

import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


lowerCAmelCase_			      =      {
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 4_80_00,
        '''sample_size''': 6_55_36,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 4_80_00,
        '''sample_size''': 6_55_36,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 4_80_00,
        '''sample_size''': 13_10_72,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 1_60_00,
        '''sample_size''': 6_55_36,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 1_60_00,
        '''sample_size''': 6_55_36,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 1_60_00,
        '''sample_size''': 6_55_36,
    },
}


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						return torch.atana(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			) / math.pi * 2


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      torch.sin(t * math.pi / 2			) ** 2
						snake_case_  =      (1 - sigma**2) ** 0.5
						return alpha_sigma_to_t(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)





class       snake_case_  (						__A   ):







			'''simple docstring'''

			pass






class       snake_case_  (						nn.Module   ):







			'''simple docstring'''

			def __init__(	self					:							List[Any]				,  _UpperCamelCase					:							int				) ->Optional[int]:
									super().__init__()

									snake_case_  =      DiffusionAttnUnetaD(_UpperCamelCase				,  n_attn_layers=4				)
									snake_case_  =      deepcopy(self.diffusion				)
									snake_case_  =      torch.quasirandom.SobolEngine(1				,  scramble=_UpperCamelCase				)






def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      MODELS_MAP[model_name]['''url''']
						os.system(F'''wget {url} ./'''			)

						return F'''./{model_name}.ckpt'''


lowerCAmelCase_			      =      {
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
}
lowerCAmelCase_			      =      {
    '''8''': '''resnets.0''',
    '''9''': '''attentions.0''',
    '''10''': '''resnets.1''',
    '''11''': '''attentions.1''',
    '''12''': '''resnets.2''',
    '''13''': '''attentions.2''',
}
lowerCAmelCase_			      =      {
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
    '''8''': '''resnets.3''',
    '''9''': '''attentions.3''',
    '''10''': '''resnets.4''',
    '''11''': '''attentions.4''',
    '''12''': '''resnets.5''',
    '''13''': '''attentions.5''',
}
lowerCAmelCase_			      =      {
    '''0''': '''resnets.0''',
    '''1''': '''resnets.1''',
    '''2''': '''resnets.2''',
    '''4''': '''resnets.0''',
    '''5''': '''resnets.1''',
    '''6''': '''resnets.2''',
}

lowerCAmelCase_			      =      {
    '''skip''': '''conv_skip''',
    '''main.0''': '''conv_1''',
    '''main.1''': '''group_norm_1''',
    '''main.3''': '''conv_2''',
    '''main.4''': '''group_norm_2''',
}

lowerCAmelCase_			      =      {
    '''norm''': '''group_norm''',
    '''qkv_proj''': ['''query''', '''key''', '''value'''],
    '''out_proj''': ['''proj_attn'''],
}


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						if name.startswith('''skip'''			):
												return name.replace('''skip'''    ,						RES_CONV_MAP['''skip''']			)

						# name has to be of format main.{digit}
						if not name.startswith('''main.'''			):
												raise ValueError(F'''ResConvBlock error with {name}'''			)

						return name.replace(name[:6]    ,						RES_CONV_MAP[name[:6]]			)


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						for key, value in ATTN_MAP.items():
												if name.startswith(SCREAMING_SNAKE_CASE__			) and not isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
																		return name.replace(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)
												elif name.startswith(SCREAMING_SNAKE_CASE__			):
																		return [name.replace(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			) for v in value]
						raise ValueError(F'''Attn error with {name}'''			)


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=13			):
						snake_case_  =      input_string

						if string.split('''.'''			)[0] == "timestep_embed":
												return string.replace('''timestep_embed'''    ,						'''time_proj'''			)

						snake_case_  =      0
						if string.startswith('''net.3.'''			):
												depth += 1
												snake_case_  =      string[6:]
						elif string.startswith('''net.'''			):
												snake_case_  =      string[4:]

						while string.startswith('''main.7.'''			):
												depth += 1
												snake_case_  =      string[7:]

						if string.startswith('''main.'''			):
												snake_case_  =      string[5:]

