



"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


A    =			logging.get_logger(__name__)

A    =			{
    '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__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	int 							=     """xlm"""
		lowerCAmelCase_       :	Dict 							=     {
		    """hidden_size""": """emb_dim""",
		    """num_attention_heads""": """n_heads""",
		    """num_hidden_layers""": """n_layers""",
		    """n_words""": """vocab_size""",  # For backward compatibility
		}



		def __init__( self  :    Tuple      ,							snake_case  :    Optional[Any]=30_145      ,							snake_case  :    Tuple=2_048      ,							snake_case  :    Union[str, Any]=12      ,							snake_case  :    str=16      ,							snake_case  :    Dict=0.1      ,							snake_case  :    int=0.1      ,							snake_case  :    Tuple=True      ,							snake_case  :    Union[str, Any]=False      ,							snake_case  :    Any=False      ,							snake_case  :    Any=False      ,							snake_case  :    str=1      ,							snake_case  :    Tuple=True      ,							snake_case  :    Any=512      ,							snake_case  :    List[Any]=2_048**-0.5      ,							snake_case  :    Any=1E-1_2      ,							snake_case  :    Dict=0.02      ,							snake_case  :    str=0      ,							snake_case  :    int=1      ,							snake_case  :    List[Any]=2      ,							snake_case  :    Tuple=3      ,							snake_case  :    List[Any]=5      ,							snake_case  :    str=True      ,							snake_case  :    Optional[int]="first"      ,							snake_case  :    Any=True      ,							snake_case  :    List[str]=None      ,							snake_case  :    Optional[int]=True      ,							snake_case  :    List[str]=0.1      ,							snake_case  :    Optional[Any]=5      ,							snake_case  :    Tuple=5      ,							snake_case  :    Optional[Any]=0      ,							snake_case  :    str=0      ,							snake_case  :    Optional[int]=2      ,							snake_case  :    Optional[int]=0      ,							**snake_case  :    Tuple      ,							)			->						int:






									'''simple docstring'''

									A         =					vocab_size
									A         =					emb_dim
									A         =					n_layers
									A         =					n_heads
									A         =					dropout
									A         =					attention_dropout
									A         =					gelu_activation
									A         =					sinusoidal_embeddings
									A         =					causal
									A         =					asm
									A         =					n_langs
									A         =					use_lang_emb
									A         =					layer_norm_eps
									A         =					bos_index
									A         =					eos_index
									A         =					pad_index
									A         =					unk_index
									A         =					mask_index
									A         =					is_encoder
									A         =					max_position_embeddings
									A         =					embed_init_std
									A         =					init_std
									A         =					summary_type
									A         =					summary_use_proj
									A         =					summary_activation
									A         =					summary_proj_to_labels
									A         =					summary_first_dropout
									A         =					start_n_top
									A         =					end_n_top
									A         =					mask_token_id
									A         =					lang_id

									if "n_words" in kwargs:
																A         =					kwargs['n_words']

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






class      UpperCAmelCase__		(      UpperCamelCase     ):



		@property
		def 					A_		( self  :    Optional[Any]				)			->						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),
									        ('token_type_ids', dynamic_axis),
									    ]				)








"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A    =			logging.get_logger(__name__)






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Tuple 							=     ["""pixel_values"""]



		def __init__( self  :    int      ,							snake_case  :    bool = True      ,							snake_case  :    Optional[Dict[str, int]] = None      ,							snake_case  :    PILImageResampling = PILImageResampling.BICUBIC      ,							snake_case  :    bool = True      ,							snake_case  :    bool = True      ,							snake_case  :    Union[int, float] = 1 / 255      ,							snake_case  :    Dict[str, int] = None      ,							snake_case  :    bool = True      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							**snake_case  :    Union[str, Any]      ,							)			->						None:






									'''simple docstring'''

									super().__init__(**snake_case				)
									A         =					size if size is not None else {'height': 224, 'width': 224}
									A         =					get_size_dict(snake_case				)
									A         =					crop_size if crop_size is not None else {'height': 224, 'width': 224}
									A         =					get_size_dict(snake_case      ,							default_to_square=snake_case      ,							param_name='crop_size'				)

									A         =					do_resize
									A         =					do_rescale
									A         =					do_normalize
									A         =					do_center_crop
									A         =					crop_size
									A         =					size
									A         =					resample
									A         =					rescale_factor
									A         =					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									A         =					image_std if image_std is not None else IMAGENET_DEFAULT_STD



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    np.ndarray      ,							snake_case  :    Dict[str, int]      ,							snake_case  :    PILImageResampling = PILImageResampling.BILINEAR      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    Dict      ,							)			->						np.ndarray:






									'''simple docstring'''

									A         =					get_size_dict(snake_case				)

									if "shortest_edge" in size:
																A         =					get_resize_output_image_size(snake_case      ,							size=size['shortest_edge']      ,							default_to_square=snake_case				)
																# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
									elif "height" in size and "width" in size:
																A         =					(size['height'], size['width'])
									else:
																raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""				)
									return resize(snake_case      ,							size=snake_case      ,							resample=snake_case      ,							data_format=snake_case      ,							**snake_case				)



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    np.ndarray      ,							snake_case  :    Dict[str, int]      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    List[Any]      ,							)			->						np.ndarray:






									'''simple docstring'''

									A         =					get_size_dict(snake_case				)
									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(snake_case      ,							size=(size['height'], size['width'])      ,							data_format=snake_case      ,							**snake_case				)



		def 					A_		( self  :    Dict      ,							snake_case  :    np.ndarray      ,							snake_case  :    float      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    Dict				)			->						np.ndarray:






									'''simple docstring'''

									return rescale(snake_case      ,							scale=snake_case      ,							data_format=snake_case      ,							**snake_case				)



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    np.ndarray      ,							snake_case  :    Union[float, List[float]]      ,							snake_case  :    Union[float, List[float]]      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    Union[str, Any]      ,							)			->						np.ndarray:






									'''simple docstring'''

									return normalize(snake_case      ,							mean=snake_case      ,							std=snake_case      ,							data_format=snake_case      ,							**snake_case				)



		def 					A_		( self  :    int      ,							snake_case  :    ImageInput      ,							snake_case  :    Optional[bool] = None      ,							snake_case  :    Dict[str, int] = None      ,							snake_case  :    PILImageResampling = None      ,							snake_case  :    bool = None      ,							snake_case  :    int = None      ,							snake_case  :    Optional[bool] = None      ,							snake_case  :    Optional[float] = None      ,							snake_case  :    Optional[bool] = None      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							snake_case  :    Optional[Union[str, TensorType]] = None      ,							snake_case  :    Union[str, ChannelDimension] = ChannelDimension.FIRST      ,							**snake_case  :    List[str]      ,							)			->						BatchFeature:






									'''simple docstring'''

									A         =					do_resize if do_resize is not None else self.do_resize
									A         =					do_rescale if do_rescale is not None else self.do_rescale
									A         =					do_normalize if do_normalize is not None else self.do_normalize
									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(snake_case      ,							param_name='crop_size'      ,							default_to_square=snake_case				)
									A         =					resample if resample is not None else self.resample
									A         =					rescale_factor if rescale_factor is not None else self.rescale_factor
									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(snake_case				)

									if not is_batched(snake_case				):
																A         =					[images]

									if not valid_images(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:
																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.'				)

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

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

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

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

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

									A         =					[to_channel_dimension_format(snake_case      ,							snake_case				) for image in images]

									A         =					{'pixel_values': images}
									return BatchFeature(data=snake_case      ,							tensor_type=snake_case				)



