




'''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 DeformableDetrImageProcessor






class 				_a							(       unittest.TestCase      ):





       '''simple docstring'''






       def __init__(	self,				A,				A=7,				A=3,				A=30,				A=400,				A=True,				A=None,				A=True,				A=[0.5, 0.5, 0.5],				A=[0.5, 0.5, 0.5],				A=True,				A=1 / 255,				A=True,				):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Dict   =		size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
             SCREAMING_SNAKE_CASE					:	List[str]   =		parent
             SCREAMING_SNAKE_CASE					:	Dict   =		batch_size
             SCREAMING_SNAKE_CASE					:	str   =		num_channels
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		min_resolution
             SCREAMING_SNAKE_CASE					:	Dict   =		max_resolution
             SCREAMING_SNAKE_CASE					:	int   =		do_resize
             SCREAMING_SNAKE_CASE					:	Any   =		size
             SCREAMING_SNAKE_CASE					:	Tuple   =		do_normalize
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		image_mean
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		image_std
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		do_rescale
             SCREAMING_SNAKE_CASE					:	str   =		rescale_factor
             SCREAMING_SNAKE_CASE					:	int   =		do_pad







       def     UpperCamelCase_				(	self   ):







             '''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     UpperCamelCase_				(	self,				A,				A=False   ):







             '''simple docstring'''







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

             else:
                   SCREAMING_SNAKE_CASE					:	List[Any]   =		[]
                   for image in image_inputs:
                         SCREAMING_SNAKE_CASE					:	int   =		self.get_expected_values([image]   )
                         expected_values.append((expected_height, expected_width)   )
                   SCREAMING_SNAKE_CASE					:	List[Any]   =		max(A_,				key=lambda A   : item[0]   )[0]
                   SCREAMING_SNAKE_CASE					:	int   =		max(A_,				key=lambda A   : item[1]   )[1]

             return expected_height, expected_width






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





       '''simple docstring'''






       A		:							int								=		DeformableDetrImageProcessor if is_vision_available() else None







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Any   =		DeformableDetrImageProcessingTester(self   )







       @property
       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             return self.image_processor_tester.prepare_image_processor_dict()







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Tuple   =		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_,				'do_rescale'   )   )
             self.assertTrue(hasattr(A_,				'do_pad'   )   )
             self.assertTrue(hasattr(A_,				'size'   )   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	str   =		self.image_processing_class.from_dict(self.image_processor_dict   )
             self.assertEqual(image_processor.size,				{'shortest_edge': 18, 'longest_edge': 1_333}   )
             self.assertEqual(image_processor.do_pad,				A_   )

             SCREAMING_SNAKE_CASE					:	str   =		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     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             pass







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







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

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

             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		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
             SCREAMING_SNAKE_CASE					:	int   =		self.image_processor_tester.get_expected_values(A_,				batched=A_   )

             SCREAMING_SNAKE_CASE					:	Any   =		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     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Tuple   =		self.image_processing_class(**self.image_processor_dict   )
             # create random numpy tensors
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		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
             SCREAMING_SNAKE_CASE					:	Dict   =		image_processing(image_inputs[0],				return_tensors='pt'   ).pixel_values

             SCREAMING_SNAKE_CASE					:	int   =		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
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		image_processing(A_,				return_tensors='pt'   ).pixel_values

             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		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     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		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=A_,				torchify=A_   )
             for image in image_inputs:
                   self.assertIsInstance(A_,				torch.Tensor   )

             # Test not batched input
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		image_processing(image_inputs[0],				return_tensors='pt'   ).pixel_values

             SCREAMING_SNAKE_CASE					:	Optional[int]   =		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
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		image_processing(A_,				return_tensors='pt'   ).pixel_values

             SCREAMING_SNAKE_CASE					:	Dict   =		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     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
             with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt',				'r'   ) as f:
                   SCREAMING_SNAKE_CASE					:	str   =		json.loads(f.read()   )

             SCREAMING_SNAKE_CASE					:	Dict   =		{"image_id": 39_769, "annotations": target}

             # encode them
             SCREAMING_SNAKE_CASE					:	str   =		DeformableDetrImageProcessor()
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		image_processing(images=A_,				annotations=A_,				return_tensors='pt'   )

             # verify pixel values
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		torch.Size([1, 3, 800, 1_066]   )
             self.assertEqual(encoding['pixel_values'].shape,				A_   )

             SCREAMING_SNAKE_CASE					:	Any   =		torch.tensor([0.27_96, 0.31_38, 0.34_81]   )
             self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3],				A_,				atol=1E-4   )   )

