




'''simple docstring'''



import unittest

import numpy as np

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

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 DPTImageProcessor






class 				_a							(       unittest.TestCase      ):





       '''simple docstring'''






       def __init__(	self,				A,				A=7,				A=3,				A=18,				A=30,				A=400,				A=True,				A=None,				A=True,				A=[0.5, 0.5, 0.5],				A=[0.5, 0.5, 0.5],				):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	int   =		size if size is not None else {"height": 18, "width": 18}
             SCREAMING_SNAKE_CASE					:	int   =		parent
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		batch_size
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		num_channels
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		image_size
             SCREAMING_SNAKE_CASE					:	int   =		min_resolution
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		max_resolution
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		do_resize
             SCREAMING_SNAKE_CASE					:	str   =		size
             SCREAMING_SNAKE_CASE					:	List[str]   =		do_normalize
             SCREAMING_SNAKE_CASE					:	str   =		image_mean
             SCREAMING_SNAKE_CASE					:	List[str]   =		image_std







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             return {
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_normalize": self.do_normalize,
                 "do_resize": self.do_resize,
                 "size": self.size,
             }






@require_torch
@require_vision
class 				_a							(       UpperCAmelCase__      ,	unittest.TestCase      ):





       '''simple docstring'''






       A		:							int								=		DPTImageProcessor if is_vision_available() else None







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		DPTImageProcessingTester(self   )







       @property
       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             return self.image_processor_tester.prepare_image_processor_dict()







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	str   =		self.image_processing_class(**self.image_processor_dict   )
             self.assertTrue(hasattr(lowerCamelCase__,				'image_mean'   )   )
             self.assertTrue(hasattr(lowerCamelCase__,				'image_std'   )   )
             self.assertTrue(hasattr(lowerCamelCase__,				'do_normalize'   )   )
             self.assertTrue(hasattr(lowerCamelCase__,				'do_resize'   )   )
             self.assertTrue(hasattr(lowerCamelCase__,				'size'   )   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Optional[int]   =		self.image_processing_class.from_dict(self.image_processor_dict   )
             self.assertEqual(image_processor.size,				{'height': 18, 'width': 18}   )

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







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		self.image_processing_class(**self.image_processor_dict   )
             # create random PIL images
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		prepare_image_inputs(self.image_processor_tester,				equal_resolution=lowerCamelCase__   )
             for image in image_inputs:
                   self.assertIsInstance(lowerCamelCase__,				Image.Image   )

             # Test not batched input
             SCREAMING_SNAKE_CASE					:	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.size['height'],
                     self.image_processor_tester.size['width'],
                 ),				)

             # Test batched
             SCREAMING_SNAKE_CASE					:	List[str]   =		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'],
                 ),				)







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	List[str]   =		self.image_processing_class(**self.image_processor_dict   )
             # create random numpy tensors
             SCREAMING_SNAKE_CASE					:	Any   =		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
             SCREAMING_SNAKE_CASE					:	Tuple   =		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
             SCREAMING_SNAKE_CASE					:	str   =		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'],
                 ),				)







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	str   =		self.image_processing_class(**self.image_processor_dict   )
             # create random PyTorch tensors
             SCREAMING_SNAKE_CASE					:	List[Any]   =		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
             SCREAMING_SNAKE_CASE					:	Dict   =		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
             SCREAMING_SNAKE_CASE					:	str   =		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'''


import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property






@unittest.skipUnless(os.path.exists(UpperCAmelCase__		)			,				"Tatoeba directory does not exist."		)
class 		lowerCAmelCase__	(	unittest.TestCase		):




			@cached_property
			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					tempfile.mkdtemp()
					return TatoebaConverter(save_dir=lowerCamelCase__       )




			@slow
			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->List[str]:



					'''simple docstring'''







					self.resolver.convert_models(["heb-eng"]       )






			@slow
			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase    ,			_UpperCAmelCase						:				Tuple	    =					self.resolver.write_model_card("opus-mt-he-en"		,     dry_run=lowerCamelCase__       )
					assert mmeta["long_pair"] == "heb-eng"



