



"""simple docstring"""




import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


_lowerCAmelCase							:   int				=				logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple							)    ->  Tuple:





     '''simple docstring'''

     if "token" in model_name_or_path:
          return "rag_token"
     if "sequence" in model_name_or_path:
          return "rag_sequence"
     if "bart" in model_name_or_path:
          return "bart"
     return None

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]							)    ->  Tuple:





     '''simple docstring'''

     return max(metric_fn(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							) for gt in ground_truths							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]		,    SCREAMING_SNAKE_CASE__      :		Tuple							)    ->  Union[str, Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       [line.strip() for line in open(SCREAMING_SNAKE_CASE__		,    "r"							).readlines()]
     _UpperCAmelCase     :      List[Any]														=       []

     if args.gold_data_mode == "qa":
          _UpperCAmelCase     :      Tuple														=       pd.read_csv(SCREAMING_SNAKE_CASE__		,    sep="\t"		,    header=SCREAMING_SNAKE_CASE__							)
          for answer_list in data[1]:
               _UpperCAmelCase     :      int														=       ast.literal_eval(SCREAMING_SNAKE_CASE__							)
               answers.append(SCREAMING_SNAKE_CASE__							)
     else:
          _UpperCAmelCase     :      Any														=       [line.strip() for line in open(SCREAMING_SNAKE_CASE__		,    "r"							).readlines()]
          _UpperCAmelCase     :      List[str]														=       [[reference] for reference in references]

     _UpperCAmelCase     :      Union[str, Any]														=       0
     for prediction, ground_truths in zip(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
          total += 1
          em += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
          fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      List[str]														=       100.0 * em / total
     _UpperCAmelCase     :      Optional[Any]														=       100.0 * fa / total

     logger.info(f'F1: {fa:.2f}'							)
     logger.info(f'EM: {em:.2f}'							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Union[str, Any]		,    SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  str:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       args.k
     _UpperCAmelCase     :      Tuple														=       [line.strip() for line in open(SCREAMING_SNAKE_CASE__		,    "r"							).readlines()]
     _UpperCAmelCase     :      int														=       [line.strip() for line in open(SCREAMING_SNAKE_CASE__		,    "r"							).readlines()]

     _UpperCAmelCase     :      List[str]														=       0
     for hypo, reference in zip(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
          _UpperCAmelCase     :      Tuple														=       set(hypo.split("\t"							)[:k]							)
          _UpperCAmelCase     :      Any														=       set(reference.split("\t"							)							)
          total += 1
          em += len(hypo_provenance & ref_provenance							) / k

     _UpperCAmelCase     :      Optional[int]														=       100.0 * em / total
     logger.info(f'Precision@{k}: {em: .2f}'							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		Dict		,    SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  Optional[Any]:





     '''simple docstring'''

     def strip_title(SCREAMING_SNAKE_CASE__      :		Union[str, Any]							):
          if title.startswith("\""							):
               _UpperCAmelCase     :      Dict														=       title[1:]
          if title.endswith("\""							):
               _UpperCAmelCase     :      List[Any]														=       title[:-1]
          return title

     _UpperCAmelCase     :      Tuple														=       rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
         SCREAMING_SNAKE_CASE__		,    return_tensors="pt"		,    padding=SCREAMING_SNAKE_CASE__		,    truncation=SCREAMING_SNAKE_CASE__		,    )["input_ids"].to(args.device							)

     _UpperCAmelCase     :      Union[str, Any]														=       rag_model.rag.question_encoder(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Tuple														=       question_enc_outputs[0]

