from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel





class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		["""vqvae"""]


						def __init__(       self	:				Any    ,		UpperCamelCase__	:				AutoencoderKL    ,		UpperCamelCase__	:				UNetaDConditionModel    ,		UpperCamelCase__	:				Mel    ,		UpperCamelCase__	:				Union[DDIMScheduler, DDPMScheduler]    ,		):





												'''simple docstring'''





												super().__init__()
												self.register_modules(unet=UpperCamelCase__    ,		scheduler=UpperCamelCase__    ,		mel=UpperCamelCase__    ,		vqvae=UpperCamelCase__						)


						def        __A       (       self	:				int						):





												'''simple docstring'''





												return 50 if isinstance(self.scheduler    ,		UpperCamelCase__						) else 1000


						@torch.no_grad()
						def __call__(       self	:				List[str]    ,		UpperCamelCase__	:				int = 1    ,		UpperCamelCase__	:				str = None    ,		UpperCamelCase__	:				np.ndarray = None    ,		UpperCamelCase__	:				int = 0    ,		UpperCamelCase__	:				int = 0    ,		UpperCamelCase__	:				int = None    ,		UpperCamelCase__	:				torch.Generator = None    ,		UpperCamelCase__	:				float = 0    ,		UpperCamelCase__	:				float = 0    ,		UpperCamelCase__	:				torch.Generator = None    ,		UpperCamelCase__	:				float = 0    ,		UpperCamelCase__	:				torch.Tensor = None    ,		UpperCamelCase__	:				torch.Tensor = None    ,		UpperCamelCase__	:				List[Any]=True    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							steps or self.get_default_steps()
												self.scheduler.set_timesteps(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							step_generator or generator
												# For backwards compatibility
												if type(self.unet.config.sample_size						) == int:
																		SCREAMING_SNAKE_CASE							:    Dict							=							(self.unet.config.sample_size, self.unet.config.sample_size)
												if noise is None:
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							randn_tensor(
																		    (
																		        batch_size,
																		        self.unet.config.in_channels,
																		        self.unet.config.sample_size[0],
																		        self.unet.config.sample_size[1],
																		    )    ,		generator=UpperCamelCase__    ,		device=self.device    ,		)
												SCREAMING_SNAKE_CASE							:    List[str]							=							noise
												SCREAMING_SNAKE_CASE							:    str							=							None

												if audio_file is not None or raw_audio is not None:
																		self.mel.load_audio(UpperCamelCase__    ,		UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.mel.audio_slice_to_image(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    List[str]							=							np.frombuffer(input_image.tobytes()    ,		dtype='''uint8'''						).reshape(
																		    (input_image.height, input_image.width)						)
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							(input_image / 255) * 2 - 1
																		SCREAMING_SNAKE_CASE							:    Any							=							torch.tensor(input_image[np.newaxis, :, :]    ,		dtype=torch.float						).to(self.device						)

																		if self.vqvae is not None:
																								SCREAMING_SNAKE_CASE							:    Dict							=							self.vqvae.encode(torch.unsqueeze(UpperCamelCase__    ,		0						)						).latent_dist.sample(
																								    generator=UpperCamelCase__						)[0]
																								SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.vqvae.config.scaling_factor * input_images

																		if start_step > 0:
																								SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.scheduler.add_noise(UpperCamelCase__    ,		UpperCamelCase__    ,		self.scheduler.timesteps[start_step - 1]						)

																		SCREAMING_SNAKE_CASE							:    List[str]							=							(
																		    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
																		)
																		SCREAMING_SNAKE_CASE							:    int							=							int(mask_start_secs * pixels_per_second						)
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							int(mask_end_secs * pixels_per_second						)
																		SCREAMING_SNAKE_CASE							:    Any							=							self.scheduler.add_noise(UpperCamelCase__    ,		UpperCamelCase__    ,		torch.tensor(self.scheduler.timesteps[start_step:]						)						)

												for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]						)						):
																		if isinstance(self.unet    ,		UpperCamelCase__						):
																								SCREAMING_SNAKE_CASE							:    Tuple							=							self.unet(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__						)['''sample''']
																		else:
																								SCREAMING_SNAKE_CASE							:    List[str]							=							self.unet(UpperCamelCase__    ,		UpperCamelCase__						)['''sample''']

																		if isinstance(self.scheduler    ,		UpperCamelCase__						):
																								SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.scheduler.step(
																								    model_output=UpperCamelCase__    ,		timestep=UpperCamelCase__    ,		sample=UpperCamelCase__    ,		eta=UpperCamelCase__    ,		generator=UpperCamelCase__    ,		)['''prev_sample''']
																		else:
																								SCREAMING_SNAKE_CASE							:    int							=							self.scheduler.step(
																								    model_output=UpperCamelCase__    ,		timestep=UpperCamelCase__    ,		sample=UpperCamelCase__    ,		generator=UpperCamelCase__    ,		)['''prev_sample''']

