"""simple docstring"""





from typing import List, Optional, Tuple, Union

import torch

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


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


class   UpperCAmelCase_			(					_a):


      def __init__(     self	,    a	,    a							) ->      int:
             super().__init__()
             self.register_modules(unet=a	,    scheduler=a							)


      @torch.no_grad()
      def __call__(     self	,    a = 1	,    a = 1_0_0	,    a = None	,    a = None	,    a = True	,    ) ->      Union[AudioPipelineOutput, Tuple]:

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

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

             lowercase__    :	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}."""							)

             lowercase__    :	List[Any]     					=			int(a							)
             if sample_size % down_scale_factor != 0:
                    lowercase__    :	Tuple     					=			(
                        (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.'							)
             lowercase__    :	Union[str, Any]     					=			int(a							)

             lowercase__    :	str     					=			next(iter(self.unet.parameters()							)							).dtype
             lowercase__    :	Optional[Any]     					=			(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."""							)

             lowercase__    :	Tuple     					=			randn_tensor(a	,    generator=a	,    device=self.device	,    dtype=a							)

             # set step values
             self.scheduler.set_timesteps(a	,    device=audio.device							)
             lowercase__    :	Union[str, Any]     					=			self.scheduler.timesteps.to(a							)

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

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

             lowercase__    :	str     					=			audio.clamp(-1	,    1							).float().cpu().numpy()

             lowercase__    :	List[str]     					=			audio[:, :, :original_sample_size]

             if not return_dict:
                    return (audio,)

             return AudioPipelineOutput(audios=a							)


"""simple docstring"""





import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image
else:


       class   UpperCAmelCase_			:


             @staticmethod
             def 	_UpperCAmelCase (     *a	,    **a							) ->      int:
                    pass





def        a_  (   _lowerCAmelCase						:				Image  ):





       '''simple docstring'''

       lowercase__    :	List[str]     					=			hashlib.mda(image.tobytes()  )
       return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class   UpperCAmelCase_			(					unittest.TestCase):
      lowerCamelCase__	:					Union[str, Any]										=						MODEL_FOR_DEPTH_ESTIMATION_MAPPING


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Dict:
             lowercase__    :	Union[str, Any]     					=			DepthEstimationPipeline(model=a	,    image_processor=a							)
             return depth_estimator, [
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
             ]


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      Optional[int]:
             lowercase__    :	Tuple     					=			depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
             self.assertEqual({'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)}	,    a							)
             import datasets

             lowercase__    :	Tuple     					=			datasets.load_dataset('hf-internal-testing/fixtures_image_utils'	,    'image'	,    split='test'							)
             lowercase__    :	List[Any]     					=			depth_estimator(
                 [
                     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
                     'http://images.cocodataset.org/val2017/000000039769.jpg',
                     # RGBA
                     dataset[0]['file'],
                     # LA
                     dataset[1]['file'],
                     # L
                     dataset[2]['file'],
                 ]							)
             self.assertEqual(
                 [
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                 ]	,    a	,    )


      @require_tf
      @unittest.skip('Depth estimation is not implemented in TF'							)
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             pass


      @slow
      @require_torch
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Tuple     					=			'Intel/dpt-large'
             lowercase__    :	Optional[int]     					=			pipeline('depth-estimation'	,    model=a							)
             lowercase__    :	List[Any]     					=			depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'							)
             lowercase__    :	Optional[Any]     					=			hashimage(outputs['depth']							)

             # This seems flaky.
             # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
             self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()							)	,    29.304							)
             self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()							)	,    2.662							)


      @require_torch
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             # This is highly irregular to have no small tests.
             self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'							)

