





'''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


lowercase_					= logging.get_logger(__name__)


if is_vision_available():
      import PIL


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = ['''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       )
        _SCREAMING_SNAKE_CASE									=			size if size is not None else {"""shortest_edge""": 224}
        _SCREAMING_SNAKE_CASE									=			get_size_dict(A ,		default_to_square=A       )
        _SCREAMING_SNAKE_CASE									=			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        _SCREAMING_SNAKE_CASE									=			get_size_dict(A ,		default_to_square=A ,		param_name="""crop_size"""       )

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


    def 			snake_case_(		self ,		A ,		A ,		A = PILImageResampling.BICUBIC ,		A = None ,		**A ,		)		->   np.ndarray:
        _SCREAMING_SNAKE_CASE									=			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()}'       )
        _SCREAMING_SNAKE_CASE									=			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 			snake_case_(		self ,		A ,		A ,		A = None ,		**A ,		)		->   np.ndarray:
        _SCREAMING_SNAKE_CASE									=			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 			snake_case_(		self ,		A ,		A ,		A = None ,		**A ,		)		->   List[str]:
        return rescale(A ,		scale=A ,		data_format=A ,		**A       )


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





    def 			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:
        _SCREAMING_SNAKE_CASE									=			do_resize if do_resize is not None else self.do_resize
        _SCREAMING_SNAKE_CASE									=			size if size is not None else self.size
        _SCREAMING_SNAKE_CASE									=			get_size_dict(A ,		param_name="""size""" ,		default_to_square=A       )
        _SCREAMING_SNAKE_CASE									=			resample if resample is not None else self.resample
        _SCREAMING_SNAKE_CASE									=			do_center_crop if do_center_crop is not None else self.do_center_crop
        _SCREAMING_SNAKE_CASE									=			crop_size if crop_size is not None else self.crop_size
        _SCREAMING_SNAKE_CASE									=			get_size_dict(A ,		param_name="""crop_size""" ,		default_to_square=A       )
        _SCREAMING_SNAKE_CASE									=			do_rescale if do_rescale is not None else self.do_rescale
        _SCREAMING_SNAKE_CASE									=			rescale_factor if rescale_factor is not None else self.rescale_factor
        _SCREAMING_SNAKE_CASE									=			do_normalize if do_normalize is not None else self.do_normalize
        _SCREAMING_SNAKE_CASE									=			image_mean if image_mean is not None else self.image_mean
        _SCREAMING_SNAKE_CASE									=			image_std if image_std is not None else self.image_std
        _SCREAMING_SNAKE_CASE									=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        _SCREAMING_SNAKE_CASE									=			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:
            _SCREAMING_SNAKE_CASE									=			[convert_to_rgb(A       ) for image in images]

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

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

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

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

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

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

        _SCREAMING_SNAKE_CASE									=			{"""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),
            ]       )