     _UpperCAmelCase     :      Any														=       rag_model.retriever(
         SCREAMING_SNAKE_CASE__		,    question_enc_pool_output.cpu().detach().to(torch.floataa							).numpy()		,    prefix=rag_model.rag.generator.config.prefix		,    n_docs=rag_model.config.n_docs		,    return_tensors="pt"		,    )
     _UpperCAmelCase     :      Union[str, Any]														=       rag_model.retriever.index.get_doc_dicts(result.doc_ids							)
     _UpperCAmelCase     :      List[str]														=       []
     for docs in all_docs:
          _UpperCAmelCase     :      str														=       [strip_title(SCREAMING_SNAKE_CASE__							) for title in docs["title"]]
          provenance_strings.append("\t".join(SCREAMING_SNAKE_CASE__							)							)
     return provenance_strings

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]		,    SCREAMING_SNAKE_CASE__      :		Tuple							)    ->  Any:





     '''simple docstring'''

     with torch.no_grad():
          _UpperCAmelCase     :      Dict														=       rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
              SCREAMING_SNAKE_CASE__		,    return_tensors="pt"		,    padding=SCREAMING_SNAKE_CASE__		,    truncation=SCREAMING_SNAKE_CASE__							)

          _UpperCAmelCase     :      Optional[int]														=       inputs_dict.input_ids.to(args.device							)
          _UpperCAmelCase     :      Union[str, Any]														=       inputs_dict.attention_mask.to(args.device							)
          _UpperCAmelCase     :      Union[str, Any]														=       rag_model.generate(  # rag_model overwrites generate
              SCREAMING_SNAKE_CASE__		,    attention_mask=SCREAMING_SNAKE_CASE__		,    num_beams=args.num_beams		,    min_length=args.min_length		,    max_length=args.max_length		,    early_stopping=SCREAMING_SNAKE_CASE__		,    num_return_sequences=1		,    bad_words_ids=[[0, 0]]		,    )
          _UpperCAmelCase     :      Optional[Any]														=       rag_model.retriever.generator_tokenizer.batch_decode(SCREAMING_SNAKE_CASE__		,    skip_special_tokens=SCREAMING_SNAKE_CASE__							)

          if args.print_predictions:
               for q, a in zip(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
                    logger.info("Q: {} - A: {}".format(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)							)

          return answers

def 		__snake_case		( )    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      List[Any]														=       argparse.ArgumentParser()
     parser.add_argument(
         "--model_type"		,    choices=["rag_sequence", "rag_token", "bart"]		,    type=SCREAMING_SNAKE_CASE__		,    help=(
             "RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
             " model_name_or_path"
         )		,    )
     parser.add_argument(
         "--index_name"		,    default=SCREAMING_SNAKE_CASE__		,    choices=["exact", "compressed", "legacy"]		,    type=SCREAMING_SNAKE_CASE__		,    help="RAG model retriever type"		,    )
     parser.add_argument(
         "--index_path"		,    default=SCREAMING_SNAKE_CASE__		,    type=SCREAMING_SNAKE_CASE__		,    help="Path to the retrieval index"		,    )
     parser.add_argument("--n_docs"		,    default=5		,    type=SCREAMING_SNAKE_CASE__		,    help="Number of retrieved docs"							)
     parser.add_argument(
         "--model_name_or_path"		,    default=SCREAMING_SNAKE_CASE__		,    type=SCREAMING_SNAKE_CASE__		,    required=SCREAMING_SNAKE_CASE__		,    help="Path to pretrained checkpoints or model identifier from huggingface.co/models"		,    )
     parser.add_argument(
         "--eval_mode"		,    choices=["e2e", "retrieval"]		,    default="e2e"		,    type=SCREAMING_SNAKE_CASE__		,    help=(
             "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
             " precision@k."
         )		,    )
     parser.add_argument("--k"		,    default=1		,    type=SCREAMING_SNAKE_CASE__		,    help="k for the precision@k calculation"							)
     parser.add_argument(
         "--evaluation_set"		,    default=SCREAMING_SNAKE_CASE__		,    type=SCREAMING_SNAKE_CASE__		,    required=SCREAMING_SNAKE_CASE__		,    help="Path to a file containing evaluation samples"		,    )
     parser.add_argument(
         "--gold_data_path"		,    default=SCREAMING_SNAKE_CASE__		,    type=SCREAMING_SNAKE_CASE__		,    required=SCREAMING_SNAKE_CASE__		,    help="Path to a tab-separated file with gold samples"		,    )
     parser.add_argument(
         "--gold_data_mode"		,    default="qa"		,    type=SCREAMING_SNAKE_CASE__		,    choices=["qa", "ans"]		,    help=(
             "Format of the gold data file"
             "qa - a single line in the following format: question [tab] answer_list"
             "ans - a single line of the gold file contains the expected answer string"
         )		,    )
     parser.add_argument(
         "--predictions_path"		,    type=SCREAMING_SNAKE_CASE__		,    default="predictions.txt"		,    help="Name of the predictions file, to be stored in the checkpoints directory"		,    )
     parser.add_argument(
         "--eval_all_checkpoints"		,    action="store_true"		,    help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"		,    )
     parser.add_argument(
         "--eval_batch_size"		,    default=8		,    type=SCREAMING_SNAKE_CASE__		,    help="Batch size per GPU/CPU for evaluation."		,    )
     parser.add_argument(
         "--recalculate"		,    help="Recalculate predictions even if the prediction file exists"		,    action="store_true"		,    )
     parser.add_argument(
         "--num_beams"		,    default=4		,    type=SCREAMING_SNAKE_CASE__		,    help="Number of beams to be used when generating answers"		,    )
     parser.add_argument("--min_length"		,    default=1		,    type=SCREAMING_SNAKE_CASE__		,    help="Min length of the generated answers"							)
     parser.add_argument("--max_length"		,    default=50		,    type=SCREAMING_SNAKE_CASE__		,    help="Max length of the generated answers"							)

