


"""simple docstring"""







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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase_       :       List[Any]      						=  logging.get_logger(__name__)


if is_vision_available():
							import PIL






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






	"""simple docstring"""




	__lowerCAmelCase			 =  ["pixel_values"]


	def __init__(		self			,   __A = True			,   __A = None			,   __A = PILImageResampling.BICUBIC			,   __A = True			,   __A = None			,   __A = True			,   __A = 1 / 255			,   __A = True			,   __A = None			,   __A = None			,   __A = True			,   **__A			,   ) ->					None:
					super().__init__(**__A						)
					a					=size if size is not None else {'''shortest_edge''': 224}
					a					=get_size_dict(__A			,   default_to_square=__A						)
					a					=crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
					a					=get_size_dict(__A			,   default_to_square=__A			,   param_name='''crop_size'''						)

					a					=do_resize
					a					=size
					a					=resample
					a					=do_center_crop
					a					=crop_size
					a					=do_rescale
					a					=rescale_factor
					a					=do_normalize
					a					=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					a					=image_std if image_std is not None else OPENAI_CLIP_STD
					a					=do_convert_rgb


	def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = PILImageResampling.BICUBIC			,   __A = None			,   **__A			,   ) ->					np.ndarray:
					a					=get_size_dict(__A			,   default_to_square=__A						)
					if "shortest_edge" not in size:
									raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''						)
					a					=get_resize_output_image_size(__A			,   size=size['''shortest_edge''']			,   default_to_square=__A						)
					return resize(__A			,   size=__A			,   resample=__A			,   data_format=__A			,   **__A						)


	def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = None			,   **__A			,   ) ->					np.ndarray:
					a					=get_size_dict(__A						)
					if "height" not in size or "width" not in size:
									raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''						)
					return center_crop(__A			,   size=(size['''height'''], size['''width'''])			,   data_format=__A			,   **__A						)


	def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = None			,   **__A			,   ) ->					Any:
					return rescale(__A			,   scale=__A			,   data_format=__A			,   **__A						)


	def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A = None			,   **__A			,   ) ->					np.ndarray:
					return normalize(__A			,   mean=__A			,   std=__A			,   data_format=__A			,   **__A						)





	def 				SCREAMING_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			,   ) ->					PIL.Image.Image:
					a					=do_resize if do_resize is not None else self.do_resize
					a					=size if size is not None else self.size
					a					=get_size_dict(__A			,   param_name='''size'''			,   default_to_square=__A						)
					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					=crop_size if crop_size is not None else self.crop_size
					a					=get_size_dict(__A			,   param_name='''crop_size'''			,   default_to_square=__A						)
					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					=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					=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					a					=make_list_of_images(__A						)

					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.'''						)

					if do_resize and size is None:
									raise ValueError('''Size 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.'''						)

					# PIL RGBA images are converted to RGB
					if do_convert_rgb:
									a					=[convert_to_rgb(__A						) for image in images]

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

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

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

					if do_rescale:
									a					=[self.rescale(image=__A			,   scale=__A						) for image in images]

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

					a					=[to_channel_dimension_format(__A			,   __A						) for image in images]

					a					={'''pixel_values''': images}
					return BatchFeature(data=__A			,   tensor_type=__A						)



"""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_       :       Optional[int]      						=  {
    """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 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  "xlm-roberta"


 def __init__(		self			,   __A=3_0522			,   __A=768			,   __A=12			,   __A=12			,   __A=3072			,   __A="gelu"			,   __A=0.1			,   __A=0.1			,   __A=512			,   __A=2			,   __A=0.02			,   __A=1E-1_2			,   __A=1			,   __A=0			,   __A=2			,   __A="absolute"			,   __A=True			,   __A=None			,   **__A			,   ) ->					str:
     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 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Mapping[str, Mapping[int, str]]:
     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),
         ]						)