
'''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


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

UpperCAmelCase    					=   256

class 						a   (				__magic_name__    ):
				_snake_case		    = ['''melgan''']




				def __init__(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				SpectrogramNotesEncoder,    SCREAMING_SNAKE_CASE_	:				SpectrogramContEncoder,    SCREAMING_SNAKE_CASE_	:				TaFilmDecoder,    SCREAMING_SNAKE_CASE_	:				DDPMScheduler,    SCREAMING_SNAKE_CASE_	:				OnnxRuntimeModel if is_onnx_available() else Any,    ):
										super().__init__()

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

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




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

										snake_case,					snake_case       :     str =    self.continuous_encoder(
										    encoder_inputs=UpperCAmelCase__,    encoder_inputs_mask=UpperCAmelCase__      )

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




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										snake_case       :     List[str] =    noise_time
										if not torch.is_tensor(UpperCAmelCase__      ):
																snake_case       :     Tuple =    torch.tensor([timesteps],    dtype=torch.long,    device=input_tokens.device      )
										elif torch.is_tensor(UpperCAmelCase__      ) and len(timesteps.shape      ) == 0:
																snake_case       :     List[str] =    timesteps[None].to(input_tokens.device      )

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

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




				@torch.no_grad()
				def __call__(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				List[List[int]],    SCREAMING_SNAKE_CASE_	:				Optional[torch.Generator] = None,    SCREAMING_SNAKE_CASE_	:				int = 1_00,    SCREAMING_SNAKE_CASE_	:				bool = True,    SCREAMING_SNAKE_CASE_	:				str = "numpy",    SCREAMING_SNAKE_CASE_	:				Optional[Callable[[int, int, torch.FloatTensor], None]] = None,    SCREAMING_SNAKE_CASE_	:				int = 1,    ):
										if (callback_steps is None) or (
										    callback_steps is not None and (not isinstance(UpperCAmelCase__,    UpperCAmelCase__      ) or callback_steps <= 0)
										):
																raise ValueError(
																    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
																    F""" {type(UpperCAmelCase__      )}."""      )

										snake_case       :     List[Any] =    np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims],    dtype=np.floataa      )
										snake_case       :     List[str] =    np.zeros([1, 0, self.n_dims],    np.floataa      )
										snake_case       :     Tuple =    torch.ones((1, TARGET_FEATURE_LENGTH),    dtype=UpperCAmelCase__,    device=self.device      )

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

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

																snake_case       :     Optional[int] =    self.encode(
																    input_tokens=torch.IntTensor([encoder_input_tokens]      ).to(device=self.device      ),    continuous_inputs=UpperCAmelCase__,    continuous_mask=UpperCAmelCase__,    )

																# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
																snake_case       :     Tuple =    randn_tensor(
																    shape=encoder_continuous_inputs.shape,    generator=UpperCAmelCase__,    device=self.device,    dtype=self.decoder.dtype,    )

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

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

																						# Compute previous output: x_t -> x_t-1
																						snake_case       :     Any =    self.scheduler.step(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    generator=UpperCAmelCase__      ).prev_sample

																snake_case       :     Any =    self.scale_to_features(UpperCAmelCase__,    input_range=[-1.0, 1.0]      )
																snake_case       :     Optional[Any] =    mel[:1]
																snake_case       :     Tuple =    mel.cpu().float().numpy()

																snake_case       :     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(UpperCAmelCase__,    UpperCAmelCase__      )

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

										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       :     List[Any] =    self.melgan(input_features=full_pred_mel.astype(np.floataa      )      )
										else:
																snake_case       :     Optional[Any] =    full_pred_mel

										if not return_dict:
																return (output,)

										return AudioPipelineOutput(audios=UpperCAmelCase__      )



'''simple docstring'''





import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse("0.8.3"):
						raise Exception("requires gluonnlp == 0.8.3")

if version.parse(mx.__version__) != version.parse("1.5.0"):
						raise Exception("requires mxnet == 1.5.0")

logging.set_verbosity_info()
UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   "The Nymphenburg Palace is a beautiful palace in Munich!"

