





"""simple docstring"""




from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase_     :						Dict						=					logging.get_logger(__name__)

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : Tuple	    =   ["""pixel_values"""]

      def __init__(     self					,      snake_case_ = True					,      snake_case_ = 3_2					,      snake_case_=PILImageResampling.BILINEAR					,      snake_case_ = True					,      **snake_case_					,      ):

        """simple docstring"""

        A_  :       str										=  do_resize
        A_  :       Any										=  do_rescale
        A_  :       int										=  size_divisor
        A_  :       Optional[int]										=  resample
        super().__init__(**snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_ = None					,      **snake_case_  ):

        """simple docstring"""

        A_						,       A_  :       Dict										=  get_image_size(snake_case_  )
        # Rounds the height and width down to the closest multiple of size_divisor
        A_  :       List[Any]										=  height // size_divisor * size_divisor
        A_  :       str										=  width // size_divisor * size_divisor
        A_  :       Tuple										=  resize(snake_case_					,      (new_h, new_w)					,      resample=snake_case_					,      data_format=snake_case_					,      **snake_case_  )
        return image

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_ = None					,      **snake_case_  ):

        """simple docstring"""

        return rescale(image=snake_case_					,      scale=snake_case_					,      data_format=snake_case_					,      **snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_=None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = ChannelDimension.FIRST					,      **snake_case_					,      ):

        """simple docstring"""

        A_  :       int										=  do_resize if do_resize is not None else self.do_resize
        A_  :       int										=  do_rescale if do_rescale is not None else self.do_rescale
        A_  :       Optional[int]										=  size_divisor if size_divisor is not None else self.size_divisor
        A_  :       int										=  resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
          raise ValueError('size_divisor is required for resizing'  )

        A_  :       Dict										=  make_list_of_images(snake_case_  )

        if not valid_images(snake_case_  ):
          raise ValueError('Invalid image(s)'  )

        # All transformations expect numpy arrays.
        A_  :       Dict										=  [to_numpy_array(snake_case_  ) for img in images]

        if do_resize:
          A_  :       Any										=  [self.resize(snake_case_					,      size_divisor=snake_case_					,      resample=snake_case_  ) for image in images]

        if do_rescale:
          A_  :       Union[str, Any]										=  [self.rescale(snake_case_					,      scale=1 / 2_5_5  ) for image in images]

        A_  :       Dict										=  [to_channel_dimension_format(snake_case_					,      snake_case_  ) for image in images]

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






"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase_     :						str						=					logging.get_logger(__name__)

lowerCamelCase_     :						Any						=					{
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : Tuple	    =   """xmod"""

      def __init__(     self					,      snake_case_=3_0_5_2_2					,      snake_case_=7_6_8					,      snake_case_=1_2					,      snake_case_=1_2					,      snake_case_=3_0_7_2					,      snake_case_="gelu"					,      snake_case_=0.1					,      snake_case_=0.1					,      snake_case_=5_1_2					,      snake_case_=2					,      snake_case_=0.02					,      snake_case_=1E-12					,      snake_case_=1					,      snake_case_=0					,      snake_case_=2					,      snake_case_="absolute"					,      snake_case_=True					,      snake_case_=None					,      snake_case_=False					,      snake_case_=2					,      snake_case_=False					,      snake_case_=True					,      snake_case_=True					,      snake_case_=("en_XX",)					,      snake_case_=None					,      **snake_case_					,      ):

        """simple docstring"""

        super().__init__(pad_token_id=snake_case_					,      bos_token_id=snake_case_					,      eos_token_id=snake_case_					,      **snake_case_  )

        A_  :       Union[str, Any]										=  vocab_size
        A_  :       Any										=  hidden_size
        A_  :       List[str]										=  num_hidden_layers
        A_  :       Tuple										=  num_attention_heads
        A_  :       int										=  hidden_act
        A_  :       Any										=  intermediate_size
        A_  :       Any										=  hidden_dropout_prob
        A_  :       Dict										=  attention_probs_dropout_prob
        A_  :       Union[str, Any]										=  max_position_embeddings
        A_  :       List[Any]										=  type_vocab_size
        A_  :       List[str]										=  initializer_range
        A_  :       Any										=  layer_norm_eps
        A_  :       Optional[Any]										=  position_embedding_type
        A_  :       int										=  use_cache
        A_  :       Dict										=  classifier_dropout
        A_  :       int										=  pre_norm
        A_  :       Optional[Any]										=  adapter_reduction_factor
        A_  :       List[Any]										=  adapter_layer_norm
        A_  :       int										=  adapter_reuse_layer_norm
        A_  :       Dict										=  ln_before_adapter
        A_  :       List[str]										=  list(snake_case_  )
        A_  :       Union[str, Any]										=  default_language


class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

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