						# mid block
						if string[:2].isdigit():
												snake_case_  =      string[:2]
												snake_case_  =      string[2:]
						else:
												snake_case_  =      string[0]
												snake_case_  =      string[1:]

						if depth == max_depth:
												snake_case_  =      MID_NUM_TO_LAYER[layer_num]
												snake_case_  =      '''mid_block'''
						elif depth > 0 and int(SCREAMING_SNAKE_CASE__			) < 7:
												snake_case_  =      DOWN_NUM_TO_LAYER[layer_num]
												snake_case_  =      F'''down_blocks.{depth}'''
						elif depth > 0 and int(SCREAMING_SNAKE_CASE__			) > 7:
												snake_case_  =      UP_NUM_TO_LAYER[layer_num]
												snake_case_  =      F'''up_blocks.{max_depth - depth - 1}'''
						elif depth == 0:
												snake_case_  =      DEPTH_0_TO_LAYER[layer_num]
												snake_case_  =      F'''up_blocks.{max_depth - 1}''' if int(SCREAMING_SNAKE_CASE__			) > 3 else '''down_blocks.0'''

						if not string_left.startswith('''.'''			):
												raise ValueError(F'''Naming error with {input_string} and string_left: {string_left}.'''			)

						snake_case_  =      string_left[1:]

						if "resnets" in new_layer:
												snake_case_  =      convert_resconv_naming(SCREAMING_SNAKE_CASE__			)
						elif "attentions" in new_layer:
												snake_case_  =      convert_attn_naming(SCREAMING_SNAKE_CASE__			)
												snake_case_  =      new_string_left

						if not isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
												snake_case_  =      prefix + '''.''' + new_layer + '''.''' + string_left
						else:
												snake_case_  =      [prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
						return new_string


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      {}
						for k, v in state_dict.items():
												if k.endswith('''kernel'''			):
																		# up- and downsample layers, don't have trainable weights
																		continue

												snake_case_  =      rename(SCREAMING_SNAKE_CASE__			)

												# check if we need to transform from Conv => Linear for attention
												if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
																		snake_case_  =      transform_conv_attns(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)
												else:
																		snake_case_  =      v

						return new_state_dict


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						if len(SCREAMING_SNAKE_CASE__			) == 1:
												if len(v.shape			) == 3:
																		# weight
																		snake_case_  =      v[:, :, 0]
												else:
																		# bias
																		snake_case_  =      v
						else:
												# qkv matrices
												snake_case_  =      v.shape[0]
												snake_case_  =      trippled_shape // 3
												for i in range(3			):
																		if len(v.shape			) == 3:
																								snake_case_  =      v[i * single_shape : (i + 1) * single_shape, :, 0]
																		else:
																								snake_case_  =      v[i * single_shape : (i + 1) * single_shape]
						return new_state_dict


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''			)

						snake_case_  =      args.model_path.split('''/'''			)[-1].split('''.'''			)[0]
						if not os.path.isfile(args.model_path			):
												assert (
												    model_name == args.model_path
												), F'''Make sure to provide one of the official model names {MODELS_MAP.keys()}'''
												snake_case_  =      download(SCREAMING_SNAKE_CASE__			)

						snake_case_  =      MODELS_MAP[model_name]['''sample_rate''']
						snake_case_  =      MODELS_MAP[model_name]['''sample_size''']

						snake_case_  =      Object()
						snake_case_  =      sample_size
						snake_case_  =      sample_rate
						snake_case_  =      0

						snake_case_  =      UNetaDModel(sample_size=SCREAMING_SNAKE_CASE__    ,						sample_rate=SCREAMING_SNAKE_CASE__			)
						snake_case_  =      diffusers_model.state_dict()

						snake_case_  =      DiffusionUncond(SCREAMING_SNAKE_CASE__			)
						orig_model.load_state_dict(torch.load(args.model_path    ,						map_location=SCREAMING_SNAKE_CASE__			)['''state_dict''']			)
						snake_case_  =      orig_model.diffusion_ema.eval()
						snake_case_  =      orig_model.state_dict()
						snake_case_  =      rename_orig_weights(SCREAMING_SNAKE_CASE__			)

						snake_case_  =      set(renamed_state_dict.keys()			) - set(diffusers_state_dict.keys()			)
						snake_case_  =      set(diffusers_state_dict.keys()			) - set(renamed_state_dict.keys()			)

						assert len(SCREAMING_SNAKE_CASE__			) == 0, F'''Problem with {renamed_minus_diffusers}'''
						assert all(k.endswith('''kernel'''			) for k in list(SCREAMING_SNAKE_CASE__			)			), F'''Problem with {diffusers_minus_renamed}'''

