





"""simple docstring"""

from typing import Dict, Iterable, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


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

class        lowercase      (    a__):
				__lowerCAmelCase    :    int         =   ["""pixel_values"""]







				def __init__(							self   :					Union[str, Any]      ,     _lowerCamelCase   :					str = True      ,     _lowerCamelCase   :					Optional[Any] = None      ,     _lowerCamelCase   :					Union[str, Any] = PILImageResampling.BICUBIC      ,     _lowerCamelCase   :					Optional[int] = True      ,     _lowerCamelCase   :					Optional[Any] = None      ,     _lowerCamelCase   :					Optional[Any] = True      ,     _lowerCamelCase   :					Optional[int] = 1 / 2_55      ,     _lowerCamelCase   :					int = True      ,     _lowerCamelCase   :					str = IMAGENET_DEFAULT_MEAN      ,     _lowerCamelCase   :					Union[str, Any] = IMAGENET_DEFAULT_STD      ,     **_lowerCamelCase   :					Dict      ,     ):



					"""simple docstring"""

					super().__init__(**lowercase__       )
					A_  :				Any	   =		size if size is not None else {'''shortest_edge''': 2_24}
					A_  :				Union[str, Any]	   =		get_size_dict(lowercase__      ,     default_to_square=lowercase__       )
					A_  :				Optional[int]	   =		crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
					A_  :				str	   =		get_size_dict(lowercase__      ,     param_name='''crop_size'''       )

					A_  :				Optional[Any]	   =		do_resize
					A_  :				Tuple	   =		size
					A_  :				Dict	   =		resample
					A_  :				Optional[int]	   =		do_center_crop
					A_  :				List[str]	   =		crop_size
					A_  :				int	   =		do_rescale
					A_  :				Dict	   =		rescale_factor
					A_  :				List[str]	   =		do_normalize
					A_  :				Tuple	   =		image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
					A_  :				List[Any]	   =		image_std if image_std is not None else IMAGENET_DEFAULT_STD







				def 					a_  (							self   :					List[Any]      ,     _lowerCamelCase   :					List[str]      ,     _lowerCamelCase   :					Tuple      ,     _lowerCamelCase   :					Optional[Any] = PILImageResampling.BICUBIC      ,     _lowerCamelCase   :					Tuple = None      ,     **_lowerCamelCase   :					Dict      ,     ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		get_size_dict(lowercase__      ,     default_to_square=lowercase__       )
					# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
					if "shortest_edge" in size:
						A_  :				Union[str, Any]	   =		int((2_56 / 2_24) * size['''shortest_edge''']       )
						A_  :				Tuple	   =		get_resize_output_image_size(lowercase__      ,     size=lowercase__      ,     default_to_square=lowercase__       )
						A_  :				str	   =		{'''height''': output_size[0], '''width''': output_size[1]}
					if "height" not in size_dict or "width" not in size_dict:
						raise ValueError(
						    F"""Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}"""       )
					return resize(
					    lowercase__      ,     size=(size_dict['''height'''], size_dict['''width'''])      ,     resample=lowercase__      ,     data_format=lowercase__      ,     **lowercase__       )







				def 					a_  (							self   :					Union[str, Any]      ,     _lowerCamelCase   :					Optional[Any]      ,     _lowerCamelCase   :					str      ,     _lowerCamelCase   :					Optional[int] = None      ,     **_lowerCamelCase   :					Optional[Any]      ,     ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		get_size_dict(lowercase__       )
					if "height" not in size or "width" not in size:
						raise ValueError(F"""Size dict must have keys \'height\' and \'width\'. Got {size.keys()}"""       )
					return center_crop(lowercase__      ,     size=(size['''height'''], size['''width'''])      ,     data_format=lowercase__      ,     **lowercase__       )







				def 					a_  (							self   :					str      ,     _lowerCamelCase   :					Tuple      ,     _lowerCamelCase   :					Any      ,     _lowerCamelCase   :					Optional[Any] = None      ,     **_lowerCamelCase   :					Tuple      ,     ):



					"""simple docstring"""

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







				def 					a_  (							self   :					List[str]      ,     _lowerCamelCase   :					Dict      ,     _lowerCamelCase   :					str      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					List[str] = None      ,     **_lowerCamelCase   :					List[Any]      ,     ):



					"""simple docstring"""

