


"""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()
a__       :							Union[str, Any]        =  logging.get_logger(__name__)

a__       :							Any        =  '''The Nymphenburg Palace is a beautiful palace in Munich!'''




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       {
					    "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,
					}

					__SCREAMING_SNAKE_CASE		     =       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
					__SCREAMING_SNAKE_CASE		     =       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=lowerCAmelCase_						,		output_all_encodings=lowerCAmelCase_						,		use_residual=predefined_args["use_residual"]						,		activation=predefined_args.get("activation"						,		"gelu"     )						,		layer_norm_eps=predefined_args.get("layer_norm_eps"						,		lowerCAmelCase_     )						,		)

					# Vocab information needs to be fetched first
					# It's the same as RoBERTa, so RobertaTokenizer can be used later
					__SCREAMING_SNAKE_CASE		     =       "openwebtext_ccnews_stories_books_cased"

					# Specify download folder to Gluonnlp's vocab
					__SCREAMING_SNAKE_CASE		     =       os.path.join(get_home_dir()						,		"models"     )
					__SCREAMING_SNAKE_CASE		     =       _load_vocab(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		cls=lowerCAmelCase_     )

					__SCREAMING_SNAKE_CASE		     =       nlp.model.BERTModel(
					    lowerCAmelCase_						,		len(lowerCAmelCase_     )						,		units=predefined_args["units"]						,		embed_size=predefined_args["embed_size"]						,		embed_dropout=predefined_args["embed_dropout"]						,		word_embed=predefined_args["word_embed"]						,		use_pooler=lowerCAmelCase_						,		use_token_type_embed=lowerCAmelCase_						,		token_type_vocab_size=predefined_args["token_type_vocab_size"]						,		use_classifier=lowerCAmelCase_						,		use_decoder=lowerCAmelCase_						,		)

					original_bort.load_parameters(lowerCAmelCase_						,		cast_dtype=lowerCAmelCase_						,		ignore_extra=lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       original_bort._collect_params_with_prefix()

					# Build our config 🤗
					__SCREAMING_SNAKE_CASE		     =       {
					    "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(lowerCAmelCase_     ),
					}

					__SCREAMING_SNAKE_CASE		     =       BertConfig.from_dict(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       BertForMaskedLM(lowerCAmelCase_     )
					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(lowerCAmelCase_     ) -> 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(lowerCAmelCase_						,		lowerCAmelCase_     ):
										__SCREAMING_SNAKE_CASE		     =       hf_param.shape

										__SCREAMING_SNAKE_CASE		     =       to_torch(params[gluon_param]     )
										__SCREAMING_SNAKE_CASE		     =       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

					__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
					    hf_bort_model.bert.embeddings.word_embeddings.weight						,		"word_embed.0.weight"     )
					__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
					    hf_bort_model.bert.embeddings.position_embeddings.weight						,		"encoder.position_weight"     )
					__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
					    hf_bort_model.bert.embeddings.LayerNorm.bias						,		"encoder.layer_norm.beta"     )
					__SCREAMING_SNAKE_CASE		     =       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)
					__SCREAMING_SNAKE_CASE		     =       torch.zeros_like(
					    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data     )

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

										# self attention
										__SCREAMING_SNAKE_CASE		     =       layer.attention.self

										__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
										    self_attn.key.bias.data						,		f"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""     )

										__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
										    self_attn.key.weight.data						,		f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""     )
										__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
										    self_attn.query.bias.data						,		f"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""     )
										__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
										    self_attn.query.weight.data						,		f"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""     )
										__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
										    self_attn.value.bias.data						,		f"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""     )
										__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
										    self_attn.value.weight.data						,		f"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""     )

										# self attention output
										__SCREAMING_SNAKE_CASE		     =       layer.attention.output

										__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
										    self_output.dense.bias						,		f"""encoder.transformer_cells.{i}.proj.bias"""     )
										__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
										    self_output.dense.weight						,		f"""encoder.transformer_cells.{i}.proj.weight"""     )
										__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
										    self_output.LayerNorm.bias						,		f"""encoder.transformer_cells.{i}.layer_norm.beta"""     )
										__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
										    self_output.LayerNorm.weight						,		f"""encoder.transformer_cells.{i}.layer_norm.gamma"""     )

										# intermediate
										__SCREAMING_SNAKE_CASE		     =       layer.intermediate

										__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
										    intermediate.dense.bias						,		f"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""     )
										__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
										    intermediate.dense.weight						,		f"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""     )

