





'''simple docstring'''

from jiwer import compute_measures

import datasets


__lowerCamelCase : Dict											=			"\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

__lowerCamelCase : Optional[int]											=			"\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

__lowerCamelCase : Optional[int]											=			"\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,       _KWARGS_DESCRIPTION  )
class       UpperCAmelCase (						datasets.Metric  ):




		def 				UpperCAmelCase__   (self      :	int				)					-> List[Any]:
						return datasets.MetricInfo(
						    description=_DESCRIPTION							,			citation=_CITATION							,			inputs_description=_KWARGS_DESCRIPTION							,			features=datasets.Features(
						        {
						            "predictions": datasets.Value("string"							,			id="sequence"				),
						            "references": datasets.Value("string"							,			id="sequence"				),
						        }				)							,			codebase_urls=["https://github.com/jitsi/jiwer/"]							,			reference_urls=[
						        "https://en.wikipedia.org/wiki/Word_error_rate",
						    ]							,			)





		def 				UpperCAmelCase__   (self      :	Optional[int]							,			A__      :	Tuple=None							,			A__      :	Dict=None							,			A__      :	Tuple=False				)					-> Optional[int]:
						if concatenate_texts:
										return compute_measures(A__							,			A__				)["wer"]
						else:
										lowercase         = 0
										lowercase         = 0
										for prediction, reference in zip(A__							,			A__				):
														lowercase         = compute_measures(A__							,			A__				)
														incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
														total += measures["substitutions"] + measures["deletions"] + measures["hits"]
										return incorrect / total











'''simple docstring'''

import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase : List[str]											=			logging.get_logger(__name__)


__lowerCamelCase : List[Any]											=			"https://openaipublic.azureedge.net/jukebox/models/"
__lowerCamelCase : Tuple											=			{
    "jukebox-1b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "1b_lyrics/prior_level_2.pth.tar",
    ],
    "jukebox-5b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "5b_lyrics/prior_level_2.pth.tar",
    ],
}







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				if key.endswith(".model.1.bias"							) and len(key.split("."							)							) > 10:
								lowercase         = key.replace(".model.1.bias"		,							".conv1d_1.bias"							)
				elif key.endswith(".model.1.weight"							) and len(key.split("."							)							) > 10:
								lowercase         = key.replace(".model.1.weight"		,							".conv1d_1.weight"							)
				elif key.endswith(".model.3.bias"							) and len(key.split("."							)							) > 10:
								lowercase         = key.replace(".model.3.bias"		,							".conv1d_2.bias"							)
				elif key.endswith(".model.3.weight"							) and len(key.split("."							)							) > 10:
								lowercase         = key.replace(".model.3.weight"		,							".conv1d_2.weight"							)

				if "conditioner_blocks.0." in key:
								lowercase         = key.replace("conditioner_blocks.0"		,							"conditioner_blocks"							)

				if "prime_prior" in key:
								lowercase         = key.replace("prime_prior"		,							"encoder"							)

				if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
								lowercase         = key.replace(".emb."		,							"."							)

				if key.endswith("k"							):  # replace vqvae.X.k with vqvae.X.codebook
								return key.replace(".k"		,							".codebook"							)
				if "y_emb." in key:
								return key.replace("y_emb."		,							"metadata_embedding."							)

				if "x_emb.emb." in key:
								lowercase         = key.replace("0.x_emb.emb"		,							"embed_tokens"							)

				if "prime_state_ln" in key:
								return key.replace("prime_state_ln"		,							"encoder.final_layer_norm"							)
				if ".ln" in key:
								return key.replace(".ln"		,							".layer_norm"							)
				if "_ln" in key:
								return key.replace("_ln"		,							"_layer_norm"							)

				if "prime_state_proj" in key:
								return key.replace("prime_state_proj"		,							"encoder.proj_in"							)
				if "prime_x_out" in key:
								return key.replace("prime_x_out"		,							"encoder.lm_head"							)
				if "prior.x_out" in key:
								return key.replace("x_out"		,							"fc_proj_out"							)
				if "x_emb" in key:
								return key.replace("x_emb"		,							"embed_tokens"							)

				return key







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = {}
				import re

				lowercase         = re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"							)
				lowercase         = re.compile(
				    R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"							)
				lowercase         = re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"							)

				lowercase         = re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"							)
				lowercase         = re.compile(
				    R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"							)
				lowercase         = re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"							)

				lowercase         = re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"							)
				lowercase         = re.compile(
				    R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"							)
				lowercase         = re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"							)

				for original_key, value in state_dict.items():
								# rename vqvae.encoder keys
								if re_encoder_block_conv_in.fullmatch(lowerCAmelCase_							):
												lowercase         = re_encoder_block_conv_in.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = int(groups[2]							) * 2 + int(groups[3]							)
												lowercase         = f'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'
												lowercase         = re_encoder_block_conv_in.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

								elif re_encoder_block_resnet.fullmatch(lowerCAmelCase_							):
												lowercase         = re_encoder_block_resnet.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = int(groups[2]							) * 2 + int(groups[3]							)
												lowercase         = {"1": 1, "3": 2}[groups[-2]]
												lowercase         = f'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'
												lowercase         = f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
												lowercase         = prefix + resnet_block
												lowercase         = re_encoder_block_resnet.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

								elif re_encoder_block_proj_out.fullmatch(lowerCAmelCase_							):
												lowercase         = re_encoder_block_proj_out.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = f'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
												lowercase         = re_encoder_block_proj_out.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