						for key, value in renamed_state_dict.items():
												assert (
												    diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
												), F'''Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'''
												if key == "time_proj.weight":
																		snake_case_  =      value.squeeze()

												snake_case_  =      value

						diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE__			)

						snake_case_  =      100
						snake_case_  =      33

						snake_case_  =      IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE__			)

						snake_case_  =      torch.manual_seed(SCREAMING_SNAKE_CASE__			)
						snake_case_  =      torch.randn([1, 2, config.sample_size]    ,						generator=SCREAMING_SNAKE_CASE__			).to(SCREAMING_SNAKE_CASE__			)

						snake_case_  =      torch.linspace(1    ,						0    ,						steps + 1    ,						device=SCREAMING_SNAKE_CASE__			)[:-1]
						snake_case_  =      get_crash_schedule(SCREAMING_SNAKE_CASE__			)

						snake_case_  =      DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE__    ,						scheduler=SCREAMING_SNAKE_CASE__			)

						snake_case_  =      torch.manual_seed(33			)
						snake_case_  =      pipe(num_inference_steps=SCREAMING_SNAKE_CASE__    ,						generator=SCREAMING_SNAKE_CASE__			).audios

						snake_case_  =      sampling.iplms_sample(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						{}			)
						snake_case_  =      generated.clamp(-1    ,						1			)

						snake_case_  =      (generated - audio).abs().sum()
						snake_case_  =      (generated - audio).abs().max()

						if args.save:
												pipe.save_pretrained(args.checkpoint_path			)

						print('''Diff sum'''    ,						SCREAMING_SNAKE_CASE__			)
						print('''Diff max'''    ,						SCREAMING_SNAKE_CASE__			)

						assert diff_max < 1E-3, F'''Diff max: {diff_max} is too much :-/'''

						print(F'''Conversion for {model_name} successful!'''			)


if __name__ == "__main__":
					lowerCAmelCase_			      =      argparse.ArgumentParser()

					parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
					parser.add_argument(
					    '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
					)
					parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
					lowerCAmelCase_			      =      parser.parse_args()

					main(args)



import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
   from transformers import WhisperFeatureExtractor

if is_torch_available():
   import torch

SCREAMING_SNAKE_CASE						:Optional[int]			=  random.Random()



def       _lowerCAmelCase    (				lowerCAmelCase_				:List[str]		,    lowerCAmelCase_				:Tuple=1.0		,    lowerCAmelCase_				:Optional[int]=None		,    lowerCAmelCase_				:List[Any]=None				)->Dict:
   '''simple docstring'''






   if rng is None:
      snake_case_            =       global_rng

   snake_case_            =       []
   for batch_idx in range(shape[0]				):
      values.append([]				)
      for _ in range(shape[1]				):
         values[-1].append(rng.random() * scale				)

   return values


@require_torch
@require_torchaudio
class      __lowerCAmelCase  (			unittest.TestCase       ):






  """simple docstring"""





  def __init__(  self							:							Optional[int]     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							List[str]=7     ,					_lowerCAmelCase							:							List[Any]=4_0_0     ,					_lowerCAmelCase							:							Optional[Any]=2_0_0_0     ,					_lowerCAmelCase							:							Tuple=1_0     ,					_lowerCAmelCase							:							Optional[int]=1_6_0     ,					_lowerCAmelCase							:							List[Any]=8     ,					_lowerCAmelCase							:							List[Any]=0.0     ,					_lowerCAmelCase							:							Dict=4_0_0_0     ,					_lowerCAmelCase							:							str=False     ,					_lowerCAmelCase							:							List[str]=True     ,					) ->			str:




     """simple docstring"""
     snake_case_            =       parent
     snake_case_            =       batch_size
     snake_case_            =       min_seq_length
     snake_case_            =       max_seq_length
     snake_case_            =       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     snake_case_            =       padding_value
     snake_case_            =       sampling_rate
     snake_case_            =       return_attention_mask
     snake_case_            =       do_normalize
     snake_case_            =       feature_size
     snake_case_            =       chunk_length
     snake_case_            =       hop_length



  def        lowerCAmelCase__	(  self							:							int	) ->			str:




     """simple docstring"""
     return {
         "feature_size": self.feature_size,
         "hop_length": self.hop_length,
         "chunk_length": self.chunk_length,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "return_attention_mask": self.return_attention_mask,
         "do_normalize": self.do_normalize,
     }



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							Optional[Any]=False     ,					_lowerCAmelCase							:							List[Any]=False	) ->			int:




     """simple docstring"""
     def _flatten(_lowerCAmelCase							:							Union[str, Any]	):
        return list(itertools.chain(*_lowerCAmelCase	)	)

     if equal_length:
        snake_case_            =       [floats_list((self.max_seq_length, self.feature_size)	) for _ in range(self.batch_size	)]
     else:
        # make sure that inputs increase in size
        snake_case_            =       [
            floats_list((x, self.feature_size)	)
            for x in range(self.min_seq_length     ,					self.max_seq_length     ,					self.seq_length_diff	)
        ]
     if numpify:
        snake_case_            =       [np.asarray(_lowerCAmelCase	) for x in speech_inputs]
     return speech_inputs






@require_torch
@require_torchaudio
class      __lowerCAmelCase  (			a			,	unittest.TestCase       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							WhisperFeatureExtractor if is_speech_available() else None



  def        lowerCAmelCase__	(  self							:							str	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       WhisperFeatureExtractionTester(self	)



  def        lowerCAmelCase__	(  self							:							Tuple	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       self.feature_extraction_class(**self.feat_extract_dict	)

     with tempfile.TemporaryDirectory() as tmpdirname:
        snake_case_            =       feat_extract_first.save_pretrained(_lowerCAmelCase	)[0]
        check_json_file_has_correct_format(_lowerCAmelCase	)
        snake_case_            =       self.feature_extraction_class.from_pretrained(_lowerCAmelCase	)

     snake_case_            =       feat_extract_first.to_dict()
     snake_case_            =       feat_extract_second.to_dict()
     snake_case_            =       feat_extract_first.mel_filters
     snake_case_            =       feat_extract_second.mel_filters
     self.assertTrue(np.allclose(_lowerCAmelCase     ,					_lowerCAmelCase	)	)
     self.assertEqual(_lowerCAmelCase     ,					_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			str:




     """simple docstring"""
     snake_case_            =       self.feature_extraction_class(**self.feat_extract_dict	)

     with tempfile.TemporaryDirectory() as tmpdirname:
        snake_case_            =       os.path.join(_lowerCAmelCase     ,					"feat_extract.json"	)
        feat_extract_first.to_json_file(_lowerCAmelCase	)
        snake_case_            =       self.feature_extraction_class.from_json_file(_lowerCAmelCase	)

     snake_case_            =       feat_extract_first.to_dict()
     snake_case_            =       feat_extract_second.to_dict()
     snake_case_            =       feat_extract_first.mel_filters
     snake_case_            =       feat_extract_second.mel_filters
     self.assertTrue(np.allclose(_lowerCAmelCase     ,					_lowerCAmelCase	)	)
     self.assertEqual(_lowerCAmelCase     ,					_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Any	) ->			Optional[int]:




     """simple docstring"""
     # Tests that all call wrap to encode_plus and batch_encode_plus
     snake_case_            =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     # create three inputs of length 800, 1000, and 1200
     snake_case_            =       [floats_list((1, x)	)[0] for x in range(8_0_0     ,					1_4_0_0     ,					2_0_0	)]
     snake_case_            =       [np.asarray(_lowerCAmelCase	) for speech_input in speech_inputs]

     # Test feature size
     snake_case_            =       feature_extractor(_lowerCAmelCase     ,					padding="max_length"     ,					return_tensors="np"	).input_features
     self.assertTrue(input_features.ndim == 3	)
     self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames	)
     self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size	)

     # Test not batched input
     snake_case_            =       feature_extractor(speech_inputs[0]     ,					return_tensors="np"	).input_features
     snake_case_            =       feature_extractor(np_speech_inputs[0]     ,					return_tensors="np"	).input_features
     self.assertTrue(np.allclose(_lowerCAmelCase     ,					_lowerCAmelCase     ,					atol=1e-3	)	)

