






"""simple docstring"""







import copy

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


_UpperCamelCase     			=  logging.get_logger(__name__)

_UpperCamelCase     			=  {}







class 					lowerCamelCase__					(       snake_case ):
    SCREAMING_SNAKE_CASE		=			'''fsmt_decoder'''






    def __init__( self	,A=0	,A=0					):
          super().__init__()
          UpperCAmelCase							=       vocab_size
          UpperCAmelCase							=       bos_token_id







class 					lowerCamelCase__					(       snake_case ):
    SCREAMING_SNAKE_CASE		=			'''fsmt'''
    SCREAMING_SNAKE_CASE		=			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






    def __init__( self	,A=["en", "de"]	,A=42_024	,A=42_024	,A="relu"	,A=1_024	,A=200	,A=1_024	,A=4_096	,A=12	,A=16	,A=0.0	,A=4_096	,A=12	,A=16	,A=0.0	,A=0.0	,A=0.1	,A=0.0	,A=0.02	,A=2	,A=True	,A=True	,A=False	,A=5	,A=1.0	,A=False	,A=True	,A=1	,A=0	,A=2	,A=2	,**A	,):
          UpperCAmelCase							=       langs
          UpperCAmelCase							=       src_vocab_size
          UpperCAmelCase							=       tgt_vocab_size
          UpperCAmelCase							=       d_model  # encoder_embed_dim and decoder_embed_dim

          UpperCAmelCase							=       encoder_ffn_dim
          UpperCAmelCase							=       UpperCAmelCase							=       encoder_layers
          UpperCAmelCase							=       encoder_attention_heads
          UpperCAmelCase							=       encoder_layerdrop
          UpperCAmelCase							=       decoder_layerdrop
          UpperCAmelCase							=       decoder_ffn_dim
          UpperCAmelCase							=       decoder_layers
          UpperCAmelCase							=       decoder_attention_heads
          UpperCAmelCase							=       max_position_embeddings
          UpperCAmelCase							=       init_std  # Normal(0, this parameter)
          UpperCAmelCase							=       activation_function

          UpperCAmelCase							=       DecoderConfig(vocab_size=A	,bos_token_id=A					)
          if "decoder" in common_kwargs:
                del common_kwargs["decoder"]

          UpperCAmelCase							=       scale_embedding  # scale factor will be sqrt(d_model) if True

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

          UpperCAmelCase							=       use_cache
          super().__init__(
              pad_token_id=A	,bos_token_id=A	,eos_token_id=A	,decoder_start_token_id=A	,is_encoder_decoder=A	,tie_word_embeddings=A	,forced_eos_token_id=A	,max_length=A	,num_beams=A	,length_penalty=A	,early_stopping=A	,**A	,)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       copy.deepcopy(self.__dict__					)
          UpperCAmelCase							=       self.decoder.to_dict()
          UpperCAmelCase							=       self.__class__.model_type
          return output











"""simple docstring"""







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 					lowerCamelCase__					(       snake_case ):
    SCREAMING_SNAKE_CASE		=			['''pixel_values''']






    def __init__( self	,A = True	,A = None	,A = PILImageResampling.BILINEAR	,A = True	,A = 1 / 255	,A = True	,A = None	,A = None	,**A	,):
          super().__init__(**A					)
          UpperCAmelCase							=       size if size is not None else {"""height""": 224, """width""": 224}
          UpperCAmelCase							=       get_size_dict(A					)
          UpperCAmelCase							=       do_resize
          UpperCAmelCase							=       do_rescale
          UpperCAmelCase							=       do_normalize
          UpperCAmelCase							=       size
          UpperCAmelCase							=       resample
          UpperCAmelCase							=       rescale_factor
          UpperCAmelCase							=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCAmelCase							=       image_std if image_std is not None else IMAGENET_STANDARD_STD






    def 				_UpperCamelCase							( self	,A	,A	,A = PILImageResampling.BILINEAR	,A = None	,**A	,):
          UpperCAmelCase							=       get_size_dict(A					)
          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(
              A	,size=(size["""height"""], size["""width"""])	,resample=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A	,A = None	,**A					):
          return rescale(A	,scale=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A = None	,**A	,):
          return normalize(A	,mean=A	,std=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = ChannelDimension.FIRST	,**A	,):
          UpperCAmelCase							=       do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase							=       do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase							=       do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase							=       resample if resample is not None else self.resample
          UpperCAmelCase							=       rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase							=       image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase							=       image_std if image_std is not None else self.image_std

          UpperCAmelCase							=       size if size is not None else self.size
          UpperCAmelCase							=       get_size_dict(A					)

          UpperCAmelCase							=       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_rescale and rescale_factor is None:
                raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

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

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

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

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

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

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