								# rename vqvae.decoder keys
								elif re_decoder_block_conv_out.fullmatch(lowerCAmelCase_							):
												lowercase         = re_decoder_block_conv_out.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = int(groups[2]							) * 2 + int(groups[3]							) - 2
												lowercase         = f'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'
												lowercase         = re_decoder_block_conv_out.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

								elif re_decoder_block_resnet.fullmatch(lowerCAmelCase_							):
												lowercase         = re_decoder_block_resnet.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = int(groups[2]							) * 2 + int(groups[3]							) - 2
												lowercase         = {"1": 1, "3": 2}[groups[-2]]
												lowercase         = f'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'
												lowercase         = f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
												lowercase         = prefix + resnet_block
												lowercase         = re_decoder_block_resnet.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

								elif re_decoder_block_proj_in.fullmatch(lowerCAmelCase_							):
												lowercase         = re_decoder_block_proj_in.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = f'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
												lowercase         = re_decoder_block_proj_in.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

								# rename prior cond.model to upsampler.upsample_block and resnet
								elif re_prior_cond_conv_out.fullmatch(lowerCAmelCase_							):
												lowercase         = re_prior_cond_conv_out.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = int(groups[1]							) * 2 + int(groups[2]							) - 2
												lowercase         = f'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
												lowercase         = re_prior_cond_conv_out.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

								elif re_prior_cond_resnet.fullmatch(lowerCAmelCase_							):
												lowercase         = re_prior_cond_resnet.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = int(groups[1]							) * 2 + int(groups[2]							) - 2
												lowercase         = {"1": 1, "3": 2}[groups[-2]]
												lowercase         = f'conditioner_blocks.upsampler.upsample_block.{block_index}.'
												lowercase         = f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
												lowercase         = prefix + resnet_block
												lowercase         = re_prior_cond_resnet.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

								elif re_prior_cond_proj_in.fullmatch(lowerCAmelCase_							):
												lowercase         = re_prior_cond_proj_in.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = f'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
												lowercase         = re_prior_cond_proj_in.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

								# keep original key
								else:
												lowercase         = original_key

								lowercase         = replace_key(lowerCAmelCase_							)

								if f'{key_prefix}.{key}' not in model_state_dict or key is None:
												print(f'failed converting {original_key} to {key}, does not match'							)

								# handle missmatched shape
								elif value.shape != model_state_dict[f'{key_prefix}.{key}'].shape:
												lowercase         = model_state_dict[f'{key_prefix}.{key}']
												print(f'{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'							)
												lowercase         = original_key

								lowercase         = original_key
								lowercase         = value

				return new_dict







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

				"""simple docstring"""






				for file in MODEL_MAPPING[model_name]:
								if not os.path.isfile(f'{pytorch_dump_folder_path}/{file.split("/"							)[-1]}'							):
												lowercase         = requests.get(f'{PREFIX}{file}'		,							allow_redirects=lowerCAmelCase_							)
												os.makedirs(f'{pytorch_dump_folder_path}/'		,							exist_ok=lowerCAmelCase_							)
												open(f'{pytorch_dump_folder_path}/{file.split("/"							)[-1]}'		,							"wb"							).write(r.content							)

				lowercase         = MODEL_MAPPING[model_name.split("/"							)[-1]]

				lowercase         = JukeboxConfig.from_pretrained(lowerCAmelCase_							)
				lowercase         = JukeboxModel(lowerCAmelCase_							)

				lowercase         = []
				lowercase         = {}
				for i, dict_name in enumerate(lowerCAmelCase_							):
								lowercase         = torch.load(f'{pytorch_dump_folder_path}/{dict_name.split("/"							)[-1]}'							)["model"]

								lowercase         = {}
								for k in old_dic.keys():
												if k.endswith(".b"							):
																lowercase         = old_dic[k]
												elif k.endswith(".w"							):
																lowercase         = old_dic[k]
												elif "level_2" not in dict_name and "cond.model." in k:
																lowercase         = old_dic[k]
												else:
																lowercase         = old_dic[k]

								lowercase         = "vqvae" if i == 0 else f'priors.{3 - i}'
								lowercase         = fix_jukebox_keys(lowerCAmelCase_		,							model.state_dict()		,							lowerCAmelCase_		,							lowerCAmelCase_							)
								weight_dict.append(lowerCAmelCase_							)

				lowercase         = weight_dict.pop(0							)
				model.vqvae.load_state_dict(lowerCAmelCase_							)
				for i in range(len(lowerCAmelCase_							)							):
								model.priors[i].load_state_dict(weight_dict[2 - i]							)

				Path(lowerCAmelCase_							).mkdir(exist_ok=lowerCAmelCase_							)
				with open(f'{pytorch_dump_folder_path}/mapping.json'		,							"w"							) as txtfile:
								json.dump(lowerCAmelCase_		,							lowerCAmelCase_							)

				print(f'Saving model {model_name} to {pytorch_dump_folder_path}'							)
				model.save_pretrained(lowerCAmelCase_							)

				return weight_dict


if __name__ == "__main__":
			__lowerCamelCase : int											=			argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--model_name",
			    default="jukebox-5b-lyrics",
			    type=str,
			    help="Name of the model you'd like to convert.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path",
			    default="jukebox-5b-lyrics-converted",
			    type=str,
			    help="Path to the output PyTorch model directory.",
			)
			__lowerCamelCase : List[str]											=			parser.parse_args()
			convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)




