


import copy

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


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

__UpperCamelCase							:							Optional[int]    							=					{}

class    __magic_name__						(  __snake_case):
     A:		List[str]								=   "fsmt_decoder"


     def __init__(   self				:  Optional[Any]				,						lowerCamelCase__				:  Tuple=0				,						lowerCamelCase__				:  List[str]=0      )	->   Any:





      '''simple docstring'''






      super().__init__()
      UpperCamelCase__       :     Optional[Any] 							= vocab_size
      UpperCamelCase__       :     Union[str, Any] 							= bos_token_id



class    __magic_name__						(  __snake_case):
     A:		Dict								=   "fsmt"
     A:		int								=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


     def __init__(   self				:  int				,						lowerCamelCase__				:  Optional[Any]=["en", "de"]				,						lowerCamelCase__				:  Any=42024				,						lowerCamelCase__				:  Any=42024				,						lowerCamelCase__				:  List[str]="relu"				,						lowerCamelCase__				:  Any=1024				,						lowerCamelCase__				:  Optional[int]=200				,						lowerCamelCase__				:  str=1024				,						lowerCamelCase__				:  Any=4096				,						lowerCamelCase__				:  Dict=12				,						lowerCamelCase__				:  Tuple=16				,						lowerCamelCase__				:  List[Any]=0.0				,						lowerCamelCase__				:  str=4096				,						lowerCamelCase__				:  Union[str, Any]=12				,						lowerCamelCase__				:  Any=16				,						lowerCamelCase__				:  List[str]=0.0				,						lowerCamelCase__				:  str=0.0				,						lowerCamelCase__				:  Optional[int]=0.1				,						lowerCamelCase__				:  List[Any]=0.0				,						lowerCamelCase__				:  Tuple=0.02				,						lowerCamelCase__				:  str=2				,						lowerCamelCase__				:  List[str]=True				,						lowerCamelCase__				:  Dict=True				,						lowerCamelCase__				:  Any=False				,						lowerCamelCase__				:  Any=5				,						lowerCamelCase__				:  Optional[int]=1.0				,						lowerCamelCase__				:  List[str]=False				,						lowerCamelCase__				:  List[Any]=True				,						lowerCamelCase__				:  List[str]=1				,						lowerCamelCase__				:  int=0				,						lowerCamelCase__				:  Optional[Any]=2				,						lowerCamelCase__				:  Any=2				,						**lowerCamelCase__				:  Optional[Any]				,						)	->   int:





      '''simple docstring'''






      UpperCamelCase__       :     Dict 							= langs
      UpperCamelCase__       :     Tuple 							= src_vocab_size
      UpperCamelCase__       :     Optional[int] 							= tgt_vocab_size
      UpperCamelCase__       :     Optional[Any] 							= d_model  # encoder_embed_dim and decoder_embed_dim

      UpperCamelCase__       :     List[str] 							= encoder_ffn_dim
      UpperCamelCase__       :     List[Any] 							= encoder_layers
      UpperCamelCase__       :     List[Any] 							= encoder_attention_heads
      UpperCamelCase__       :     Union[str, Any] 							= encoder_layerdrop
      UpperCamelCase__       :     List[Any] 							= decoder_layerdrop
      UpperCamelCase__       :     str 							= decoder_ffn_dim
      UpperCamelCase__       :     Optional[int] 							= decoder_layers
      UpperCamelCase__       :     int 							= decoder_attention_heads
      UpperCamelCase__       :     List[Any] 							= max_position_embeddings
      UpperCamelCase__       :     Dict 							= init_std  # Normal(0, this parameter)
      UpperCamelCase__       :     Dict 							= activation_function

      UpperCamelCase__       :     List[str] 							= DecoderConfig(vocab_size=lowerCamelCase_				,						bos_token_id=lowerCamelCase_      )
      if "decoder" in common_kwargs:
       del common_kwargs["decoder"]

      UpperCamelCase__       :     Any 							= scale_embedding  # scale factor will be sqrt(d_model) if True

      # 3 Types of Dropout
      UpperCamelCase__       :     int 							= attention_dropout
      UpperCamelCase__       :     int 							= activation_dropout
      UpperCamelCase__       :     Any 							= dropout

