
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


lowercase__		:Any   =    logging.get_logger(__name__)


if is_vision_available():
							import PIL







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       List[Any]       					=['''pixel_values''']




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

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




							def  A__      (				self   ,A__   ,A__   ,A__ = PILImageResampling.BICUBIC   ,A__ = None   ,**A__   ,):
												lowercase							=						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()}')
												lowercase							=						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  A__      (				self   ,A__   ,A__   ,A__ = None   ,**A__   ,):
												lowercase							=						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  A__      (				self   ,A__   ,A__   ,A__ = None   ,**A__   ,):
												return rescale(A__   ,scale=A__   ,data_format=A__   ,**A__)




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





							def  A__      (				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__   ,):
												lowercase							=						do_resize if do_resize is not None else self.do_resize
												lowercase							=						size if size is not None else self.size
												lowercase							=						get_size_dict(A__   ,param_name='''size'''   ,default_to_square=A__)
												lowercase							=						resample if resample is not None else self.resample
												lowercase							=						do_center_crop if do_center_crop is not None else self.do_center_crop
												lowercase							=						crop_size if crop_size is not None else self.crop_size
												lowercase							=						get_size_dict(A__   ,param_name='''crop_size'''   ,default_to_square=A__)
												lowercase							=						do_rescale if do_rescale is not None else self.do_rescale
												lowercase							=						rescale_factor if rescale_factor is not None else self.rescale_factor
												lowercase							=						do_normalize if do_normalize is not None else self.do_normalize
												lowercase							=						image_mean if image_mean is not None else self.image_mean
												lowercase							=						image_std if image_std is not None else self.image_std
												lowercase							=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

												lowercase							=						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:
																	lowercase							=						[convert_to_rgb(A__) for image in images]

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

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

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

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

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

												lowercase							=						[to_channel_dimension_format(A__   ,A__) for image in images]

												lowercase							=						{'''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


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''data2vec-text'''


    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-12 ,		A=1 ,		A=0 ,		A=2 ,		A="absolute" ,		A=True ,		A=None ,		**A ,		)		->   int:
        super().__init__(pad_token_id=A ,		bos_token_id=A ,		eos_token_id=A ,		**A       )

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




class 					a_     (				snake_case_       ):

    '''simple docstring'''


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



