






"""simple docstring"""

import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import SwinaSRImageProcessor
			from transformers.image_transforms import get_image_size

class     A_				(   unittest.TestCase	):







				def __init__(				self				:    List[Any]    ,     __lowerCamelCase				:    Dict    ,     __lowerCamelCase				:    Any=7    ,     __lowerCamelCase				:    str=3    ,     __lowerCamelCase				:    Optional[int]=1_8    ,     __lowerCamelCase				:    int=3_0    ,     __lowerCamelCase				:    Dict=4_0_0    ,     __lowerCamelCase				:    int=True    ,     __lowerCamelCase				:    Any=1 / 2_5_5    ,     __lowerCamelCase				:    Tuple=True    ,     __lowerCamelCase				:    List[Any]=8    ,     )					->       int:
										__magic_name__      =  parent
										__magic_name__      =  batch_size
										__magic_name__      =  num_channels
										__magic_name__      =  image_size
										__magic_name__      =  min_resolution
										__magic_name__      =  max_resolution
										__magic_name__      =  do_rescale
										__magic_name__      =  rescale_factor
										__magic_name__      =  do_pad
										__magic_name__      =  pad_size







				def      _snake_case       (				self				:    List[str]						)					->       int:
										return {
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_pad": self.do_pad,
										    "pad_size": self.pad_size,
										}







				def      _snake_case       (				self				:    Union[str, Any]    ,     __lowerCamelCase				:    str=False    ,     __lowerCamelCase				:    Tuple=False    ,     __lowerCamelCase				:    Optional[int]=False						)					->       Union[str, Any]:
										assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

										if equal_resolution:
																__magic_name__      =  []
																for i in range(self.batch_size						):
																						image_inputs.append(
																						    np.random.randint(
																						        2_5_5    ,     size=(self.num_channels, self.max_resolution, self.max_resolution)    ,     dtype=np.uinta						)						)
										else:
																__magic_name__      =  []
																for i in range(self.batch_size						):
																						__magic_name__   ,     __magic_name__      =  np.random.choice(np.arange(self.min_resolution    ,     self.max_resolution						)    ,     2						)
																						image_inputs.append(np.random.randint(2_5_5    ,     size=(self.num_channels, width, height)    ,     dtype=np.uinta						)						)

										if not numpify and not torchify:
																# PIL expects the channel dimension as last dimension
																__magic_name__      =  [Image.fromarray(np.moveaxis(__lowerCamelCase    ,     0    ,     -1						)						) for x in image_inputs]

										if torchify:
																__magic_name__      =  [torch.from_numpy(__lowerCamelCase						) for x in image_inputs]

										return image_inputs





@require_torch
@require_vision
class     A_				(   snake_case_       ,				unittest.TestCase	):
				UpperCAmelCase__										=					SwinaSRImageProcessor if is_vision_available() else None







				def      _snake_case       (				self				:    Dict						)					->       Optional[int]:
										__magic_name__      =  SwinaSRImageProcessingTester(self						)







				@property
				def      _snake_case       (				self				:    List[Any]						)					->       int:
										return self.image_processor_tester.prepare_image_processor_dict()







				def      _snake_case       (				self				:    str						)					->       Optional[Any]:
										__magic_name__      =  self.image_processing_class(**self.image_processor_dict						)
										self.assertTrue(hasattr(__lowerCamelCase    ,     "do_rescale"						)						)
										self.assertTrue(hasattr(__lowerCamelCase    ,     "rescale_factor"						)						)
										self.assertTrue(hasattr(__lowerCamelCase    ,     "do_pad"						)						)
										self.assertTrue(hasattr(__lowerCamelCase    ,     "pad_size"						)						)







				def      _snake_case       (				self				:    str						)					->       Dict:
										pass







				def      _snake_case       (				self				:    List[str]    ,     __lowerCamelCase				:    Optional[int]						)					->       Optional[int]:
										__magic_name__   ,     __magic_name__      =  get_image_size(__lowerCamelCase						)
										__magic_name__      =  self.image_processor_tester.pad_size

										__magic_name__      =  (old_height // size + 1) * size - old_height
										__magic_name__      =  (old_width // size + 1) * size - old_width
										return old_height + pad_height, old_width + pad_width







				def      _snake_case       (				self				:    Dict						)					->       Any:
										# Initialize image_processor
										__magic_name__      =  self.image_processing_class(**self.image_processor_dict						)
										# create random PIL images
										__magic_name__      =  self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase						)
										for image in image_inputs:
																self.assertIsInstance(__lowerCamelCase    ,     Image.Image						)

										# Test not batched input
										__magic_name__      =  image_processor(image_inputs[0]    ,     return_tensors="pt"						).pixel_values
										__magic_name__   ,     __magic_name__      =  self.calculate_expected_size(np.array(image_inputs[0]						)						)
										self.assertEqual(
										    encoded_images.shape    ,     (
										        1,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )    ,     )







				def      _snake_case       (				self				:    Optional[int]						)					->       Optional[Any]:
										# Initialize image_processor
										__magic_name__      =  self.image_processing_class(**self.image_processor_dict						)
										# create random numpy tensors
										__magic_name__      =  self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase    ,     numpify=__lowerCamelCase						)
										for image in image_inputs:
																self.assertIsInstance(__lowerCamelCase    ,     np.ndarray						)

										# Test not batched input
										__magic_name__      =  image_processor(image_inputs[0]    ,     return_tensors="pt"						).pixel_values
										__magic_name__   ,     __magic_name__      =  self.calculate_expected_size(image_inputs[0]						)
										self.assertEqual(
										    encoded_images.shape    ,     (
										        1,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )    ,     )







				def      _snake_case       (				self				:    Dict						)					->       int:
										# Initialize image_processor
										__magic_name__      =  self.image_processing_class(**self.image_processor_dict						)
										# create random PyTorch tensors
										__magic_name__      =  self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase    ,     torchify=__lowerCamelCase						)
										for image in image_inputs:
																self.assertIsInstance(__lowerCamelCase    ,     torch.Tensor						)

										# Test not batched input
										__magic_name__      =  image_processor(image_inputs[0]    ,     return_tensors="pt"						).pixel_values
										__magic_name__   ,     __magic_name__      =  self.calculate_expected_size(image_inputs[0]						)
										self.assertEqual(
										    encoded_images.shape    ,     (
										        1,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )    ,     )











"""simple docstring"""

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     A_				(   unittest.TestCase	):







				@slow
				def      _snake_case       (				self				:    Tuple						)					->       Dict:
										__magic_name__      =  CamembertModel.from_pretrained("camembert-base"						)
										model.to(__lowerCamelCase						)

										__magic_name__      =  torch.tensor(
										    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,     device=__lowerCamelCase    ,     dtype=torch.long    ,     )  # J'aime le camembert !
										with torch.no_grad():
																__magic_name__      =  model(__lowerCamelCase						)["last_hidden_state"]
										__magic_name__      =  torch.Size((1, 1_0, 7_6_8)						)
										self.assertEqual(output.shape    ,     __lowerCamelCase						)
										# compare the actual values for a slice.
										__magic_name__      =  torch.tensor(
										    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,     device=__lowerCamelCase    ,     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]    ,     __lowerCamelCase    ,     atol=1e-4						)						)



