import copy

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


lowerCAmelCase      :				Dict     			=logging.get_logger(__name__)

lowerCAmelCase      :				int     			={}




class 						__snake_case						(			lowerCAmelCase__		):





      '''simple docstring'''






      _snake_case     		=							'fsmt_decoder'



      def __init__(       self  :					Optional[Any]     ,	_UpperCamelCase  :					Optional[Any]=0     ,	_UpperCamelCase  :					Dict=0)					->Any:


          """simple docstring"""


          super().__init__()
          _lowerCamelCase  :		Union[str, Any]			    =    vocab_size
          _lowerCamelCase  :		List[Any]			    =    bos_token_id





class 						__snake_case						(			lowerCAmelCase__		):





      '''simple docstring'''






      _snake_case     		=							'fsmt'
      _snake_case     		=							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



      def __init__(       self  :					str     ,	_UpperCamelCase  :					Optional[Any]=["en", "de"]     ,	_UpperCamelCase  :					List[str]=4_2024     ,	_UpperCamelCase  :					List[str]=4_2024     ,	_UpperCamelCase  :					Optional[int]="relu"     ,	_UpperCamelCase  :					Union[str, Any]=1024     ,	_UpperCamelCase  :					Dict=200     ,	_UpperCamelCase  :					Dict=1024     ,	_UpperCamelCase  :					List[str]=4096     ,	_UpperCamelCase  :					str=12     ,	_UpperCamelCase  :					Optional[Any]=16     ,	_UpperCamelCase  :					List[str]=0.0     ,	_UpperCamelCase  :					List[Any]=4096     ,	_UpperCamelCase  :					Dict=12     ,	_UpperCamelCase  :					Optional[int]=16     ,	_UpperCamelCase  :					Optional[int]=0.0     ,	_UpperCamelCase  :					Union[str, Any]=0.0     ,	_UpperCamelCase  :					Dict=0.1     ,	_UpperCamelCase  :					List[Any]=0.0     ,	_UpperCamelCase  :					Optional[Any]=0.0_2     ,	_UpperCamelCase  :					Optional[int]=2     ,	_UpperCamelCase  :					int=True     ,	_UpperCamelCase  :					List[str]=True     ,	_UpperCamelCase  :					Any=False     ,	_UpperCamelCase  :					Union[str, Any]=5     ,	_UpperCamelCase  :					Tuple=1.0     ,	_UpperCamelCase  :					List[str]=False     ,	_UpperCamelCase  :					Tuple=True     ,	_UpperCamelCase  :					Union[str, Any]=1     ,	_UpperCamelCase  :					Union[str, Any]=0     ,	_UpperCamelCase  :					Optional[int]=2     ,	_UpperCamelCase  :					Dict=2     ,	**_UpperCamelCase  :					Optional[Any]     ,	)					->List[str]:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    langs
          _lowerCamelCase  :		List[Any]			    =    src_vocab_size
          _lowerCamelCase  :		Optional[int]			    =    tgt_vocab_size
          _lowerCamelCase  :		Tuple			    =    d_model  # encoder_embed_dim and decoder_embed_dim

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

          _lowerCamelCase  :		Tuple			    =    DecoderConfig(vocab_size=_lowercase     ,	bos_token_id=_lowercase)
          if "decoder" in common_kwargs:
              del common_kwargs["decoder"]

          _lowerCamelCase  :		str			    =    scale_embedding  # scale factor will be sqrt(d_model) if True

          # 3 Types of Dropout
          _lowerCamelCase  :		Union[str, Any]			    =    attention_dropout
          _lowerCamelCase  :		str			    =    activation_dropout
          _lowerCamelCase  :		str			    =    dropout

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




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[int])					->Optional[int]:


          """simple docstring"""


          _lowerCamelCase  :		Optional[int]			    =    copy.deepcopy(self.__dict__)
          _lowerCamelCase  :		List[Any]			    =    self.decoder.to_dict()
          _lowerCamelCase  :		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 center_crop, 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, is_vision_available, logging


if is_vision_available():
	import PIL


__A						:		Dict          =						logging.get_logger(__name__)

class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= ["pixel_values"]




					def __init__(   self:		str,     _lowercase:		bool = True,     _lowercase:		Dict[str, int] = None,     _lowercase:		PILImageResampling = PIL.Image.NEAREST,     _lowercase:		bool = False,     _lowercase:		Dict[str, int] = None,     _lowercase:		Union[int, float] = 1 / 255,     _lowercase:		bool = False,     _lowercase:		bool = True,     _lowercase:		bool = True,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		bool = True,     **_lowercase:		Union[str, Any],     ):


									'''simple docstring'''


									super().__init__(**_lowercase)
									__lowerCAmelCase					=  size if size is not None else {"""height""": 346, """width""": 346}
									__lowerCAmelCase					=  get_size_dict(_lowercase)
									__lowerCAmelCase					=  crop_size if crop_size is not None else {"""height""": 289, """width""": 289}
									__lowerCAmelCase					=  get_size_dict(_lowercase,     param_name="""crop_size""")

