

"""simple docstring"""


from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


a					:List[Any]    = logging.get_logger(__name__)

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[str]       = ["""pixel_values"""]

     def __init__(						self	,    _a = True	,    _a = 1 / 255	,    _a = True	,    _a = 8	,    **_a	,    )  ->      None:


           """simple docstring"""


           super().__init__(**_a  )

           SCREAMING_SNAKE_CASE__   :      str      		=	do_rescale
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	rescale_factor
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	do_pad
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	pad_size

     def 					_a			(						self	,    _a	,    _a	,    _a = None	,    **_a  )  ->      np.ndarray:


           """simple docstring"""


           return rescale(_a	,    scale=_a	,    data_format=_a	,    **_a  )

     def 					_a			(						self	,    _a	,    _a	,    _a = None  )  ->      Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	get_image_size(_a  )
           SCREAMING_SNAKE_CASE__   :      int      		=	(old_height // size + 1) * size - old_height
           SCREAMING_SNAKE_CASE__   :      Any      		=	(old_width // size + 1) * size - old_width

           return pad(_a	,    ((0, pad_height), (0, pad_width))	,    mode="""symmetric"""	,    data_format=_a  )





     def 					_a			(						self	,    _a	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = ChannelDimension.FIRST	,    **_a	,    )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	do_rescale if do_rescale is not None else self.do_rescale
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	rescale_factor if rescale_factor is not None else self.rescale_factor
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	do_pad if do_pad is not None else self.do_pad
           SCREAMING_SNAKE_CASE__   :      Dict      		=	pad_size if pad_size is not None else self.pad_size

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	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_rescale and rescale_factor is None:
                 raise ValueError("""Rescale factor must be specified if do_rescale is True."""  )

           # All transformations expect numpy arrays.
           SCREAMING_SNAKE_CASE__   :      Dict      		=	[to_numpy_array(_a  ) for image in images]

           if do_rescale:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[self.rescale(image=_a	,    scale=_a  ) for image in images]

           if do_pad:
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	[self.pad(_a	,    size=_a  ) for image in images]

           SCREAMING_SNAKE_CASE__   :      Any      		=	[to_channel_dimension_format(_a	,    _a  ) for image in images]

           SCREAMING_SNAKE_CASE__   :      int      		=	{"""pixel_values""": images}
           return BatchFeature(data=_a	,    tensor_type=_a  )







"""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      __a							(unittest.TestCase):






     '''simple docstring'''

     @slow
     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	tf.convert_to_tensor(
               [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]	,    dtype=tf.intaa	,    )  # J'aime le camembert !"

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model(_a  )["""last_hidden_state"""]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tf.TensorShape((1, 10, 768)  )
           self.assertEqual(output.shape	,    _a  )
           # compare the actual values for a slice.
           SCREAMING_SNAKE_CASE__   :      Optional[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  )  )




