



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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_snake_case		=  logging.get_logger(__name__)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		['pixel_values']







							def __init__(   self,			__a = True,			__a = None,			__a = PILImageResampling.BICUBIC,			__a = True,			__a = True,			__a = 1 / 255,			__a = None,			__a = True,			__a = None,			__a = None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a)
														_lowerCAmelCase     :							Optional[int]    =    size if size is not None else {"height": 224, "width": 224}
														_lowerCAmelCase     :							Optional[Any]    =    get_size_dict(__a)
														_lowerCAmelCase     :							str    =    crop_size if crop_size is not None else {"height": 224, "width": 224}
														_lowerCAmelCase     :							Tuple    =    get_size_dict(__a,			default_to_square=__a,			param_name="crop_size")

														_lowerCAmelCase     :							Optional[int]    =    do_resize
														_lowerCAmelCase     :							Optional[int]    =    do_rescale
														_lowerCAmelCase     :							List[str]    =    do_normalize
														_lowerCAmelCase     :							int    =    do_center_crop
														_lowerCAmelCase     :							int    =    crop_size
														_lowerCAmelCase     :							List[Any]    =    size
														_lowerCAmelCase     :							Union[str, Any]    =    resample
														_lowerCAmelCase     :							Union[str, Any]    =    rescale_factor
														_lowerCAmelCase     :							List[str]    =    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
														_lowerCAmelCase     :							int    =    image_std if image_std is not None else IMAGENET_DEFAULT_STD







							def    snake_case__      (   self,			__a,			__a,			__a = PILImageResampling.BILINEAR,			__a = None,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    get_size_dict(__a)

														if "shortest_edge" in size:
																					_lowerCAmelCase     :							Dict    =    get_resize_output_image_size(__a,			size=size["shortest_edge"],			default_to_square=__a)
																					# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
														elif "height" in size and "width" in size:
																					_lowerCAmelCase     :							int    =    (size["height"], size["width"])
														else:
																					raise ValueError(f"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}")
														return resize(__a,			size=__a,			resample=__a,			data_format=__a,			**__a)







							def    snake_case__      (   self,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    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    snake_case__      (   self,			__a,			__a,			__a = None,			**__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = ChannelDimension.FIRST,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    do_resize if do_resize is not None else self.do_resize
														_lowerCAmelCase     :							str    =    do_rescale if do_rescale is not None else self.do_rescale
														_lowerCAmelCase     :							Any    =    do_normalize if do_normalize is not None else self.do_normalize
														_lowerCAmelCase     :							Optional[int]    =    do_center_crop if do_center_crop is not None else self.do_center_crop
														_lowerCAmelCase     :							Union[str, Any]    =    crop_size if crop_size is not None else self.crop_size
														_lowerCAmelCase     :							Union[str, Any]    =    get_size_dict(__a,			param_name="crop_size",			default_to_square=__a)
														_lowerCAmelCase     :							int    =    resample if resample is not None else self.resample
														_lowerCAmelCase     :							Optional[Any]    =    rescale_factor if rescale_factor is not None else self.rescale_factor
														_lowerCAmelCase     :							str    =    image_mean if image_mean is not None else self.image_mean
														_lowerCAmelCase     :							str    =    image_std if image_std is not None else self.image_std

														_lowerCAmelCase     :							str    =    size if size is not None else self.size
														_lowerCAmelCase     :							Union[str, Any]    =    get_size_dict(__a)

														if not is_batched(__a):
																					_lowerCAmelCase     :							int    =    [images]

														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.")

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

														if do_resize:
																					_lowerCAmelCase     :							Union[str, Any]    =    [self.resize(image=__a,			size=__a,			resample=__a) for image in images]

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

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

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

														_lowerCAmelCase     :							str    =    [to_channel_dimension_format(__a,			__a) for image in images]

														_lowerCAmelCase     :							str    =    {"pixel_values": images}
														return BatchFeature(data=__a,			tensor_type=__a)


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


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

__lowerCAmelCase			:     Tuple           =   {
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		"""pegasus"""
  a__           =		["""past_key_values"""]
  a__           =		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  def __init__( self			: Optional[int]			,							UpperCamelCase__			: Optional[int]=5_0265			,							UpperCamelCase__			: Optional[int]=1024			,							UpperCamelCase__			: Any=12			,							UpperCamelCase__			: Union[str, Any]=4096			,							UpperCamelCase__			: Any=16			,							UpperCamelCase__			: Union[str, Any]=12			,							UpperCamelCase__			: List[str]=4096			,							UpperCamelCase__			: Tuple=16			,							UpperCamelCase__			: Optional[int]=0.0			,							UpperCamelCase__			: List[Any]=0.0			,							UpperCamelCase__			: List[str]=True			,							UpperCamelCase__			: List[Any]=True			,							UpperCamelCase__			: List[Any]="gelu"			,							UpperCamelCase__			: List[Any]=1024			,							UpperCamelCase__			: Optional[Any]=0.1			,							UpperCamelCase__			: str=0.0			,							UpperCamelCase__			: Any=0.0			,							UpperCamelCase__			: Union[str, Any]=0.02			,							UpperCamelCase__			: Any=0			,							UpperCamelCase__			: int=False			,							UpperCamelCase__			: Any=0			,							UpperCamelCase__			: List[str]=1			,							UpperCamelCase__			: Tuple=1			,							**UpperCamelCase__			: Union[str, Any]			,							)			->	str:







    """simple docstring"""






    __magic_name__				     =       vocab_size
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       d_model
    __magic_name__				     =       encoder_ffn_dim
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       encoder_attention_heads
    __magic_name__				     =       decoder_ffn_dim
    __magic_name__				     =       decoder_layers
    __magic_name__				     =       decoder_attention_heads
    __magic_name__				     =       dropout
    __magic_name__				     =       attention_dropout
    __magic_name__				     =       activation_dropout
    __magic_name__				     =       activation_function
    __magic_name__				     =       init_std
    __magic_name__				     =       encoder_layerdrop
    __magic_name__				     =       decoder_layerdrop
    __magic_name__				     =       use_cache
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       scale_embedding  # scale factor will be sqrt(d_model) if True
    super().__init__(
        pad_token_id=UpperCamelCase__			,							eos_token_id=UpperCamelCase__			,							is_encoder_decoder=UpperCamelCase__			,							decoder_start_token_id=UpperCamelCase__			,							forced_eos_token_id=UpperCamelCase__			,							**UpperCamelCase__			,							)
  @property
  def 							_lowercase							( self			: List[Any]    )			->	int:







    """simple docstring"""






    return self.encoder_attention_heads





  @property
  def 							_lowercase							( self			: Dict    )			->	int:







    """simple docstring"""






    return self.d_model

