





"""simple docstring"""




import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor


class 			__UpperCamelCase							(		unittest.TestCase		):






							def __init__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__=7 ,   lowerCAmelCase__=3 ,   lowerCAmelCase__=30 ,   lowerCAmelCase__=400 ,   lowerCAmelCase__=True ,   lowerCAmelCase__=None ,   lowerCAmelCase__=True ,   lowerCAmelCase__=[0.5, 0.5, 0.5] ,   lowerCAmelCase__=[0.5, 0.5, 0.5] ,   lowerCAmelCase__=True ,   lowerCAmelCase__=1 / 255 ,   lowerCAmelCase__=True ,   )      ->					Tuple:
								# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
								a :						Optional[Any]      =							size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
								a :						Union[str, Any]      =							parent
								a :						Union[str, Any]      =							batch_size
								a :						str      =							num_channels
								a :						int      =							min_resolution
								a :						int      =							max_resolution
								a :						List[Any]      =							do_resize
								a :						List[str]      =							size
								a :						Any      =							do_normalize
								a :						Union[str, Any]      =							image_mean
								a :						Optional[Any]      =							image_std
								a :						Optional[Any]      =							do_rescale
								a :						Union[str, Any]      =							rescale_factor
								a :						Dict      =							do_pad






							def        __a  (  self )      ->					str:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_rescale": self.do_rescale,
								    "rescale_factor": self.rescale_factor,
								    "do_pad": self.do_pad,
								}






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__=False )      ->					Optional[Any]:
								if not batched:
									a :						List[Any]      =							image_inputs[0]
									if isinstance(lowerCAmelCase__ ,   Image.Image ):
										a :						Optional[int]      =							image.size
									else:
										a :						Tuple      =							image.shape[1], image.shape[2]
									if w < h:
										a :						Optional[int]      =							int(self.size["shortest_edge"] * h / w )
										a :						Any      =							self.size['shortest_edge']
									elif w > h:
										a :						Tuple      =							self.size['shortest_edge']
										a :						Optional[Any]      =							int(self.size["shortest_edge"] * w / h )
									else:
										a :						Optional[int]      =							self.size['shortest_edge']
										a :						Union[str, Any]      =							self.size['shortest_edge']

								else:
									a :						int      =							[]
									for image in image_inputs:
										a :						int      =							self.get_expected_values([image] )
										expected_values.append((expected_height, expected_width) )
									a :						Optional[Any]      =							max(lowerCAmelCase__ ,   key=lambda lowerCAmelCase__ : item[0] )[0]
									a :						Union[str, Any]      =							max(lowerCAmelCase__ ,   key=lambda lowerCAmelCase__ : item[1] )[1]

								return expected_height, expected_width


@require_torch
@require_vision
class 			__UpperCamelCase							(		a__						,			unittest.TestCase		):
							lowerCamelCase  :   List[Any]    =ConditionalDetrImageProcessor if is_vision_available() else None






							def        __a  (  self )      ->					Dict:
								a :						Union[str, Any]      =							ConditionalDetrImageProcessingTester(self )






							@property
							def        __a  (  self )      ->					List[Any]:
								return self.image_processor_tester.prepare_image_processor_dict()






							def        __a  (  self )      ->					int:
								a :						Optional[int]      =							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        __a  (  self )      ->					List[str]:
								a :						Tuple      =							self.image_processing_class.from_dict(self.image_processor_dict )
								self.assertEqual(image_processor.size ,   {"shortest_edge": 18, "longest_edge": 1333} )
								self.assertEqual(image_processor.do_pad ,   lowerCAmelCase__ )

								a :						str      =							self.image_processing_class.from_dict(
								    self.image_processor_dict ,   size=42 ,   max_size=84 ,   pad_and_return_pixel_mask=lowerCAmelCase__ )
								self.assertEqual(image_processor.size ,   {"shortest_edge": 42, "longest_edge": 84} )
								self.assertEqual(image_processor.do_pad ,   lowerCAmelCase__ )






							def        __a  (  self )      ->					Any:
								pass






							def        __a  (  self )      ->					Union[str, Any]:
								# Initialize image_processing
								a :						str      =							self.image_processing_class(**self.image_processor_dict )
								# create random PIL images
								a :						Optional[int]      =							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 :						List[Any]      =							image_processing(image_inputs[0] ,   return_tensors="pt" ).pixel_values

								a :						List[str]      =							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 :						Any      =							self.image_processor_tester.get_expected_values(lowerCAmelCase__ ,   batched=lowerCAmelCase__ )

								a :						Dict      =							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,
								        expected_height,
								        expected_width,
								    ) ,   )






							def        __a  (  self )      ->					Any:
								# Initialize image_processing
								a :						Dict      =							self.image_processing_class(**self.image_processor_dict )
								# create random numpy tensors
								a :						Optional[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
								a :						Union[str, Any]      =							image_processing(image_inputs[0] ,   return_tensors="pt" ).pixel_values

								a :						Any      =							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 :						int      =							image_processing(lowerCAmelCase__ ,   return_tensors="pt" ).pixel_values

								a :						Tuple      =							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        __a  (  self )      ->					int:
								# Initialize image_processing
								a :						Dict      =							self.image_processing_class(**self.image_processor_dict )
								# create random PyTorch tensors
								a :						Optional[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
								a :						str      =							image_processing(image_inputs[0] ,   return_tensors="pt" ).pixel_values

								a :						Optional[int]      =							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 :						int      =							image_processing(lowerCAmelCase__ ,   return_tensors="pt" ).pixel_values

								a :						Tuple      =							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,
								    ) ,   )






