






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

	__UpperCamelCase   :						int							=  True
	__UpperCamelCase   :						List[Any]							=  ""
except Exception as e:
	__UpperCamelCase   :						int							=  False
	__UpperCamelCase   :						Dict							=  (
	    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 					__SCREAMING_SNAKE_CASE(		lowercase_      ,					lowercase_		):
							_UpperCAmelCase	      =    "mel_config.json"
							@register_to_config
							def __init__(       self: int    ,		UpperCamelCase: int = 2_56    ,		UpperCamelCase: int = 2_56    ,		UpperCamelCase: int = 2_20_50    ,		UpperCamelCase: int = 20_48    ,		UpperCamelCase: int = 5_12    ,		UpperCamelCase: int = 80    ,		UpperCamelCase: int = 32    ,		)						->						List[str]:
											snake_case__													=       hop_length
											snake_case__													=       sample_rate
											snake_case__													=       n_fft
											snake_case__													=       top_db
											snake_case__													=       n_iter
											self.set_resolution(UpperCamelCase_    ,		UpperCamelCase_  )
											snake_case__													=       None

											if not _librosa_can_be_imported:
															raise ValueError(_import_error  )
							def lowerCAmelCase_	(       self: Dict    ,		UpperCamelCase: int    ,		UpperCamelCase: int  )						->						Any:
											snake_case__													=       x_res
											snake_case__													=       y_res
											snake_case__													=       self.y_res
											snake_case__													=       self.x_res * self.hop_length - 1
							def lowerCAmelCase_	(       self: Dict    ,		UpperCamelCase: str = None    ,		UpperCamelCase: np.ndarray = None  )						->						Any:



											if audio_file is not None:
															snake_case__							,  snake_case__													=       librosa.load(UpperCamelCase_    ,		mono=UpperCamelCase_    ,		sr=self.sr  )
											else:
															snake_case__													=       raw_audio

											# Pad with silence if necessary.
											if len(self.audio  ) < self.x_res * self.hop_length:
															snake_case__													=       np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio  ),)  )]  )
							def lowerCAmelCase_	(       self: Tuple  )						->						Dict:
											return len(self.audio  ) // self.slice_size
							def lowerCAmelCase_	(       self: Any    ,		UpperCamelCase: int = 0  )						->						List[str]:
											return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
							def lowerCAmelCase_	(       self: Optional[int]  )						->						List[Any]:
											return self.sr
							def lowerCAmelCase_	(       self: List[str]    ,		UpperCamelCase: int  )						->						int:
											snake_case__													=       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  )
											snake_case__													=       librosa.power_to_db(UpperCamelCase_    ,		ref=np.max    ,		top_db=self.top_db  )
											snake_case__													=       (((log_S + self.top_db) * 2_55 / self.top_db).clip(0    ,		2_55  ) + 0.5).astype(np.uinta  )
											snake_case__													=       Image.fromarray(UpperCamelCase_  )
											return image


							def lowerCAmelCase_	(       self: Union[str, Any]    ,		UpperCamelCase: Image.Image  )						->						Dict:
											snake_case__													=       np.frombuffer(image.tobytes()    ,		dtype='uint8'  ).reshape((image.height, image.width)  )
											snake_case__													=       bytedata.astype('float'  ) * self.top_db / 2_55 - self.top_db
											snake_case__													=       librosa.db_to_power(UpperCamelCase_  )
											snake_case__													=       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












import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__UpperCamelCase   :						List[Any]							=  logging.getLogger(__name__)
def        a_	( )     ->     int:







				"""simple docstring"""
				snake_case__													=       argparse.ArgumentParser(description='PyTorch Transformer Language Model'					)
				parser.add_argument('--model_name' ,				type=_A ,				default='transfo-xl-wt103' ,				help='pretrained model name'					)
				parser.add_argument(
				    '--split' ,				type=_A ,				default='test' ,				choices=['all', 'valid', 'test'] ,				help='which split to evaluate'					)
				parser.add_argument('--batch_size' ,				type=_A ,				default=10 ,				help='batch size'					)
				parser.add_argument('--tgt_len' ,				type=_A ,				default=128 ,				help='number of tokens to predict'					)
				parser.add_argument('--ext_len' ,				type=_A ,				default=0 ,				help='length of the extended context'					)
				parser.add_argument('--mem_len' ,				type=_A ,				default=1600 ,				help='length of the retained previous heads'					)
				parser.add_argument('--clamp_len' ,				type=_A ,				default=1000 ,				help='max positional embedding index'					)
				parser.add_argument('--no_cuda' ,				action='store_true' ,				help='Do not use CUDA even though CUA is available'					)
				parser.add_argument('--work_dir' ,				type=_A ,				required=_A ,				help='path to the work_dir'					)
				parser.add_argument('--no_log' ,				action='store_true' ,				help='do not log the eval result'					)
				parser.add_argument('--same_length' ,				action='store_true' ,				help='set same length attention with masking'					)
				parser.add_argument('--server_ip' ,				type=_A ,				default='' ,				help='Can be used for distant debugging.'					)
				parser.add_argument('--server_port' ,				type=_A ,				default='' ,				help='Can be used for distant debugging.'					)
				snake_case__													=       parser.parse_args()
				assert args.ext_len >= 0, "extended context length must be non-negative"

				if args.server_ip and args.server_port:
								# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
								import ptvsd

								print('Waiting for debugger attach'					)
								ptvsd.enable_attach(address=(args.server_ip, args.server_port) ,				redirect_output=_A					)
								ptvsd.wait_for_attach()

				snake_case__													=       torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'					)
				logger.info('device: {}'.format(_A					)					)

				# Load a pre-processed dataset
				# You can also build the corpus yourself using TransfoXLCorpus methods
				# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
				# and tokenizing the dataset
				# The pre-processed corpus is a convertion (using the conversion script )
				snake_case__													=       TransfoXLCorpus.from_pretrained(args.model_name					)

				snake_case__													=       corpus.get_iterator('valid' ,				args.batch_size ,				args.tgt_len ,				device=_A ,				ext_len=args.ext_len					)
				snake_case__													=       corpus.get_iterator('test' ,				args.batch_size ,				args.tgt_len ,				device=_A ,				ext_len=args.ext_len					)

				# Load a pre-trained model
				snake_case__													=       TransfoXLLMHeadModel.from_pretrained(args.model_name					)
				model.to(_A					)

				logger.info(
				    'Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
				        args.batch_size ,				args.tgt_len ,				args.ext_len ,				args.mem_len ,				args.clamp_len					)					)

				model.reset_memory_length(args.mem_len					)
				if args.clamp_len > 0:
								snake_case__													=       args.clamp_len
				if args.same_length:
								snake_case__													=       True

				###############################################################################
				# Evaluation code
				###############################################################################
				def evaluate(_A					):
								# Turn on evaluation mode which disables dropout.
								model.eval()
								snake_case__							,  snake_case__													=       0, 0.0
								snake_case__													=       time.time()
								with torch.no_grad():
												snake_case__													=       None
												for idx, (data, target, seq_len) in enumerate(_A					):
																snake_case__													=       model(_A ,				lm_labels=_A ,				mems=_A					)
																snake_case__							,  snake_case__							,  snake_case__													=       ret
																snake_case__													=       loss.mean()
																total_loss += seq_len * loss.item()
																total_len += seq_len
												snake_case__													=       time.time() - start_time
								logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(_A ,				1000 * total_time / (idx + 1)					)					)
								return total_loss / total_len

				# Run on test data.
				if args.split == "all":
								snake_case__													=       evaluate(_A					)
								snake_case__													=       evaluate(_A					)
				elif args.split == "valid":
								snake_case__													=       evaluate(_A					)
								snake_case__													=       None
				elif args.split == "test":
								snake_case__													=       evaluate(_A					)
								snake_case__													=       None

				def format_log(_A ,				_A					):
								snake_case__													=       '| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(_A ,				_A ,				math.exp(_A					)					)
								return log_str

				snake_case__													=       ''
				if valid_loss is not None:
								log_str += format_log(_A ,				'valid'					)
				if test_loss is not None:
								log_str += format_log(_A ,				'test'					)

				logger.info('=' * 100					)
				logger.info(_A					)
				logger.info('=' * 100					)


if __name__ == "__main__":
							main()