def      A (       A_							:					str    ,							A_							:					str       ):
						snake_case       :     str =    {
						    '''attention_cell''': '''multi_head''',
						    '''num_layers''': 4,
						    '''units''': 1024,
						    '''hidden_size''': 768,
						    '''max_length''': 512,
						    '''num_heads''': 8,
						    '''scaled''': True,
						    '''dropout''': 0.1,
						    '''use_residual''': True,
						    '''embed_size''': 1024,
						    '''embed_dropout''': 0.1,
						    '''word_embed''': None,
						    '''layer_norm_eps''': 1e-5,
						    '''token_type_vocab_size''': 2,
						}

						snake_case       :     int =    bort_4_8_768_1024_hparams

						# Let's construct the original Bort model here
						# Taken from official BERT implementation, see:
						# https://github.com/alexa/bort/blob/master/bort/bort.py
						snake_case       :     int =    BERTEncoder(
						    attention_cell=predefined_args['''attention_cell''']    ,							num_layers=predefined_args['''num_layers''']    ,							units=predefined_args['''units''']    ,							hidden_size=predefined_args['''hidden_size''']    ,							max_length=predefined_args['''max_length''']    ,							num_heads=predefined_args['''num_heads''']    ,							scaled=predefined_args['''scaled''']    ,							dropout=predefined_args['''dropout''']    ,							output_attention=A_    ,							output_all_encodings=A_    ,							use_residual=predefined_args['''use_residual''']    ,							activation=predefined_args.get('''activation'''    ,							'''gelu'''       )    ,							layer_norm_eps=predefined_args.get('''layer_norm_eps'''    ,							A_       )    ,							)

						# Vocab information needs to be fetched first
						# It's the same as RoBERTa, so RobertaTokenizer can be used later
						snake_case       :     List[Any] =    '''openwebtext_ccnews_stories_books_cased'''

						# Specify download folder to Gluonnlp's vocab
						snake_case       :     Optional[Any] =    os.path.join(get_home_dir()    ,							'''models'''       )
						snake_case       :     Dict =    _load_vocab(A_    ,							A_    ,							A_    ,							cls=A_       )

						snake_case       :     Tuple =    nlp.model.BERTModel(
						    A_    ,							len(A_       )    ,							units=predefined_args['''units''']    ,							embed_size=predefined_args['''embed_size''']    ,							embed_dropout=predefined_args['''embed_dropout''']    ,							word_embed=predefined_args['''word_embed''']    ,							use_pooler=A_    ,							use_token_type_embed=A_    ,							token_type_vocab_size=predefined_args['''token_type_vocab_size''']    ,							use_classifier=A_    ,							use_decoder=A_    ,							)

						original_bort.load_parameters(A_    ,							cast_dtype=A_    ,							ignore_extra=A_       )
						snake_case       :     Union[str, Any] =    original_bort._collect_params_with_prefix()

						# Build our config 🤗
						snake_case       :     Optional[int] =    {
						    '''architectures''': ['''BertForMaskedLM'''],
						    '''attention_probs_dropout_prob''': predefined_args['''dropout'''],
						    '''hidden_act''': '''gelu''',
						    '''hidden_dropout_prob''': predefined_args['''dropout'''],
						    '''hidden_size''': predefined_args['''embed_size'''],
						    '''initializer_range''': 0.02,
						    '''intermediate_size''': predefined_args['''hidden_size'''],
						    '''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
						    '''max_position_embeddings''': predefined_args['''max_length'''],
						    '''model_type''': '''bort''',
						    '''num_attention_heads''': predefined_args['''num_heads'''],
						    '''num_hidden_layers''': predefined_args['''num_layers'''],
						    '''pad_token_id''': 1,  # 2 = BERT, 1 = RoBERTa
						    '''type_vocab_size''': 1,  # 2 = BERT, 1 = RoBERTa
						    '''vocab_size''': len(A_       ),
						}

						snake_case       :     Optional[int] =    BertConfig.from_dict(A_       )
						snake_case       :     int =    BertForMaskedLM(A_       )
						hf_bort_model.eval()

						# Parameter mapping table (Gluonnlp to Transformers)
						# * denotes layer index
						#
						# | Gluon Parameter                                                | Transformers Parameter
						# | -------------------------------------------------------------- | ----------------------
						# | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
						# | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
						# | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
						# | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
						# | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
						# | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
						# | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
						# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
						# | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
						# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
						# | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
						# | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
						# | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
						# | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
						# | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
						# | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
						# | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
						# | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
						# | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
						# | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

						# Helper function to convert MXNET Arrays to PyTorch
						def to_torch(A_							:					List[str]       ) -> nn.Parameter:
												return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()       )       )

						# Check param shapes and map new HF param back
						def check_and_map_params(A_							:					Tuple    ,							A_							:					Dict       ):
												snake_case       :     Tuple =    hf_param.shape

												snake_case       :     List[Any] =    to_torch(params[gluon_param]       )
												snake_case       :     int =    gluon_param.shape

												assert (
												    shape_hf == shape_gluon
												), F"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""

												return gluon_param

						snake_case       :     List[str] =    check_and_map_params(
						    hf_bort_model.bert.embeddings.word_embeddings.weight    ,							'''word_embed.0.weight'''       )
						snake_case       :     Optional[Any] =    check_and_map_params(
						    hf_bort_model.bert.embeddings.position_embeddings.weight    ,							'''encoder.position_weight'''       )
						snake_case       :     Dict =    check_and_map_params(
						    hf_bort_model.bert.embeddings.LayerNorm.bias    ,							'''encoder.layer_norm.beta'''       )
						snake_case       :     Tuple =    check_and_map_params(
						    hf_bort_model.bert.embeddings.LayerNorm.weight    ,							'''encoder.layer_norm.gamma'''       )

						# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
						snake_case       :     str =    torch.zeros_like(
						    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data       )

						for i in range(hf_bort_config.num_hidden_layers       ):
												snake_case       :     BertLayer =    hf_bort_model.bert.encoder.layer[i]

												# self attention
												snake_case       :     BertSelfAttention =    layer.attention.self

												snake_case       :     Optional[int] =    check_and_map_params(
												    self_attn.key.bias.data    ,							F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""       )

												snake_case       :     Any =    check_and_map_params(
												    self_attn.key.weight.data    ,							F"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""       )
												snake_case       :     Tuple =    check_and_map_params(
												    self_attn.query.bias.data    ,							F"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""       )
												snake_case       :     Dict =    check_and_map_params(
												    self_attn.query.weight.data    ,							F"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""       )
												snake_case       :     int =    check_and_map_params(
												    self_attn.value.bias.data    ,							F"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""       )
												snake_case       :     int =    check_and_map_params(
												    self_attn.value.weight.data    ,							F"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""       )

												# self attention output
												snake_case       :     BertSelfOutput =    layer.attention.output

												snake_case       :     Optional[Any] =    check_and_map_params(
												    self_output.dense.bias    ,							F"""encoder.transformer_cells.{i}.proj.bias"""       )
												snake_case       :     Tuple =    check_and_map_params(
												    self_output.dense.weight    ,							F"""encoder.transformer_cells.{i}.proj.weight"""       )
												snake_case       :     int =    check_and_map_params(
												    self_output.LayerNorm.bias    ,							F"""encoder.transformer_cells.{i}.layer_norm.beta"""       )
												snake_case       :     Optional[int] =    check_and_map_params(
												    self_output.LayerNorm.weight    ,							F"""encoder.transformer_cells.{i}.layer_norm.gamma"""       )

												# intermediate
												snake_case       :     BertIntermediate =    layer.intermediate

												snake_case       :     Optional[Any] =    check_and_map_params(
												    intermediate.dense.bias    ,							F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""       )
												snake_case       :     Optional[int] =    check_and_map_params(
												    intermediate.dense.weight    ,							F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""       )

												# output
												snake_case       :     BertOutput =    layer.output

												snake_case       :     List[Any] =    check_and_map_params(
												    bert_output.dense.bias    ,							F"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""       )
												snake_case       :     int =    check_and_map_params(
												    bert_output.dense.weight    ,							F"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""       )
												snake_case       :     List[str] =    check_and_map_params(
												    bert_output.LayerNorm.bias    ,							F"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""       )
												snake_case       :     List[str] =    check_and_map_params(
												    bert_output.LayerNorm.weight    ,							F"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma"""       )

						# Save space and energy 🎄
						hf_bort_model.half()

						# Compare output of both models
						snake_case       :     Dict =    RobertaTokenizer.from_pretrained('''roberta-base'''       )

						snake_case       :     Any =    tokenizer.encode_plus(A_       )['''input_ids''']

						# Get gluon output
						snake_case       :     Dict =    mx.nd.array([input_ids]       )
						snake_case       :     Optional[int] =    original_bort(inputs=A_    ,							token_types=[]       )

						# Get Transformer output (save and reload model again)
						hf_bort_model.save_pretrained(A_       )
						snake_case       :     Optional[int] =    BertModel.from_pretrained(A_       )
						hf_bort_model.eval()

						snake_case       :     Any =    tokenizer.encode_plus(A_    ,							return_tensors='''pt'''       )
						snake_case       :     Any =    hf_bort_model(**A_       )[0]

						snake_case       :     Optional[Any] =    output_gluon[0].asnumpy()
						snake_case       :     Union[str, Any] =    output_hf[0].detach().numpy()

						snake_case       :     Tuple =    np.max(np.abs(hf_layer - gluon_layer       )       ).item()
						snake_case       :     Tuple =    np.allclose(A_    ,							A_    ,							atol=1e-3       )

						if success:
												print('''✔️ Both model do output the same tensors'''       )
						else:
												print('''❌ Both model do **NOT** output the same tensors'''       )
												print('''Absolute difference is:'''    ,							A_       )


if __name__ == "__main__":
						UpperCAmelCase    					=   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--bort_checkpoint_path", default=None, type=str, required=True, help="Path the official Bort params file."
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						UpperCAmelCase    					=   parser.parse_args()
						convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)

