





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


__A				:    Optional[int]       =   logging.get_logger(__name__)  # pylint: disable=invalid-name

__A				:    Optional[int]       =   256

class 	A_       (a_			):
	UpperCAmelCase__					=			['''melgan''']
	def __init__(    self							,					_A							,					_A							,					_A							,					_A							,					_A							,					):






				'''simple docstring'''







				super().__init__()

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

				self.register_modules(
				    notes_encoder=_A							,					continuous_encoder=_A							,					decoder=_A							,					scheduler=_A							,					melgan=_A							,					)
	def      _lowercase				(    self							,					_A							,					_A=(-1.0, 1.0)							,					_A=False		):






				'''simple docstring'''







				UpperCAmelCase		,			UpperCAmelCase   		=				output_range
				if clip:
							UpperCAmelCase   		=				torch.clip(_A							,					self.min_value							,					self.max_value		)
				# Scale to [0, 1].
				UpperCAmelCase   		=				(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      _lowercase				(    self							,					_A							,					_A=(-1.0, 1.0)							,					_A=False		):






				'''simple docstring'''







				UpperCAmelCase		,			UpperCAmelCase   		=				input_range
				UpperCAmelCase   		=				torch.clip(_A							,					_A							,					_A		) if clip else outputs
				# Scale to [0, 1].
				UpperCAmelCase   		=				(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      _lowercase				(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				input_tokens > 0
				UpperCAmelCase		,			UpperCAmelCase   		=				self.notes_encoder(
				    encoder_input_tokens=_A							,					encoder_inputs_mask=_A		)

				UpperCAmelCase		,			UpperCAmelCase   		=				self.continuous_encoder(
				    encoder_inputs=_A							,					encoder_inputs_mask=_A		)

				return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
	def      _lowercase				(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







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

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

				UpperCAmelCase   		=				self.decoder(
				    encodings_and_masks=_A							,					decoder_input_tokens=_A							,					decoder_noise_time=_A		)
				return logits







	@torch.no_grad()
	def __call__(    self							,					_A							,					_A = None							,					_A = 1_0_0							,					_A = True							,					_A = "numpy"							,					_A = None							,					_A = 1							,					):






				'''simple docstring'''







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

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

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

							UpperCAmelCase   		=				self.scale_features(
							    _A							,					output_range=[-1.0, 1.0]							,					clip=_A		)

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

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

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

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

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

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

							UpperCAmelCase   		=				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(_A							,					_A		)

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

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

				if not return_dict:
							return (output,)

				return AudioPipelineOutput(audios=_A		)










import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


__A				:    Dict       =   logging.get_logger(__name__)

__A				:    Any       =   {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A				:    Tuple       =   {
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

__A				:    List[Any]       =   {
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


__A				:    List[Any]       =   {
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}

class 	A_       (a_			):
	UpperCAmelCase__					=			VOCAB_FILES_NAMES
	UpperCAmelCase__					=			PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase__					=			PRETRAINED_INIT_CONFIGURATION
	UpperCAmelCase__					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase__					=			SqueezeBertTokenizer
	def __init__(    self							,					_A=None							,					_A=None							,					_A=True							,					_A="[UNK]"							,					_A="[SEP]"							,					_A="[PAD]"							,					_A="[CLS]"							,					_A="[MASK]"							,					_A=True							,					_A=None							,					**_A							,					):






				'''simple docstring'''







				super().__init__(
				    _A							,					tokenizer_file=_A							,					do_lower_case=_A							,					unk_token=_A							,					sep_token=_A							,					pad_token=_A							,					cls_token=_A							,					mask_token=_A							,					tokenize_chinese_chars=_A							,					strip_accents=_A							,					**_A							,					)

				UpperCAmelCase   		=				json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
				if (
				    normalizer_state.get('''lowercase'''							,					_A		) != do_lower_case
				    or normalizer_state.get('''strip_accents'''							,					_A		) != strip_accents
				    or normalizer_state.get('''handle_chinese_chars'''							,					_A		) != tokenize_chinese_chars
				):
							UpperCAmelCase   		=				getattr(_A							,					normalizer_state.pop('''type'''		)		)
							UpperCAmelCase   		=				do_lower_case
							UpperCAmelCase   		=				strip_accents
							UpperCAmelCase   		=				tokenize_chinese_chars
							UpperCAmelCase   		=				normalizer_class(**_A		)

				UpperCAmelCase   		=				do_lower_case
	def      _lowercase				(    self							,					_A							,					_A=None		):






				'''simple docstring'''







				UpperCAmelCase   		=				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

				if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

				return output
	def      _lowercase				(    self							,					_A							,					_A = None		):






				'''simple docstring'''







				UpperCAmelCase   		=				[self.sep_token_id]
				UpperCAmelCase   		=				[self.cls_token_id]
				if token_ids_a is None:
							return len(cls + token_ids_a + sep		) * [0]
				return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







	def      _lowercase				(    self							,					_A							,					_A = None		):






				'''simple docstring'''







				UpperCAmelCase   		=				self._tokenizer.model.save(_A							,					name=_A		)
				return tuple(_A		)



