
"""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  _snake_case	(    unittest.TestCase							):






							def __init__(       self    :      Union[str, Any]    ,			snake_case__    :      List[str]    ,			snake_case__    :      Optional[int]=7    ,			snake_case__    :      str=3    ,			snake_case__    :      Optional[int]=1_8    ,			snake_case__    :      Optional[int]=3_0    ,			snake_case__    :      Dict=4_0_0    ,			snake_case__    :      str=True    ,			snake_case__    :      List[str]=1 / 2_5_5    ,			snake_case__    :      Dict=True    ,			snake_case__    :      Optional[int]=8    ,			):






												"""simple docstring"""



												__a:    Dict		     =       parent
												__a:    Dict		     =       batch_size
												__a:    Union[str, Any]		     =       num_channels
												__a:    List[Any]		     =       image_size
												__a:    int		     =       min_resolution
												__a:    Any		     =       max_resolution
												__a:    int		     =       do_rescale
												__a:    Union[str, Any]		     =       rescale_factor
												__a:    Any		     =       do_pad
												__a:    Tuple		     =       pad_size






							def 				__lowercase	(       self    :      Optional[int]     ):






												"""simple docstring"""



												return {
												    "do_rescale": self.do_rescale,
												    "rescale_factor": self.rescale_factor,
												    "do_pad": self.do_pad,
												    "pad_size": self.pad_size,
												}






							def 				__lowercase	(       self    :      List[str]    ,			snake_case__    :      Dict=False    ,			snake_case__    :      str=False    ,			snake_case__    :      Union[str, Any]=False     ):






												"""simple docstring"""



												assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

												if equal_resolution:
																	__a:    List[Any]		     =       []
																	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:
																	__a:    str		     =       []
																	for i in range(self.batch_size     ):
																						__a     ,					__a:    List[str]		     =       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
																	__a:    str		     =       [Image.fromarray(np.moveaxis(snake_case__    ,			0    ,			-1     )     ) for x in image_inputs]

												if torchify:
																	__a:    Any		     =       [torch.from_numpy(snake_case__     ) for x in image_inputs]

												return image_inputs






@require_torch
@require_vision
class  _snake_case	(    _lowercase			,					unittest.TestCase							):
							SCREAMING_SNAKE_CASE		  =    SwinaSRImageProcessor if is_vision_available() else None






							def 				__lowercase	(       self    :      Dict     ):






												"""simple docstring"""



												__a:    str		     =       SwinaSRImageProcessingTester(self     )






							@property
							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												return self.image_processor_tester.prepare_image_processor_dict()






							def 				__lowercase	(       self    :      Dict     ):






												"""simple docstring"""



												__a:    List[Any]		     =       self.image_processing_class(**self.image_processor_dict     )
												self.assertTrue(hasattr(snake_case__    ,			"""do_rescale"""     )     )
												self.assertTrue(hasattr(snake_case__    ,			"""rescale_factor"""     )     )
												self.assertTrue(hasattr(snake_case__    ,			"""do_pad"""     )     )
												self.assertTrue(hasattr(snake_case__    ,			"""pad_size"""     )     )






							def 				__lowercase	(       self    :      Optional[int]     ):






												"""simple docstring"""



												pass






							def 				__lowercase	(       self    :      List[str]    ,			snake_case__    :      Dict     ):






												"""simple docstring"""



												__a     ,					__a:    Any		     =       get_image_size(snake_case__     )
												__a:    Optional[int]		     =       self.image_processor_tester.pad_size

												__a:    List[str]		     =       (old_height // size + 1) * size - old_height
												__a:    List[str]		     =       (old_width // size + 1) * size - old_width
												return old_height + pad_height, old_width + pad_width






							def 				__lowercase	(       self    :      Optional[int]     ):






												"""simple docstring"""



												__a:    Tuple		     =       self.image_processing_class(**self.image_processor_dict     )
												# create random PIL images
												__a:    Tuple		     =       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__     )
												for image in image_inputs:
																	self.assertIsInstance(snake_case__    ,			Image.Image     )

												# Test not batched input
												__a:    Optional[Any]		     =       image_processor(image_inputs[0]    ,			return_tensors="""pt"""     ).pixel_values
												__a     ,					__a:    Optional[int]		     =       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 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												__a:    Any		     =       self.image_processing_class(**self.image_processor_dict     )
												# create random numpy tensors
												__a:    int		     =       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__    ,			numpify=snake_case__     )
												for image in image_inputs:
																	self.assertIsInstance(snake_case__    ,			np.ndarray     )

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






							def 				__lowercase	(       self    :      Dict     ):






												"""simple docstring"""



												__a:    Optional[Any]		     =       self.image_processing_class(**self.image_processor_dict     )
												# create random PyTorch tensors
												__a:    Optional[int]		     =       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__    ,			torchify=snake_case__     )
												for image in image_inputs:
																	self.assertIsInstance(snake_case__    ,			torch.Tensor     )

												# Test not batched input
												__a:    Union[str, Any]		     =       image_processor(image_inputs[0]    ,			return_tensors="""pt"""     ).pixel_values
												__a     ,					__a:    List[Any]		     =       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 BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property




class  _snake_case	(    unittest.TestCase							):






							@cached_property
							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""     )






							@cached_property
							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""     )






							def 				__lowercase	(       self    :      int     ):






												"""simple docstring"""



												__a:    Optional[int]		     =       self.tokenizer_ab
												__a:    Union[str, Any]		     =       """ I am a small frog."""
												__a:    str		     =       tok([src_text]    ,			padding=snake_case__    ,			truncation=snake_case__     )["""input_ids"""]
												__a:    Optional[Any]		     =       tok.batch_decode(snake_case__    ,			skip_special_tokens=snake_case__    ,			clean_up_tokenization_spaces=snake_case__     )[0]
												assert src_text == decoded






							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												__a:    str		     =       self.rust_tokenizer_ab
												__a:    Optional[Any]		     =       """ I am a small frog."""
												__a:    str		     =       tok([src_text]    ,			padding=snake_case__    ,			truncation=snake_case__     )["""input_ids"""]
												__a:    Union[str, Any]		     =       tok.batch_decode(snake_case__    ,			skip_special_tokens=snake_case__    ,			clean_up_tokenization_spaces=snake_case__     )[0]
												assert src_text == decoded






							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												assert self.tokenizer_ab.add_prefix_space
												assert self.tokenizer_ab([""" Sam""", """Sam"""]     ).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]






							def 				__lowercase	(       self    :      Any     ):






												"""simple docstring"""



												assert self.rust_tokenizer_ab.add_prefix_space
												assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]     ).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]
