
'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import DonutImageProcessor


class 						__UpperCAmelCase      (					unittest.TestCase						):


							def __init__(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_=7    ,   lowerCAmelCase_=3    ,   lowerCAmelCase_=18    ,   lowerCAmelCase_=30    ,   lowerCAmelCase_=4_00    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=[0.5, 0.5, 0.5]    ,   lowerCAmelCase_=[0.5, 0.5, 0.5]    ,   ):





								"""simple docstring"""






								_snake_case   						=		parent
								_snake_case   						=		batch_size
								_snake_case   						=		num_channels
								_snake_case   						=		image_size
								_snake_case   						=		min_resolution
								_snake_case   						=		max_resolution
								_snake_case   						=		do_resize
								_snake_case   						=		size if size is not None else {'height': 18, 'width': 20}
								_snake_case   						=		do_thumbnail
								_snake_case   						=		do_align_axis
								_snake_case   						=		do_pad
								_snake_case   						=		do_normalize
								_snake_case   						=		image_mean
								_snake_case   						=		image_std




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_thumbnail": self.do_thumbnail,
								    "do_align_long_axis": self.do_align_axis,
								    "do_pad": self.do_pad,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}


@require_torch
@require_vision
class 						__UpperCAmelCase      (					_lowerCamelCase						,   unittest.TestCase						):
							__lowercase      		=       DonutImageProcessor if is_vision_available() else None


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		DonutImageProcessingTester(self  )


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return self.image_processor_tester.prepare_image_processor_dict()


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.image_processing_class(**self.image_processor_dict  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'do_resize'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'size'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'do_thumbnail'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'do_align_long_axis'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'do_pad'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'do_normalize'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'image_mean'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'image_std'  )  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.image_processing_class.from_dict(self.image_processor_dict  )
								self.assertEqual(image_processor.size    ,   {'height': 18, 'width': 20}  )

								_snake_case   						=		self.image_processing_class.from_dict(self.image_processor_dict    ,   size=42  )
								self.assertEqual(image_processor.size    ,   {'height': 42, 'width': 42}  )

								# Previous config had dimensions in (width, height) order
								_snake_case   						=		self.image_processing_class.from_dict(self.image_processor_dict    ,   size=(42, 84)  )
								self.assertEqual(image_processor.size    ,   {'height': 84, 'width': 42}  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								pass


							@is_flaky()
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								_snake_case   						=		prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=lowerCAmelCase_  )
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_    ,   Image.Image  )

								# Test not batched input
								_snake_case   						=		image_processing(image_inputs[0]    ,   return_tensors='pt'  ).pixel_values
								self.assertEqual(
								    encoded_images.shape    ,   (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )    ,   )

								# Test batched
								_snake_case   						=		image_processing(lowerCAmelCase_    ,   return_tensors='pt'  ).pixel_values
								self.assertEqual(
								    encoded_images.shape    ,   (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )    ,   )


							@is_flaky()
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.image_processing_class(**self.image_processor_dict  )
								# create random numpy tensors
								_snake_case   						=		prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=lowerCAmelCase_    ,   numpify=lowerCAmelCase_  )
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_    ,   np.ndarray  )

								# Test not batched input
								_snake_case   						=		image_processing(image_inputs[0]    ,   return_tensors='pt'  ).pixel_values
								self.assertEqual(
								    encoded_images.shape    ,   (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )    ,   )

								# Test batched
								_snake_case   						=		image_processing(lowerCAmelCase_    ,   return_tensors='pt'  ).pixel_values
								self.assertEqual(
								    encoded_images.shape    ,   (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )    ,   )




							@is_flaky()
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.image_processing_class(**self.image_processor_dict  )
								# create random PyTorch tensors
								_snake_case   						=		prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=lowerCAmelCase_    ,   torchify=lowerCAmelCase_  )
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_    ,   torch.Tensor  )

								# Test not batched input
								_snake_case   						=		image_processing(image_inputs[0]    ,   return_tensors='pt'  ).pixel_values
								self.assertEqual(
								    encoded_images.shape    ,   (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )    ,   )

								# Test batched
								_snake_case   						=		image_processing(lowerCAmelCase_    ,   return_tensors='pt'  ).pixel_values
								self.assertEqual(
								    encoded_images.shape    ,   (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )    ,   )



'''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  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		TFCamembertModel.from_pretrained('jplu/tf-camembert-base'  )

								_snake_case   						=		tf.convert_to_tensor(
								    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]    ,   dtype=tf.intaa    ,   )  # J'aime le camembert !"

								_snake_case   						=		model(lowerCAmelCase_  )['last_hidden_state']
								_snake_case   						=		tf.TensorShape((1, 10, 7_68)  )
								self.assertEqual(output.shape    ,   lowerCAmelCase_  )
								# compare the actual values for a slice.
								_snake_case   						=		tf.convert_to_tensor(
								    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,   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  )  )

