
import copy

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


__lowerCAmelCase   :     Tuple      =						logging.get_logger(__name__)

__lowerCAmelCase   :     Optional[Any]      =						{}


class   __lowerCAmelCase		(		lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Optional[Any]       				=      '''fsmt_decoder'''




      def __init__( self						:    Optional[Any]			,    _snake_case						:    Tuple=0			,    _snake_case						:    Any=0			):
          super().__init__()
          __lowercase						:     List[Any]		      =    vocab_size
          __lowercase						:     Tuple		      =    bos_token_id






class   __lowerCAmelCase		(		lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Optional[Any]       				=      '''fsmt'''
      A__		:     int       				=      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




      def __init__( self						:    Dict			,    _snake_case						:    Any=["en", "de"]			,    _snake_case						:    Optional[Any]=4_2024			,    _snake_case						:    Tuple=4_2024			,    _snake_case						:    Dict="relu"			,    _snake_case						:    int=1024			,    _snake_case						:    Dict=200			,    _snake_case						:    Dict=1024			,    _snake_case						:    Optional[Any]=4096			,    _snake_case						:    Tuple=12			,    _snake_case						:    int=16			,    _snake_case						:    List[str]=0.0			,    _snake_case						:    List[str]=4096			,    _snake_case						:    Optional[int]=12			,    _snake_case						:    List[str]=16			,    _snake_case						:    List[Any]=0.0			,    _snake_case						:    Union[str, Any]=0.0			,    _snake_case						:    Optional[Any]=0.1			,    _snake_case						:    Union[str, Any]=0.0			,    _snake_case						:    Optional[Any]=0.02			,    _snake_case						:    Any=2			,    _snake_case						:    Optional[Any]=True			,    _snake_case						:    int=True			,    _snake_case						:    Dict=False			,    _snake_case						:    List[Any]=5			,    _snake_case						:    Any=1.0			,    _snake_case						:    Optional[Any]=False			,    _snake_case						:    Tuple=True			,    _snake_case						:    Tuple=1			,    _snake_case						:    int=0			,    _snake_case						:    Tuple=2			,    _snake_case						:    Optional[Any]=2			,    **_snake_case						:    Any			,    ):
          __lowercase						:     str		      =    langs
          __lowercase						:     Dict		      =    src_vocab_size
          __lowercase						:     int		      =    tgt_vocab_size
          __lowercase						:     List[Any]		      =    d_model  # encoder_embed_dim and decoder_embed_dim

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

          __lowercase						:     Dict		      =    DecoderConfig(vocab_size=_snake_case			,    bos_token_id=_snake_case			)
          if "decoder" in common_kwargs:
              del common_kwargs["decoder"]

          __lowercase						:     Optional[int]		      =    scale_embedding  # scale factor will be sqrt(d_model) if True

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

          __lowercase						:     Optional[Any]		      =    use_cache
          super().__init__(
              pad_token_id=_snake_case			,    bos_token_id=_snake_case			,    eos_token_id=_snake_case			,    decoder_start_token_id=_snake_case			,    is_encoder_decoder=_snake_case			,    tie_word_embeddings=_snake_case			,    forced_eos_token_id=_snake_case			,    max_length=_snake_case			,    num_beams=_snake_case			,    length_penalty=_snake_case			,    early_stopping=_snake_case			,    **_snake_case			,    )




      def 						snake_case_     ( self						:    Optional[Any]			):
          __lowercase						:     List[Any]		      =    copy.deepcopy(self.__dict__			)
          __lowercase						:     str		      =    self.decoder.to_dict()
          __lowercase						:     int		      =    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


__lowerCAmelCase   :     Dict      =						logging.get_logger(__name__)


class   __lowerCAmelCase		(		lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Union[str, Any]       				=      ['''pixel_values''']




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




      def 						snake_case_     ( self						:    int			,    _snake_case						:    np.ndarray			,    _snake_case						:    Dict[str, int]			,    _snake_case						:    PILImageResampling = PILImageResampling.BILINEAR			,    _snake_case						:    Optional[Union[str, ChannelDimension]] = None			,    **_snake_case						:    Optional[int]			,    ):
          __lowercase						:     List[Any]		      =    get_size_dict(_snake_case			)
          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(
              _snake_case			,    size=(size['''height'''], size['''width'''])			,    resample=_snake_case			,    data_format=_snake_case			,    **_snake_case			)




      def 						snake_case_     ( self						:    Union[str, Any]			,    _snake_case						:    np.ndarray			,    _snake_case						:    float			,    _snake_case						:    Optional[Union[str, ChannelDimension]] = None			,    **_snake_case						:    Dict			):
          return rescale(_snake_case			,    scale=_snake_case			,    data_format=_snake_case			,    **_snake_case			)




      def 						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						:    Any			,    ):
          return normalize(_snake_case			,    mean=_snake_case			,    std=_snake_case			,    data_format=_snake_case			,    **_snake_case			)




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

          __lowercase						:     Optional[int]		      =    size if size is not None else self.size
          __lowercase						:     Tuple		      =    get_size_dict(_snake_case			)

          __lowercase						:     Optional[int]		      =    make_list_of_images(_snake_case			)

          if not valid_images(_snake_case			):
              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.
          __lowercase						:     Optional[Any]		      =    [to_numpy_array(_snake_case			) for image in images]

          if do_resize:
              __lowercase						:     List[Any]		      =    [self.resize(image=_snake_case			,    size=_snake_case			,    resample=_snake_case			) for image in images]

          if do_rescale:
              __lowercase						:     Tuple		      =    [self.rescale(image=_snake_case			,    scale=_snake_case			) for image in images]

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

          __lowercase						:     Tuple		      =    [to_channel_dimension_format(_snake_case			,    _snake_case			) for image in images]

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