							@slow
							def        __a  (  self )      ->					Dict:
								# prepare image and target
								a :						Dict      =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
								with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" ,   "r" ) as f:
									a :						Dict      =							json.loads(f.read() )

								a :						Optional[Any]      =							{'image_id': 3_9769, 'annotations': target}

								# encode them
								a :						Optional[Any]      =							ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50" )
								a :						List[Any]      =							image_processing(images=lowerCAmelCase__ ,   annotations=lowerCAmelCase__ ,   return_tensors="pt" )

								# verify pixel values
								a :						Optional[int]      =							torch.Size([1, 3, 800, 1066] )
								self.assertEqual(encoding["pixel_values"].shape ,   lowerCAmelCase__ )

								a :						Optional[int]      =							torch.tensor([0.2_796, 0.3_138, 0.3_481] )
								self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] ,   lowerCAmelCase__ ,   atol=1E-4 ) )

								# verify area
								a :						Union[str, Any]      =							torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["area"] ,   lowerCAmelCase__ ) )
								# verify boxes
								a :						Dict      =							torch.Size([6, 4] )
								self.assertEqual(encoding["labels"][0]["boxes"].shape ,   lowerCAmelCase__ )
								a :						Optional[Any]      =							torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] ,   lowerCAmelCase__ ,   atol=1E-3 ) )
								# verify image_id
								a :						Any      =							torch.tensor([3_9769] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,   lowerCAmelCase__ ) )
								# verify is_crowd
								a :						str      =							torch.tensor([0, 0, 0, 0, 0, 0] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,   lowerCAmelCase__ ) )
								# verify class_labels
								a :						Union[str, Any]      =							torch.tensor([75, 75, 63, 65, 17, 17] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,   lowerCAmelCase__ ) )
								# verify orig_size
								a :						List[Any]      =							torch.tensor([480, 640] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,   lowerCAmelCase__ ) )
								# verify size
								a :						Optional[int]      =							torch.tensor([800, 1066] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,   lowerCAmelCase__ ) )






							@slow
							def        __a  (  self )      ->					Any:
								# prepare image, target and masks_path
								a :						List[Any]      =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
								with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" ,   "r" ) as f:
									a :						List[str]      =							json.loads(f.read() )

								a :						Optional[int]      =							{'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

								a :						Optional[Any]      =							pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )

								# encode them
								a :						Any      =							ConditionalDetrImageProcessor(format="coco_panoptic" )
								a :						Tuple      =							image_processing(images=lowerCAmelCase__ ,   annotations=lowerCAmelCase__ ,   masks_path=lowerCAmelCase__ ,   return_tensors="pt" )

								# verify pixel values
								a :						Any      =							torch.Size([1, 3, 800, 1066] )
								self.assertEqual(encoding["pixel_values"].shape ,   lowerCAmelCase__ )

								a :						Tuple      =							torch.tensor([0.2_796, 0.3_138, 0.3_481] )
								self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] ,   lowerCAmelCase__ ,   atol=1E-4 ) )

								# verify area
								a :						Optional[Any]      =							torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["area"] ,   lowerCAmelCase__ ) )
								# verify boxes
								a :						Tuple      =							torch.Size([6, 4] )
								self.assertEqual(encoding["labels"][0]["boxes"].shape ,   lowerCAmelCase__ )
								a :						Optional[Any]      =							torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] ,   lowerCAmelCase__ ,   atol=1E-3 ) )
								# verify image_id
								a :						Dict      =							torch.tensor([3_9769] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,   lowerCAmelCase__ ) )
								# verify is_crowd
								a :						List[Any]      =							torch.tensor([0, 0, 0, 0, 0, 0] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,   lowerCAmelCase__ ) )
								# verify class_labels
								a :						str      =							torch.tensor([17, 17, 63, 75, 75, 93] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,   lowerCAmelCase__ ) )
								# verify masks
								a :						str      =							82_2873
								self.assertEqual(encoding["labels"][0]["masks"].sum().item() ,   lowerCAmelCase__ )
								# verify orig_size
								a :						Union[str, Any]      =							torch.tensor([480, 640] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,   lowerCAmelCase__ ) )
								# verify size
								a :						List[Any]      =							torch.tensor([800, 1066] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,   lowerCAmelCase__ ) )