     parser.add_argument(
         "--print_predictions"		,    action="store_true"		,    help="If True, prints predictions while evaluating."		,    )
     parser.add_argument(
         "--print_docs"		,    action="store_true"		,    help="If True, prints docs retried while generating."		,    )
     _UpperCAmelCase     :      Optional[Any]														=       parser.parse_args()
     _UpperCAmelCase     :      Dict														=       torch.device("cuda" if torch.cuda.is_available() else "cpu"							)
     return args

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      List[Any]														=       {}
     if args.model_type is None:
          _UpperCAmelCase     :      str														=       infer_model_type(args.model_name_or_path							)
          assert args.model_type is not None
     if args.model_type.startswith("rag"							):
          _UpperCAmelCase     :      Tuple														=       RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
          _UpperCAmelCase     :      str														=       args.n_docs
          if args.index_name is not None:
               _UpperCAmelCase     :      Optional[Any]														=       args.index_name
          if args.index_path is not None:
               _UpperCAmelCase     :      List[Any]														=       args.index_path
     else:
          _UpperCAmelCase     :      int														=       BartForConditionalGeneration

     _UpperCAmelCase     :      List[str]														=       (
         [f.path for f in os.scandir(args.model_name_or_path							) if f.is_dir()]
         if args.eval_all_checkpoints
         else [args.model_name_or_path]
     )

     logger.info("Evaluate the following checkpoints: %s"		,    SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      List[str]														=       get_scores if args.eval_mode == "e2e" else get_precision_at_k
     _UpperCAmelCase     :      Any														=       evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval

     for checkpoint in checkpoints:
          if os.path.exists(args.predictions_path							) and (not args.recalculate):
               logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path							)							)
               score_fn(SCREAMING_SNAKE_CASE__		,    args.predictions_path		,    args.gold_data_path							)
               continue

          logger.info("***** Running evaluation for {} *****".format(SCREAMING_SNAKE_CASE__							)							)
          logger.info("  Batch size = %d"		,    args.eval_batch_size							)
          logger.info("  Predictions will be stored under {}".format(args.predictions_path							)							)

