




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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


SCREAMING_SNAKE_CASE_						:					Any			   =       logging.get_logger(__name__)







class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     __UpperCAmelCase	: Optional[Any]						=    ["""pixel_values"""]

     def __init__(      self :       int						,				A :       bool = True						,				A :       Dict[str, int] = None						,				A :       PILImageResampling = PIL.Image.NEAREST						,				A :       bool = False						,				A :       Dict[str, int] = None						,				A :       Union[int, float] = 1 / 2_55						,				A :       bool = False						,				A :       bool = True						,				A :       bool = True						,				A :       Optional[Union[float, List[float]]] = None						,				A :       Optional[Union[float, List[float]]] = None						,				A :       bool = True						,				**A :       Optional[int]						,				)			->			None:






         """simple docstring"""
         super().__init__(**A    )
         a__      =       size if size is not None else {"""height""": 3_46, """width""": 3_46}
         a__      =       get_size_dict(A    )
         a__      =       crop_size if crop_size is not None else {"""height""": 2_89, """width""": 2_89}
         a__      =       get_size_dict(A						,				param_name="""crop_size"""    )

         a__      =       do_resize
         a__      =       size
         a__      =       resample
         a__      =       do_center_crop
         a__      =       crop_size
         a__      =       do_rescale
         a__      =       rescale_factor
         a__      =       rescale_offset
         a__      =       do_normalize
         a__      =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         a__      =       image_std if image_std is not None else IMAGENET_STANDARD_STD
         a__      =       include_top

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]						,				A :       np.ndarray						,				A :       Dict[str, int]						,				A :       PILImageResampling = PIL.Image.NEAREST						,				A :       Optional[Union[str, ChannelDimension]] = None						,				**A :       Dict						,				)			->			np.ndarray:






         """simple docstring"""
         a__      =       get_size_dict(A    )
         if "height" not in size or "width" not in size:
             raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""    )
         return resize(
             A						,				size=(size["""height"""], size["""width"""])						,				resample=A						,				data_format=A						,				**A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple						,				A :       np.ndarray						,				A :       Dict[str, int]						,				A :       Optional[Union[str, ChannelDimension]] = None						,				**A :       Optional[Any]						,				)			->			np.ndarray:






         """simple docstring"""
         a__      =       get_size_dict(A    )
         if "height" not in size or "width" not in size:
             raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""    )
         return center_crop(A						,				size=(size["""height"""], size["""width"""])						,				data_format=A						,				**A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict						,				A :       np.ndarray						,				A :       Union[int, float]						,				A :       bool = True						,				A :       Optional[Union[str, ChannelDimension]] = None						,				**A :       List[str]						,				)			->			Any:






         """simple docstring"""
         if offset:
             a__      =       (image - 1_2_7.5) * scale
             if data_format is not None:
                 a__      =       to_channel_dimension_format(A						,				A    )
             a__      =       rescaled_image.astype(np.floataa    )
         else:
             a__      =       rescale(A						,				scale=A						,				data_format=A						,				**A    )
         return rescaled_image

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]						,				A :       np.ndarray						,				A :       Union[float, List[float]]						,				A :       Union[float, List[float]]						,				A :       Optional[Union[str, ChannelDimension]] = None						,				**A :       str						,				)			->			np.ndarray:






         """simple docstring"""
         return normalize(A						,				mean=A						,				std=A						,				data_format=A						,				**A    )






     def 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]						,				A :       ImageInput						,				A :       bool = None						,				A :       Dict[str, int] = None						,				A :       Any=None						,				A :       bool = None						,				A :       Dict[str, int] = None						,				A :       bool = None						,				A :       float = None						,				A :       bool = None						,				A :       bool = None						,				A :       Optional[Union[float, List[float]]] = None						,				A :       Optional[Union[float, List[float]]] = None						,				A :       bool = None						,				A :       Optional[Union[str, TensorType]] = None						,				A :       ChannelDimension = ChannelDimension.FIRST						,				**A :       Tuple						,				)			->			PIL.Image.Image:






         """simple docstring"""
         a__      =       do_resize if do_resize is not None else self.do_resize
         a__      =       resample if resample is not None else self.resample
         a__      =       do_center_crop if do_center_crop is not None else self.do_center_crop
         a__      =       do_rescale if do_rescale is not None else self.do_rescale
         a__      =       rescale_factor if rescale_factor is not None else self.rescale_factor
         a__      =       rescale_offset if rescale_offset is not None else self.rescale_offset
         a__      =       do_normalize if do_normalize is not None else self.do_normalize
         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__      =       include_top if include_top is not None else self.include_top

         a__      =       size if size is not None else self.size
         a__      =       get_size_dict(A    )
         a__      =       crop_size if crop_size is not None else self.crop_size
         a__      =       get_size_dict(A						,				param_name="""crop_size"""    )

         a__      =       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 or resample is None:
             raise ValueError("""Size and resample 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__      =       [to_numpy_array(A    ) for image in images]

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

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

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

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

         if include_top:
             a__      =       [self.normalize(image=A						,				mean=[0, 0, 0]						,				std=A    ) for image in images]

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

         a__      =       {"""pixel_values""": images}
         return BatchFeature(data=A						,				tensor_type=A    )












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
 import torch

 from transformers import CamembertModel







@require_torch
@require_sentencepiece
@require_tokenizers
class 		__UpperCAmelCase(		unittest.TestCase ):





     """simple docstring"""



     @slow
     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			List[Any]:






         """simple docstring"""
         a__      =       CamembertModel.from_pretrained("""camembert-base"""    )
         model.to(A    )

         a__      =       torch.tensor(
             [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]						,				device=A						,				dtype=torch.long						,				)  # J'aime le camembert !
         with torch.no_grad():
             a__      =       model(A    )["""last_hidden_state"""]
         a__      =       torch.Size((1, 10, 7_68)    )
         self.assertEqual(output.shape						,				A    )
         # compare the actual values for a slice.
         a__      =       torch.tensor(
             [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]						,				device=A						,				dtype=torch.float						,				)
         # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
         # camembert.eval()
         # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

         self.assertTrue(torch.allclose(output[:, :3, :3]						,				A						,				atol=1E-4    )    )






