






"""simple docstring"""
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
			lowerCamelCase_       			=   {
			    '''linear''': PIL.Image.Resampling.BILINEAR,
			    '''bilinear''': PIL.Image.Resampling.BILINEAR,
			    '''bicubic''': PIL.Image.Resampling.BICUBIC,
			    '''lanczos''': PIL.Image.Resampling.LANCZOS,
			    '''nearest''': PIL.Image.Resampling.NEAREST,
			}
else:
			lowerCamelCase_       			=   {
			    '''linear''': PIL.Image.LINEAR,
			    '''bilinear''': PIL.Image.BILINEAR,
			    '''bicubic''': PIL.Image.BICUBIC,
			    '''lanczos''': PIL.Image.LANCZOS,
			    '''nearest''': PIL.Image.NEAREST,
			}

def        snake_case   (     A__		):
			UpperCAmelCase_  :				List[str]      =      (images / 2 + 0.5).clamp(0			,1		)
			UpperCAmelCase_  :				Any      =      images.cpu().permute(0			,2			,3			,1		).float().numpy()
			UpperCAmelCase_  :				List[str]      =      numpy_to_pil(A__		)
			return images




def        snake_case   (     A__		):
			if images.ndim == 3:
						UpperCAmelCase_  :				Any      =      images[None, ...]
			UpperCAmelCase_  :				str      =      (images * 2_55).round().astype("uint8"		)
			if images.shape[-1] == 1:
						# special case for grayscale (single channel) images
						UpperCAmelCase_  :				Optional[int]      =      [Image.fromarray(image.squeeze()			,mode="L"		) for image in images]
			else:
						UpperCAmelCase_  :				Union[str, Any]      =      [Image.fromarray(A__		) for image in images]

			return pil_images












"""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      :				Optional[Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				List[Any]      =      TFCamembertModel.from_pretrained("jplu/tf-camembert-base"		)

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

				UpperCAmelCase_  :				Any      =      model(lowerCAmelCase_		)["last_hidden_state"]
				UpperCAmelCase_  :				str      =      tf.TensorShape((1, 10, 768)		)
				self.assertEqual(output.shape			,			lowerCAmelCase_		)
				# compare the actual values for a slice.
				UpperCAmelCase_  :				Tuple      =      tf.convert_to_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]]]			,			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		)		)