										# output
										__SCREAMING_SNAKE_CASE		     =       layer.output

										__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
										    bert_output.dense.bias						,		f"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""     )
										__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
										    bert_output.dense.weight						,		f"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""     )
										__SCREAMING_SNAKE_CASE		     =       check_and_map_params(
										    bert_output.LayerNorm.bias						,		f"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""     )
										__SCREAMING_SNAKE_CASE		     =       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
					__SCREAMING_SNAKE_CASE		     =       RobertaTokenizer.from_pretrained("roberta-base"     )

					__SCREAMING_SNAKE_CASE		     =       tokenizer.encode_plus(lowerCAmelCase_     )["input_ids"]

					# Get gluon output
					__SCREAMING_SNAKE_CASE		     =       mx.nd.array([input_ids]     )
					__SCREAMING_SNAKE_CASE		     =       original_bort(inputs=lowerCAmelCase_						,		token_types=[]     )

					# Get Transformer output (save and reload model again)
					hf_bort_model.save_pretrained(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       BertModel.from_pretrained(lowerCAmelCase_     )
					hf_bort_model.eval()

					__SCREAMING_SNAKE_CASE		     =       tokenizer.encode_plus(lowerCAmelCase_						,		return_tensors="pt"     )
					__SCREAMING_SNAKE_CASE		     =       hf_bort_model(**lowerCAmelCase_     )[0]

					__SCREAMING_SNAKE_CASE		     =       output_gluon[0].asnumpy()
					__SCREAMING_SNAKE_CASE		     =       output_hf[0].detach().numpy()

					__SCREAMING_SNAKE_CASE		     =       np.max(np.abs(hf_layer - gluon_layer     )     ).item()
					__SCREAMING_SNAKE_CASE		     =       np.allclose(lowerCAmelCase_						,		lowerCAmelCase_						,		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:"						,		lowerCAmelCase_     )


if __name__ == "__main__":
		a__       :							int        =  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.'''
		)
		a__       :							List[str]        =  parser.parse_args()
		convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""







import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
a__       :							Union[str, Any]        =  logging.get_logger('''transformers.models.encodec''')