																		if mask is not None:
																								if mask_start > 0:
																														SCREAMING_SNAKE_CASE							:    Dict							=							mask[:, step, :, :mask_start]
																								if mask_end > 0:
																														SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							mask[:, step, :, -mask_end:]

												if self.vqvae is not None:
																		# 0.18215 was scaling factor used in training to ensure unit variance
																		SCREAMING_SNAKE_CASE							:    Tuple							=							1 / self.vqvae.config.scaling_factor * images
																		SCREAMING_SNAKE_CASE							:    Any							=							self.vqvae.decode(UpperCamelCase__						)['''sample''']

												SCREAMING_SNAKE_CASE							:    List[Any]							=							(images / 2 + 0.5).clamp(0    ,		1						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							images.cpu().permute(0    ,		2    ,		3    ,		1						).numpy()
												SCREAMING_SNAKE_CASE							:    List[str]							=							(images * 255).round().astype('''uint8'''						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							list(
												    (Image.fromarray(_[:, :, 0]						) for _ in images)
												    if images.shape[3] == 1
												    else (Image.fromarray(UpperCamelCase__    ,		mode='''RGB'''						).convert('''L'''						) for _ in images)						)

												SCREAMING_SNAKE_CASE							:    str							=							[self.mel.image_to_audio(UpperCamelCase__						) for _ in images]
												if not return_dict:
																		return images, (self.mel.get_sample_rate(), audios)

												return BaseOutput(**AudioPipelineOutput(np.array(UpperCamelCase__						)[:, np.newaxis, :]						)    ,		**ImagePipelineOutput(UpperCamelCase__						)						)


						@torch.no_grad()
						def        __A       (       self	:				List[str]    ,		UpperCamelCase__	:				List[Image.Image]    ,		UpperCamelCase__	:				int = 50						):





												'''simple docstring'''





												assert isinstance(self.scheduler    ,		UpperCamelCase__						)
												self.scheduler.set_timesteps(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							np.array(
												    [np.frombuffer(image.tobytes()    ,		dtype='''uint8'''						).reshape((1, image.height, image.width)						) for image in images]						)
												SCREAMING_SNAKE_CASE							:    Any							=							(sample / 255) * 2 - 1
												SCREAMING_SNAKE_CASE							:    Any							=							torch.Tensor(UpperCamelCase__						).to(self.device						)

												for t in self.progress_bar(torch.flip(self.scheduler.timesteps    ,		(0,)						)						):
																		SCREAMING_SNAKE_CASE							:    Any							=							t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							self.scheduler.alphas_cumprod[t]
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							(
																		    self.scheduler.alphas_cumprod[prev_timestep]
																		    if prev_timestep >= 0
																		    else self.scheduler.final_alpha_cumprod
																		)
																		SCREAMING_SNAKE_CASE							:    List[str]							=							1 - alpha_prod_t
																		SCREAMING_SNAKE_CASE							:    Any							=							self.unet(UpperCamelCase__    ,		UpperCamelCase__						)['''sample''']
																		SCREAMING_SNAKE_CASE							:    Any							=							(1 - alpha_prod_t_prev) ** 0.5 * model_output
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
																		SCREAMING_SNAKE_CASE							:    Dict							=							sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

												return sample



						@staticmethod
						def        __A       (       UpperCamelCase__	:				torch.Tensor    ,		UpperCamelCase__	:				torch.Tensor    ,		UpperCamelCase__	:				float						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							acos(torch.dot(torch.flatten(UpperCamelCase__						)    ,		torch.flatten(UpperCamelCase__						)						) / torch.norm(UpperCamelCase__						) / torch.norm(UpperCamelCase__						)						)
												return sin((1 - alpha) * theta						) * xa / sin(UpperCamelCase__						) + sin(alpha * theta						) * xa / sin(UpperCamelCase__						)






import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
					from PIL import Image
else:





					class 					lowercase__						:


											@staticmethod
											def        __A       (       *UpperCamelCase__	:				Union[str, Any]    ,		**UpperCamelCase__	:				Optional[Any]						):





																	'''simple docstring'''





																	pass





@is_pipeline_test
@require_vision
@require_torch
class 					lowercase__						(   unittest.TestCase):
						UpperCamelCase_     =		MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				Tuple    ,		UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							pipeline(
												    '''zero-shot-object-detection'''    ,		model='''hf-internal-testing/tiny-random-owlvit-object-detection'''						)

												SCREAMING_SNAKE_CASE							:    List[str]							=							[
												    {
												        '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
												        '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
												    }
												]
												return object_detector, examples


