






"""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_				(      *snake_case__ ,		**snake_case__				)			->			Dict:



          """simple docstring"""





          pass






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			hashlib.mda(image.tobytes()		)
       return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class   UpperCamelCase_ (       unittest.TestCase  ):
  _A       :					int				=	MODEL_FOR_DEPTH_ESTIMATION_MAPPING



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			Tuple:



         """simple docstring"""





         UpperCAmelCase	    =			DepthEstimationPipeline(model=snake_case__ ,		image_processor=snake_case__				)
         return depth_estimator, [
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__				)			->			Tuple:



         """simple docstring"""





         UpperCAmelCase	    =			depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
         self.assertEqual({"""predicted_depth""": ANY(torch.Tensor				), """depth""": ANY(Image.Image				)} ,		snake_case__				)
         import datasets

         UpperCAmelCase	    =			datasets.load_dataset("""hf-internal-testing/fixtures_image_utils""" ,		"""image""" ,		split="""test"""				)
         UpperCAmelCase	    =			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				)},
             ] ,		snake_case__ ,		)



  @require_tf
  @unittest.skip("""Depth estimation is not implemented in TF"""				)
  def 		UpperCamelCase_				(      self				)			->			List[str]:



         """simple docstring"""





         pass



  @slow
  @require_torch
  def 		UpperCamelCase_				(      self				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			"""Intel/dpt-large"""
         UpperCAmelCase	    =			pipeline("""depth-estimation""" ,		model=snake_case__				)
         UpperCAmelCase	    =			depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""				)
         UpperCAmelCase	    =			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				)			->			Dict:



         """simple docstring"""





         self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT"""				)








"""simple docstring"""







import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_	:    List[Any]            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Optional[Any]            =      {
    '''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json''',
    '''facebook/encodec_48khz''': '''https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json''',
}





class   UpperCamelCase_ (       a_  ):
  _A       :					Dict				=	'encodec'



  def __init__(      self ,		snake_case__=[1.5, 3.0, 6.0, 12.0, 24.0] ,		snake_case__=2_40_00 ,		snake_case__=1 ,		snake_case__=False ,		snake_case__=None ,		snake_case__=None ,		snake_case__=1_28 ,		snake_case__=32 ,		snake_case__=1 ,		snake_case__=[8, 5, 4, 2] ,		snake_case__="weight_norm" ,		snake_case__=7 ,		snake_case__=7 ,		snake_case__=3 ,		snake_case__=2 ,		snake_case__=True ,		snake_case__="reflect" ,		snake_case__=2 ,		snake_case__=2 ,		snake_case__=1.0 ,		snake_case__=10_24 ,		snake_case__=None ,		snake_case__=True ,		**snake_case__ ,		)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			target_bandwidths
         UpperCAmelCase	    =			sampling_rate
         UpperCAmelCase	    =			audio_channels
         UpperCAmelCase	    =			normalize
         UpperCAmelCase	    =			chunk_length_s
         UpperCAmelCase	    =			overlap
         UpperCAmelCase	    =			hidden_size
         UpperCAmelCase	    =			num_filters
         UpperCAmelCase	    =			num_residual_layers
         UpperCAmelCase	    =			upsampling_ratios
         UpperCAmelCase	    =			norm_type
         UpperCAmelCase	    =			kernel_size
         UpperCAmelCase	    =			last_kernel_size
         UpperCAmelCase	    =			residual_kernel_size
         UpperCAmelCase	    =			dilation_growth_rate
         UpperCAmelCase	    =			use_causal_conv
         UpperCAmelCase	    =			pad_mode
         UpperCAmelCase	    =			compress
         UpperCAmelCase	    =			num_lstm_layers
         UpperCAmelCase	    =			trim_right_ratio
         UpperCAmelCase	    =			codebook_size
         UpperCAmelCase	    =			codebook_dim if codebook_dim is not None else hidden_size
         UpperCAmelCase	    =			use_conv_shortcut

         if self.norm_type not in ["weight_norm", "time_group_norm"]:
                raise ValueError(
                    f'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'''				)

         super().__init__(**snake_case__				)



  @property
  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """simple docstring"""





         if self.chunk_length_s is None:
                return None
         else:
                return int(self.chunk_length_s * self.sampling_rate				)



  @property
  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """simple docstring"""





         if self.chunk_length_s is None or self.overlap is None:
                return None
         else:
                return max(1 ,		int((1.0 - self.overlap) * self.chunk_length				)				)



  @property
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			np.prod(self.upsampling_ratios				)
         return math.ceil(self.sampling_rate / hop_length				)



  @property
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)				)
