
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,
    get_resize_output_image_size,
    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


UpperCAmelCase__		:     Tuple     			=      logging.get_logger(__name__)






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Optional[int] 				=    ['''pixel_values''']


   def __init__(					self     :							List[Any]			,			lowerCAmelCase_     :							bool = True			,			lowerCAmelCase_     :							Dict[str, int] = None			,			lowerCAmelCase_     :							float = None			,			lowerCAmelCase_     :							PILImageResampling = PILImageResampling.BILINEAR			,			lowerCAmelCase_     :							bool = True			,			lowerCAmelCase_     :							Union[int, float] = 1 / 2_5_5			,			lowerCAmelCase_     :							bool = True			,			lowerCAmelCase_     :							Optional[Union[float, List[float]]] = None			,			lowerCAmelCase_     :							Optional[Union[float, List[float]]] = None			,			**lowerCAmelCase_     :							int			,			):

      """simple docstring"""
      super().__init__(**lowerCAmelCase_     )
      _A:  List[str]					=       size if size is not None else {'''shortest_edge''': 3_8_4}
      _A:  Optional[Any]					=       get_size_dict(lowerCAmelCase_			,			default_to_square=lowerCAmelCase_     )

      _A:  int					=       do_resize
      _A:  int					=       size
      # Default value set here for backwards compatibility where the value in config is None
      _A:  List[str]					=       crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
      _A:  int					=       resample
      _A:  int					=       do_rescale
      _A:  List[str]					=       rescale_factor
      _A:  List[Any]					=       do_normalize
      _A:  Dict					=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _A:  Any					=       image_std if image_std is not None else IMAGENET_STANDARD_STD


   def  __magic_name__				(					self     :							Tuple			,			lowerCAmelCase_     :							np.ndarray			,			lowerCAmelCase_     :							Dict[str, int]			,			lowerCAmelCase_     :							float			,			lowerCAmelCase_     :							PILImageResampling = PILImageResampling.BICUBIC			,			lowerCAmelCase_     :							Optional[Union[str, ChannelDimension]] = None			,			**lowerCAmelCase_     :							Optional[int]			,			):

      """simple docstring"""
      _A:  int					=       get_size_dict(lowerCAmelCase_			,			default_to_square=lowerCAmelCase_     )
      if "shortest_edge" not in size:
         raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""     )
      _A:  Dict					=       size['''shortest_edge''']

      if shortest_edge < 3_8_4:
         # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
         _A:  int					=       int(shortest_edge / crop_pct     )
         _A:  str					=       get_resize_output_image_size(lowerCAmelCase_			,			size=lowerCAmelCase_			,			default_to_square=lowerCAmelCase_     )
         _A:  Tuple					=       resize(image=lowerCAmelCase_			,			size=lowerCAmelCase_			,			resample=lowerCAmelCase_			,			data_format=lowerCAmelCase_			,			**lowerCAmelCase_     )
         # then crop to (shortest_edge, shortest_edge)
         return center_crop(image=lowerCAmelCase_			,			size=(shortest_edge, shortest_edge)			,			data_format=lowerCAmelCase_			,			**lowerCAmelCase_     )
      else:
         # warping (no cropping) when evaluated at 384 or larger
         return resize(
             lowerCAmelCase_			,			size=(shortest_edge, shortest_edge)			,			resample=lowerCAmelCase_			,			data_format=lowerCAmelCase_			,			**lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Optional[int]			,			lowerCAmelCase_     :							np.ndarray			,			lowerCAmelCase_     :							Union[int, float]			,			lowerCAmelCase_     :							Optional[Union[str, ChannelDimension]] = None			,			**lowerCAmelCase_     :							Any			,			):

      """simple docstring"""
      return rescale(lowerCAmelCase_			,			scale=lowerCAmelCase_			,			data_format=lowerCAmelCase_			,			**lowerCAmelCase_     )


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

      """simple docstring"""
      return normalize(lowerCAmelCase_			,			mean=lowerCAmelCase_			,			std=lowerCAmelCase_			,			data_format=lowerCAmelCase_			,			**lowerCAmelCase_     )


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

      """simple docstring"""
      _A:  Union[str, Any]					=       do_resize if do_resize is not None else self.do_resize
      _A:  Tuple					=       crop_pct if crop_pct is not None else self.crop_pct
      _A:  Optional[Any]					=       resample if resample is not None else self.resample
      _A:  Dict					=       do_rescale if do_rescale is not None else self.do_rescale
      _A:  Dict					=       rescale_factor if rescale_factor is not None else self.rescale_factor
      _A:  List[str]					=       do_normalize if do_normalize is not None else self.do_normalize
      _A:  List[str]					=       image_mean if image_mean is not None else self.image_mean
      _A:  int					=       image_std if image_std is not None else self.image_std

      _A:  Optional[Any]					=       size if size is not None else self.size
      _A:  Dict					=       get_size_dict(lowerCAmelCase_			,			default_to_square=lowerCAmelCase_     )

      _A:  List[str]					=       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 or resample is None:
         raise ValueError('''Size and resample must be specified if do_resize is True.'''     )

      if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
         raise ValueError('''crop_pct must be specified if size < 384.'''     )

      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.
      _A:  Optional[Any]					=       [to_numpy_array(lowerCAmelCase_     ) for image in images]

      if do_resize:
         _A:  Tuple					=       [self.resize(image=lowerCAmelCase_			,			size=lowerCAmelCase_			,			crop_pct=lowerCAmelCase_			,			resample=lowerCAmelCase_     ) for image in images]

      if do_rescale:
         _A:  List[Any]					=       [self.rescale(image=lowerCAmelCase_			,			scale=lowerCAmelCase_     ) for image in images]

      if do_normalize:
         _A:  List[str]					=       [self.normalize(image=lowerCAmelCase_			,			mean=lowerCAmelCase_			,			std=lowerCAmelCase_     ) for image in images]