      UpperCamelCase__       :     Any 							= use_cache
      super().__init__(
          pad_token_id=lowerCamelCase_				,						bos_token_id=lowerCamelCase_				,						eos_token_id=lowerCamelCase_				,						decoder_start_token_id=lowerCamelCase_				,						is_encoder_decoder=lowerCamelCase_				,						tie_word_embeddings=lowerCamelCase_				,						forced_eos_token_id=lowerCamelCase_				,						max_length=lowerCamelCase_				,						num_beams=lowerCamelCase_				,						length_penalty=lowerCamelCase_				,						early_stopping=lowerCamelCase_				,						**lowerCamelCase_				,						)


     def   UpperCAmelCase__						(   self				:  List[str]      )	->   Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__       :     str 							= copy.deepcopy(self.__dict__      )
      UpperCamelCase__       :     int 							= self.decoder.to_dict()
      UpperCamelCase__       :     Dict 							= 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 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


UpperCamelCase_									=   logging.get_logger(__name__)







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : Optional[Any]       =						["pixel_values"]


   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Optional[Dict[str, int]] = None  ,lowerCamelCase_:     PILImageResampling = PILImageResampling.BILINEAR  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Union[int, float] = 1 / 255  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,**lowerCamelCase_:     Dict  ,)	->      None:
       super().__init__(**lowerCamelCase_							)
       UpperCAmelCase_     :       Optional[Any]  		=					size if size is not None else {"""height""": 224, """width""": 224}
       UpperCAmelCase_     :       str  		=					get_size_dict(lowerCamelCase_							)
       UpperCAmelCase_     :       Tuple  		=					do_resize
       UpperCAmelCase_     :       Optional[Any]  		=					do_rescale
       UpperCAmelCase_     :       Optional[Any]  		=					do_normalize
       UpperCAmelCase_     :       int  		=					size
       UpperCAmelCase_     :       List[Any]  		=					resample
       UpperCAmelCase_     :       Optional[Any]  		=					rescale_factor
       UpperCAmelCase_     :       Dict  		=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       UpperCAmelCase_     :       Any  		=					image_std if image_std is not None else IMAGENET_STANDARD_STD


   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     np.ndarray  ,lowerCamelCase_:     Dict[str, int]  ,lowerCamelCase_:     PILImageResampling = PILImageResampling.BILINEAR  ,lowerCamelCase_:     Optional[Union[str, ChannelDimension]] = None  ,**lowerCamelCase_:     Optional[int]  ,)	->      np.ndarray:
       UpperCAmelCase_     :       Dict  		=					get_size_dict(lowerCamelCase_							)
       if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''							)
       return resize(
           lowerCamelCase_  ,size=(size["""height"""], size["""width"""])  ,resample=lowerCamelCase_  ,data_format=lowerCamelCase_  ,**lowerCamelCase_							)


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


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






   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     ImageInput  ,lowerCamelCase_:     Optional[bool] = None  ,lowerCamelCase_:     Dict[str, int] = None  ,lowerCamelCase_:     PILImageResampling = None  ,lowerCamelCase_:     Optional[bool] = None  ,lowerCamelCase_:     Optional[float] = None  ,lowerCamelCase_:     Optional[bool] = None  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,lowerCamelCase_:     Optional[Union[str, TensorType]] = None  ,lowerCamelCase_:     Union[str, ChannelDimension] = ChannelDimension.FIRST  ,**lowerCamelCase_:     int  ,)	->      List[Any]:
       UpperCAmelCase_     :       str  		=					do_resize if do_resize is not None else self.do_resize
       UpperCAmelCase_     :       Dict  		=					do_rescale if do_rescale is not None else self.do_rescale
       UpperCAmelCase_     :       Dict  		=					do_normalize if do_normalize is not None else self.do_normalize
       UpperCAmelCase_     :       int  		=					resample if resample is not None else self.resample
       UpperCAmelCase_     :       str  		=					rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCAmelCase_     :       Dict  		=					image_mean if image_mean is not None else self.image_mean
       UpperCAmelCase_     :       int  		=					image_std if image_std is not None else self.image_std

       UpperCAmelCase_     :       Tuple  		=					size if size is not None else self.size
       UpperCAmelCase_     :       Union[str, Any]  		=					get_size_dict(lowerCamelCase_							)

       UpperCAmelCase_     :       Optional[int]  		=					make_list_of_images(lowerCamelCase_							)

       if not valid_images(lowerCamelCase_							):
           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_rescale and rescale_factor is None:
           raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

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

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

       if do_rescale:
           UpperCAmelCase_     :       Optional[Any]  		=					[self.rescale(image=lowerCamelCase_  ,scale=lowerCamelCase_							) for image in images]

       if do_normalize:
           UpperCAmelCase_     :       str  		=					[self.normalize(image=lowerCamelCase_  ,mean=lowerCamelCase_  ,std=lowerCamelCase_							) for image in images]

       UpperCAmelCase_     :       Any  		=					[to_channel_dimension_format(lowerCamelCase_  ,lowerCamelCase_							) for image in images]

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