									__lowerCAmelCase					=  do_resize
									__lowerCAmelCase					=  size
									__lowerCAmelCase					=  resample
									__lowerCAmelCase					=  do_center_crop
									__lowerCAmelCase					=  crop_size
									__lowerCAmelCase					=  do_rescale
									__lowerCAmelCase					=  rescale_factor
									__lowerCAmelCase					=  rescale_offset
									__lowerCAmelCase					=  do_normalize
									__lowerCAmelCase					=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									__lowerCAmelCase					=  image_std if image_std is not None else IMAGENET_STANDARD_STD
									__lowerCAmelCase					=  include_top




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		np.ndarray,     _lowercase:		Dict[str, int],     _lowercase:		PILImageResampling = PIL.Image.NEAREST,     _lowercase:		Optional[Union[str, ChannelDimension]] = None,     **_lowercase:		Dict,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  get_size_dict(_lowercase)
									if "height" not in size or "width" not in size:
													raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}''')
									return resize(
									    _lowercase,     size=(size["""height"""], size["""width"""]),     resample=_lowercase,     data_format=_lowercase,     **_lowercase)




					def     _lowercase						(   self:		List[str],     _lowercase:		np.ndarray,     _lowercase:		Dict[str, int],     _lowercase:		Optional[Union[str, ChannelDimension]] = None,     **_lowercase:		Tuple,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  get_size_dict(_lowercase)
									if "height" not in size or "width" not in size:
													raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}''')
									return center_crop(_lowercase,     size=(size["""height"""], size["""width"""]),     data_format=_lowercase,     **_lowercase)




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		np.ndarray,     _lowercase:		Union[int, float],     _lowercase:		bool = True,     _lowercase:		Optional[Union[str, ChannelDimension]] = None,     **_lowercase:		Dict,     ):


									'''simple docstring'''


									if offset:
													__lowerCAmelCase					=  (image - 127.5) * scale
													if data_format is not None:
																	__lowerCAmelCase					=  to_channel_dimension_format(_lowercase,     _lowercase)
													__lowerCAmelCase					=  rescaled_image.astype(np.floataa)
									else:
													__lowerCAmelCase					=  rescale(_lowercase,     scale=_lowercase,     data_format=_lowercase,     **_lowercase)
									return rescaled_image




					def     _lowercase						(   self:		Dict,     _lowercase:		np.ndarray,     _lowercase:		Union[float, List[float]],     _lowercase:		Union[float, List[float]],     _lowercase:		Optional[Union[str, ChannelDimension]] = None,     **_lowercase:		Any,     ):


									'''simple docstring'''


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







					def     _lowercase						(   self:		Dict,     _lowercase:		ImageInput,     _lowercase:		bool = None,     _lowercase:		Dict[str, int] = None,     _lowercase:		str=None,     _lowercase:		bool = None,     _lowercase:		Dict[str, int] = None,     _lowercase:		bool = None,     _lowercase:		float = None,     _lowercase:		bool = None,     _lowercase:		bool = None,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		bool = None,     _lowercase:		Optional[Union[str, TensorType]] = None,     _lowercase:		ChannelDimension = ChannelDimension.FIRST,     **_lowercase:		Tuple,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  do_resize if do_resize is not None else self.do_resize
									__lowerCAmelCase					=  resample if resample is not None else self.resample
									__lowerCAmelCase					=  do_center_crop if do_center_crop is not None else self.do_center_crop
									__lowerCAmelCase					=  do_rescale if do_rescale is not None else self.do_rescale
									__lowerCAmelCase					=  rescale_factor if rescale_factor is not None else self.rescale_factor
									__lowerCAmelCase					=  rescale_offset if rescale_offset is not None else self.rescale_offset
									__lowerCAmelCase					=  do_normalize if do_normalize is not None else self.do_normalize
									__lowerCAmelCase					=  image_mean if image_mean is not None else self.image_mean
									__lowerCAmelCase					=  image_std if image_std is not None else self.image_std
									__lowerCAmelCase					=  include_top if include_top is not None else self.include_top

									__lowerCAmelCase					=  size if size is not None else self.size
									__lowerCAmelCase					=  get_size_dict(_lowercase)
									__lowerCAmelCase					=  crop_size if crop_size is not None else self.crop_size
									__lowerCAmelCase					=  get_size_dict(_lowercase,     param_name="""crop_size""")

									__lowerCAmelCase					=  make_list_of_images(_lowercase)

									if not valid_images(_lowercase):
													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 or resample is None:
													raise ValueError("""Size and resample 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.""")

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

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

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

									if do_rescale:
													__lowerCAmelCase					=  [self.rescale(image=_lowercase,     scale=_lowercase,     offset=_lowercase) for image in images]

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

									if include_top:
													__lowerCAmelCase					=  [self.normalize(image=_lowercase,     mean=[0, 0, 0],     std=_lowercase) for image in images]

									__lowerCAmelCase					=  [to_channel_dimension_format(_lowercase,     _lowercase) for image in images]

									__lowerCAmelCase					=  {"""pixel_values""": images}
									return BatchFeature(data=_lowercase,     tensor_type=_lowercase)