             # verify area
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['area'],				A_   )   )
             # verify boxes
             SCREAMING_SNAKE_CASE					:	List[str]   =		torch.Size([6, 4]   )
             self.assertEqual(encoding['labels'][0]['boxes'].shape,				A_   )
             SCREAMING_SNAKE_CASE					:	List[Any]   =		torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0],				A_,				atol=1E-3   )   )
             # verify image_id
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		torch.tensor([39_769]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],				A_   )   )
             # verify is_crowd
             SCREAMING_SNAKE_CASE					:	str   =		torch.tensor([0, 0, 0, 0, 0, 0]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],				A_   )   )
             # verify class_labels
             SCREAMING_SNAKE_CASE					:	str   =		torch.tensor([75, 75, 63, 65, 17, 17]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],				A_   )   )
             # verify orig_size
             SCREAMING_SNAKE_CASE					:	Dict   =		torch.tensor([480, 640]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],				A_   )   )
             # verify size
             SCREAMING_SNAKE_CASE					:	Any   =		torch.tensor([800, 1_066]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['size'],				A_   )   )







       @slow
       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Any   =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
             with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt',				'r'   ) as f:
                   SCREAMING_SNAKE_CASE					:	Optional[int]   =		json.loads(f.read()   )

             SCREAMING_SNAKE_CASE					:	Any   =		{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

             SCREAMING_SNAKE_CASE					:	Tuple   =		pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'   )

             # encode them
             SCREAMING_SNAKE_CASE					:	List[Any]   =		DeformableDetrImageProcessor(format='coco_panoptic'   )
             SCREAMING_SNAKE_CASE					:	Dict   =		image_processing(images=A_,				annotations=A_,				masks_path=A_,				return_tensors='pt'   )

             # verify pixel values
             SCREAMING_SNAKE_CASE					:	Any   =		torch.Size([1, 3, 800, 1_066]   )
             self.assertEqual(encoding['pixel_values'].shape,				A_   )

             SCREAMING_SNAKE_CASE					:	List[Any]   =		torch.tensor([0.27_96, 0.31_38, 0.34_81]   )
             self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3],				A_,				atol=1E-4   )   )

             # verify area
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['area'],				A_   )   )
             # verify boxes
             SCREAMING_SNAKE_CASE					:	Dict   =		torch.Size([6, 4]   )
             self.assertEqual(encoding['labels'][0]['boxes'].shape,				A_   )
             SCREAMING_SNAKE_CASE					:	Any   =		torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0],				A_,				atol=1E-3   )   )
             # verify image_id
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		torch.tensor([39_769]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],				A_   )   )
             # verify is_crowd
             SCREAMING_SNAKE_CASE					:	Dict   =		torch.tensor([0, 0, 0, 0, 0, 0]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],				A_   )   )
             # verify class_labels
             SCREAMING_SNAKE_CASE					:	Any   =		torch.tensor([17, 17, 63, 75, 75, 93]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],				A_   )   )
             # verify masks
             SCREAMING_SNAKE_CASE					:	Dict   =		822_873
             self.assertEqual(encoding['labels'][0]['masks'].sum().item(),				A_   )
             # verify orig_size
             SCREAMING_SNAKE_CASE					:	List[Any]   =		torch.tensor([480, 640]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],				A_   )   )
             # verify size
             SCREAMING_SNAKE_CASE					:	List[Any]   =		torch.tensor([800, 1_066]   )
             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       :     str   =     logging.get_logger(__name__)  # pylint: disable=invalid-name



class 					A__   (       __snake_case			):





      def __init__( self       ,							A_       ,							A_   ):
       '''simple docstring'''




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





      @torch.no_grad()
      def __call__( self       ,							A_ = 1       ,							A_ = 100       ,							A_ = None       ,							A_ = None       ,							A_ = True       ,							):
       '''simple docstring'''




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

       UpperCamelCase	:					Optional[Any]       	=   audio_length_in_s * self.unet.config.sample_rate

       UpperCamelCase	:					Any       	=   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	:					Union[str, Any]       	=   int(A_   )
       if sample_size % down_scale_factor != 0:
        UpperCamelCase	:					List[str]       	=   (
            (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	:					Any       	=   int(A_   )

       UpperCamelCase	:					Union[str, Any]       	=   next(iter(self.unet.parameters()   )   ).dtype
       UpperCamelCase	:					Optional[int]       	=   (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	:					Optional[Any]       	=   randn_tensor(A_       ,							generator=A_       ,							device=self.device       ,							dtype=A_   )

       # set step values
       self.scheduler.set_timesteps(A_       ,							device=audio.device   )
       UpperCamelCase	:					Optional[int]       	=   self.scheduler.timesteps.to(A_   )

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

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

       UpperCamelCase	:					Optional[Any]       	=   audio.clamp(-1       ,							1   ).float().cpu().numpy()

       UpperCamelCase	:					Dict       	=   audio[:, :, :original_sample_size]

       if not return_dict:
        return (audio,)

       return AudioPipelineOutput(audios=A_   )
