
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        lowerCAmelCase__      (   unittest.TestCase	):

						def __init__(			self       ,							a       ,							a=7       ,							a=3       ,							a=30       ,							a=4_00       ,							a=True       ,							a=None       ,							a=True       ,							a=[0.5, 0.5, 0.5]       ,							a=[0.5, 0.5, 0.5]       ,							a=True       ,							a=1 / 2_55       ,							a=True       ,							)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
								_UpperCamelCase						    =	parent
								_UpperCamelCase						    =	batch_size
								_UpperCamelCase						    =	num_channels
								_UpperCamelCase						    =	min_resolution
								_UpperCamelCase						    =	max_resolution
								_UpperCamelCase						    =	do_resize
								_UpperCamelCase						    =	size
								_UpperCamelCase						    =	do_normalize
								_UpperCamelCase						    =	image_mean
								_UpperCamelCase						    =	image_std
								_UpperCamelCase						    =	do_rescale
								_UpperCamelCase						    =	rescale_factor
								_UpperCamelCase						    =	do_pad

						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								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       ,							a       ,							a=False				)					->      Tuple:


								'''simple docstring'''

								if not batched:
										_UpperCamelCase						    =	image_inputs[0]
										if isinstance(a       ,							Image.Image				):
												_UpperCamelCase    ,	_UpperCamelCase						    =	image.size
										else:
												_UpperCamelCase    ,	_UpperCamelCase						    =	image.shape[1], image.shape[2]
										if w < h:
												_UpperCamelCase						    =	int(self.size["""shortest_edge"""] * h / w				)
												_UpperCamelCase						    =	self.size["""shortest_edge"""]
										elif w > h:
												_UpperCamelCase						    =	self.size["""shortest_edge"""]
												_UpperCamelCase						    =	int(self.size["""shortest_edge"""] * w / h				)
										else:
												_UpperCamelCase						    =	self.size["""shortest_edge"""]
												_UpperCamelCase						    =	self.size["""shortest_edge"""]

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

								return expected_height, expected_width







@require_torch
@require_vision
class        lowerCAmelCase__      (   __lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: Union[str, Any]	=		ConditionalDetrImageProcessor if is_vision_available() else None

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	ConditionalDetrImageProcessingTester(self				)

						@property
						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								return self.image_processor_tester.prepare_image_processor_dict()

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


								'''simple docstring'''

								_UpperCamelCase						    =	self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(a       ,							"""image_mean"""				)				)
								self.assertTrue(hasattr(a       ,							"""image_std"""				)				)
								self.assertTrue(hasattr(a       ,							"""do_normalize"""				)				)
								self.assertTrue(hasattr(a       ,							"""do_resize"""				)				)
								self.assertTrue(hasattr(a       ,							"""size"""				)				)

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


								'''simple docstring'''

								_UpperCamelCase						    =	self.image_processing_class.from_dict(self.image_processor_dict				)
								self.assertEqual(image_processor.size       ,							{"""shortest_edge""": 18, """longest_edge""": 13_33}				)
								self.assertEqual(image_processor.do_pad       ,							a				)

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

						def  A_				(			self				)					->      int:


								'''simple docstring'''

								pass

						def  A_				(			self				)					->      str:


								'''simple docstring'''

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

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

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.image_processor_tester.get_expected_values(a				)

								self.assertEqual(
								    encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

								# Test batched
								_UpperCamelCase    ,	_UpperCamelCase						    =	self.image_processor_tester.get_expected_values(a       ,							batched=a				)

								_UpperCamelCase						    =	image_processing(a       ,							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				)					->      Optional[int]:


								'''simple docstring'''

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

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

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.image_processor_tester.get_expected_values(a				)

								self.assertEqual(
								    encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

								# Test batched
								_UpperCamelCase						    =	image_processing(a       ,							return_tensors="""pt"""				).pixel_values

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.image_processor_tester.get_expected_values(a       ,							batched=a				)

								self.assertEqual(
								    encoded_images.shape       ,							(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )       ,							)

						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

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

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

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.image_processor_tester.get_expected_values(a				)

								self.assertEqual(
								    encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

								# Test batched
								_UpperCamelCase						    =	image_processing(a       ,							return_tensors="""pt"""				).pixel_values

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.image_processor_tester.get_expected_values(a       ,							batched=a				)

								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				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
								with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""       ,							"""r"""				) as f:
										_UpperCamelCase						    =	json.loads(f.read()				)

								_UpperCamelCase						    =	{"""image_id""": 3_97_69, """annotations""": target}

								# encode them
								_UpperCamelCase						    =	ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""				)
								_UpperCamelCase						    =	image_processing(images=a       ,							annotations=a       ,							return_tensors="""pt"""				)

								# verify pixel values
								_UpperCamelCase						    =	torch.Size([1, 3, 8_00, 10_66]				)
								self.assertEqual(encoding["""pixel_values"""].shape       ,							a				)

								_UpperCamelCase						    =	torch.tensor([0.2796, 0.3138, 0.3481]				)
								self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,							a       ,							atol=1e-4				)				)

