import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow




@require_torch
class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->Any:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						"""ylacombe/bark-small"""
          _lowerCamelCase     :		Tuple 						=						tempfile.mkdtemp()
          _lowerCamelCase     :		Tuple 						=						"""en_speaker_1"""
          _lowerCamelCase     :		Tuple 						=						"""This is a test string"""
          _lowerCamelCase     :		Optional[int] 						=						"""speaker_embeddings_path.json"""
          _lowerCamelCase     :		int 						=						"""speaker_embeddings"""



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  **_UpperCamelCase     :				int)				->List[str]:


          """simple docstring"""


          return AutoTokenizer.from_pretrained(self.checkpoint    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Union[str, Any]:


          """simple docstring"""


          shutil.rmtree(self.tmpdirname)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		str 						=						self.get_tokenizer()

          _lowerCamelCase     :		int 						=						BarkProcessor(tokenizer=_UpperCamelCase)

          processor.save_pretrained(self.tmpdirname)
          _lowerCamelCase     :		Optional[int] 						=						BarkProcessor.from_pretrained(self.tmpdirname)

          self.assertEqual(processor.tokenizer.get_vocab()    ,  tokenizer.get_vocab())



      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		str 						=						BarkProcessor.from_pretrained(
              pretrained_processor_name_or_path=self.checkpoint    ,  speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,  )
          processor.save_pretrained(
              self.tmpdirname    ,  speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,  speaker_embeddings_directory=self.speaker_embeddings_directory    ,  )

          _lowerCamelCase     :		List[str] 						=						self.get_tokenizer(bos_token="""(BOS)"""    ,  eos_token="""(EOS)""")

          _lowerCamelCase     :		List[Any] 						=						BarkProcessor.from_pretrained(
              self.tmpdirname    ,  self.speaker_embeddings_dict_path    ,  bos_token="""(BOS)"""    ,  eos_token="""(EOS)"""    ,  )

          self.assertEqual(processor.tokenizer.get_vocab()    ,  tokenizer_add_kwargs.get_vocab())



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						BarkProcessor.from_pretrained(
              pretrained_processor_name_or_path=self.checkpoint    ,  speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,  )

          _lowerCamelCase     :		int 						=						35
          _lowerCamelCase     :		Optional[int] 						=						2
          _lowerCamelCase     :		Optional[Any] 						=						8

          _lowerCamelCase     :		str 						=						{
              """semantic_prompt""": np.ones(_UpperCamelCase),
              """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)),
              """fine_prompt""": np.ones((nb_codebooks_total, seq_len)),
          }

          # test providing already loaded voice_preset
          _lowerCamelCase     :		List[str] 						=						processor(text=self.input_string    ,  voice_preset=_UpperCamelCase)

          _lowerCamelCase     :		Union[str, Any] 						=						inputs["""history_prompt"""]
          for key in voice_preset:
              self.assertListEqual(voice_preset[key].tolist()    ,  processed_voice_preset.get(_UpperCamelCase    ,  np.array([])).tolist())

          # test loading voice preset from npz file
          _lowerCamelCase     :		int 						=						os.path.join(self.tmpdirname    ,  """file.npz""")
          np.savez(_UpperCamelCase    ,  **_UpperCamelCase)
          _lowerCamelCase     :		str 						=						processor(text=self.input_string    ,  voice_preset=_UpperCamelCase)
          _lowerCamelCase     :		str 						=						inputs["""history_prompt"""]

          for key in voice_preset:
              self.assertListEqual(voice_preset[key].tolist()    ,  processed_voice_preset.get(_UpperCamelCase    ,  np.array([])).tolist())

          # test loading voice preset from the hub
          _lowerCamelCase     :		List[str] 						=						processor(text=self.input_string    ,  voice_preset=self.voice_preset)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.get_tokenizer()

          _lowerCamelCase     :		Union[str, Any] 						=						BarkProcessor(tokenizer=_UpperCamelCase)

          _lowerCamelCase     :		str 						=						processor(text=self.input_string)

          _lowerCamelCase     :		Union[str, Any] 						=						tokenizer(
              self.input_string    ,  padding="""max_length"""    ,  max_length=256    ,  add_special_tokens=_UpperCamelCase    ,  return_attention_mask=_UpperCamelCase    ,  return_token_type_ids=_UpperCamelCase    ,  )

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]    ,  encoded_processor[key].squeeze().tolist())





from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				float)				->float:


          """simple docstring"""


          return 0.0







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		int 						=						min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
    _lowerCamelCase     :		Tuple 						=						max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
    return lowest, highest







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						512
    _lowerCamelCase     :		Tuple 						=						[1] + [0] * (size - 1)
    _lowerCamelCase     :		Optional[Any] 						=						[filter_type.process(__A	) for item in inputs]

    _lowerCamelCase     :		Optional[Any] 						=						[0] * (samplerate - size)  # zero-padding
    outputs += filler
    _lowerCamelCase     :		Tuple 						=						np.abs(np.fft.fft(__A	)	)
    _lowerCamelCase     :		List[Any] 						=						20 * np.logaa(__A	)

    # Frequencies on log scale from 24 to nyquist frequency
    plt.xlim(24   , samplerate / 2 - 1	)
    plt.xlabel("""Frequency (Hz)"""	)
    plt.xscale("""log"""	)

    # Display within reasonable bounds
    _lowerCamelCase     :		Any 						=						get_bounds(__A   , __A	)
    plt.ylim(max([-80, bounds[0]]	)   , min([80, bounds[1]]	)	)
    plt.ylabel("""Gain (dB)"""	)

    plt.plot(__A	)
    plt.show()







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						512
    _lowerCamelCase     :		Union[str, Any] 						=						[1] + [0] * (size - 1)
    _lowerCamelCase     :		int 						=						[filter_type.process(__A	) for item in inputs]

    _lowerCamelCase     :		Optional[Any] 						=						[0] * (samplerate - size)  # zero-padding
    outputs += filler
    _lowerCamelCase     :		Any 						=						np.angle(np.fft.fft(__A	)	)

    # Frequencies on log scale from 24 to nyquist frequency
    plt.xlim(24   , samplerate / 2 - 1	)
    plt.xlabel("""Frequency (Hz)"""	)
    plt.xscale("""log"""	)

    plt.ylim(-2 * pi   , 2 * pi	)
    plt.ylabel("""Phase shift (Radians)"""	)
    plt.plot(np.unwrap(__A   , -2 * pi	)	)
    plt.show()