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				Any    ,		UpperCamelCase__	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							object_detector(examples[0]    ,		threshold=0.0						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							len(UpperCamelCase__						)
												self.assertGreater(UpperCamelCase__    ,		0						)
												self.assertEqual(
												    UpperCamelCase__    ,		[
												        {
												            '''score''': ANY(UpperCamelCase__						),
												            '''label''': ANY(UpperCamelCase__						),
												            '''box''': {'''xmin''': ANY(UpperCamelCase__						), '''ymin''': ANY(UpperCamelCase__						), '''xmax''': ANY(UpperCamelCase__						), '''ymax''': ANY(UpperCamelCase__						)},
												        }
												        for i in range(UpperCamelCase__						)
												    ]    ,		)


						@require_tf
						@unittest.skip('''Zero Shot Object Detection not implemented in TF'''						)
						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												pass


						@require_torch
						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							pipeline(
												    '''zero-shot-object-detection'''    ,		model='''hf-internal-testing/tiny-random-owlvit-object-detection'''						)

												SCREAMING_SNAKE_CASE							:    str							=							object_detector(
												    '''./tests/fixtures/tests_samples/COCO/000000039769.png'''    ,		candidate_labels=['''cat''', '''remote''', '''couch''']    ,		threshold=0.64    ,		)

												self.assertEqual(
												    nested_simplify(UpperCamelCase__    ,		decimals=4						)    ,		[
												        {'''score''': 0.7235, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												        {'''score''': 0.7218, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												        {'''score''': 0.7184, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												        {'''score''': 0.6748, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												        {'''score''': 0.6656, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												        {'''score''': 0.6614, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												        {'''score''': 0.6456, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
												        {'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
												        {'''score''': 0.6419, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
												    ]    ,		)

												SCREAMING_SNAKE_CASE							:    str							=							object_detector(
												    [
												        {
												            '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
												            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
												        }
												    ]    ,		threshold=0.64    ,		)

												self.assertEqual(
												    nested_simplify(UpperCamelCase__    ,		decimals=4						)    ,		[
												        [
												            {'''score''': 0.7235, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												            {'''score''': 0.7218, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												            {'''score''': 0.7184, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												            {'''score''': 0.6748, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												            {'''score''': 0.6656, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												            {'''score''': 0.6614, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												            {'''score''': 0.6456, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
												            {'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
												            {'''score''': 0.6419, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
												        ]
												    ]    ,		)


						@require_torch
						@slow
						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							pipeline('''zero-shot-object-detection'''						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							object_detector(
												    '''http://images.cocodataset.org/val2017/000000039769.jpg'''    ,		candidate_labels=['''cat''', '''remote''', '''couch''']    ,		)
												self.assertEqual(
												    nested_simplify(UpperCamelCase__    ,		decimals=4						)    ,		[
												        {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												        {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												        {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
												        {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
												        {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
												    ]    ,		)

												SCREAMING_SNAKE_CASE							:    int							=							object_detector(
												    [
												        {
												            '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
												            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
												        },
												        {
												            '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
												            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
												        },
												    ]    ,		)
												self.assertEqual(
												    nested_simplify(UpperCamelCase__    ,		decimals=4						)    ,		[
												        [
												            {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												            {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												            {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
												            {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
												            {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
												        ],
												        [
												            {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												            {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												            {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
												            {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
												            {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
												        ],
												    ]    ,		)


						@require_tf
						@unittest.skip('''Zero Shot Object Detection not implemented in TF'''						)
						def        __A       (       self	:				str						):





												'''simple docstring'''





												pass


						@require_torch
						@slow
						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							0.2
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							pipeline('''zero-shot-object-detection'''						)

												SCREAMING_SNAKE_CASE							:    Dict							=							object_detector(
												    '''http://images.cocodataset.org/val2017/000000039769.jpg'''    ,		candidate_labels=['''cat''', '''remote''', '''couch''']    ,		threshold=UpperCamelCase__    ,		)
												self.assertEqual(
												    nested_simplify(UpperCamelCase__    ,		decimals=4						)    ,		[
												        {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												        {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												        {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
												    ]    ,		)



						@require_torch
						@slow
						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							2
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							pipeline('''zero-shot-object-detection'''						)

												SCREAMING_SNAKE_CASE							:    List[str]							=							object_detector(
												    '''http://images.cocodataset.org/val2017/000000039769.jpg'''    ,		candidate_labels=['''cat''', '''remote''', '''couch''']    ,		top_k=UpperCamelCase__    ,		)
												self.assertEqual(
												    nested_simplify(UpperCamelCase__    ,		decimals=4						)    ,		[
												        {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												        {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												    ]    ,		)





