

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		=logging.get_logger(__name__)






class        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  Optional[Any]						      =      ['''pixel_values''']
							def __init__(		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int] = None   ,SCREAMING_SNAKE_CASE__		:			PILImageResampling = PIL.Image.BICUBIC   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int] = None   ,SCREAMING_SNAKE_CASE__		:			Union[int, float] = 1 / 2_5_5   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, List[float]]] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, List[float]]] = None   ,**SCREAMING_SNAKE_CASE__		:			int   ,):
								super().__init__(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Dict					       = size if size is not None else {'height': 2_5_6, 'width': 2_5_6}
								__lowerCamelCase				:				int					       = get_size_dict(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Optional[int]					       = crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
								__lowerCamelCase				:				Any					       = get_size_dict(SCREAMING_SNAKE_CASE__   ,param_name='crop_size')

								__lowerCamelCase				:				Optional[int]					       = do_resize
								__lowerCamelCase				:				List[Any]					       = size
								__lowerCamelCase				:				Any					       = resample
								__lowerCamelCase				:				Optional[int]					       = do_center_crop
								__lowerCamelCase				:				Any					       = crop_size
								__lowerCamelCase				:				Optional[int]					       = do_rescale
								__lowerCamelCase				:				int					       = rescale_factor
								__lowerCamelCase				:				str					       = do_normalize
								__lowerCamelCase				:				str					       = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__lowerCamelCase				:				List[Any]					       = image_std if image_std is not None else IMAGENET_STANDARD_STD
							def     lowerCAmelCase     (		self		:			str   ,SCREAMING_SNAKE_CASE__		:			np.ndarray   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int]   ,SCREAMING_SNAKE_CASE__		:			PILImageResampling = PIL.Image.BICUBIC   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, ChannelDimension]] = None   ,**SCREAMING_SNAKE_CASE__		:			Any   ,):
								__lowerCamelCase				:				List[str]					       = get_size_dict(SCREAMING_SNAKE_CASE__)
								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(
								    SCREAMING_SNAKE_CASE__   ,size=(size['height'], size['width'])   ,resample=SCREAMING_SNAKE_CASE__   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Any   ,SCREAMING_SNAKE_CASE__		:			np.ndarray   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int]   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, ChannelDimension]] = None   ,**SCREAMING_SNAKE_CASE__		:			str   ,):
								__lowerCamelCase				:				List[str]					       = get_size_dict(SCREAMING_SNAKE_CASE__)
								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(SCREAMING_SNAKE_CASE__   ,size=(size['height'], size['width'])   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			int   ,SCREAMING_SNAKE_CASE__		:			np.ndarray   ,SCREAMING_SNAKE_CASE__		:			Union[int, float]   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, ChannelDimension]] = None   ,**SCREAMING_SNAKE_CASE__		:			int   ,):
								return rescale(SCREAMING_SNAKE_CASE__   ,scale=SCREAMING_SNAKE_CASE__   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			np.ndarray   ,SCREAMING_SNAKE_CASE__		:			Union[float, List[float]]   ,SCREAMING_SNAKE_CASE__		:			Union[float, List[float]]   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, ChannelDimension]] = None   ,**SCREAMING_SNAKE_CASE__		:			str   ,):
								return normalize(SCREAMING_SNAKE_CASE__   ,mean=SCREAMING_SNAKE_CASE__   ,std=SCREAMING_SNAKE_CASE__   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)


							def     lowerCAmelCase     (		self		:			int   ,SCREAMING_SNAKE_CASE__		:			ImageInput   ,SCREAMING_SNAKE_CASE__		:			bool = None   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int] = None   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=None   ,SCREAMING_SNAKE_CASE__		:			bool = None   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int] = None   ,SCREAMING_SNAKE_CASE__		:			bool = None   ,SCREAMING_SNAKE_CASE__		:			float = None   ,SCREAMING_SNAKE_CASE__		:			bool = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, List[float]]] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, List[float]]] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, TensorType]] = None   ,SCREAMING_SNAKE_CASE__		:			ChannelDimension = ChannelDimension.FIRST   ,**SCREAMING_SNAKE_CASE__		:			Dict   ,):
								__lowerCamelCase				:				Optional[int]					       = do_resize if do_resize is not None else self.do_resize
								__lowerCamelCase				:				int					       = resample if resample is not None else self.resample
								__lowerCamelCase				:				Optional[int]					       = do_center_crop if do_center_crop is not None else self.do_center_crop
								__lowerCamelCase				:				int					       = do_rescale if do_rescale is not None else self.do_rescale
								__lowerCamelCase				:				List[str]					       = rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowerCamelCase				:				Dict					       = do_normalize if do_normalize is not None else self.do_normalize
								__lowerCamelCase				:				Any					       = image_mean if image_mean is not None else self.image_mean
								__lowerCamelCase				:				str					       = image_std if image_std is not None else self.image_std

