





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, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import MobileNetVaImageProcessor



class       __lowerCAmelCase (    unittest.TestCase  ):






			def __init__(self      ,							__magic_name__      ,							__magic_name__=7      ,							__magic_name__=3      ,							__magic_name__=18      ,							__magic_name__=30      ,							__magic_name__=400      ,							__magic_name__=True      ,							__magic_name__=None      ,							__magic_name__=True      ,							__magic_name__=None      ,							)  ->     List[Any]:


							'''simple docstring'''



							snake_case_		:					int			    =						size if size is not None else {'''shortest_edge''': 20}
							snake_case_		:					Optional[int]			    =						crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
							snake_case_		:					Union[str, Any]			    =						parent
							snake_case_		:					List[Any]			    =						batch_size
							snake_case_		:					int			    =						num_channels
							snake_case_		:					int			    =						image_size
							snake_case_		:					Any			    =						min_resolution
							snake_case_		:					Tuple			    =						max_resolution
							snake_case_		:					Any			    =						do_resize
							snake_case_		:					str			    =						size
							snake_case_		:					str			    =						do_center_crop
							snake_case_		:					Optional[Any]			    =						crop_size






			def     lowerCamelCase						(self					)  ->     str:


							'''simple docstring'''



							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							}




@require_torch
@require_vision
class       __lowerCAmelCase (    _a,  unittest.TestCase  ):
			lowerCamelCase_  :  Union[str, Any]     =   MobileNetVaImageProcessor if is_vision_available() else None






			def     lowerCamelCase						(self					)  ->     Dict:


							'''simple docstring'''



							snake_case_		:					Dict			    =						MobileNetVaImageProcessingTester(self					)






			@property
			def     lowerCamelCase						(self					)  ->     Optional[int]:


							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()






			def     lowerCamelCase						(self					)  ->     int:


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(__magic_name__      ,							'''do_resize'''					)					)
							self.assertTrue(hasattr(__magic_name__      ,							'''size'''					)					)
							self.assertTrue(hasattr(__magic_name__      ,							'''do_center_crop'''					)					)
							self.assertTrue(hasattr(__magic_name__      ,							'''center_crop'''					)					)






			def     lowerCamelCase						(self					)  ->     Optional[Any]:


							'''simple docstring'''



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

							snake_case_		:					Dict			    =						self.image_processing_class.from_dict(self.image_processor_dict      ,							size=42      ,							crop_size=84					)
							self.assertEqual(image_processor.size      ,							{'''shortest_edge''': 42}					)
							self.assertEqual(image_processor.crop_size      ,							{'''height''': 84, '''width''': 84}					)






			def     lowerCamelCase						(self					)  ->     Any:


							'''simple docstring'''



							pass






			def     lowerCamelCase						(self					)  ->     Optional[int]:


							'''simple docstring'''



							snake_case_		:					List[Any]			    =						self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							snake_case_		:					Dict			    =						prepare_image_inputs(self.image_processor_tester      ,							equal_resolution=__magic_name__					)
							for image in image_inputs:
											self.assertIsInstance(__magic_name__      ,							Image.Image					)

							# Test not batched input
							snake_case_		:					Union[str, Any]			    =						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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )      ,							)

							# Test batched
							snake_case_		:					List[Any]			    =						image_processing(__magic_name__      ,							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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )      ,							)






			def     lowerCamelCase						(self					)  ->     str:


							'''simple docstring'''



							snake_case_		:					List[Any]			    =						self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							snake_case_		:					List[str]			    =						prepare_image_inputs(self.image_processor_tester      ,							equal_resolution=__magic_name__      ,							numpify=__magic_name__					)
							for image in image_inputs:
											self.assertIsInstance(__magic_name__      ,							np.ndarray					)

							# Test not batched input
							snake_case_		:					str			    =						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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )      ,							)

							# Test batched
							snake_case_		:					List[Any]			    =						image_processing(__magic_name__      ,							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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )      ,							)






			def     lowerCamelCase						(self					)  ->     int:


							'''simple docstring'''



							snake_case_		:					List[Any]			    =						self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							snake_case_		:					List[Any]			    =						prepare_image_inputs(self.image_processor_tester      ,							equal_resolution=__magic_name__      ,							torchify=__magic_name__					)
							for image in image_inputs:
											self.assertIsInstance(__magic_name__      ,							torch.Tensor					)

							# Test not batched input
							snake_case_		:					Optional[int]			    =						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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )      ,							)

							# Test batched
							snake_case_		:					Union[str, Any]			    =						image_processing(__magic_name__      ,							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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )      ,							)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCAmelCase_												=       {}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_												=       ['''MBart50Tokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_												=       ['''MBart50TokenizerFast''']


if TYPE_CHECKING:
	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mbartaa import MBartaaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
	import sys

	lowerCAmelCase_												=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