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







				def 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					List[Any]      ,     _lowerCamelCase   :					Dict = None      ,     _lowerCamelCase   :					Any = None      ,     _lowerCamelCase   :					Any = None      ,     _lowerCamelCase   :					Optional[Any] = None      ,     _lowerCamelCase   :					Union[str, Any] = None      ,     _lowerCamelCase   :					Tuple = None      ,     _lowerCamelCase   :					Tuple = None      ,     _lowerCamelCase   :					str = None      ,     _lowerCamelCase   :					Optional[Any] = None      ,     _lowerCamelCase   :					str = None      ,     _lowerCamelCase   :					int = None      ,     _lowerCamelCase   :					str = ChannelDimension.FIRST      ,     **_lowerCamelCase   :					Tuple      ,     ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		do_resize if do_resize is not None else self.do_resize
					A_  :				Dict	   =		resample if resample is not None else self.resample
					A_  :				Any	   =		do_center_crop if do_center_crop is not None else self.do_center_crop
					A_  :				Optional[Any]	   =		do_rescale if do_rescale is not None else self.do_rescale
					A_  :				Optional[int]	   =		rescale_factor if rescale_factor is not None else self.rescale_factor
					A_  :				List[str]	   =		do_normalize if do_normalize is not None else self.do_normalize
					A_  :				List[Any]	   =		image_mean if image_mean is not None else self.image_mean
					A_  :				Union[str, Any]	   =		image_std if image_std is not None else self.image_std

					A_  :				Tuple	   =		size if size is not None else self.size
					A_  :				Optional[Any]	   =		get_size_dict(lowercase__      ,     default_to_square=lowercase__       )
					A_  :				Union[str, Any]	   =		crop_size if crop_size is not None else self.crop_size
					A_  :				str	   =		get_size_dict(lowercase__      ,     param_name='''crop_size'''       )

					A_  :				str	   =		make_list_of_images(lowercase__       )

					if not valid_images(lowercase__       ):
						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.'''       )

					# All transformations expect numpy arrays.
					A_  :				List[Any]	   =		[to_numpy_array(lowercase__       ) for image in images]

					if do_resize:
						A_  :				Optional[int]	   =		[self.resize(lowercase__      ,     lowercase__      ,     lowercase__       ) for image in images]

					if do_center_crop:
						A_  :				Tuple	   =		[self.center_crop(lowercase__      ,     lowercase__       ) for image in images]

					if do_rescale:
						A_  :				Optional[Any]	   =		[self.rescale(lowercase__      ,     lowercase__       ) for image in images]

					if do_normalize:
						A_  :				str	   =		[self.normalize(lowercase__      ,     lowercase__      ,     lowercase__       ) for image in images]

					A_  :				int	   =		[to_channel_dimension_format(lowercase__      ,     lowercase__       ) for image in images]

					A_  :				Dict	   =		{'''pixel_values''': images}
					return BatchFeature(data=lowercase__      ,     tensor_type=lowercase__       )











"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


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

_lowerCamelCase  :			Tuple        =       {
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Optional[Any]         =   """camembert"""







				def __init__(							self   :					List[Any]      ,     _lowerCamelCase   :					Optional[Any]=3_05_22      ,     _lowerCamelCase   :					List[str]=7_68      ,     _lowerCamelCase   :					Optional[Any]=12      ,     _lowerCamelCase   :					List[str]=12      ,     _lowerCamelCase   :					Any=30_72      ,     _lowerCamelCase   :					List[str]="gelu"      ,     _lowerCamelCase   :					Optional[Any]=0.1      ,     _lowerCamelCase   :					Optional[int]=0.1      ,     _lowerCamelCase   :					Union[str, Any]=5_12      ,     _lowerCamelCase   :					str=2      ,     _lowerCamelCase   :					Optional[Any]=0.02      ,     _lowerCamelCase   :					int=1E-12      ,     _lowerCamelCase   :					str=1      ,     _lowerCamelCase   :					Union[str, Any]=0      ,     _lowerCamelCase   :					Optional[int]=2      ,     _lowerCamelCase   :					List[str]="absolute"      ,     _lowerCamelCase   :					str=True      ,     _lowerCamelCase   :					List[Any]=None      ,     **_lowerCamelCase   :					Union[str, Any]      ,     ):



					"""simple docstring"""

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

					A_  :				Any	   =		vocab_size
					A_  :				Optional[Any]	   =		hidden_size
					A_  :				Any	   =		num_hidden_layers
					A_  :				List[Any]	   =		num_attention_heads
					A_  :				List[Any]	   =		hidden_act
					A_  :				Optional[int]	   =		intermediate_size
					A_  :				Tuple	   =		hidden_dropout_prob
					A_  :				Union[str, Any]	   =		attention_probs_dropout_prob
					A_  :				int	   =		max_position_embeddings
					A_  :				Tuple	   =		type_vocab_size
					A_  :				Dict	   =		initializer_range
					A_  :				Tuple	   =		layer_norm_eps
					A_  :				List[Any]	   =		position_embedding_type
					A_  :				Any	   =		use_cache
					A_  :				Dict	   =		classifier_dropout

class        lowercase      (    __UpperCAmelCase):







				@property
				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

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