a__       :							Optional[Any]        =  {
    '''quantizer.vq.layers.*._codebook.inited''': '''quantizer.layers.*.codebook.inited''',
    '''quantizer.vq.layers.*._codebook.cluster_size''': '''quantizer.layers.*.codebook.cluster_size''',
    '''quantizer.vq.layers.*._codebook.embed''': '''quantizer.layers.*.codebook.embed''',
    '''quantizer.vq.layers.*._codebook.embed_avg''': '''quantizer.layers.*.codebook.embed_avg''',
}
a__       :							int        =  {
    '''encoder.model.0.conv.conv''': '''encoder.layers.0.conv''',
    '''encoder.model.1.block.1.conv.conv''': '''encoder.layers.1.block.1.conv''',
    '''encoder.model.1.block.3.conv.conv''': '''encoder.layers.1.block.3.conv''',
    '''encoder.model.1.shortcut.conv.conv''': '''encoder.layers.1.shortcut.conv''',
    '''encoder.model.3.conv.conv''': '''encoder.layers.3.conv''',
    '''encoder.model.4.block.1.conv.conv''': '''encoder.layers.4.block.1.conv''',
    '''encoder.model.4.block.3.conv.conv''': '''encoder.layers.4.block.3.conv''',
    '''encoder.model.4.shortcut.conv.conv''': '''encoder.layers.4.shortcut.conv''',
    '''encoder.model.6.conv.conv''': '''encoder.layers.6.conv''',
    '''encoder.model.7.block.1.conv.conv''': '''encoder.layers.7.block.1.conv''',
    '''encoder.model.7.block.3.conv.conv''': '''encoder.layers.7.block.3.conv''',
    '''encoder.model.7.shortcut.conv.conv''': '''encoder.layers.7.shortcut.conv''',
    '''encoder.model.9.conv.conv''': '''encoder.layers.9.conv''',
    '''encoder.model.10.block.1.conv.conv''': '''encoder.layers.10.block.1.conv''',
    '''encoder.model.10.block.3.conv.conv''': '''encoder.layers.10.block.3.conv''',
    '''encoder.model.10.shortcut.conv.conv''': '''encoder.layers.10.shortcut.conv''',
    '''encoder.model.12.conv.conv''': '''encoder.layers.12.conv''',
    '''encoder.model.13.lstm''': '''encoder.layers.13.lstm''',
    '''encoder.model.15.conv.conv''': '''encoder.layers.15.conv''',
}
a__       :							List[Any]        =  {
    '''encoder.model.0.conv.norm''': '''encoder.layers.0.norm''',
    '''encoder.model.1.block.1.conv.norm''': '''encoder.layers.1.block.1.norm''',
    '''encoder.model.1.block.3.conv.norm''': '''encoder.layers.1.block.3.norm''',
    '''encoder.model.1.shortcut.conv.norm''': '''encoder.layers.1.shortcut.norm''',
    '''encoder.model.3.conv.norm''': '''encoder.layers.3.norm''',
    '''encoder.model.4.block.1.conv.norm''': '''encoder.layers.4.block.1.norm''',
    '''encoder.model.4.block.3.conv.norm''': '''encoder.layers.4.block.3.norm''',
    '''encoder.model.4.shortcut.conv.norm''': '''encoder.layers.4.shortcut.norm''',
    '''encoder.model.6.conv.norm''': '''encoder.layers.6.norm''',
    '''encoder.model.7.block.1.conv.norm''': '''encoder.layers.7.block.1.norm''',
    '''encoder.model.7.block.3.conv.norm''': '''encoder.layers.7.block.3.norm''',
    '''encoder.model.7.shortcut.conv.norm''': '''encoder.layers.7.shortcut.norm''',
    '''encoder.model.9.conv.norm''': '''encoder.layers.9.norm''',
    '''encoder.model.10.block.1.conv.norm''': '''encoder.layers.10.block.1.norm''',
    '''encoder.model.10.block.3.conv.norm''': '''encoder.layers.10.block.3.norm''',
    '''encoder.model.10.shortcut.conv.norm''': '''encoder.layers.10.shortcut.norm''',
    '''encoder.model.12.conv.norm''': '''encoder.layers.12.norm''',
    '''encoder.model.15.conv.norm''': '''encoder.layers.15.norm''',
}
a__       :							List[Any]        =  {
    '''decoder.model.0.conv.conv''': '''decoder.layers.0.conv''',
    '''decoder.model.1.lstm''': '''decoder.layers.1.lstm''',
    '''decoder.model.3.convtr.convtr''': '''decoder.layers.3.conv''',
    '''decoder.model.4.block.1.conv.conv''': '''decoder.layers.4.block.1.conv''',
    '''decoder.model.4.block.3.conv.conv''': '''decoder.layers.4.block.3.conv''',
    '''decoder.model.4.shortcut.conv.conv''': '''decoder.layers.4.shortcut.conv''',
    '''decoder.model.6.convtr.convtr''': '''decoder.layers.6.conv''',
    '''decoder.model.7.block.1.conv.conv''': '''decoder.layers.7.block.1.conv''',
    '''decoder.model.7.block.3.conv.conv''': '''decoder.layers.7.block.3.conv''',
    '''decoder.model.7.shortcut.conv.conv''': '''decoder.layers.7.shortcut.conv''',
    '''decoder.model.9.convtr.convtr''': '''decoder.layers.9.conv''',
    '''decoder.model.10.block.1.conv.conv''': '''decoder.layers.10.block.1.conv''',
    '''decoder.model.10.block.3.conv.conv''': '''decoder.layers.10.block.3.conv''',
    '''decoder.model.10.shortcut.conv.conv''': '''decoder.layers.10.shortcut.conv''',
    '''decoder.model.12.convtr.convtr''': '''decoder.layers.12.conv''',
    '''decoder.model.13.block.1.conv.conv''': '''decoder.layers.13.block.1.conv''',
    '''decoder.model.13.block.3.conv.conv''': '''decoder.layers.13.block.3.conv''',
    '''decoder.model.13.shortcut.conv.conv''': '''decoder.layers.13.shortcut.conv''',
    '''decoder.model.15.conv.conv''': '''decoder.layers.15.conv''',
}
a__       :							Union[str, Any]        =  {
    '''decoder.model.0.conv.norm''': '''decoder.layers.0.norm''',
    '''decoder.model.3.convtr.norm''': '''decoder.layers.3.norm''',
    '''decoder.model.4.block.1.conv.norm''': '''decoder.layers.4.block.1.norm''',
    '''decoder.model.4.block.3.conv.norm''': '''decoder.layers.4.block.3.norm''',
    '''decoder.model.4.shortcut.conv.norm''': '''decoder.layers.4.shortcut.norm''',
    '''decoder.model.6.convtr.norm''': '''decoder.layers.6.norm''',
    '''decoder.model.7.block.1.conv.norm''': '''decoder.layers.7.block.1.norm''',
    '''decoder.model.7.block.3.conv.norm''': '''decoder.layers.7.block.3.norm''',
    '''decoder.model.7.shortcut.conv.norm''': '''decoder.layers.7.shortcut.norm''',
    '''decoder.model.9.convtr.norm''': '''decoder.layers.9.norm''',
    '''decoder.model.10.block.1.conv.norm''': '''decoder.layers.10.block.1.norm''',
    '''decoder.model.10.block.3.conv.norm''': '''decoder.layers.10.block.3.norm''',
    '''decoder.model.10.shortcut.conv.norm''': '''decoder.layers.10.shortcut.norm''',
    '''decoder.model.12.convtr.norm''': '''decoder.layers.12.norm''',
    '''decoder.model.13.block.1.conv.norm''': '''decoder.layers.13.block.1.norm''',
    '''decoder.model.13.block.3.conv.norm''': '''decoder.layers.13.block.3.norm''',
    '''decoder.model.13.shortcut.conv.norm''': '''decoder.layers.13.shortcut.norm''',
    '''decoder.model.15.conv.norm''': '''decoder.layers.15.norm''',
}
a__       :							List[Any]        =  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
a__       :							Any        =  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
a__       :							List[Any]        =  []
a__       :							str        =  []




