






"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 MobileViTImageProcessor





class   UpperCamelCase_ (       unittest.TestCase  ):



  def __init__(      self ,		snake_case__ ,		snake_case__=7 ,		snake_case__=3 ,		snake_case__=18 ,		snake_case__=30 ,		snake_case__=4_00 ,		snake_case__=True ,		snake_case__=None ,		snake_case__=True ,		snake_case__=None ,		snake_case__=True ,		)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			size if size is not None else {"""shortest_edge""": 20}
         UpperCAmelCase	    =			crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
         UpperCAmelCase	    =			parent
         UpperCAmelCase	    =			batch_size
         UpperCAmelCase	    =			num_channels
         UpperCAmelCase	    =			image_size
         UpperCAmelCase	    =			min_resolution
         UpperCAmelCase	    =			max_resolution
         UpperCAmelCase	    =			do_resize
         UpperCAmelCase	    =			size
         UpperCAmelCase	    =			do_center_crop
         UpperCAmelCase	    =			crop_size
         UpperCAmelCase	    =			do_flip_channel_order



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



         """simple docstring"""





         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
             "do_flip_channel_order": self.do_flip_channel_order,
         }





@require_torch
@require_vision
class   UpperCamelCase_ (       a_   ,					unittest.TestCase  ):
  _A       :					Union[str, Any]				=	MobileViTImageProcessor if is_vision_available() else None



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



         """simple docstring"""





         UpperCAmelCase	    =			MobileViTImageProcessingTester(self				)



  @property
  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         return self.image_processor_tester.prepare_image_processor_dict()



  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class(**self.image_processor_dict				)
         self.assertTrue(hasattr(UpperCamelCase__ ,		"""do_resize"""				)				)
         self.assertTrue(hasattr(UpperCamelCase__ ,		"""size"""				)				)
         self.assertTrue(hasattr(UpperCamelCase__ ,		"""do_center_crop"""				)				)
         self.assertTrue(hasattr(UpperCamelCase__ ,		"""center_crop"""				)				)
         self.assertTrue(hasattr(UpperCamelCase__ ,		"""do_flip_channel_order"""				)				)



  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class.from_dict(self.image_processor_dict				)
         self.assertEqual(image_processor.size ,		{"""shortest_edge""": 20}				)
         self.assertEqual(image_processor.crop_size ,		{"""height""": 18, """width""": 18}				)

         UpperCAmelCase	    =			self.image_processing_class.from_dict(self.image_processor_dict ,		size=42 ,		crop_size=84				)
         self.assertEqual(image_processor.size ,		{"""shortest_edge""": 42}				)
         self.assertEqual(image_processor.crop_size ,		{"""height""": 84, """width""": 84}				)



  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         pass



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



         """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=UpperCamelCase__				)
         for image in image_inputs:
                self.assertIsInstance(UpperCamelCase__ ,		Image.Image				)

         # Test not batched input
         UpperCAmelCase	    =			image_processing(image_inputs[0] ,		return_tensors="""pt"""				).pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             ) ,		)

         # Test batched
         UpperCAmelCase	    =			image_processing(UpperCamelCase__ ,		return_tensors="""pt"""				).pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             ) ,		)



  def 		UpperCamelCase_				(      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=UpperCamelCase__ ,		numpify=UpperCamelCase__				)
         for image in image_inputs:
                self.assertIsInstance(UpperCamelCase__ ,		np.ndarray				)

         # Test not batched input
         UpperCAmelCase	    =			image_processing(image_inputs[0] ,		return_tensors="""pt"""				).pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             ) ,		)

         # Test batched
         UpperCAmelCase	    =			image_processing(UpperCamelCase__ ,		return_tensors="""pt"""				).pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             ) ,		)



  def 		UpperCamelCase_				(      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=UpperCamelCase__ ,		torchify=UpperCamelCase__				)
         for image in image_inputs:
                self.assertIsInstance(UpperCamelCase__ ,		torch.Tensor				)

         # Test not batched input
         UpperCAmelCase	    =			image_processing(image_inputs[0] ,		return_tensors="""pt"""				).pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             ) ,		)

         # Test batched
         UpperCAmelCase	    =			image_processing(UpperCamelCase__ ,		return_tensors="""pt"""				).pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             ) ,		)








"""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)				)
