






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


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

UpperCamelCase								=		256

class 				lowerCAmelCase_	(		__snake_case			):
				_UpperCamelCase					:       Optional[int]							       = ["""melgan"""]


				def __init__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						):
											super().__init__()

											# From MELGAN
											_lowercase							:   int 							=   math.log(1E-5      )  # Matches MelGAN training.
											_lowercase							:   Tuple 							=   4.0  # Largest value for most examples
											_lowercase							:   Tuple 							=   1_2_8

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


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase=(-1.0, 1.0) ,						_lowerCAmelCase=False      ):
											_lowercase							:   Tuple 							=   output_range
											if clip:
																		_lowercase							:   List[str] 							=   torch.clip(_lowerCAmelCase ,						self.min_value ,						self.max_value      )
											# Scale to [0, 1].
											_lowercase							:   Optional[Any] 							=   (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 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase=(-1.0, 1.0) ,						_lowerCAmelCase=False      ):
											_lowercase							:   Optional[int] 							=   input_range
											_lowercase							:   Optional[int] 							=   torch.clip(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ) if clip else outputs
											# Scale to [0, 1].
											_lowercase							:   int 							=   (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 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   str 							=   input_tokens > 0
											_lowercase							:   List[str] 							=   self.notes_encoder(
											    encoder_input_tokens=_lowerCAmelCase ,						encoder_inputs_mask=_lowerCAmelCase      )

											_lowercase							:   List[Any] 							=   self.continuous_encoder(
											    encoder_inputs=_lowerCAmelCase ,						encoder_inputs_mask=_lowerCAmelCase      )

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


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   str 							=   noise_time
											if not torch.is_tensor(_lowerCAmelCase      ):
																		_lowercase							:   Dict 							=   torch.tensor([timesteps] ,						dtype=torch.long ,						device=input_tokens.device      )
											elif torch.is_tensor(_lowerCAmelCase      ) and len(timesteps.shape      ) == 0:
																		_lowercase							:   Any 							=   timesteps[None].to(input_tokens.device      )

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

											_lowercase							:   List[str] 							=   self.decoder(
											    encodings_and_masks=_lowerCAmelCase ,						decoder_input_tokens=_lowerCAmelCase ,						decoder_noise_time=_lowerCAmelCase      )
											return logits


				@torch.no_grad()
				def __call__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase = None ,						_lowerCAmelCase = 1_0_0 ,						_lowerCAmelCase = True ,						_lowerCAmelCase = "numpy" ,						_lowerCAmelCase = None ,						_lowerCAmelCase = 1 ,						):
											if (callback_steps is None) or (
											    callback_steps is not None and (not isinstance(_lowerCAmelCase ,						_lowerCAmelCase      ) or callback_steps <= 0)
											):
																		raise ValueError(
																		    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
																		    F""" {type(_lowerCAmelCase      )}."""      )

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

											for i, encoder_input_tokens in enumerate(_lowerCAmelCase      ):
																		if i == 0:
																									_lowercase							:   Tuple 							=   torch.from_numpy(pred_mel[:1].copy()      ).to(
																									    device=self.device ,						dtype=self.decoder.dtype      )
																									# The first chunk has no previous context.
																									_lowercase							:   Tuple 							=   torch.zeros((1, TARGET_FEATURE_LENGTH) ,						dtype=_lowerCAmelCase ,						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.
																									_lowercase							:   Any 							=   ones

																		_lowercase							:   str 							=   self.scale_features(
																		    _lowerCAmelCase ,						output_range=[-1.0, 1.0] ,						clip=_lowerCAmelCase      )

																		_lowercase							:   Union[str, Any] 							=   self.encode(
																		    input_tokens=torch.IntTensor([encoder_input_tokens]      ).to(device=self.device      ) ,						continuous_inputs=_lowerCAmelCase ,						continuous_mask=_lowerCAmelCase ,						)

																		# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
																		_lowercase							:   Union[str, Any] 							=   randn_tensor(
																		    shape=encoder_continuous_inputs.shape ,						generator=_lowerCAmelCase ,						device=self.device ,						dtype=self.decoder.dtype ,						)

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

																		# Denoising diffusion loop
																		for j, t in enumerate(self.progress_bar(self.scheduler.timesteps      )      ):
																									_lowercase							:   Optional[int] 							=   self.decode(
																									    encodings_and_masks=_lowerCAmelCase ,						input_tokens=_lowerCAmelCase ,						noise_time=t / self.scheduler.config.num_train_timesteps ,						)

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

																		_lowercase							:   Any 							=   self.scale_to_features(_lowerCAmelCase ,						input_range=[-1.0, 1.0]      )
																		_lowercase							:   int 							=   mel[:1]
																		_lowercase							:   Optional[int] 							=   mel.cpu().float().numpy()

																		_lowercase							:   Optional[int] 							=   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(_lowerCAmelCase ,						_lowerCAmelCase      )

																		logger.info('Generated segment' ,						_lowerCAmelCase      )