								# verify area
								_UpperCamelCase						    =	torch.tensor([5887.9600, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,							a				)				)
								# verify boxes
								_UpperCamelCase						    =	torch.Size([6, 4]				)
								self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,							a				)
								_UpperCamelCase						    =	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,							a       ,							atol=1e-3				)				)
								# verify image_id
								_UpperCamelCase						    =	torch.tensor([3_97_69]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,							a				)				)
								# verify is_crowd
								_UpperCamelCase						    =	torch.tensor([0, 0, 0, 0, 0, 0]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,							a				)				)
								# verify class_labels
								_UpperCamelCase						    =	torch.tensor([75, 75, 63, 65, 17, 17]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,							a				)				)
								# verify orig_size
								_UpperCamelCase						    =	torch.tensor([4_80, 6_40]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,							a				)				)
								# verify size
								_UpperCamelCase						    =	torch.tensor([8_00, 10_66]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,							a				)				)







						@slow
						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
								with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""       ,							"""r"""				) as f:
										_UpperCamelCase						    =	json.loads(f.read()				)

								_UpperCamelCase						    =	{"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

								_UpperCamelCase						    =	pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""				)

								# encode them
								_UpperCamelCase						    =	ConditionalDetrImageProcessor(format="""coco_panoptic"""				)
								_UpperCamelCase						    =	image_processing(images=a       ,							annotations=a       ,							masks_path=a       ,							return_tensors="""pt"""				)

								# verify pixel values
								_UpperCamelCase						    =	torch.Size([1, 3, 8_00, 10_66]				)
								self.assertEqual(encoding["""pixel_values"""].shape       ,							a				)

								_UpperCamelCase						    =	torch.tensor([0.2796, 0.3138, 0.3481]				)
								self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,							a       ,							atol=1e-4				)				)

								# verify area
								_UpperCamelCase						    =	torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 5879.6562, 7634.1147]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,							a				)				)
								# verify boxes
								_UpperCamelCase						    =	torch.Size([6, 4]				)
								self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,							a				)
								_UpperCamelCase						    =	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,							a       ,							atol=1e-3				)				)
								# verify image_id
								_UpperCamelCase						    =	torch.tensor([3_97_69]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,							a				)				)
								# verify is_crowd
								_UpperCamelCase						    =	torch.tensor([0, 0, 0, 0, 0, 0]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,							a				)				)
								# verify class_labels
								_UpperCamelCase						    =	torch.tensor([17, 17, 63, 75, 75, 93]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,							a				)				)
								# verify masks
								_UpperCamelCase						    =	82_28_73
								self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()       ,							a				)
								# verify orig_size
								_UpperCamelCase						    =	torch.tensor([4_80, 6_40]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,							a				)				)
								# verify size
								_UpperCamelCase						    =	torch.tensor([8_00, 10_66]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,							a				)				)

from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


lowerCamelCase__	=							logging.get_logger(__name__)  # pylint: disable=invalid-name







class        lowerCAmelCase__      (   __lowercase	):

						def __init__(			self       ,							a       ,							a				)					->      Any:


								'''simple docstring'''

								super().__init__()
								self.register_modules(unet=a       ,							scheduler=a				)







						@torch.no_grad()
						def __call__(			self       ,							a = 1       ,							a = 1_00       ,							a = None       ,							a = None       ,							a = True       ,							)					->      Union[AudioPipelineOutput, Tuple]:


								'''simple docstring'''

								if audio_length_in_s is None:
										_UpperCamelCase						    =	self.unet.config.sample_size / self.unet.config.sample_rate

								_UpperCamelCase						    =	audio_length_in_s * self.unet.config.sample_rate

								_UpperCamelCase						    =	2 ** len(self.unet.up_blocks				)
								if sample_size < 3 * down_scale_factor:
										raise ValueError(
										    F'{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'
										    F' {3 * down_scale_factor / self.unet.config.sample_rate}.'				)

								_UpperCamelCase						    =	int(a				)
								if sample_size % down_scale_factor != 0:
										_UpperCamelCase						    =	(
										    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
										) * down_scale_factor
										logger.info(
										    F'{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'
										    F' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'
										    """ process."""				)
								_UpperCamelCase						    =	int(a				)

								_UpperCamelCase						    =	next(iter(self.unet.parameters()				)				).dtype
								_UpperCamelCase						    =	(batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(a       ,							a				) and len(a				) != batch_size:
										raise ValueError(
										    F'You have passed a list of generators of length {len(a				)}, but requested an effective batch'
										    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'				)

								_UpperCamelCase						    =	randn_tensor(a       ,							generator=a       ,							device=self.device       ,							dtype=a				)

								# set step values
								self.scheduler.set_timesteps(a       ,							device=audio.device				)
								_UpperCamelCase						    =	self.scheduler.timesteps.to(a				)

								for t in self.progress_bar(self.scheduler.timesteps				):
										# 1. predict noise model_output
										_UpperCamelCase						    =	self.unet(a       ,							a				).sample

										# 2. compute previous image: x_t -> t_t-1
										_UpperCamelCase						    =	self.scheduler.step(a       ,							a       ,							a				).prev_sample

								_UpperCamelCase						    =	audio.clamp(-1       ,							1				).float().cpu().numpy()

								_UpperCamelCase						    =	audio[:, :, :original_sample_size]

								if not return_dict:
										return (audio,)

								return AudioPipelineOutput(audios=a				)



