


"""simple docstring"""







import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
a__				:							Optional[int]						=     logging.get_logger(__name__)


a__				:							List[Any]						=     ['''model.decoder.embed_positions.weights''']




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					if "emb" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("emb"  ,						"model.decoder.embed_tokens"   )
					if "transformer" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("transformer"  ,						"model.decoder"   )
					if "cross_attention" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("cross_attention"  ,						"encoder_attn"   )
					if "linear1" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("linear1"  ,						"fc1"   )
					if "linear2" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("linear2"  ,						"fc2"   )
					if "norm1" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("norm1"  ,						"self_attn_layer_norm"   )
					if "norm_cross" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("norm_cross"  ,						"encoder_attn_layer_norm"   )
					if "norm2" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("norm2"  ,						"final_layer_norm"   )
					if "out_norm" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("out_norm"  ,						"model.decoder.layer_norm"   )
					if "linears" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("linears"  ,						"lm_heads"   )
					if "condition_provider.conditioners.description.output_proj" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("condition_provider.conditioners.description.output_proj"  ,						"enc_to_dec_proj"   )
					return name




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				list(state_dict.keys()   )
					__SCREAMING_SNAKE_CASE						   =				{}
					for key in keys:
										__SCREAMING_SNAKE_CASE						   =				state_dict.pop(lowerCAmelCase_   )
										__SCREAMING_SNAKE_CASE						   =				rename_keys(lowerCAmelCase_   )
										if "in_proj_weight" in key:
															# split fused qkv proj
															__SCREAMING_SNAKE_CASE						   =				val[:hidden_size, :]
															__SCREAMING_SNAKE_CASE						   =				val[hidden_size : 2 * hidden_size, :]
															__SCREAMING_SNAKE_CASE						   =				val[-hidden_size:, :]
										elif "enc_to_dec_proj" in key:
															__SCREAMING_SNAKE_CASE						   =				val
										else:
															__SCREAMING_SNAKE_CASE						   =				val
					return state_dict, enc_dec_proj_state_dict




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					if checkpoint == "small":
										# default config values
										__SCREAMING_SNAKE_CASE						   =				1024
										__SCREAMING_SNAKE_CASE						   =				24
										__SCREAMING_SNAKE_CASE						   =				16
					elif checkpoint == "medium":
										__SCREAMING_SNAKE_CASE						   =				1536
										__SCREAMING_SNAKE_CASE						   =				48
										__SCREAMING_SNAKE_CASE						   =				24
					elif checkpoint == "large":
										__SCREAMING_SNAKE_CASE						   =				2048
										__SCREAMING_SNAKE_CASE						   =				48
										__SCREAMING_SNAKE_CASE						   =				32
					else:
										raise ValueError(f"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""   )
					__SCREAMING_SNAKE_CASE						   =				MusicgenDecoderConfig(
					    hidden_size=lowerCAmelCase_  ,						ffn_dim=hidden_size * 4  ,						num_hidden_layers=lowerCAmelCase_  ,						num_attention_heads=lowerCAmelCase_  ,						)
					return config




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




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				MusicGen.get_pretrained(lowerCAmelCase_  ,						device=lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				decoder_config_from_checkpoint(lowerCAmelCase_   )

					__SCREAMING_SNAKE_CASE						   =				fairseq_model.lm.state_dict()
					__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				rename_state_dict(
					    lowerCAmelCase_  ,						hidden_size=decoder_config.hidden_size   )

					__SCREAMING_SNAKE_CASE						   =				TaEncoderModel.from_pretrained("t5-base"   )
					__SCREAMING_SNAKE_CASE						   =				EncodecModel.from_pretrained("facebook/encodec_32khz"   )
					__SCREAMING_SNAKE_CASE						   =				MusicgenForCausalLM(lowerCAmelCase_   ).eval()

					# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
					__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				decoder.load_state_dict(lowerCAmelCase_  ,						strict=lowerCAmelCase_   )

					for key in missing_keys.copy():
										if key.startswith(("text_encoder", "audio_encoder")   ) or key in EXPECTED_MISSING_KEYS:
															missing_keys.remove(lowerCAmelCase_   )

					if len(lowerCAmelCase_   ) > 0:
										raise ValueError(f"""Missing key(s) in state_dict: {missing_keys}"""   )

					if len(lowerCAmelCase_   ) > 0:
										raise ValueError(f"""Unexpected key(s) in state_dict: {unexpected_keys}"""   )

					# init the composite model
					__SCREAMING_SNAKE_CASE						   =				MusicgenForConditionalGeneration(text_encoder=lowerCAmelCase_  ,						audio_encoder=lowerCAmelCase_  ,						decoder=lowerCAmelCase_   )

					# load the pre-trained enc-dec projection (from the decoder state dict)
					model.enc_to_dec_proj.load_state_dict(lowerCAmelCase_   )

					# check we can do a forward pass
					__SCREAMING_SNAKE_CASE						   =				torch.arange(0  ,						8  ,						dtype=torch.long   ).reshape(2  ,						-1   )
					__SCREAMING_SNAKE_CASE						   =				input_ids.reshape(2 * 4  ,						-1   )

					with torch.no_grad():
										__SCREAMING_SNAKE_CASE						   =				model(input_ids=lowerCAmelCase_  ,						decoder_input_ids=lowerCAmelCase_   ).logits

					if logits.shape != (8, 1, 2048):
										raise ValueError("Incorrect shape for logits"   )

					# now construct the processor
					__SCREAMING_SNAKE_CASE						   =				AutoTokenizer.from_pretrained("t5-base"   )
					__SCREAMING_SNAKE_CASE						   =				AutoFeatureExtractor.from_pretrained("facebook/encodec_32khz"  ,						padding_side="left"   )

					__SCREAMING_SNAKE_CASE						   =				MusicgenProcessor(feature_extractor=lowerCAmelCase_  ,						tokenizer=lowerCAmelCase_   )

					# set the appropriate bos/pad token ids
					__SCREAMING_SNAKE_CASE						   =				2048
					__SCREAMING_SNAKE_CASE						   =				2048

					# set other default generation config params
					__SCREAMING_SNAKE_CASE						   =				int(30 * audio_encoder.config.frame_rate   )
					__SCREAMING_SNAKE_CASE						   =				True
					__SCREAMING_SNAKE_CASE						   =				3.0

					if pytorch_dump_folder is not None:
										Path(lowerCAmelCase_   ).mkdir(exist_ok=lowerCAmelCase_   )
										logger.info(f"""Saving model {checkpoint} to {pytorch_dump_folder}"""   )
										model.save_pretrained(lowerCAmelCase_   )
										processor.save_pretrained(lowerCAmelCase_   )

					if repo_id:
										logger.info(f"""Pushing model {checkpoint} to {repo_id}"""   )
										model.push_to_hub(lowerCAmelCase_   )
										processor.push_to_hub(lowerCAmelCase_   )


if __name__ == "__main__":
		a__				:							Union[str, Any]						=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint''',
		    default='''small''',
		    type=str,
		    help='''Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder''',
		    required=True,
		    default=None,
		    type=str,
		    help='''Path to the output PyTorch model directory.''',
		)
		parser.add_argument(
		    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
		)
		parser.add_argument(
		    '''--device''', default='''cpu''', type=str, help='''Torch device to run the conversion, either cpu or cuda.'''
		)

		a__				:							int						=     parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)




"""simple docstring"""







from jiwer import compute_measures

import datasets


a__				:							Optional[int]						=     '''\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''

a__				:							List[str]						=     '''\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.

The 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.

This 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.

Word error rate can then be computed as:

WER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).

This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
'''

a__				:							Dict						=     '''
Compute WER score of transcribed segments against references.

Args:
    references: List of references for each speech input.
    predictions: List of transcriptions to score.
    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.

Returns:
    (float): the word error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> wer = datasets.load_metric("wer")
    >>> wer_score = wer.compute(predictions=predictions, references=references)
    >>> print(wer_score)
    0.5
'''




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

	"""simple docstring"""





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