




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


SCREAMING_SNAKE_CASE:     Union[str, Any]									= logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE:     int									= 2_5_6



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      =["melgan"]



						def __init__(			self	:   Dict					,							snake_case__	:   SpectrogramNotesEncoder					,							snake_case__	:   SpectrogramContEncoder					,							snake_case__	:   TaFilmDecoder					,							snake_case__	:   DDPMScheduler					,							snake_case__	:   OnnxRuntimeModel if is_onnx_available() else Any					,							):

								"""simple docstring"""

								super().__init__()

								# From MELGAN
								SCREAMING_SNAKE_CASE_					      =							math.log(1e-5  )  # Matches MelGAN training.
								SCREAMING_SNAKE_CASE_					      =							4.0  # Largest value for most examples
								SCREAMING_SNAKE_CASE_					      =							1_28

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



						def 	__a					(			self	:   Union[str, Any]					,							snake_case__	:   List[str]					,							snake_case__	:   Optional[Any]=(-1.0, 1.0)					,							snake_case__	:   Optional[Any]=False  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							output_range
								if clip:
										SCREAMING_SNAKE_CASE_					      =							torch.clip(snake_case__					,							self.min_value					,							self.max_value  )
								# Scale to [0, 1].
								SCREAMING_SNAKE_CASE_					      =							(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	:   int					,							snake_case__	:   Dict					,							snake_case__	:   List[Any]=(-1.0, 1.0)					,							snake_case__	:   Tuple=False  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							input_range
								SCREAMING_SNAKE_CASE_					      =							torch.clip(snake_case__					,							snake_case__					,							snake_case__  ) if clip else outputs
								# Scale to [0, 1].
								SCREAMING_SNAKE_CASE_					      =							(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	:   str					,							snake_case__	:   Tuple					,							snake_case__	:   Tuple					,							snake_case__	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							input_tokens > 0
								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							self.notes_encoder(
								    encoder_input_tokens=snake_case__					,							encoder_inputs_mask=snake_case__  )

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							self.continuous_encoder(
								    encoder_inputs=snake_case__					,							encoder_inputs_mask=snake_case__  )

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



						def 	__a					(			self	:   int					,							snake_case__	:   Optional[Any]					,							snake_case__	:   List[str]					,							snake_case__	:   List[str]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							noise_time
								if not torch.is_tensor(snake_case__  ):
										SCREAMING_SNAKE_CASE_					      =							torch.tensor([timesteps]					,							dtype=torch.long					,							device=input_tokens.device  )
								elif torch.is_tensor(snake_case__  ) and len(timesteps.shape  ) == 0:
										SCREAMING_SNAKE_CASE_					      =							timesteps[None].to(input_tokens.device  )

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

								SCREAMING_SNAKE_CASE_					      =							self.decoder(
								    encodings_and_masks=snake_case__					,							decoder_input_tokens=snake_case__					,							decoder_noise_time=snake_case__  )
								return logits






						@torch.no_grad()
						def __call__(			self	:   Tuple					,							snake_case__	:   List[List[int]]					,							snake_case__	:   Optional[torch.Generator] = None					,							snake_case__	:   int = 1_00					,							snake_case__	:   bool = True					,							snake_case__	:   str = "numpy"					,							snake_case__	:   Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,							snake_case__	:   int = 1					,							):

								"""simple docstring"""

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

								SCREAMING_SNAKE_CASE_					      =							np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]					,							dtype=np.floataa  )
								SCREAMING_SNAKE_CASE_					      =							np.zeros([1, 0, self.n_dims]					,							np.floataa  )
								SCREAMING_SNAKE_CASE_					      =							torch.ones((1, TARGET_FEATURE_LENGTH)					,							dtype=snake_case__					,							device=self.device  )

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

										SCREAMING_SNAKE_CASE_					      =							self.scale_features(
										    snake_case__					,							output_range=[-1.0, 1.0]					,							clip=snake_case__  )

										SCREAMING_SNAKE_CASE_					      =							self.encode(
										    input_tokens=torch.IntTensor([encoder_input_tokens]  ).to(device=self.device  )					,							continuous_inputs=snake_case__					,							continuous_mask=snake_case__					,							)

										# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
										SCREAMING_SNAKE_CASE_					      =							randn_tensor(
										    shape=encoder_continuous_inputs.shape					,							generator=snake_case__					,							device=self.device					,							dtype=self.decoder.dtype					,							)

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

										# Denoising diffusion loop
										for j, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
												SCREAMING_SNAKE_CASE_					      =							self.decode(
												    encodings_and_masks=snake_case__					,							input_tokens=snake_case__					,							noise_time=t / self.scheduler.config.num_train_timesteps					,							)

												# Compute previous output: x_t -> x_t-1
												SCREAMING_SNAKE_CASE_					      =							self.scheduler.step(snake_case__					,							snake_case__					,							snake_case__					,							generator=snake_case__  ).prev_sample

										SCREAMING_SNAKE_CASE_					      =							self.scale_to_features(snake_case__					,							input_range=[-1.0, 1.0]  )
										SCREAMING_SNAKE_CASE_					      =							mel[:1]
										SCREAMING_SNAKE_CASE_					      =							mel.cpu().float().numpy()

										SCREAMING_SNAKE_CASE_					      =							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(snake_case__					,							snake_case__  )

										logger.info('Generated segment'					,							snake_case__  )

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

								if not return_dict:
										return (output,)

								return AudioPipelineOutput(audios=snake_case__  )





from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

SCREAMING_SNAKE_CASE:     str									= _symbol_database.Default()


SCREAMING_SNAKE_CASE:     Dict									= _descriptor_pool.Default().AddSerializedFile(
    b'''\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'''
)

SCREAMING_SNAKE_CASE:     Optional[Any]									= globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, '''sentencepiece_model_pb2''', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
						SCREAMING_SNAKE_CASE:     List[Any]									= None
						SCREAMING_SNAKE_CASE:     str									= b'''H\003'''
						# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
						# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
						# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
						# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
						# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
						SCREAMING_SNAKE_CASE:     List[Any]									= 4_5
						SCREAMING_SNAKE_CASE:     List[Any]									= 1_5_8_1
						SCREAMING_SNAKE_CASE:     Union[str, Any]									= 1_5_1_7
						SCREAMING_SNAKE_CASE:     Any									= 1_5_7_0
						SCREAMING_SNAKE_CASE:     Union[str, Any]									= 1_5_8_4
						SCREAMING_SNAKE_CASE:     str									= 1_7_9_3
						SCREAMING_SNAKE_CASE:     List[Any]									= 1_7_9_5
						SCREAMING_SNAKE_CASE:     Any									= 1_9_1_6
						SCREAMING_SNAKE_CASE:     Optional[Any]									= 1_8_6_4
						SCREAMING_SNAKE_CASE:     Union[str, Any]									= 1_9_0_5
						SCREAMING_SNAKE_CASE:     str									= 1_9_1_9
						SCREAMING_SNAKE_CASE:     Any									= 2_4_2_9
						SCREAMING_SNAKE_CASE:     Dict									= 2_2_0_8
						SCREAMING_SNAKE_CASE:     Optional[Any]									= 2_4_1_8
						SCREAMING_SNAKE_CASE:     Optional[Any]									= 2_3_2_3
						SCREAMING_SNAKE_CASE:     Tuple									= 2_4_0_7
# @@protoc_insertion_point(module_scope)