
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  :  Optional[Any]      ,				snake_case__  :  Tuple      ,				snake_case__  :  bool = True      ,				snake_case__  :  Dict[str, int] = None      ,				snake_case__  :  int = 32      ,				snake_case__  :  bool = True      ,				snake_case__  :  Union[int, float] = 1 / 255      ,				snake_case__  :  bool = True      ,				snake_case__  :  bool = True      ,				snake_case__  :  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]      ,				snake_case__  :  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]      ,				snake_case__  :  bool = True      ,				snake_case__  :  int=7      ,				snake_case__  :  Optional[Any]=30      ,				snake_case__  :  List[Any]=400      ,				snake_case__  :  Optional[int]=3      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							parent
								__lowerCAmelCase         =							do_resize
								__lowerCAmelCase         =							size if size is not None else {"shortest_edge": 288}
								__lowerCAmelCase         =							size_divisor
								__lowerCAmelCase         =							do_rescale
								__lowerCAmelCase         =							rescale_factor
								__lowerCAmelCase         =							do_normalize
								__lowerCAmelCase         =							do_center_crop
								__lowerCAmelCase         =							image_mean
								__lowerCAmelCase         =							image_std
								__lowerCAmelCase         =							do_pad
								__lowerCAmelCase         =							batch_size
								__lowerCAmelCase         =							num_channels
								__lowerCAmelCase         =							min_resolution
								__lowerCAmelCase         =							max_resolution







							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""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       UpperCAmelCase__					(						self  :  Dict      ,				snake_case__  :  Tuple      ,				snake_case__  :  Union[str, Any]=False ):
								"""simple docstring"""







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

									__lowerCAmelCase         =							int((1_333 / 800) * size )
									if max(snake_case__      ,				snake_case__ ) > max_size:
										__lowerCAmelCase         =							max_size / max(snake_case__      ,				snake_case__ )
										__lowerCAmelCase         =							newh * scale
										__lowerCAmelCase         =							neww * scale

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

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

								return expected_height, expected_width

@require_torch
@require_vision
class       a   (   __UpperCAmelCase      ,	unittest.TestCase					):
							lowercase_		:						List[str]   							=  BridgeTowerImageProcessor if is_vision_available() else None







							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							BridgeTowerImageProcessingTester(self )







							@property
							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								return self.image_processor_tester.prepare_image_processor_dict()







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.image_processing_class(**self.image_processor_dict )
								self.assertTrue(hasattr(snake_case__      ,				"image_mean" ) )
								self.assertTrue(hasattr(snake_case__      ,				"image_std" ) )
								self.assertTrue(hasattr(snake_case__      ,				"do_normalize" ) )
								self.assertTrue(hasattr(snake_case__      ,				"do_resize" ) )
								self.assertTrue(hasattr(snake_case__      ,				"size" ) )
								self.assertTrue(hasattr(snake_case__      ,				"size_divisor" ) )







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								pass







							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







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

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

								__lowerCAmelCase   ,						__lowerCAmelCase         =							self.image_processor_tester.get_expected_values(snake_case__ )
								self.assertEqual(
								    encoded_images.shape      ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,				)

								# Test batched
								__lowerCAmelCase         =							image_processing(snake_case__      ,				return_tensors="pt" ).pixel_values

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







							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







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

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

								__lowerCAmelCase   ,						__lowerCAmelCase         =							self.image_processor_tester.get_expected_values(snake_case__ )
								self.assertEqual(
								    encoded_images.shape      ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,				)

								# Test batched
								__lowerCAmelCase         =							image_processing(snake_case__      ,				return_tensors="pt" ).pixel_values

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







							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.image_processing_class(**self.image_processor_dict )
								# create random PyTorch tensors
								__lowerCAmelCase         =							prepare_image_inputs(self.image_processor_tester      ,				equal_resolution=snake_case__      ,				torchify=snake_case__ )
								for image in image_inputs:
									self.assertIsInstance(snake_case__      ,				torch.Tensor )

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

								__lowerCAmelCase   ,						__lowerCAmelCase         =							self.image_processor_tester.get_expected_values(snake_case__ )
								self.assertEqual(
								    encoded_images.shape      ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,				)

								# Test batched
								__lowerCAmelCase         =							image_processing(snake_case__      ,				return_tensors="pt" ).pixel_values

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








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
					import torch

					from transformers.generation import DisjunctiveConstraint

@require_torch
class       a		(   unittest.TestCase					):







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							[[1, 2, 4], [1, 2, 3, 4]]
								__lowerCAmelCase         =							DisjunctiveConstraint(snake_case__ )
								self.assertTrue(isinstance(dc.token_ids      ,				snake_case__ ) )

								with self.assertRaises(snake_case__ ):
									DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )

								with self.assertRaises(snake_case__ ):
									DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )







							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							[[1, 2], [1, 2, 3, 4]]

								with self.assertRaises(snake_case__ ):
									DisjunctiveConstraint(snake_case__ )  # fails here







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							[[1, 2, 3], [1, 2, 4]]

								__lowerCAmelCase         =							DisjunctiveConstraint(snake_case__ )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(1 )
								__lowerCAmelCase         =							stepped is True and completed is False and reset is False
								self.assertTrue(snake_case__ )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.current_seq == [1] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(2 )
								__lowerCAmelCase         =							stepped is True and completed is False and reset is False
								self.assertTrue(snake_case__ )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.current_seq == [1, 2] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(3 )
								__lowerCAmelCase         =							stepped is True and completed is True and reset is False
								self.assertTrue(snake_case__ )
								self.assertTrue(dc.completed )  # Completed!
								self.assertTrue(dc.current_seq == [1, 2, 3] )







							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

								__lowerCAmelCase         =							DisjunctiveConstraint(snake_case__ )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(1 )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.current_seq == [1] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(2 )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.current_seq == [1, 2] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(4 )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.current_seq == [1, 2, 4] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(5 )
								self.assertTrue(dc.completed )  # Completed!
								self.assertTrue(dc.current_seq == [1, 2, 4, 5] )

								dc.reset()

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(1 )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.remaining() == 3 )
								self.assertTrue(dc.current_seq == [1] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(2 )
								self.assertTrue(not dc.completed )
								self.assertTrue(dc.remaining() == 2 )
								self.assertTrue(dc.current_seq == [1, 2] )

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							dc.update(5 )
								self.assertTrue(dc.completed )  # Completed!
								self.assertTrue(dc.remaining() == 0 )
								self.assertTrue(dc.current_seq == [1, 2, 5] )