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					for attribute in key.split("."     ):
										__SCREAMING_SNAKE_CASE		     =       getattr(lowerCAmelCase_						,		lowerCAmelCase_     )

					if weight_type is not None:
										__SCREAMING_SNAKE_CASE		     =       getattr(lowerCAmelCase_						,		lowerCAmelCase_     ).shape
					else:
										__SCREAMING_SNAKE_CASE		     =       hf_pointer.shape

					if hf_shape != value.shape:
										raise ValueError(
										    f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
										    f""" {value.shape} for {full_name}"""     )

					if weight_type == "weight":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_g":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_v":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "running_mean":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "running_var":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "num_batches_tracked":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_ih_l0":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_hh_l0":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias_ih_l0":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias_hh_l0":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_ih_l1":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_hh_l1":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias_ih_l1":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias_hh_l1":
										__SCREAMING_SNAKE_CASE		     =       value
					else:
										__SCREAMING_SNAKE_CASE		     =       value

					logger.info(f"""{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."""     )




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					for key in ignore_keys:
										if key.endswith(".*"     ):
															if name.startswith(key[:-1]     ):
																				return True
										elif ".*." in key:
															__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       key.split(".*."     )
															if prefix in name and suffix in name:
																				return True
										elif key in name:
															return True
					return False




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       []

					if model_name == "encodec_24khz" or "encodec_32khz":
										__SCREAMING_SNAKE_CASE		     =       MAPPING_24K
					elif model_name == "encodec_48khz":
										__SCREAMING_SNAKE_CASE		     =       MAPPING_48K
					else:
										raise ValueError(f"""Unsupported model: {model_name}"""     )

					for name, value in orig_dict.items():
										if should_ignore(lowerCAmelCase_						,		lowerCAmelCase_     ):
															logger.info(f"""{name} was ignored"""     )
															continue

										__SCREAMING_SNAKE_CASE		     =       False
										for key, mapped_key in MAPPING.items():
															if "*" in key:
																				__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       key.split(".*."     )
																				if prefix in name and suffix in name:
																									__SCREAMING_SNAKE_CASE		     =       suffix

															if key in name:
																				# HACK otherwise .embed gets initialized with .embed_avg too
																				if key.endswith("embed"     ) and name.endswith("embed_avg"     ):
																									continue