     # Test batched
     snake_case_            =       feature_extractor(_lowerCAmelCase     ,					return_tensors="np"	).input_features
     snake_case_            =       feature_extractor(_lowerCAmelCase     ,					return_tensors="np"	).input_features
     for enc_seq_a, enc_seq_a in zip(_lowerCAmelCase     ,					_lowerCAmelCase	):
        self.assertTrue(np.allclose(_lowerCAmelCase     ,					_lowerCAmelCase     ,					atol=1e-3	)	)

     # Test 2-D numpy arrays are batched.
     snake_case_            =       [floats_list((1, x)	)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
     snake_case_            =       np.asarray(_lowerCAmelCase	)
     snake_case_            =       feature_extractor(_lowerCAmelCase     ,					return_tensors="np"	).input_features
     snake_case_            =       feature_extractor(_lowerCAmelCase     ,					return_tensors="np"	).input_features
     for enc_seq_a, enc_seq_a in zip(_lowerCAmelCase     ,					_lowerCAmelCase	):
        self.assertTrue(np.allclose(_lowerCAmelCase     ,					_lowerCAmelCase     ,					atol=1e-3	)	)

     # Test truncation required
     snake_case_            =       [floats_list((1, x)	)[0] for x in range(2_0_0     ,					(feature_extractor.n_samples + 5_0_0)     ,					2_0_0	)]
     snake_case_            =       [np.asarray(_lowerCAmelCase	) for speech_input in speech_inputs]

     snake_case_            =       [x[: feature_extractor.n_samples] for x in speech_inputs]
     snake_case_            =       [np.asarray(_lowerCAmelCase	) for speech_input in speech_inputs_truncated]

     snake_case_            =       feature_extractor(_lowerCAmelCase     ,					return_tensors="np"	).input_features
     snake_case_            =       feature_extractor(_lowerCAmelCase     ,					return_tensors="np"	).input_features
     for enc_seq_a, enc_seq_a in zip(_lowerCAmelCase     ,					_lowerCAmelCase	):
        self.assertTrue(np.allclose(_lowerCAmelCase     ,					_lowerCAmelCase     ,					atol=1e-3	)	)



  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Tuple:




     """simple docstring"""
     import torch

     snake_case_            =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     snake_case_            =       np.random.rand(1_0_0     ,					3_2	).astype(np.floataa	)
     snake_case_            =       np_speech_inputs.tolist()

     for inputs in [py_speech_inputs, np_speech_inputs]:
        snake_case_            =       feature_extractor.pad([{"input_features": inputs}]     ,					return_tensors="np"	)
        self.assertTrue(np_processed.input_features.dtype == np.floataa	)
        snake_case_            =       feature_extractor.pad([{"input_features": inputs}]     ,					return_tensors="pt"	)
        self.assertTrue(pt_processed.input_features.dtype == torch.floataa	)



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							Dict	) ->			int:




     """simple docstring"""
     snake_case_            =       load_dataset("hf-internal-testing/librispeech_asr_dummy"     ,					"clean"     ,					split="validation"	)
     # automatic decoding with librispeech
     snake_case_            =       ds.sort("id"	).select(range(_lowerCAmelCase	)	)[:num_samples]["audio"]

     return [x["array"] for x in speech_samples]



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




     """simple docstring"""
     # fmt: off
     snake_case_            =       torch.tensor(
         [
             0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
             0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
             0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
             -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
         ]	)
     # fmt: on

     snake_case_            =       self._load_datasamples(1	)
     snake_case_            =       WhisperFeatureExtractor()
     snake_case_            =       feature_extractor(_lowerCAmelCase     ,					return_tensors="pt"	).input_features
     self.assertEqual(input_features.shape     ,					(1, 8_0, 3_0_0_0)	)
     self.assertTrue(torch.allclose(input_features[0, 0, :3_0]     ,					_lowerCAmelCase     ,					atol=1e-4	)	)



  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			List[str]:




     """simple docstring"""
     snake_case_            =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     snake_case_            =       self._load_datasamples(1	)[0]
     snake_case_            =       ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5_5_3_5  # Rescale to [0, 65535] to show issue
     snake_case_            =       feat_extract.zero_mean_unit_var_norm([audio]     ,					attention_mask=_lowerCAmelCase	)[0]

     self.assertTrue(np.all(np.mean(_lowerCAmelCase	) < 1e-3	)	)
     self.assertTrue(np.all(np.abs(np.var(_lowerCAmelCase	) - 1	) < 1e-3	)	)




