





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


A   : Optional[int]             =			logging.get_logger(__name__)


if is_vision_available():
		import PIL







class 			lowerCamelCase					(						__SCREAMING_SNAKE_CASE  ):
					_SCREAMING_SNAKE_CASE							   =    ["pixel_values"]


					def __init__(					self	:  Optional[Any]       ,     __snake_case	:  bool = True       ,     __snake_case	:  Dict[str, int] = None       ,     __snake_case	:  PILImageResampling = PILImageResampling.BICUBIC       ,     __snake_case	:  bool = True       ,     __snake_case	:  Dict[str, int] = None       ,     __snake_case	:  bool = True       ,     __snake_case	:  Union[int, float] = 1 / 2_55       ,     __snake_case	:  bool = True       ,     __snake_case	:  Optional[Union[float, List[float]]] = None       ,     __snake_case	:  Optional[Union[float, List[float]]] = None       ,     __snake_case	:  bool = True       ,     **__snake_case	:  Dict       ,     ):





											'''simple docstring'''





											super().__init__(**_a       )
											_snake_case:   Tuple  					=		size if size is not None else {"""shortest_edge""": 2_24}
											_snake_case:   Optional[Any]  					=		get_size_dict(_a       ,     default_to_square=_a       )
											_snake_case:   Tuple  					=		crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
											_snake_case:   Optional[Any]  					=		get_size_dict(_a       ,     default_to_square=_a       ,     param_name='crop_size'       )

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ,     __snake_case	:  np.ndarray       ,     __snake_case	:  Dict[str, int]       ,     __snake_case	:  PILImageResampling = PILImageResampling.BICUBIC       ,     __snake_case	:  Optional[Union[str, ChannelDimension]] = None       ,     **__snake_case	:  Optional[Any]       ,     ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		get_size_dict(_a       ,     default_to_square=_a       )
											if "shortest_edge" not in size:
																	raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''       )
											_snake_case:   Tuple  					=		get_resize_output_image_size(_a       ,     size=size['shortest_edge']       ,     default_to_square=_a       )
											return resize(_a       ,     size=_a       ,     resample=_a       ,     data_format=_a       ,     **_a       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ,     __snake_case	:  np.ndarray       ,     __snake_case	:  Dict[str, int]       ,     __snake_case	:  Optional[Union[str, ChannelDimension]] = None       ,     **__snake_case	:  Optional[int]       ,     ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		get_size_dict(_a       )
											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(_a       ,     size=(size['height'], size['width'])       ,     data_format=_a       ,     **_a       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ,     __snake_case	:  np.ndarray       ,     __snake_case	:  Union[int, float]       ,     __snake_case	:  Optional[Union[str, ChannelDimension]] = None       ,     **__snake_case	:  Any       ,     ):





											'''simple docstring'''





											return rescale(_a       ,     scale=_a       ,     data_format=_a       ,     **_a       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ,     __snake_case	:  np.ndarray       ,     __snake_case	:  Union[float, List[float]]       ,     __snake_case	:  Union[float, List[float]]       ,     __snake_case	:  Optional[Union[str, ChannelDimension]] = None       ,     **__snake_case	:  List[str]       ,     ):





											'''simple docstring'''





											return normalize(_a       ,     mean=_a       ,     std=_a       ,     data_format=_a       ,     **_a       )




