
"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
      import PIL

lowercase_       :						Dict     					=	logging.get_logger(__name__)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any				)  ->    List[List[ImageInput]]:
  if isinstance(lowerCamelCase__,						(list, tuple)				) and isinstance(videos[0],						(list, tuple)				) and is_valid_image(videos[0][0]				):
    return videos

  elif isinstance(lowerCamelCase__,						(list, tuple)				) and is_valid_image(videos[0]				):
    return [videos]

  elif is_valid_image(lowerCamelCase__				):
    return [[videos]]

  raise ValueError(f'''Could not make batched video from {videos}'''				)





class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			["""pixel_values"""]
     def __init__(     self  ,     snake_case__ = True  ,     snake_case__ = None  ,     snake_case__ = PILImageResampling.BILINEAR  ,     snake_case__ = True  ,     snake_case__ = None  ,     snake_case__ = True  ,     snake_case__ = 1 / 255  ,     snake_case__ = True  ,     snake_case__ = True  ,     snake_case__ = None  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       super().__init__(**snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = size if size is not None else {"shortest_edge": 256}
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = get_size_dict(snake_case__  ,     default_to_square=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = crop_size if crop_size is not None else {"height": 224, "width": 224}
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = get_size_dict(snake_case__  ,     param_name="crop_size"				)

       _SCREAMING_SNAKE_CASE   :				Tuple							    = do_resize
       _SCREAMING_SNAKE_CASE   :				Dict							    = size
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = do_center_crop
       _SCREAMING_SNAKE_CASE   :				List[str]							    = crop_size
       _SCREAMING_SNAKE_CASE   :				str							    = resample
       _SCREAMING_SNAKE_CASE   :				List[str]							    = do_rescale
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = rescale_factor
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = offset
       _SCREAMING_SNAKE_CASE   :				List[str]							    = do_normalize
       _SCREAMING_SNAKE_CASE   :				str							    = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       _SCREAMING_SNAKE_CASE   :				Tuple							    = image_std if image_std is not None else IMAGENET_STANDARD_STD
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__ = PILImageResampling.BILINEAR  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = get_size_dict(snake_case__  ,     default_to_square=snake_case__				)
       if "shortest_edge" in size:
         _SCREAMING_SNAKE_CASE   :				Tuple							    = get_resize_output_image_size(snake_case__  ,     size["shortest_edge"]  ,     default_to_square=snake_case__				)
       elif "height" in size and "width" in size:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = (size["height"], size["width"])
       else:
         raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''				)
       return resize(snake_case__  ,     size=snake_case__  ,     resample=snake_case__  ,     data_format=snake_case__  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = get_size_dict(snake_case__				)
       if "height" not in size or "width" not in size:
         raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''				)
       return center_crop(snake_case__  ,     size=(size["height"], size["width"])  ,     data_format=snake_case__  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__ = True  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = image.astype(np.floataa				)
       if offset:
         _SCREAMING_SNAKE_CASE   :				List[str]							    = image - (scale / 2)
       return rescale(snake_case__  ,     scale=snake_case__  ,     data_format=snake_case__  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       return normalize(snake_case__  ,     mean=snake_case__  ,     std=snake_case__  ,     data_format=snake_case__  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = ChannelDimension.FIRST  ,     ):




       """simple docstring"""


       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."				)

       if offset and not do_rescale:
         raise ValueError("For offset, do_rescale must also be set to True."				)

       # All transformations expect numpy arrays.
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = to_numpy_array(snake_case__				)

       if do_resize:
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.resize(image=snake_case__  ,     size=snake_case__  ,     resample=snake_case__				)

       if do_center_crop:
         _SCREAMING_SNAKE_CASE   :				int							    = self.center_crop(snake_case__  ,     size=snake_case__				)

       if do_rescale:
         _SCREAMING_SNAKE_CASE   :				Any							    = self.rescale(image=snake_case__  ,     scale=snake_case__  ,     offset=snake_case__				)

       if do_normalize:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.normalize(image=snake_case__  ,     mean=snake_case__  ,     std=snake_case__				)

       _SCREAMING_SNAKE_CASE   :				str							    = to_channel_dimension_format(snake_case__  ,     snake_case__				)
       return image




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = ChannelDimension.FIRST  ,     **snake_case__  ,     ):




       """simple docstring"""


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

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = size if size is not None else self.size
       _SCREAMING_SNAKE_CASE   :				Dict							    = get_size_dict(snake_case__  ,     default_to_square=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Any							    = crop_size if crop_size is not None else self.crop_size
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = get_size_dict(snake_case__  ,     param_name="crop_size"				)

       if not valid_images(snake_case__				):
         raise ValueError(
             "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
             "torch.Tensor, tf.Tensor or jax.ndarray."				)

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = make_batched(snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Tuple							    = [
           [
               self._preprocess_image(
                   image=snake_case__  ,     do_resize=snake_case__  ,     size=snake_case__  ,     resample=snake_case__  ,     do_center_crop=snake_case__  ,     crop_size=snake_case__  ,     do_rescale=snake_case__  ,     rescale_factor=snake_case__  ,     offset=snake_case__  ,     do_normalize=snake_case__  ,     image_mean=snake_case__  ,     image_std=snake_case__  ,     data_format=snake_case__  ,     )
               for img in video
           ]
           for video in videos
       ]

       _SCREAMING_SNAKE_CASE   :				Dict							    = {"pixel_values": videos}
       return BatchFeature(data=snake_case__  ,     tensor_type=snake_case__				)





"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase      (							unittest.TestCase  ):
     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = TFCamembertModel.from_pretrained("jplu/tf-camembert-base"				)

       _SCREAMING_SNAKE_CASE   :				str							    = tf.convert_to_tensor(
           [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]  ,     dtype=tf.intaa  ,     )  # J'aime le camembert !"

       _SCREAMING_SNAKE_CASE   :				str							    = model(snake_case__				)["last_hidden_state"]
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tf.TensorShape((1, 10, 768)				)
       self.assertEqual(output.shape  ,     snake_case__				)
       # compare the actual values for a slice.
       _SCREAMING_SNAKE_CASE   :				int							    = tf.convert_to_tensor(
           [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]  ,     dtype=tf.floataa  ,     )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(np.allclose(output[:, :3, :3].numpy()  ,     expected_slice.numpy()  ,     atol=1E-4				)				)