          if args.model_type.startswith("rag"							):
               _UpperCAmelCase     :      Tuple														=       RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE__		,    **SCREAMING_SNAKE_CASE__							)
               _UpperCAmelCase     :      Any														=       model_class.from_pretrained(SCREAMING_SNAKE_CASE__		,    retriever=SCREAMING_SNAKE_CASE__		,    **SCREAMING_SNAKE_CASE__							)
               model.retriever.init_retrieval()
          else:
               _UpperCAmelCase     :      Tuple														=       model_class.from_pretrained(SCREAMING_SNAKE_CASE__		,    **SCREAMING_SNAKE_CASE__							)
          model.to(args.device							)

          with open(args.evaluation_set		,    "r"							) as eval_file, open(args.predictions_path		,    "w"							) as preds_file:
               _UpperCAmelCase     :      Optional[int]														=       []
               for line in tqdm(SCREAMING_SNAKE_CASE__							):
                    questions.append(line.strip()							)
                    if len(SCREAMING_SNAKE_CASE__							) == args.eval_batch_size:
                         _UpperCAmelCase     :      Optional[Any]														=       evaluate_batch_fn(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
                         preds_file.write("\n".join(SCREAMING_SNAKE_CASE__							) + "\n"							)
                         preds_file.flush()
                         _UpperCAmelCase     :      Optional[Any]														=       []
               if len(SCREAMING_SNAKE_CASE__							) > 0:
                    _UpperCAmelCase     :      int														=       evaluate_batch_fn(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
                    preds_file.write("\n".join(SCREAMING_SNAKE_CASE__							)							)
                    preds_file.flush()

               score_fn(SCREAMING_SNAKE_CASE__		,    args.predictions_path		,    args.gold_data_path							)


if __name__ == "__main__":
 _lowerCAmelCase							:   int				=				get_args()
 main(args)





"""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()
_lowerCAmelCase							:   Optional[Any]				=				logging.get_logger(__name__)


_lowerCAmelCase							:   List[str]				=				["model.decoder.embed_positions.weights"]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  List[str]:





     '''simple docstring'''

     if "emb" in name:
          _UpperCAmelCase     :      int														=       name.replace("emb"		,    "model.decoder.embed_tokens"							)
     if "transformer" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("transformer"		,    "model.decoder"							)
     if "cross_attention" in name:
          _UpperCAmelCase     :      Any														=       name.replace("cross_attention"		,    "encoder_attn"							)
     if "linear1" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("linear1"		,    "fc1"							)
     if "linear2" in name:
          _UpperCAmelCase     :      Any														=       name.replace("linear2"		,    "fc2"							)
     if "norm1" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("norm1"		,    "self_attn_layer_norm"							)
     if "norm_cross" in name:
          _UpperCAmelCase     :      List[Any]														=       name.replace("norm_cross"		,    "encoder_attn_layer_norm"							)
     if "norm2" in name:
          _UpperCAmelCase     :      Dict														=       name.replace("norm2"		,    "final_layer_norm"							)
     if "out_norm" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("out_norm"		,    "model.decoder.layer_norm"							)
     if "linears" in name:
          _UpperCAmelCase     :      Dict														=       name.replace("linears"		,    "lm_heads"							)
     if "condition_provider.conditioners.description.output_proj" in name:
          _UpperCAmelCase     :      Optional[Any]														=       name.replace("condition_provider.conditioners.description.output_proj"		,    "enc_to_dec_proj"							)
     return name

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		OrderedDict		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  Tuple[Dict, Dict]:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       list(state_dict.keys()							)
     _UpperCAmelCase     :      Dict														=       {}
     for key in keys:
          _UpperCAmelCase     :      Optional[Any]														=       state_dict.pop(SCREAMING_SNAKE_CASE__							)
          _UpperCAmelCase     :      Any														=       rename_keys(SCREAMING_SNAKE_CASE__							)
          if "in_proj_weight" in key:
               # split fused qkv proj
               _UpperCAmelCase     :      Optional[int]														=       val[:hidden_size, :]
               _UpperCAmelCase     :      str														=       val[hidden_size : 2 * hidden_size, :]
               _UpperCAmelCase     :      List[Any]														=       val[-hidden_size:, :]
          elif "enc_to_dec_proj" in key:
               _UpperCAmelCase     :      Union[str, Any]														=       val
          else:
               _UpperCAmelCase     :      int														=       val
     return state_dict, enc_dec_proj_state_dict

