





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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A 			=  logging.get_logger(__name__)




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  ['''pixel_values''']







					def __init__(						self	,			_UpperCAmelCase = True	,			_UpperCAmelCase = None	,			_UpperCAmelCase = PILImageResampling.BILINEAR	,			_UpperCAmelCase = True	,			_UpperCAmelCase = None	,			_UpperCAmelCase = True	,			_UpperCAmelCase = 1 / 255	,			_UpperCAmelCase = True	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			**_UpperCAmelCase	,			):
									super().__init__(**_UpperCAmelCase     )
									__a :						List[Any]			       =  size if size is not None else {'''shortest_edge''': 256}
									__a :						str			       =  get_size_dict(_UpperCAmelCase	,			default_to_square=_UpperCAmelCase     )
									__a :						Optional[int]			       =  crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
									__a :						Any			       =  get_size_dict(_UpperCAmelCase     )
									__a :						Optional[Any]			       =  do_resize
									__a :						List[Any]			       =  size
									__a :						Dict			       =  resample
									__a :						Tuple			       =  do_center_crop
									__a :						Optional[Any]			       =  crop_size
									__a :						Any			       =  do_rescale
									__a :						List[str]			       =  rescale_factor
									__a :						Optional[Any]			       =  do_normalize
									__a :						List[Any]			       =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									__a :						List[Any]			       =  image_std if image_std is not None else IMAGENET_STANDARD_STD







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase = PILImageResampling.BICUBIC	,			_UpperCAmelCase = None	,			**_UpperCAmelCase	,			):
									__a :						Optional[Any]			       =  get_size_dict(_UpperCAmelCase	,			default_to_square=_UpperCAmelCase     )
									if "shortest_edge" not in size:
													raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""     )
									__a :						str			       =  get_resize_output_image_size(_UpperCAmelCase	,			size=size['''shortest_edge''']	,			default_to_square=_UpperCAmelCase     )
									return resize(_UpperCAmelCase	,			size=_UpperCAmelCase	,			resample=_UpperCAmelCase	,			data_format=_UpperCAmelCase	,			**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			**_UpperCAmelCase	,			):
									__a :						Dict			       =  get_size_dict(_UpperCAmelCase     )
									return center_crop(_UpperCAmelCase	,			size=(size['''height'''], size['''width'''])	,			data_format=_UpperCAmelCase	,			**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			**_UpperCAmelCase     ):
									return rescale(_UpperCAmelCase	,			scale=_UpperCAmelCase	,			data_format=_UpperCAmelCase	,			**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			**_UpperCAmelCase	,			):
									return normalize(_UpperCAmelCase	,			mean=_UpperCAmelCase	,			std=_UpperCAmelCase	,			data_format=_UpperCAmelCase	,			**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = ChannelDimension.FIRST	,			**_UpperCAmelCase	,			):
									__a :						Optional[Any]			       =  do_resize if do_resize is not None else self.do_resize
									__a :						Optional[int]			       =  size if size is not None else self.size
									__a :						int			       =  get_size_dict(_UpperCAmelCase	,			default_to_square=_UpperCAmelCase     )
									__a :						List[str]			       =  resample if resample is not None else self.resample
									__a :						int			       =  do_center_crop if do_center_crop is not None else self.do_center_crop
									__a :						str			       =  crop_size if crop_size is not None else self.crop_size
									__a :						List[str]			       =  get_size_dict(_UpperCAmelCase     )
									__a :						Union[str, Any]			       =  do_rescale if do_rescale is not None else self.do_rescale
									__a :						Optional[int]			       =  rescale_factor if rescale_factor is not None else self.rescale_factor
									__a :						Optional[int]			       =  do_normalize if do_normalize is not None else self.do_normalize
									__a :						Optional[Any]			       =  image_mean if image_mean is not None else self.image_mean
									__a :						Dict			       =  image_std if image_std is not None else self.image_std

									__a :						List[str]			       =  make_list_of_images(_UpperCAmelCase     )

									if not valid_images(_UpperCAmelCase     ):
													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.'''     )

									# All transformations expect numpy arrays.
									__a :						Union[str, Any]			       =  [to_numpy_array(_UpperCAmelCase     ) for image in images]

									if do_resize:
													__a :						int			       =  [self.resize(image=_UpperCAmelCase	,			size=_UpperCAmelCase	,			resample=_UpperCAmelCase     ) for image in images]

									if do_center_crop:
													__a :						Optional[Any]			       =  [self.center_crop(image=_UpperCAmelCase	,			size=_UpperCAmelCase     ) for image in images]

									if do_rescale:
													__a :						int			       =  [self.rescale(image=_UpperCAmelCase	,			scale=_UpperCAmelCase     ) for image in images]

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

									__a :						List[Any]			       =  [to_channel_dimension_format(_UpperCAmelCase	,			_UpperCAmelCase     ) for image in images]

									__a :						Optional[Any]			       =  {'''pixel_values''': images}
									return BatchFeature(data=_UpperCAmelCase	,			tensor_type=_UpperCAmelCase     )






"""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 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''git_vision_model'''







					def __init__(						self	,			_UpperCAmelCase=768	,			_UpperCAmelCase=3072	,			_UpperCAmelCase=12	,			_UpperCAmelCase=12	,			_UpperCAmelCase=3	,			_UpperCAmelCase=224	,			_UpperCAmelCase=16	,			_UpperCAmelCase="quick_gelu"	,			_UpperCAmelCase=1e-5	,			_UpperCAmelCase=0.0	,			_UpperCAmelCase=0.0_2	,			**_UpperCAmelCase	,			):
									super().__init__(**_UpperCAmelCase     )

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







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			**_UpperCAmelCase     ):
									cls._set_token_in_kwargs(_UpperCAmelCase     )

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

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






class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''git'''







					def __init__(						self	,			_UpperCAmelCase=None	,			_UpperCAmelCase=30522	,			_UpperCAmelCase=768	,			_UpperCAmelCase=6	,			_UpperCAmelCase=12	,			_UpperCAmelCase=3072	,			_UpperCAmelCase="gelu"	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=1024	,			_UpperCAmelCase=0.0_2	,			_UpperCAmelCase=1e-1_2	,			_UpperCAmelCase=0	,			_UpperCAmelCase="absolute"	,			_UpperCAmelCase=True	,			_UpperCAmelCase=False	,			_UpperCAmelCase=101	,			_UpperCAmelCase=102	,			_UpperCAmelCase=None	,			**_UpperCAmelCase	,			):
									super().__init__(bos_token_id=_UpperCAmelCase	,			eos_token_id=_UpperCAmelCase	,			pad_token_id=_UpperCAmelCase	,			**_UpperCAmelCase     )

									if vision_config is None:
													__a :						Union[str, Any]			       =  {}
													logger.info('''vision_config is None. initializing the GitVisionConfig with default values.'''     )

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

									__a :						str			       =  bos_token_id
									__a :						Dict			       =  eos_token_id







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[int]			       =  copy.deepcopy(self.__dict__     )
									__a :						int			       =  self.vision_config.to_dict()
									__a :						Tuple			       =  self.__class__.model_type
									return output