


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


SCREAMING_SNAKE_CASE__       			=				logging.get_logger(__name__)


if is_vision_available():
							import PIL







class     __lowerCamelCase     (  snake_case_     ):


							"""simple docstring"""

							lowerCAmelCase__          =				["pixel_values"]




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






								'''simple docstring'''






								super().__init__(**UpperCAmelCase  )
								lowercase_			 = size if size is not None else {"shortest_edge": 224}
								lowercase_			 = get_size_dict(UpperCAmelCase						,       default_to_square=UpperCAmelCase  )
								lowercase_			 = crop_size if crop_size is not None else {"height": 224, "width": 224}
								lowercase_			 = get_size_dict(UpperCAmelCase  )

								lowercase_			 = do_resize
								lowercase_			 = size
								lowercase_			 = resample
								lowercase_			 = do_center_crop
								lowercase_			 = crop_size
								lowercase_			 = do_rescale
								lowercase_			 = rescale_factor
								lowercase_			 = do_normalize
								lowercase_			 = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								lowercase_			 = image_std if image_std is not None else OPENAI_CLIP_STD
								lowercase_			 = do_convert_rgb




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase = PILImageResampling.BICUBIC						,       UpperCAmelCase = None						,       **UpperCAmelCase						,       )     ->       np.ndarray:






								'''simple docstring'''






								lowercase_			 = get_size_dict(UpperCAmelCase						,       default_to_square=UpperCAmelCase  )
								lowercase_			 = get_resize_output_image_size(
								    UpperCAmelCase						,       size=(size["height"], size["width"])						,       default_to_square=UpperCAmelCase  )
								return resize(UpperCAmelCase						,       size=UpperCAmelCase						,       resample=UpperCAmelCase						,       data_format=UpperCAmelCase						,       **UpperCAmelCase  )




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase = None						,       **UpperCAmelCase						,       )     ->       np.ndarray:






								'''simple docstring'''






								lowercase_			 = get_size_dict(UpperCAmelCase  )
								return center_crop(UpperCAmelCase						,       size=(size["height"], size["width"])						,       data_format=UpperCAmelCase						,       **UpperCAmelCase  )




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase = None						,       **UpperCAmelCase						,       )     ->       List[Any]:






								'''simple docstring'''






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




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase = None						,       **UpperCAmelCase						,       )     ->       np.ndarray:






								'''simple docstring'''






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




							def 				A__      (  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 = None						,       UpperCAmelCase = ChannelDimension.FIRST						,       **UpperCAmelCase						,       )     ->       PIL.Image.Image:






								'''simple docstring'''






								lowercase_			 = do_resize if do_resize is not None else self.do_resize
								lowercase_			 = size if size is not None else self.size
								lowercase_			 = get_size_dict(UpperCAmelCase						,       default_to_square=UpperCAmelCase  )
								lowercase_			 = resample if resample is not None else self.resample
								lowercase_			 = do_center_crop if do_center_crop is not None else self.do_center_crop
								lowercase_			 = crop_size if crop_size is not None else self.crop_size
								lowercase_			 = get_size_dict(UpperCAmelCase  )
								lowercase_			 = do_rescale if do_rescale is not None else self.do_rescale
								lowercase_			 = rescale_factor if rescale_factor is not None else self.rescale_factor
								lowercase_			 = do_normalize if do_normalize is not None else self.do_normalize
								lowercase_			 = image_mean if image_mean is not None else self.image_mean
								lowercase_			 = image_std if image_std is not None else self.image_std
								lowercase_			 = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								lowercase_			 = 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."  )

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

								# All transformations expect numpy arrays.
								lowercase_			 = [to_numpy_array(UpperCAmelCase  ) for image in images]

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

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

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

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

								lowercase_			 = [to_channel_dimension_format(UpperCAmelCase						,       UpperCAmelCase  ) for image in images]

								lowercase_			 = {"pixel_values": images}
								return BatchFeature(data=UpperCAmelCase						,       tensor_type=UpperCAmelCase  )





from typing import Callable, Optional, Union

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


SCREAMING_SNAKE_CASE__       			=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       			=				{
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}







class     __lowerCamelCase     (  snake_case_     ):


							"""simple docstring"""

							lowerCAmelCase__          =				"prophetnet"
							lowerCAmelCase__          =				["past_key_values"]
							lowerCAmelCase__          =				{
							    "num_attention_heads": "num_encoder_attention_heads",
							}




							def __init__(  self						,       UpperCAmelCase = 0.1						,       UpperCAmelCase = "gelu"						,       UpperCAmelCase = 30522						,       UpperCAmelCase = 1024						,       UpperCAmelCase = 4096						,       UpperCAmelCase = 12						,       UpperCAmelCase = 16						,       UpperCAmelCase = 4096						,       UpperCAmelCase = 12						,       UpperCAmelCase = 16						,       UpperCAmelCase = 0.1						,       UpperCAmelCase = 0.1						,       UpperCAmelCase = 512						,       UpperCAmelCase = 0.02						,       UpperCAmelCase = True						,       UpperCAmelCase = True						,       UpperCAmelCase = 0						,       UpperCAmelCase = 2						,       UpperCAmelCase = 32						,       UpperCAmelCase = 128						,       UpperCAmelCase = False						,       UpperCAmelCase = 0.0						,       UpperCAmelCase = True						,       UpperCAmelCase = 0						,       UpperCAmelCase = 1						,       UpperCAmelCase = 2						,       **UpperCAmelCase						,       )     ->       Dict:






								'''simple docstring'''






								lowercase_			 = vocab_size
								lowercase_			 = hidden_size
								lowercase_			 = encoder_ffn_dim
								lowercase_			 = num_encoder_layers
								lowercase_			 = num_encoder_attention_heads
								lowercase_			 = decoder_ffn_dim
								lowercase_			 = num_decoder_layers
								lowercase_			 = num_decoder_attention_heads
								lowercase_			 = max_position_embeddings
								lowercase_			 = init_std  # Normal(0, this parameter)
								lowercase_			 = activation_function

								# parameters for prophetnet
								lowercase_			 = ngram
								lowercase_			 = num_buckets
								lowercase_			 = relative_max_distance
								lowercase_			 = disable_ngram_loss
								lowercase_			 = eps

								# 3 Types of Dropout
								lowercase_			 = attention_dropout
								lowercase_			 = activation_dropout
								lowercase_			 = dropout

								lowercase_			 = use_cache

								super().__init__(
								    pad_token_id=UpperCAmelCase						,       bos_token_id=UpperCAmelCase						,       eos_token_id=UpperCAmelCase						,       is_encoder_decoder=UpperCAmelCase						,       add_cross_attention=UpperCAmelCase						,       decoder_start_token_id=UpperCAmelCase						,       **UpperCAmelCase						,       )




							@property
							def 				A__      (  self  )     ->       int:






								'''simple docstring'''






								return self.num_encoder_layers + self.num_decoder_layers




							@num_hidden_layers.setter
							def 				A__      (  self						,       UpperCAmelCase  )     ->       Any:






								'''simple docstring'''






								raise NotImplementedError(
								    "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
								    " `num_decoder_layers`."  )

