





import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
	from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


lowerCAmelCase_        =							logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase_        =							2_5_6



class    __lowerCAmelCase			(     a_	):
			lowerCamelCase_	:	Tuple			     = ["melgan"]






			def __init__(self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				)	->  None:


							'''simple docstring'''



							super().__init__()

							# From MELGAN
							snake_case_		:       List[str]           =    math.log(1e-5       )  # Matches MelGAN training.
							snake_case_		:       Tuple           =    4.0  # Largest value for most examples
							snake_case_		:       Any           =    128

							self.register_modules(
							    notes_encoder=__magic_name__   ,				continuous_encoder=__magic_name__   ,				decoder=__magic_name__   ,				scheduler=__magic_name__   ,				melgan=__magic_name__   ,				)






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__=(-1.0, 1.0)   ,				__magic_name__=False       )	->  Optional[Any]:


							'''simple docstring'''



							snake_case_     ,				snake_case_		:       Union[str, Any]           =    output_range
							if clip:
											snake_case_		:       Optional[int]           =    torch.clip(__magic_name__   ,				self.min_value   ,				self.max_value       )
							# Scale to [0, 1].
							snake_case_		:       Optional[int]           =    (features - self.min_value) / (self.max_value - self.min_value)
							# Scale to [min_out, max_out].
							return zero_one * (max_out - min_out) + min_out






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__=(-1.0, 1.0)   ,				__magic_name__=False       )	->  Any:


							'''simple docstring'''



							snake_case_     ,				snake_case_		:       Any           =    input_range
							snake_case_		:       str           =    torch.clip(__magic_name__   ,				__magic_name__   ,				__magic_name__       ) if clip else outputs
							# Scale to [0, 1].
							snake_case_		:       Dict           =    (outputs - min_out) / (max_out - min_out)
							# Scale to [self.min_value, self.max_value].
							return zero_one * (self.max_value - self.min_value) + self.min_value






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__       )	->  List[str]:


							'''simple docstring'''



							snake_case_		:       Any           =    input_tokens > 0
							snake_case_     ,				snake_case_		:       List[str]           =    self.notes_encoder(
							    encoder_input_tokens=__magic_name__   ,				encoder_inputs_mask=__magic_name__       )

							snake_case_     ,				snake_case_		:       Union[str, Any]           =    self.continuous_encoder(
							    encoder_inputs=__magic_name__   ,				encoder_inputs_mask=__magic_name__       )

							return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__       )	->  Any:


							'''simple docstring'''



							snake_case_		:       List[str]           =    noise_time
							if not torch.is_tensor(__magic_name__       ):
											snake_case_		:       Tuple           =    torch.tensor([timesteps]   ,				dtype=torch.long   ,				device=input_tokens.device       )
							elif torch.is_tensor(__magic_name__       ) and len(timesteps.shape       ) == 0:
											snake_case_		:       Optional[Any]           =    timesteps[None].to(input_tokens.device       )

							# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
							snake_case_		:       Any           =    timesteps * torch.ones(input_tokens.shape[0]   ,				dtype=timesteps.dtype   ,				device=timesteps.device       )

							snake_case_		:       Optional[int]           =    self.decoder(
							    encodings_and_masks=__magic_name__   ,				decoder_input_tokens=__magic_name__   ,				decoder_noise_time=__magic_name__       )
							return logits






			@torch.no_grad()
			def __call__(self   ,				__magic_name__   ,				__magic_name__ = None   ,				__magic_name__ = 100   ,				__magic_name__ = True   ,				__magic_name__ = "numpy"   ,				__magic_name__ = None   ,				__magic_name__ = 1   ,				)	->  Union[AudioPipelineOutput, Tuple]:


							'''simple docstring'''



							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(__magic_name__   ,				__magic_name__       ) or callback_steps <= 0)
							):
											raise ValueError(
											    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
											    F''' {type(__magic_name__       )}.'''       )

							snake_case_		:       List[str]           =    np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]   ,				dtype=np.floataa       )
							snake_case_		:       List[Any]           =    np.zeros([1, 0, self.n_dims]   ,				np.floataa       )
							snake_case_		:       Optional[Any]           =    torch.ones((1, TARGET_FEATURE_LENGTH)   ,				dtype=__magic_name__   ,				device=self.device       )

							for i, encoder_input_tokens in enumerate(__magic_name__       ):
											if i == 0:
															snake_case_		:       List[Any]           =    torch.from_numpy(pred_mel[:1].copy()       ).to(
															    device=self.device   ,				dtype=self.decoder.dtype       )
															# The first chunk has no previous context.
															snake_case_		:       List[str]           =    torch.zeros((1, TARGET_FEATURE_LENGTH)   ,				dtype=__magic_name__   ,				device=self.device       )
											else:
															# The full song pipeline does not feed in a context feature, so the mask
															# will be all 0s after the feature converter. Because we know we're
															# feeding in a full context chunk from the previous prediction, set it
															# to all 1s.
															snake_case_		:       Optional[Any]           =    ones

											snake_case_		:       Optional[Any]           =    self.scale_features(
											    __magic_name__   ,				output_range=[-1.0, 1.0]   ,				clip=__magic_name__       )

											snake_case_		:       str           =    self.encode(
											    input_tokens=torch.IntTensor([encoder_input_tokens]       ).to(device=self.device       )   ,				continuous_inputs=__magic_name__   ,				continuous_mask=__magic_name__   ,				)

											# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
											snake_case_		:       Dict           =    randn_tensor(
											    shape=encoder_continuous_inputs.shape   ,				generator=__magic_name__   ,				device=self.device   ,				dtype=self.decoder.dtype   ,				)