											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":
																		_lowercase							:   Dict 							=   self.melgan(input_features=full_pred_mel.astype(np.floataa      )      )
											else:
																		_lowercase							:   Dict 							=   full_pred_mel

											if not return_dict:
																		return (output,)

											return AudioPipelineOutput(audios=_lowerCAmelCase      )








import argparse
import os
import re

import packaging.version


SCREAMING_SNAKE_CASE__     					=			"examples/"
SCREAMING_SNAKE_CASE__     					=			{
    "examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
    "doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
SCREAMING_SNAKE_CASE__     					=			{
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
SCREAMING_SNAKE_CASE__     					=			"README.md"


def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




		with open(a     , "r"     , encoding="utf-8"     , newline="\n" ) as f:
				SCREAMING_SNAKE_CASE_				:Dict				     =						f.read()
		SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						REPLACE_PATTERNS[pattern]
		SCREAMING_SNAKE_CASE_				:List[str]				     =						replace.replace("VERSION"     , a )
		SCREAMING_SNAKE_CASE_				:str				     =						re_pattern.sub(a     , a )
		with open(a     , "w"     , encoding="utf-8"     , newline="\n" ) as f:
				f.write(a )


def     lowercase	(    a ):



		'''simple docstring'''




		for folder, directories, fnames in os.walk(a ):
				# Removing some of the folders with non-actively maintained examples from the walk
				if "research_projects" in directories:
						directories.remove("research_projects" )
				if "legacy" in directories:
						directories.remove("legacy" )
				for fname in fnames:
						if fname.endswith(".py" ):
								update_version_in_file(os.path.join(a     , a )     , a     , pattern="examples" )


def     lowercase	(    a     , a=False ):



		'''simple docstring'''




		for pattern, fname in REPLACE_FILES.items():
				update_version_in_file(a     , a     , a )
		if not patch:
				update_version_in_examples(a )


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						"🤗 Transformers currently provides the following architectures"
		SCREAMING_SNAKE_CASE_				:Any				     =						"1. Want to contribute a new model?"
		with open(a     , "r"     , encoding="utf-8"     , newline="\n" ) as f:
				SCREAMING_SNAKE_CASE_				:List[str]				     =						f.readlines()

		# Find the start of the list.
		SCREAMING_SNAKE_CASE_				:int				     =						0
		while not lines[start_index].startswith(_start_prompt ):
				start_index += 1
		start_index += 1

		SCREAMING_SNAKE_CASE_				:int				     =						start_index
		# Update the lines in the model list.
		while not lines[index].startswith(_end_prompt ):
				if lines[index].startswith("1." ):
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						lines[index].replace(
						    "https://huggingface.co/docs/transformers/main/model_doc"     , "https://huggingface.co/docs/transformers/model_doc"     , )
				index += 1

		with open(a     , "w"     , encoding="utf-8"     , newline="\n" ) as f:
				f.writelines(a )


def     lowercase	(    ):



		'''simple docstring'''




		with open(REPLACE_FILES["init"]     , "r" ) as f:
				SCREAMING_SNAKE_CASE_				:str				     =						f.read()
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						REPLACE_PATTERNS["init"][0].search(a ).groups()[0]
		return packaging.version.parse(a )


def     lowercase	(    a=False ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:str				     =						get_version()
		if patch and default_version.is_devrelease:
				raise ValueError("Can't create a patch version from the dev branch, checkout a released version!" )
		if default_version.is_devrelease:
				SCREAMING_SNAKE_CASE_				:int				     =						default_version.base_version
		elif patch:
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						F"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
		else:
				SCREAMING_SNAKE_CASE_				:List[str]				     =						F"{default_version.major}.{default_version.minor + 1}.0"

		# Now let's ask nicely if that's the right one.
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						input(F"Which version are you releasing? [{default_version}]" )
		if len(a ) == 0:
				SCREAMING_SNAKE_CASE_				:Any				     =						default_version

		print(F"Updating version to {version}." )
		global_version_update(a     , patch=a )
		if not patch:
				print("Cleaning main README, don't forget to run `make fix-copies`." )
				clean_main_ref_in_model_list()


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						get_version()
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						F"{current_version.major}.{current_version.minor + 1}.0.dev0"
		SCREAMING_SNAKE_CASE_				:str				     =						current_version.base_version

		# Check with the user we got that right.
		SCREAMING_SNAKE_CASE_				:Any				     =						input(F"Which version are we developing now? [{dev_version}]" )
		if len(a ) == 0:
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						dev_version

		print(F"Updating version to {version}." )
		global_version_update(a )
		print("Cleaning main README, don't forget to run `make fix-copies`." )
		clean_main_ref_in_model_list()


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE__     					=			argparse.ArgumentParser()
		parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
		parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
		SCREAMING_SNAKE_CASE__     					=			parser.parse_args()
		if not args.post_release:
				pre_release_work(patch=args.patch)
		elif args.patch:
				print("Nothing to do after a patch :-)")
		else:
				post_release_work()