"""simple docstring"""






from typing import Any







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       ):
				_validation(
				    lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       )
				# Creates data structures and fill initial step
				_lowerCamelCase	:     dict       =       {}
				_lowerCamelCase	:     dict       =       {}
				for state in states_space:
								_lowerCamelCase	:     Optional[Any]       =       observations_space[0]
								_lowerCamelCase	:     int       =       (
								    initial_probabilities[state] * emission_probabilities[state][observation]
								)
								_lowerCamelCase	:     List[Any]       =       None

				# Fills the data structure with the probabilities of
				# different transitions and pointers to previous states
				for o in range(1				,       len(lowercase__						)						):
								_lowerCamelCase	:     List[str]       =       observations_space[o]
								_lowerCamelCase	:     str       =       observations_space[o - 1]
								for state in states_space:
												# Calculates the argmax for probability function
												_lowerCamelCase	:     Dict       =       ''
												_lowerCamelCase	:     int       =       -1
												for k_state in states_space:
																_lowerCamelCase	:     Any       =       (
																    probabilities[(k_state, prior_observation)]
																    * transition_probabilities[k_state][state]
																    * emission_probabilities[state][observation]
																)
																if probability > max_probability:
																				_lowerCamelCase	:     Dict       =       probability
																				_lowerCamelCase	:     int       =       k_state

            # Update probabilities and pointers dicts
												_lowerCamelCase	:     List[str]       =       (
												    probabilities[(arg_max, prior_observation)]
												    * transition_probabilities[arg_max][state]
												    * emission_probabilities[state][observation]
												)

												_lowerCamelCase	:     Union[str, Any]       =       arg_max

    # The final observation
				_lowerCamelCase	:     Tuple       =       observations_space[len(lowercase__						) - 1]

				# argmax for given final observation
				_lowerCamelCase	:     Optional[int]       =       ''
				_lowerCamelCase	:     str       =       -1
				for k_state in states_space:
								_lowerCamelCase	:     Optional[int]       =       probabilities[(k_state, final_observation)]
								if probability > max_probability:
												_lowerCamelCase	:     int       =       probability
												_lowerCamelCase	:     Optional[int]       =       k_state
				_lowerCamelCase	:     Any       =       arg_max

				# Process pointers backwards
				_lowerCamelCase	:     Optional[Any]       =       last_state
				_lowerCamelCase	:     Union[str, Any]       =       []
				for o in range(len(lowercase__						) - 1				,       -1				,       -1						):
								result.append(lowercase__						)
								_lowerCamelCase	:     Dict       =       pointers[previous, observations_space[o]]
				result.reverse()

				return result







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       ):
				_validate_not_empty(
				    lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       )
				_validate_lists(lowercase__				,       lowercase__						)
				_validate_dicts(
				    lowercase__				,       lowercase__				,       lowercase__						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       ):
				if not all(
				    [
				        observations_space,
				        states_space,
				        initial_probabilities,
				        transition_probabilities,
				        emission_probabilities,
				    ]						):
								raise ValueError('There\'s an empty parameter'						)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				_validate_list(lowercase__				,       'observations_space'						)
				_validate_list(lowercase__				,       'states_space'						)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				if not isinstance(_object				,       lowercase__						):
								_lowerCamelCase	:     Tuple       =       f'''{var_name} must be a list'''
								raise ValueError(lowercase__						)
				else:
								for x in _object:
												if not isinstance(lowercase__				,       lowercase__						):
																_lowerCamelCase	:     int       =       f'''{var_name} must be a list of strings'''
																raise ValueError(lowercase__						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       ):
				_validate_dict(lowercase__				,       'initial_probabilities'				,       lowercase__						)
				_validate_nested_dict(lowercase__				,       'transition_probabilities'						)
				_validate_nested_dict(lowercase__				,       'emission_probabilities'						)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				_validate_dict(_object				,       lowercase__				,       lowercase__						)
				for x in _object.values():
								_validate_dict(lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__ = False						):
				if not isinstance(_object				,       lowercase__						):
								_lowerCamelCase	:     Tuple       =       f'''{var_name} must be a dict'''
								raise ValueError(lowercase__						)
				if not all(isinstance(lowercase__				,       lowercase__						) for x in _object						):
								_lowerCamelCase	:     Optional[int]       =       f'''{var_name} all keys must be strings'''
								raise ValueError(lowercase__						)
				if not all(isinstance(lowercase__				,       lowercase__						) for x in _object.values()						):
								_lowerCamelCase	:     Optional[int]       =       'nested dictionary ' if nested else ''
								_lowerCamelCase	:     Optional[Any]       =       f'''{var_name} {nested_text}all values must be {value_type.__name__}'''
								raise ValueError(lowercase__						)


if __name__ == "__main__":
			from doctest import testmod

			testmod()