




'''simple docstring'''


import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor

class     A					(	unittest.TestCase					):







							def __init__(						self :						str							,    lowerCAmelCase_ :						List[Any]							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Dict[str, int] = None							,    lowerCAmelCase_ :						int = 32							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Union[int, float] = 1 / 2_55							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Optional[Any]=7							,    lowerCAmelCase_ :						Optional[Any]=30							,    lowerCAmelCase_ :						Tuple=4_00							,    lowerCAmelCase_ :						Any=3							,    )    ->							Dict:


														"""simple docstring"""


														_a			  =       parent
														_a			  =       do_resize
														_a			  =       size if size is not None else {'''shortest_edge''': 2_88}
														_a			  =       size_divisor
														_a			  =       do_rescale
														_a			  =       rescale_factor
														_a			  =       do_normalize
														_a			  =       do_center_crop
														_a			  =       image_mean
														_a			  =       image_std
														_a			  =       do_pad
														_a			  =       batch_size
														_a			  =       num_channels
														_a			  =       min_resolution
														_a			  =       max_resolution







							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							Optional[int]:


														"""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,
														    "size_divisor": self.size_divisor,
														}







							def      __lowerCAmelCase    (						self :						List[str]							,    lowerCAmelCase_ :						str							,    lowerCAmelCase_ :						List[str]=False      )    ->							List[Any]:


														"""simple docstring"""


														if not batched:
																					_a			  =       self.size['''shortest_edge''']
																					_a			  =       image_inputs[0]
																					if isinstance(lowerCAmelCase_							,    Image.Image      ):
																												_a  ,							_a			  =       image.size
																					else:
																												_a  ,							_a			  =       image.shape[1], image.shape[2]
																					_a			  =       size / min(lowerCAmelCase_							,    lowerCAmelCase_      )
																					if h < w:
																												_a  ,							_a			  =       size, scale * w
																					else:
																												_a  ,							_a			  =       scale * h, size

																					_a			  =       int((13_33 / 8_00) * size      )
																					if max(lowerCAmelCase_							,    lowerCAmelCase_      ) > max_size:
																												_a			  =       max_size / max(lowerCAmelCase_							,    lowerCAmelCase_      )
																												_a			  =       newh * scale
																												_a			  =       neww * scale

																					_a  ,							_a			  =       int(newh + 0.5      ), int(neww + 0.5      )
																					_a  ,							_a			  =       (
																					    newh // self.size_divisor * self.size_divisor,
																					    neww // self.size_divisor * self.size_divisor,
																					)

														else:
																					_a			  =       []
																					for image in image_inputs:
																												_a  ,							_a			  =       self.get_expected_values([image]      )
																												expected_values.append((expected_height, expected_width)      )
																					_a			  =       max(lowerCAmelCase_							,    key=lambda lowerCAmelCase_      : item[0]      )[0]
																					_a			  =       max(lowerCAmelCase_							,    key=lambda lowerCAmelCase_      : item[1]      )[1]

														return expected_height, expected_width



@require_torch
@require_vision
class     A					(	_a  ,unittest.TestCase					):
							lowercase_           =       BridgeTowerImageProcessor if is_vision_available() else None







							def      __lowerCAmelCase    (						self :						Union[str, Any]      )    ->							List[Any]:


														"""simple docstring"""


														_a			  =       BridgeTowerImageProcessingTester(self      )







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


														"""simple docstring"""


														return self.image_processor_tester.prepare_image_processor_dict()







							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							List[str]:


														"""simple docstring"""


														_a			  =       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'''      )      )
														self.assertTrue(hasattr(lowerCAmelCase_							,    '''size_divisor'''      )      )







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


														"""simple docstring"""


														pass







							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							Tuple:


														"""simple docstring"""


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

														# Test not batched input
														_a			  =       image_processing(image_inputs[0]							,    return_tensors='''pt'''      ).pixel_values

														_a  ,							_a			  =       self.image_processor_tester.get_expected_values(lowerCAmelCase_      )
														self.assertEqual(
														    encoded_images.shape							,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,    )

														# Test batched
														_a			  =       image_processing(lowerCAmelCase_							,    return_tensors='''pt'''      ).pixel_values

														_a  ,							_a			  =       self.image_processor_tester.get_expected_values(lowerCAmelCase_							,    batched=lowerCAmelCase_      )
														self.assertEqual(
														    encoded_images.shape							,    (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        expected_height,
														        expected_width,
														    )							,    )







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


														"""simple docstring"""


														_a			  =       self.image_processing_class(**self.image_processor_dict      )
														# create random numpy tensors
														_a			  =       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
														_a			  =       image_processing(image_inputs[0]							,    return_tensors='''pt'''      ).pixel_values

														_a  ,							_a			  =       self.image_processor_tester.get_expected_values(lowerCAmelCase_      )
														self.assertEqual(
														    encoded_images.shape							,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,    )

														# Test batched
														_a			  =       image_processing(lowerCAmelCase_							,    return_tensors='''pt'''      ).pixel_values

														_a  ,							_a			  =       self.image_processor_tester.get_expected_values(lowerCAmelCase_							,    batched=lowerCAmelCase_      )
														self.assertEqual(
														    encoded_images.shape							,    (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        expected_height,
														        expected_width,
														    )							,    )







							def      __lowerCAmelCase    (						self :						int      )    ->							Union[str, Any]:


														"""simple docstring"""


														_a			  =       self.image_processing_class(**self.image_processor_dict      )
														# create random PyTorch tensors
														_a			  =       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
														_a			  =       image_processing(image_inputs[0]							,    return_tensors='''pt'''      ).pixel_values

														_a  ,							_a			  =       self.image_processor_tester.get_expected_values(lowerCAmelCase_      )
														self.assertEqual(
														    encoded_images.shape							,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,    )

														# Test batched
														_a			  =       image_processing(lowerCAmelCase_							,    return_tensors='''pt'''      ).pixel_values

														_a  ,							_a			  =       self.image_processor_tester.get_expected_values(lowerCAmelCase_							,    batched=lowerCAmelCase_      )
														self.assertEqual(
														    encoded_images.shape							,    (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        expected_height,
														        expected_width,
														    )							,    )







'''simple docstring'''


import os

import pytest

from transformers.dynamic_module_utils import get_imports


_snake_case   :   List[str]     							=	'\nimport os\n'

_snake_case   :   Dict     							=	'\ndef foo():\n    import os\n    return False\n'

_snake_case   :   List[Any]     							=	'\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n'

_snake_case   :   Dict     							=	'\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n'

_snake_case   :   Optional[Any]     							=	'\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n'

_snake_case   :   Optional[Any]     							=	'\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n'

_snake_case   :   Any     							=	'\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n'

_snake_case   :   List[str]     							=	'\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n'

_snake_case   :   Dict     							=	'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n'

_snake_case   :   Any     							=	'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n'

_snake_case   :   Union[str, Any]     							=	[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize('''case'''				,		UpperCamelCase   )
def 	snake_case_					(UpperCamelCase :						str				,		UpperCamelCase :						List[Any]   ):





							'''simple docstring'''


							_a			  =       os.path.join(UpperCamelCase				,		'''test_file.py'''   )
							with open(UpperCamelCase				,		'''w'''   ) as _tmp_file:
														_tmp_file.write(UpperCamelCase   )

							_a			  =       get_imports(UpperCamelCase   )
							assert parsed_imports == ["os"]

