



'''simple docstring'''



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

lowerCAmelCase_     :       Union[str, Any]								=						256
class 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   Tuple	     =				['melgan']







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

													"""simple docstring"""
													super().__init__()

													# From MELGAN
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			math.log(1e-5				)  # Matches MelGAN training.
													SCREAMING_SNAKE_CASE					:      Tuple     					=			4.0  # Largest value for most examples
													SCREAMING_SNAKE_CASE					:      int     					=			1_28

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







						def 					__lowercase    (  self   :      List[str]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Any=(-1.0, 1.0)				,    lowerCAmelCase__   :      Optional[int]=False				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[Any]     					=			output_range
													if clip:
																				SCREAMING_SNAKE_CASE					:      str     					=			torch.clip(lowerCAmelCase__				,    self.min_value				,    self.max_value				)
													# Scale to [0, 1].
													SCREAMING_SNAKE_CASE					:      Union[str, 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 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      int=(-1.0, 1.0)				,    lowerCAmelCase__   :      Union[str, Any]=False				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      int     					=			input_range
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			torch.clip(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				) if clip else outputs
													# Scale to [0, 1].
													SCREAMING_SNAKE_CASE					:      Tuple     					=			(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   :      List[Any]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			input_tokens > 0
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			self.notes_encoder(
													    encoder_input_tokens=lowerCAmelCase__				,    encoder_inputs_mask=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Dict     					=			self.continuous_encoder(
													    encoder_inputs=lowerCAmelCase__				,    encoder_inputs_mask=lowerCAmelCase__				)

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







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			noise_time
													if not torch.is_tensor(lowerCAmelCase__				):
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			torch.tensor([timesteps]				,    dtype=torch.long				,    device=input_tokens.device				)
													elif torch.is_tensor(lowerCAmelCase__				) and len(timesteps.shape				) == 0:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			timesteps[None].to(input_tokens.device				)

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

													SCREAMING_SNAKE_CASE					:      int     					=			self.decoder(
													    encodings_and_masks=lowerCAmelCase__				,    decoder_input_tokens=lowerCAmelCase__				,    decoder_noise_time=lowerCAmelCase__				)
													return logits







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

													"""simple docstring"""
													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__				)}."""				)

													SCREAMING_SNAKE_CASE					:      int     					=			np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]				,    dtype=np.floataa				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			np.zeros([1, 0, self.n_dims]				,    np.floataa				)
													SCREAMING_SNAKE_CASE					:      Any     					=			torch.ones((1, TARGET_FEATURE_LENGTH)				,    dtype=lowerCAmelCase__				,    device=self.device				)

													for i, encoder_input_tokens in enumerate(lowerCAmelCase__				):
																				if i == 0:
																											SCREAMING_SNAKE_CASE					:      str     					=			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					:      List[str]     					=			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.
																											SCREAMING_SNAKE_CASE					:      Dict     					=			ones

																				SCREAMING_SNAKE_CASE					:      str     					=			self.scale_features(
																				    lowerCAmelCase__				,    output_range=[-1.0, 1.0]				,    clip=lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE					:      List[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
																				SCREAMING_SNAKE_CASE					:      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				)				):
																											SCREAMING_SNAKE_CASE					:      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
																											SCREAMING_SNAKE_CASE					:      List[str]     					=			self.scheduler.step(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    generator=lowerCAmelCase__				).prev_sample

																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.scale_to_features(lowerCAmelCase__				,    input_range=[-1.0, 1.0]				)
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			mel[:1]
																				SCREAMING_SNAKE_CASE					:      Any     					=			mel.cpu().float().numpy()

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

													if not return_dict:
																				return (output,)

													return AudioPipelineOutput(audios=lowerCAmelCase__				)









'''simple docstring'''



import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							lowerCAmelCase_     :       Any								=						argparse.ArgumentParser(
							    description=(
							        'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
							        ' Distillation'
							    )
							)
							parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
							parser.add_argument('--model_name', default='roberta-large', type=str)
							parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
							parser.add_argument('--vocab_transform', action='store_true')
							lowerCAmelCase_     :       Dict								=						parser.parse_args()

							if args.model_type == "roberta":
														lowerCAmelCase_     :       str								=						RobertaForMaskedLM.from_pretrained(args.model_name)
														lowerCAmelCase_     :       Tuple								=						'roberta'
							elif args.model_type == "gpt2":
														lowerCAmelCase_     :       List[Any]								=						GPTaLMHeadModel.from_pretrained(args.model_name)
														lowerCAmelCase_     :       Optional[Any]								=						'transformer'

							lowerCAmelCase_     :       List[Any]								=						model.state_dict()
							lowerCAmelCase_     :       List[Any]								=						{}

							# Embeddings #
							if args.model_type == "gpt2":
														for param_name in ["wte.weight", "wpe.weight"]:
																					lowerCAmelCase_     :       List[str]								=						state_dict[f'{prefix}.{param_name}']
							else:
														for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																					lowerCAmelCase_     :       Any								=						f'{prefix}.embeddings.{w}.weight'
																					lowerCAmelCase_     :       Any								=						state_dict[param_name]
														for w in ["weight", "bias"]:
																					lowerCAmelCase_     :       str								=						f'{prefix}.embeddings.LayerNorm.{w}'
																					lowerCAmelCase_     :       str								=						state_dict[param_name]

    # Transformer Blocks #
							lowerCAmelCase_     :       List[str]								=						0
							for teacher_idx in [0, 2, 4, 7, 9, 11]:
														if args.model_type == "gpt2":
																					for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
																												for w in ["weight", "bias"]:
																																			lowerCAmelCase_     :       List[str]								=						state_dict[
																																			    f'{prefix}.h.{teacher_idx}.{layer}.{w}'
																																			]
																					lowerCAmelCase_     :       Any								=						state_dict[f'{prefix}.h.{teacher_idx}.attn.bias']
														else:
																					for layer in [
																					    "attention.self.query",
																					    "attention.self.key",
																					    "attention.self.value",
																					    "attention.output.dense",
																					    "attention.output.LayerNorm",
																					    "intermediate.dense",
																					    "output.dense",
																					    "output.LayerNorm",
																					]:
																												for w in ["weight", "bias"]:
																																			lowerCAmelCase_     :       Any								=						state_dict[
																																			    f'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
																																			]
														std_idx += 1

							# Language Modeling Head ###s
							if args.model_type == "roberta":
														for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
																					lowerCAmelCase_     :       List[Any]								=						state_dict[f'{layer}']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												lowerCAmelCase_     :       str								=						state_dict[f'lm_head.dense.{w}']
																												lowerCAmelCase_     :       Optional[int]								=						state_dict[f'lm_head.layer_norm.{w}']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					lowerCAmelCase_     :       Dict								=						state_dict[f'{prefix}.ln_f.{w}']
														lowerCAmelCase_     :       Any								=						state_dict['lm_head.weight']

							print(f'N layers selected for distillation: {std_idx}')
							print(f'Number of params transferred for distillation: {len(compressed_sd.keys())}')

							print(f'Save transferred checkpoint to {args.dump_checkpoint}.')
							torch.save(compressed_sd, args.dump_checkpoint)