								__lowerCamelCase				:				Union[str, Any]					       = size if size is not None else self.size
								__lowerCamelCase				:				Any					       = get_size_dict(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Union[str, Any]					       = crop_size if crop_size is not None else self.crop_size
								__lowerCamelCase				:				Dict					       = get_size_dict(SCREAMING_SNAKE_CASE__   ,param_name='crop_size')

								__lowerCamelCase				:				List[Any]					       = make_list_of_images(SCREAMING_SNAKE_CASE__)

								if not valid_images(SCREAMING_SNAKE_CASE__):
									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				:				Union[str, Any]					       = [to_numpy_array(SCREAMING_SNAKE_CASE__) for image in images]

								if do_resize:
									__lowerCamelCase				:				Any					       = [self.resize(image=SCREAMING_SNAKE_CASE__   ,size=SCREAMING_SNAKE_CASE__   ,resample=SCREAMING_SNAKE_CASE__) for image in images]

								if do_center_crop:
									__lowerCamelCase				:				Dict					       = [self.center_crop(image=SCREAMING_SNAKE_CASE__   ,size=SCREAMING_SNAKE_CASE__) for image in images]

								if do_rescale:
									__lowerCamelCase				:				Dict					       = [self.rescale(image=SCREAMING_SNAKE_CASE__   ,scale=SCREAMING_SNAKE_CASE__) for image in images]

								if do_normalize:
									__lowerCamelCase				:				Union[str, Any]					       = [self.normalize(image=SCREAMING_SNAKE_CASE__   ,mean=SCREAMING_SNAKE_CASE__   ,std=SCREAMING_SNAKE_CASE__) for image in images]

								__lowerCamelCase				:				Optional[int]					       = [to_channel_dimension_format(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__) for image in images]

								__lowerCamelCase				:				Optional[int]					       = {'pixel_values': images}
								return BatchFeature(data=SCREAMING_SNAKE_CASE__   ,tensor_type=SCREAMING_SNAKE_CASE__)











from collections import OrderedDict
from typing import Mapping

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


A    :					Union[str, Any] =      logging.get_logger(__name__)

A    :					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	:       Any							=    '''xlm-roberta'''






			def __init__(			self   :    Optional[Any]							,	__lowerCAmelCase   :    List[Any]=3_05_22							,	__lowerCAmelCase   :    int=7_68							,	__lowerCAmelCase   :    Tuple=12							,	__lowerCAmelCase   :    str=12							,	__lowerCAmelCase   :    Union[str, Any]=30_72							,	__lowerCAmelCase   :    Union[str, Any]="gelu"							,	__lowerCAmelCase   :    Optional[int]=0.1							,	__lowerCAmelCase   :    Dict=0.1							,	__lowerCAmelCase   :    List[str]=5_12							,	__lowerCAmelCase   :    Tuple=2							,	__lowerCAmelCase   :    Dict=0.0_2							,	__lowerCAmelCase   :    List[str]=1e-12							,	__lowerCAmelCase   :    Union[str, Any]=1							,	__lowerCAmelCase   :    str=0							,	__lowerCAmelCase   :    Optional[int]=2							,	__lowerCAmelCase   :    Tuple="absolute"							,	__lowerCAmelCase   :    Any=True							,	__lowerCAmelCase   :    Any=None							,	**__lowerCAmelCase   :    str							,	)	->	Optional[Any]:







					"""simple docstring"""






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

					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    a_       (			self   :    int   )	->	Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					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),
					    ]   )