																				__SCREAMING_SNAKE_CASE		     =       True
																				if "*" in mapped_key:
																									__SCREAMING_SNAKE_CASE		     =       name.split(lowerCAmelCase_     )[0].split("."     )[-2]
																									__SCREAMING_SNAKE_CASE		     =       mapped_key.replace("*"						,		lowerCAmelCase_     )
																				if "weight_g" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_g"
																				elif "weight_v" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_v"
																				elif "weight_ih_l0" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_ih_l0"
																				elif "weight_hh_l0" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_hh_l0"
																				elif "bias_ih_l0" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias_ih_l0"
																				elif "bias_hh_l0" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias_hh_l0"
																				elif "weight_ih_l1" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_ih_l1"
																				elif "weight_hh_l1" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_hh_l1"
																				elif "bias_ih_l1" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias_ih_l1"
																				elif "bias_hh_l1" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias_hh_l1"
																				elif "bias" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias"
																				elif "weight" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight"
																				elif "running_mean" in name:
																									__SCREAMING_SNAKE_CASE		     =       "running_mean"
																				elif "running_var" in name:
																									__SCREAMING_SNAKE_CASE		     =       "running_var"
																				elif "num_batches_tracked" in name:
																									__SCREAMING_SNAKE_CASE		     =       "num_batches_tracked"
																				else:
																									__SCREAMING_SNAKE_CASE		     =       None
																				set_recursively(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
															continue
										if not is_used:
															unused_weights.append(lowerCAmelCase_     )

					logger.warning(f"""Unused weights: {unused_weights}"""     )




@torch.no_grad()
def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_=None						,		lowerCAmelCase_=None						,		):




					'''simple docstring'''


					if config_path is not None:
										__SCREAMING_SNAKE_CASE		     =       EncodecConfig.from_pretrained(lowerCAmelCase_     )
					else:
										__SCREAMING_SNAKE_CASE		     =       EncodecConfig()

					if model_name == "encodec_24khz":
										pass  # config is already correct
					elif model_name == "encodec_32khz":
										__SCREAMING_SNAKE_CASE		     =       [8, 5, 4, 4]
										__SCREAMING_SNAKE_CASE		     =       [2.2]
										__SCREAMING_SNAKE_CASE		     =       64
										__SCREAMING_SNAKE_CASE		     =       3_2000
										__SCREAMING_SNAKE_CASE		     =       2048
										__SCREAMING_SNAKE_CASE		     =       False
										__SCREAMING_SNAKE_CASE		     =       False
										__SCREAMING_SNAKE_CASE		     =       False
					elif model_name == "encodec_48khz":
										__SCREAMING_SNAKE_CASE		     =       [8, 5, 4, 2]
										__SCREAMING_SNAKE_CASE		     =       [3.0, 6.0, 12.0, 24.0]
										__SCREAMING_SNAKE_CASE		     =       4_8000
										__SCREAMING_SNAKE_CASE		     =       2
										__SCREAMING_SNAKE_CASE		     =       False
										__SCREAMING_SNAKE_CASE		     =       "time_group_norm"
										__SCREAMING_SNAKE_CASE		     =       True
										__SCREAMING_SNAKE_CASE		     =       1.0
										__SCREAMING_SNAKE_CASE		     =       0.01
					else:
										raise ValueError(f"""Unknown model name: {model_name}"""     )

					__SCREAMING_SNAKE_CASE		     =       EncodecModel(lowerCAmelCase_     )

					__SCREAMING_SNAKE_CASE		     =       EncodecFeatureExtractor(
					    feature_size=config.audio_channels						,		sampling_rate=config.sampling_rate						,		chunk_length_s=config.chunk_length_s						,		overlap=config.overlap						,		)
					feature_extractor.save_pretrained(lowerCAmelCase_     )

					__SCREAMING_SNAKE_CASE		     =       torch.load(lowerCAmelCase_     )
					if "best_state" in original_checkpoint:
										# we might have a training state saved, in which case discard the yaml results and just retain the weights
										__SCREAMING_SNAKE_CASE		     =       original_checkpoint["best_state"]
					recursively_load_weights(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
					model.save_pretrained(lowerCAmelCase_     )

					if repo_id:
										print("Pushing to the hub..."     )
										feature_extractor.push_to_hub(lowerCAmelCase_     )
										model.push_to_hub(lowerCAmelCase_     )


if __name__ == "__main__":
		a__       :							List[str]        =  argparse.ArgumentParser()
		parser.add_argument(
		    '''--model''',
		    default='''encodec_24khz''',
		    type=str,
		    help='''The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.''',
		)
		parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
		)

		a__       :							Optional[int]        =  parser.parse_args()
		convert_checkpoint(
		    args.model,
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.push_to_hub,
		)
