






'''simple docstring'''
import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
						import librosa  # noqa: E402

						__lowerCAmelCase							=True
						__lowerCAmelCase							=""
except Exception as e:
						__lowerCAmelCase							=False
						__lowerCAmelCase							=(
						    f'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
						)


from PIL import Image  # noqa: E402


class    _snake_case   (				snake_case     ,					snake_case					):

					"""simple docstring"""





					_UpperCamelCase		 =      "mel_config.json"




					@register_to_config
					def __init__(     self   , UpperCAmelCase__ = 256   , UpperCAmelCase__ = 256   , UpperCAmelCase__ = 2_2050   , UpperCAmelCase__ = 2048   , UpperCAmelCase__ = 512   , UpperCAmelCase__ = 80   , UpperCAmelCase__ = 32   , )      ->	Optional[int]:
										a_									=   hop_length
										a_									=   sample_rate
										a_									=   n_fft
										a_									=   top_db
										a_									=   n_iter
										self.set_resolution(UpperCAmelCase__   , UpperCAmelCase__     )
										a_									=   None

										if not _librosa_can_be_imported:
															raise ValueError(_import_error     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	Tuple:
										a_									=   x_res
										a_									=   y_res
										a_									=   self.y_res
										a_									=   self.x_res * self.hop_length - 1




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__ = None   , UpperCAmelCase__ = None     )      ->	Optional[Any]:
										if audio_file is not None:
															a_							,					a_									=   librosa.load(UpperCAmelCase__   , mono=UpperCAmelCase__   , sr=self.sr     )
										else:
															a_									=   raw_audio

										# Pad with silence if necessary.
										if len(self.audio     ) < self.x_res * self.hop_length:
															a_									=   np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio     ),)     )]     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										return len(self.audio     ) // self.slice_size




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__ = 0     )      ->	np.ndarray:
										return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										return self.sr




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	Image.Image:
										a_									=   librosa.feature.melspectrogram(
										    y=self.get_audio_slice(UpperCAmelCase__     )   , sr=self.sr   , n_fft=self.n_fft   , hop_length=self.hop_length   , n_mels=self.n_mels     )
										a_									=   librosa.power_to_db(UpperCAmelCase__   , ref=np.max   , top_db=self.top_db     )
										a_									=   (((log_S + self.top_db) * 255 / self.top_db).clip(0   , 255     ) + 0.5).astype(np.uinta     )
										a_									=   Image.fromarray(UpperCAmelCase__     )
										return image




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	np.ndarray:
										a_									=   np.frombuffer(image.tobytes()   , dtype='uint8'     ).reshape((image.height, image.width)     )
										a_									=   bytedata.astype('float'     ) * self.top_db / 255 - self.top_db
										a_									=   librosa.db_to_power(UpperCAmelCase__     )
										a_									=   librosa.feature.inverse.mel_to_audio(
										    UpperCAmelCase__   , sr=self.sr   , n_fft=self.n_fft   , hop_length=self.hop_length   , n_iter=self.n_iter     )
										return audio









'''simple docstring'''
import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch


class    _snake_case   (				unittest.TestCase					):

					"""simple docstring"""





					@patch('sys.argv'   , ['fakeprogrampath', 'env']     )
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										# test transformers-cli env
										import transformers.commands.transformers_cli

										with CaptureStd() as cs:
															transformers.commands.transformers_cli.main()
										self.assertIn('Python version'   , cs.out     )
										self.assertIn('Platform'   , cs.out     )
										self.assertIn('Using distributed or parallel set-up in script?'   , cs.out     )




					@is_pt_tf_cross_test
					@patch(
					    'sys.argv'   , ['fakeprogrampath', 'pt-to-tf', '--model-name', 'hf-internal-testing/tiny-random-gptj', '--no-pr']     )
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[str]:
										import transformers.commands.transformers_cli

										shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'   , ignore_errors=UpperCAmelCase__     )  # cleans potential past runs
										transformers.commands.transformers_cli.main()

										# The original repo has no TF weights -- if they exist, they were created by the CLI
										self.assertTrue(os.path.exists('/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5'     )     )




					@require_torch
					@patch('sys.argv'   , ['fakeprogrampath', 'download', 'hf-internal-testing/tiny-random-gptj', '--cache-dir', '/tmp']     )
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[Any]:
										import transformers.commands.transformers_cli

										# # remove any previously downloaded model to start clean
										shutil.rmtree('/tmp/models--hf-internal-testing--tiny-random-gptj'   , ignore_errors=UpperCAmelCase__     )

										# run the command
										transformers.commands.transformers_cli.main()

										# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/blobs'     )     )
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/refs'     )     )
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots'     )     )




					@require_torch
					@patch(
					    'sys.argv'   , [
					        'fakeprogrampath',
					        'download',
					        'hf-internal-testing/test_dynamic_model_with_tokenizer',
					        '--trust-remote-code',
					        '--cache-dir',
					        '/tmp',
					    ]   , )
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										import transformers.commands.transformers_cli

										# # remove any previously downloaded model to start clean
										shutil.rmtree('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer'   , ignore_errors=UpperCAmelCase__     )

										# run the command
										transformers.commands.transformers_cli.main()

										# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs'     )     )
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs'     )     )
										self.assertTrue(
										    os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots'     )     )