											# set step values
											self.scheduler.set_timesteps(__magic_name__       )

											# Denoising diffusion loop
											for j, t in enumerate(self.progress_bar(self.scheduler.timesteps       )       ):
															snake_case_		:       Union[str, Any]           =    self.decode(
															    encodings_and_masks=__magic_name__   ,				input_tokens=__magic_name__   ,				noise_time=t / self.scheduler.config.num_train_timesteps   ,				)

															# Compute previous output: x_t -> x_t-1
															snake_case_		:       Tuple           =    self.scheduler.step(__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				generator=__magic_name__       ).prev_sample

											snake_case_		:       Dict           =    self.scale_to_features(__magic_name__   ,				input_range=[-1.0, 1.0]       )
											snake_case_		:       Optional[int]           =    mel[:1]
											snake_case_		:       str           =    mel.cpu().float().numpy()

											snake_case_		:       List[str]           =    np.concatenate([full_pred_mel, pred_mel[:1]]   ,				axis=1       )

											# call the callback, if provided
											if callback is not None and i % callback_steps == 0:
															callback(__magic_name__   ,				__magic_name__       )

											logger.info('''Generated segment'''   ,				__magic_name__       )

							if output_type == "numpy" and not is_onnx_available():
											raise ValueError(
											    '''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.'''       )
							elif output_type == "numpy" and self.melgan is None:
											raise ValueError(
											    '''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.'''       )

							if output_type == "numpy":
											snake_case_		:       Dict           =    self.melgan(input_features=full_pred_mel.astype(np.floataa       )       )
							else:
											snake_case_		:       Dict           =    full_pred_mel

							if not return_dict:
											return (output,)

							return AudioPipelineOutput(audios=__magic_name__       )




import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar







class        __lowercase       (							unittest.TestCase  ):



		"""simple docstring"""

		def 					__A   ( self	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  logging.get_logger()

					# the current default level is logging.WARNING
					lowerCamelCase    		=  logging.get_verbosity()

					logging.set_verbosity_error()
					self.assertEqual(logger.getEffectiveLevel()		,      logging.get_verbosity()	)

					logging.set_verbosity_warning()
					self.assertEqual(logger.getEffectiveLevel()		,      logging.get_verbosity()	)

					logging.set_verbosity_info()
					self.assertEqual(logger.getEffectiveLevel()		,      logging.get_verbosity()	)

					logging.set_verbosity_debug()
					self.assertEqual(logger.getEffectiveLevel()		,      logging.get_verbosity()	)

					# restore to the original level
					logging.set_verbosity(A	)







		def 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







					lowerCamelCase    		=  logging.get_verbosity()

					lowerCamelCase    		=  logging.get_logger("""transformers.models.bart.tokenization_bart"""	)
					lowerCamelCase    		=  """Testing 1, 2, 3"""

					# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
					if level_origin <= logging.WARNING:
								with CaptureLogger(A	) as cl:
											logger.warning(A	)
								self.assertEqual(cl.out		,      msg + """\n"""	)

					# this is setting the level for all of `transformers.*` loggers
					logging.set_verbosity_error()

					# should not be able to log warnings
					with CaptureLogger(A	) as cl:
								logger.warning(A	)
					self.assertEqual(cl.out		,      """"""	)

					# should be able to log warnings again
					logging.set_verbosity_warning()
					with CaptureLogger(A	) as cl:
								logger.warning(A	)
					self.assertEqual(cl.out		,      msg + """\n"""	)

					# restore to the original level
					logging.set_verbosity(A	)







		@mockenv(TRANSFORMERS_VERBOSITY="""error"""	)
		def 					__A   ( self	)  -> Union[str, Any]:







					'''simple docstring'''







					transformers.utils.logging._reset_library_root_logger()
					# this action activates the env var
					lowerCamelCase    		=  logging.get_logger("""transformers.models.bart.tokenization_bart"""	)

					lowerCamelCase    		=  os.getenv("""TRANSFORMERS_VERBOSITY"""		,      A	)
					lowerCamelCase    		=  logging.log_levels[env_level_str]

					lowerCamelCase    		=  logging.get_verbosity()
					self.assertEqual(
					    A		,      A		,      F'TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'		,      )

					# restore to the original level
					lowerCamelCase    		=  """"""
					transformers.utils.logging._reset_library_root_logger()







		@mockenv(TRANSFORMERS_VERBOSITY="""super-error"""	)
		def 					__A   ( self	)  -> Union[str, Any]:







					'''simple docstring'''







					transformers.utils.logging._reset_library_root_logger()
					lowerCamelCase    		=  logging.logging.getLogger()
					with CaptureLogger(A	) as cl:
								# this action activates the env var
								logging.get_logger("""transformers.models.bart.tokenization_bart"""	)
					self.assertIn("""Unknown option TRANSFORMERS_VERBOSITY=super-error"""		,      cl.out	)

					# no need to restore as nothing was changed







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







					'''simple docstring'''







					transformers.utils.logging._reset_library_root_logger()

					lowerCamelCase    		=  logging.get_logger("""transformers.models.bart.tokenization_bart"""	)
					lowerCamelCase    		=  """Testing 1, 2, 3"""

					with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""1"""	):
								# nothing should be logged as env var disables this method
								with CaptureLogger(A	) as cl:
											logger.warning_advice(A	)
								self.assertEqual(cl.out		,      """"""	)

					with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""""""	):
								# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
								with CaptureLogger(A	) as cl:
											logger.warning_advice(A	)
								self.assertEqual(cl.out		,      msg + """\n"""	)






def 	__lowerCamelCase						(							):





			'''simple docstring'''




			disable_progress_bar()
			assert are_progress_bars_disabled()

			enable_progress_bar()
			assert not are_progress_bars_disabled()