      _A:  List[Any]					=       [to_channel_dimension_format(lowerCAmelCase_			,			lowerCAmelCase_     ) for image in images]

      _A:  str					=       {'''pixel_values''': images}
      return BatchFeature(data=lowerCAmelCase_			,			tensor_type=lowerCAmelCase_     )





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__		:     List[str]     			=      logging.get_logger(__name__)

UpperCAmelCase__		:     Union[str, Any]     			=      {
    'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json',
    'xlm-mlm-ende-1024': 'https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json',
    'xlm-mlm-enfr-1024': 'https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json',
    'xlm-mlm-enro-1024': 'https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json',
    'xlm-mlm-tlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json',
    'xlm-mlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json',
    'xlm-clm-enfr-1024': 'https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json',
    'xlm-clm-ende-1024': 'https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json',
    'xlm-mlm-17-1280': 'https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json',
    'xlm-mlm-100-1280': 'https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json',
}






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    int 				=    '''xlm'''
   __UpperCamelCase		:    Optional[Any] 				=    {
       '''hidden_size''': '''emb_dim''',
       '''num_attention_heads''': '''n_heads''',
       '''num_hidden_layers''': '''n_layers''',
       '''n_words''': '''vocab_size''',  # For backward compatibility
   }


   def __init__(					self     :							List[Any]			,			lowerCAmelCase_     :							Dict=3_0_1_4_5			,			lowerCAmelCase_     :							Union[str, Any]=2_0_4_8			,			lowerCAmelCase_     :							List[str]=1_2			,			lowerCAmelCase_     :							Optional[Any]=1_6			,			lowerCAmelCase_     :							Union[str, Any]=0.1			,			lowerCAmelCase_     :							List[str]=0.1			,			lowerCAmelCase_     :							Optional[int]=True			,			lowerCAmelCase_     :							List[str]=False			,			lowerCAmelCase_     :							Tuple=False			,			lowerCAmelCase_     :							List[str]=False			,			lowerCAmelCase_     :							Optional[Any]=1			,			lowerCAmelCase_     :							List[str]=True			,			lowerCAmelCase_     :							Tuple=5_1_2			,			lowerCAmelCase_     :							Tuple=2_0_4_8**-0.5			,			lowerCAmelCase_     :							List[str]=1e-12			,			lowerCAmelCase_     :							Any=0.02			,			lowerCAmelCase_     :							int=0			,			lowerCAmelCase_     :							int=1			,			lowerCAmelCase_     :							Dict=2			,			lowerCAmelCase_     :							List[Any]=3			,			lowerCAmelCase_     :							int=5			,			lowerCAmelCase_     :							Optional[int]=True			,			lowerCAmelCase_     :							Union[str, Any]="first"			,			lowerCAmelCase_     :							int=True			,			lowerCAmelCase_     :							List[Any]=None			,			lowerCAmelCase_     :							Any=True			,			lowerCAmelCase_     :							int=0.1			,			lowerCAmelCase_     :							Tuple=5			,			lowerCAmelCase_     :							Tuple=5			,			lowerCAmelCase_     :							Optional[int]=0			,			lowerCAmelCase_     :							Optional[Any]=0			,			lowerCAmelCase_     :							str=2			,			lowerCAmelCase_     :							Dict=0			,			**lowerCAmelCase_     :							List[Any]			,			):

      """simple docstring"""
      _A:  Dict					=       vocab_size
      _A:  Dict					=       emb_dim
      _A:  Optional[Any]					=       n_layers
      _A:  str					=       n_heads
      _A:  Optional[int]					=       dropout
      _A:  Union[str, Any]					=       attention_dropout
      _A:  Any					=       gelu_activation
      _A:  Optional[Any]					=       sinusoidal_embeddings
      _A:  Any					=       causal
      _A:  Optional[int]					=       asm
      _A:  List[str]					=       n_langs
      _A:  int					=       use_lang_emb
      _A:  Any					=       layer_norm_eps
      _A:  Tuple					=       bos_index
      _A:  Any					=       eos_index
      _A:  List[str]					=       pad_index
      _A:  str					=       unk_index
      _A:  str					=       mask_index
      _A:  List[Any]					=       is_encoder
      _A:  List[Any]					=       max_position_embeddings
      _A:  str					=       embed_init_std
      _A:  int					=       init_std
      _A:  List[str]					=       summary_type
      _A:  List[str]					=       summary_use_proj
      _A:  Tuple					=       summary_activation
      _A:  Dict					=       summary_proj_to_labels
      _A:  Tuple					=       summary_first_dropout
      _A:  str					=       start_n_top
      _A:  str					=       end_n_top
      _A:  Optional[Any]					=       mask_token_id
      _A:  Any					=       lang_id

      if "n_words" in kwargs:
         _A:  Union[str, Any]					=       kwargs['''n_words''']

      super().__init__(pad_token_id=lowerCAmelCase_			,			bos_token_id=lowerCAmelCase_			,			**lowerCAmelCase_     )






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''


   @property
   def  __magic_name__				(					self     :							List[str]     ):

      """simple docstring"""
      if self.task == "multiple-choice":
         _A:  int					=       {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
         _A:  Tuple					=       {0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
              ('''token_type_ids''', dynamic_axis),
          ]     )



