



