					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     __snake_case	:  ImageInput       ,     __snake_case	:  bool = None       ,     __snake_case	:  Dict[str, int] = None       ,     __snake_case	:  PILImageResampling = None       ,     __snake_case	:  bool = None       ,     __snake_case	:  int = None       ,     __snake_case	:  bool = None       ,     __snake_case	:  float = None       ,     __snake_case	:  bool = None       ,     __snake_case	:  Optional[Union[float, List[float]]] = None       ,     __snake_case	:  Optional[Union[float, List[float]]] = None       ,     __snake_case	:  bool = None       ,     __snake_case	:  Optional[Union[str, TensorType]] = None       ,     __snake_case	:  Optional[ChannelDimension] = ChannelDimension.FIRST       ,     **__snake_case	:  int       ,     ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		do_resize if do_resize is not None else self.do_resize
											_snake_case:   Tuple  					=		size if size is not None else self.size
											_snake_case:   Optional[int]  					=		get_size_dict(_a       ,     param_name='size'       ,     default_to_square=_a       )
											_snake_case:   List[str]  					=		resample if resample is not None else self.resample
											_snake_case:   int  					=		do_center_crop if do_center_crop is not None else self.do_center_crop
											_snake_case:   Any  					=		crop_size if crop_size is not None else self.crop_size
											_snake_case:   int  					=		get_size_dict(_a       ,     param_name='crop_size'       ,     default_to_square=_a       )
											_snake_case:   List[Any]  					=		do_rescale if do_rescale is not None else self.do_rescale
											_snake_case:   int  					=		rescale_factor if rescale_factor is not None else self.rescale_factor
											_snake_case:   Any  					=		do_normalize if do_normalize is not None else self.do_normalize
											_snake_case:   int  					=		image_mean if image_mean is not None else self.image_mean
											_snake_case:   int  					=		image_std if image_std is not None else self.image_std
											_snake_case:   List[str]  					=		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

											_snake_case:   int  					=		make_list_of_images(_a       )

											if not valid_images(_a       ):
																	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:
																	_snake_case:   Optional[int]  					=		[convert_to_rgb(_a       ) for image in images]

											# All transformations expect numpy arrays.
											_snake_case:   Dict  					=		[to_numpy_array(_a       ) for image in images]

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

											if do_center_crop:
																	_snake_case:   Tuple  					=		[self.center_crop(image=_a       ,     size=_a       ) for image in images]

											if do_rescale:
																	_snake_case:   List[str]  					=		[self.rescale(image=_a       ,     scale=_a       ) for image in images]

											if do_normalize:
																	_snake_case:   Any  					=		[self.normalize(image=_a       ,     mean=_a       ,     std=_a       ) for image in images]

											_snake_case:   List[str]  					=		[to_channel_dimension_format(_a       ,     _a       ) for image in images]

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












'''simple docstring'''






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


A   : Tuple             =			logging.get_logger(__name__)

A   : Any             =			{
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    "biogpt"


					def __init__(					self	:  Union[str, Any]       ,     __snake_case	:  Tuple=4_23_84       ,     __snake_case	:  Any=10_24       ,     __snake_case	:  Optional[int]=24       ,     __snake_case	:  int=16       ,     __snake_case	:  str=40_96       ,     __snake_case	:  Dict="gelu"       ,     __snake_case	:  List[str]=0.1       ,     __snake_case	:  List[Any]=0.1       ,     __snake_case	:  List[Any]=10_24       ,     __snake_case	:  Optional[int]=0.02       ,     __snake_case	:  Union[str, Any]=1e-12       ,     __snake_case	:  List[Any]=True       ,     __snake_case	:  Union[str, Any]=True       ,     __snake_case	:  Any=0.0       ,     __snake_case	:  int=0.0       ,     __snake_case	:  Dict=1       ,     __snake_case	:  str=0       ,     __snake_case	:  List[str]=2       ,     **__snake_case	:  Optional[int]       ,     ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		vocab_size
											_snake_case:   Optional[int]  					=		max_position_embeddings
											_snake_case:   Dict  					=		hidden_size
											_snake_case:   Union[str, Any]  					=		num_hidden_layers
											_snake_case:   str  					=		num_attention_heads
											_snake_case:   Any  					=		intermediate_size
											_snake_case:   List[Any]  					=		hidden_act
											_snake_case:   int  					=		hidden_dropout_prob
											_snake_case:   List[str]  					=		attention_probs_dropout_prob
											_snake_case:   Optional[Any]  					=		initializer_range
											_snake_case:   Union[str, Any]  					=		layer_norm_eps
											_snake_case:   Dict  					=		scale_embedding
											_snake_case:   Dict  					=		use_cache
											_snake_case:   List[Any]  					=		layerdrop
											_snake_case:   int  					=		activation_dropout
											super().__init__(pad_token_id=__snake_case       ,     bos_token_id=__snake_case       ,     eos_token_id=__snake_case       ,     **__snake_case       )





