





'''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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


lowercase_         =			logging.get_logger(__name__)


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			['''pixel_values''']


    def __init__(		self			,		A = True			,		A = None			,		A = True			,		A = None			,		A = PILImageResampling.BICUBIC			,		A = True			,		A = 1 / 255			,		A = True			,		A = None			,		A = None			,		**A			,		)		->       None:
        super().__init__(**A	)
        _SCREAMING_SNAKE_CASE     =      crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
        _SCREAMING_SNAKE_CASE     =      get_size_dict(A			,		param_name="""crop_size"""	)
        _SCREAMING_SNAKE_CASE     =      size if size is not None else {"""height""": 224, """width""": 224}
        _SCREAMING_SNAKE_CASE     =      get_size_dict(A	)

        _SCREAMING_SNAKE_CASE     =      do_center_crop
        _SCREAMING_SNAKE_CASE     =      crop_size
        _SCREAMING_SNAKE_CASE     =      do_resize
        _SCREAMING_SNAKE_CASE     =      size
        _SCREAMING_SNAKE_CASE     =      resample
        _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 IMAGENET_DEFAULT_MEAN
        _SCREAMING_SNAKE_CASE     =      image_std if image_std is not None else IMAGENET_DEFAULT_STD


    def       snake_case_(		self			,		A			,		A			,		A = None			,		A = None			,		**A			,		)		->       np.ndarray:
        _SCREAMING_SNAKE_CASE     =      self.size if size is None else size
        _SCREAMING_SNAKE_CASE     =      get_size_dict(A	)
        _SCREAMING_SNAKE_CASE     =      get_size_dict(A			,		param_name="""crop_size"""	)

        _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      get_image_size(A	)
        _SCREAMING_SNAKE_CASE     =      min(A			,		A	)
        _SCREAMING_SNAKE_CASE     =      (size["""height"""] / crop_size["""height"""]) * min_dim
        _SCREAMING_SNAKE_CASE     =      (size["""width"""] / crop_size["""width"""]) * min_dim
        return center_crop(A			,		size=(cropped_height, cropped_width)			,		data_format=A			,		**A	)


    def       snake_case_(		self			,		A			,		A			,		A = PIL.Image.BICUBIC			,		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 dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'	)
        return resize(
            A			,		size=(size["""height"""], size["""width"""])			,		resample=A			,		data_format=A			,		**A	)


    def       snake_case_(		self			,		A			,		A			,		A = None			,		**A			,		)		->       List[Any]:
        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 = ChannelDimension.FIRST			,		**A			,		)		->       PIL.Image.Image:
        _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"""	)
        _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	)
        _SCREAMING_SNAKE_CASE     =      resample if resample is not None else self.resample
        _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     =      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_center_crop and crop_size is None:
            raise ValueError("""If `do_center_crop` is set to `True`, `crop_size` must be provided."""	)

        if do_resize and size is None:
            raise ValueError("""Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True."""	)

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

        if do_center_crop:
            _SCREAMING_SNAKE_CASE     =      [self.center_crop(A			,		A			,		size=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_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_         =			{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			'''rembert'''


    def __init__(		self			,		A=25_0300			,		A=1152			,		A=32			,		A=18			,		A=256			,		A=1664			,		A=4608			,		A="gelu"			,		A=0.0			,		A=0.0			,		A=0.1			,		A=512			,		A=2			,		A=0.02			,		A=1e-12			,		A=True			,		A=0			,		A=312			,		A=313			,		**A			,		)		->       Dict:
        super().__init__(pad_token_id=A			,		bos_token_id=A			,		eos_token_id=A			,		**A	)

        _SCREAMING_SNAKE_CASE     =      vocab_size
        _SCREAMING_SNAKE_CASE     =      input_embedding_size
        _SCREAMING_SNAKE_CASE     =      output_embedding_size
        _SCREAMING_SNAKE_CASE     =      max_position_embeddings
        _SCREAMING_SNAKE_CASE     =      hidden_size
        _SCREAMING_SNAKE_CASE     =      num_hidden_layers
        _SCREAMING_SNAKE_CASE     =      num_attention_heads
        _SCREAMING_SNAKE_CASE     =      intermediate_size
        _SCREAMING_SNAKE_CASE     =      hidden_act
        _SCREAMING_SNAKE_CASE     =      hidden_dropout_prob
        _SCREAMING_SNAKE_CASE     =      attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE     =      classifier_dropout_prob
        _SCREAMING_SNAKE_CASE     =      initializer_range
        _SCREAMING_SNAKE_CASE     =      type_vocab_size
        _SCREAMING_SNAKE_CASE     =      layer_norm_eps
        _SCREAMING_SNAKE_CASE     =      use_cache
        _SCREAMING_SNAKE_CASE     =      False




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),
                ("""token_type_ids""", dynamic_axis),
            ]	)





    @property
    def       snake_case_(		self	)		->       float:
        return 1e-4



