



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
   import PIL

_lowercase    	=		logging.get_logger(__name__)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[str]						)->	List[List[ImageInput]]:
    if isinstance(UpperCamelCase_			,							(list, tuple)						) and isinstance(videos[0]			,							(list, tuple)						) and is_valid_image(videos[0][0]						):
        return videos

    elif isinstance(UpperCamelCase_			,							(list, tuple)						) and is_valid_image(videos[0]						):
        return [videos]

    elif is_valid_image(UpperCamelCase_						):
        return [[videos]]

    raise ValueError(f"Could not make batched video from {videos}"						)



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					['''pixel_values''']




     def __init__(					self			,    a__ = True			,    a__ = None			,    a__ = PILImageResampling.BILINEAR			,    a__ = True			,    a__ = None			,    a__ = True			,    a__ = 1 / 2_5_5			,    a__ = True			,    a__ = True			,    a__ = None			,    a__ = None			,    **a__			,    ):
         super().__init__(**a__)
         A__					=       size if size is not None else {'''shortest_edge''': 2_5_6}
         A__					=       get_size_dict(a__			,    default_to_square=a__)
         A__					=       crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
         A__					=       get_size_dict(a__			,    param_name='''crop_size''')

         A__					=       do_resize
         A__					=       size
         A__					=       do_center_crop
         A__					=       crop_size
         A__					=       resample
         A__					=       do_rescale
         A__					=       rescale_factor
         A__					=       offset
         A__					=       do_normalize
         A__					=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         A__					=       image_std if image_std is not None else IMAGENET_STANDARD_STD




     def 		snake_case_		(					self			,    a__			,    a__			,    a__ = PILImageResampling.BILINEAR			,    a__ = None			,    **a__			,    ):
         A__					=       get_size_dict(a__			,    default_to_square=a__)
         if "shortest_edge" in size:
             A__					=       get_resize_output_image_size(a__			,    size['''shortest_edge''']			,    default_to_square=a__)
         elif "height" in size and "width" in size:
             A__					=       (size['''height'''], size['''width'''])
         else:
             raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}")
         return resize(a__			,    size=a__			,    resample=a__			,    data_format=a__			,    **a__)




     def 		snake_case_		(					self			,    a__			,    a__			,    a__ = None			,    **a__			,    ):
         A__					=       get_size_dict(a__)
         if "height" not in size or "width" not in size:
             raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}")
         return center_crop(a__			,    size=(size['''height'''], size['''width'''])			,    data_format=a__			,    **a__)




     def 		snake_case_		(					self			,    a__			,    a__			,    a__ = True			,    a__ = None			,    **a__			,    ):
         A__					=       image.astype(np.floataa)
         if offset:
             A__					=       image - (scale / 2)
         return rescale(a__			,    scale=a__			,    data_format=a__			,    **a__)




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__ = None			,    **a__			,    ):
         return normalize(a__			,    mean=a__			,    std=a__			,    data_format=a__			,    **a__)




     def 		snake_case_		(					self			,    a__			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = ChannelDimension.FIRST			,    ):
         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.''')

         if offset and not do_rescale:
             raise ValueError('''For offset, do_rescale must also be set to True.''')

         # All transformations expect numpy arrays.
         A__					=       to_numpy_array(a__)

         if do_resize:
             A__					=       self.resize(image=a__			,    size=a__			,    resample=a__)

         if do_center_crop:
             A__					=       self.center_crop(a__			,    size=a__)

         if do_rescale:
             A__					=       self.rescale(image=a__			,    scale=a__			,    offset=a__)

         if do_normalize:
             A__					=       self.normalize(image=a__			,    mean=a__			,    std=a__)

         A__					=       to_channel_dimension_format(a__			,    a__)
         return image




     def 		snake_case_		(					self			,    a__			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = ChannelDimension.FIRST			,    **a__			,    ):
         A__					=       do_resize if do_resize is not None else self.do_resize
         A__					=       resample if resample is not None else self.resample
         A__					=       do_center_crop if do_center_crop is not None else self.do_center_crop
         A__					=       do_rescale if do_rescale is not None else self.do_rescale
         A__					=       rescale_factor if rescale_factor is not None else self.rescale_factor
         A__					=       offset if offset is not None else self.offset
         A__					=       do_normalize if do_normalize is not None else self.do_normalize
         A__					=       image_mean if image_mean is not None else self.image_mean
         A__					=       image_std if image_std is not None else self.image_std

         A__					=       size if size is not None else self.size
         A__					=       get_size_dict(a__			,    default_to_square=a__)
         A__					=       crop_size if crop_size is not None else self.crop_size
         A__					=       get_size_dict(a__			,    param_name='''crop_size''')

         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.''')

         A__					=       make_batched(a__)

         A__					=       [
             [
                 self._preprocess_image(
                     image=a__			,    do_resize=a__			,    size=a__			,    resample=a__			,    do_center_crop=a__			,    crop_size=a__			,    do_rescale=a__			,    rescale_factor=a__			,    offset=a__			,    do_normalize=a__			,    image_mean=a__			,    image_std=a__			,    data_format=a__			,    )
                 for img in video
             ]
             for video in videos
         ]

         A__					=       {'''pixel_values''': videos}
         return BatchFeature(data=a__			,    tensor_type=a__)





from collections import OrderedDict
from typing import Mapping

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


_lowercase    	=		logging.get_logger(__name__)

_lowercase    	=		{
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''xlm-roberta'''




     def __init__(					self			,    a__=3_0_5_2_2			,    a__=7_6_8			,    a__=1_2			,    a__=1_2			,    a__=3_0_7_2			,    a__="gelu"			,    a__=0.1			,    a__=0.1			,    a__=5_1_2			,    a__=2			,    a__=0.0_2			,    a__=1e-12			,    a__=1			,    a__=0			,    a__=2			,    a__="absolute"			,    a__=True			,    a__=None			,    **a__			,    ):
         super().__init__(pad_token_id=a__			,    bos_token_id=a__			,    eos_token_id=a__			,    **a__)

         A__					=       vocab_size
         A__					=       hidden_size
         A__					=       num_hidden_layers
         A__					=       num_attention_heads
         A__					=       hidden_act
         A__					=       intermediate_size
         A__					=       hidden_dropout_prob
         A__					=       attention_probs_dropout_prob
         A__					=       max_position_embeddings
         A__					=       type_vocab_size
         A__					=       initializer_range
         A__					=       layer_norm_eps
         A__					=       position_embedding_type
         A__					=       use_cache
         A__					=       classifier_dropout







class      _UpperCAmelCase      (							A__  ):




     @property
     def 		snake_case_		(					self):
         if self.task == "multiple-choice":
             A__					=       {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             A__					=       {0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
             ])