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  MusicgenDecoderConfig:





     '''simple docstring'''

     if checkpoint == "small":
          # default config values
          _UpperCAmelCase     :      Union[str, Any]														=       1_024
          _UpperCAmelCase     :      Tuple														=       24
          _UpperCAmelCase     :      List[str]														=       16
     elif checkpoint == "medium":
          _UpperCAmelCase     :      List[str]														=       1_536
          _UpperCAmelCase     :      List[str]														=       48
          _UpperCAmelCase     :      str														=       24
     elif checkpoint == "large":
          _UpperCAmelCase     :      Dict														=       2_048
          _UpperCAmelCase     :      Optional[int]														=       48
          _UpperCAmelCase     :      Tuple														=       32
     else:
          raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'							)
     _UpperCAmelCase     :      Union[str, Any]														=       MusicgenDecoderConfig(
         hidden_size=SCREAMING_SNAKE_CASE__		,    ffn_dim=hidden_size * 4		,    num_hidden_layers=SCREAMING_SNAKE_CASE__		,    num_attention_heads=SCREAMING_SNAKE_CASE__		,    )
     return config

@torch.no_grad()
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]		,    SCREAMING_SNAKE_CASE__      :		int=None		,    SCREAMING_SNAKE_CASE__      :		Tuple=None		,    SCREAMING_SNAKE_CASE__      :		Dict="cpu"							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       MusicGen.get_pretrained(SCREAMING_SNAKE_CASE__		,    device=SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      str														=       decoder_config_from_checkpoint(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Optional[int]														=       fairseq_model.lm.state_dict()
     _UpperCAmelCase							,			_UpperCAmelCase     :      Union[str, Any]														=       rename_state_dict(
         SCREAMING_SNAKE_CASE__		,    hidden_size=decoder_config.hidden_size							)

     _UpperCAmelCase     :      Tuple														=       TaEncoderModel.from_pretrained("t5-base"							)
     _UpperCAmelCase     :      List[str]														=       EncodecModel.from_pretrained("facebook/encodec_32khz"							)
     _UpperCAmelCase     :      Union[str, Any]														=       MusicgenForCausalLM(SCREAMING_SNAKE_CASE__							).eval()

     # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
     _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       decoder.load_state_dict(SCREAMING_SNAKE_CASE__		,    strict=SCREAMING_SNAKE_CASE__							)

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

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

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

     # init the composite model
     _UpperCAmelCase     :      Dict														=       MusicgenForConditionalGeneration(text_encoder=SCREAMING_SNAKE_CASE__		,    audio_encoder=SCREAMING_SNAKE_CASE__		,    decoder=SCREAMING_SNAKE_CASE__							)

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

     # check we can do a forward pass
     _UpperCAmelCase     :      int														=       torch.arange(0		,    8		,    dtype=torch.long							).reshape(2		,    -1							)
     _UpperCAmelCase     :      List[Any]														=       input_ids.reshape(2 * 4		,    -1							)

     with torch.no_grad():
          _UpperCAmelCase     :      List[str]														=       model(input_ids=SCREAMING_SNAKE_CASE__		,    decoder_input_ids=SCREAMING_SNAKE_CASE__							).logits

     if logits.shape != (8, 1, 2_048):
          raise ValueError("Incorrect shape for logits"							)

     # now construct the processor
     _UpperCAmelCase     :      Optional[int]														=       AutoTokenizer.from_pretrained("t5-base"							)
     _UpperCAmelCase     :      str														=       AutoFeatureExtractor.from_pretrained("facebook/encodec_32khz"		,    padding_side="left"							)

     _UpperCAmelCase     :      Optional[Any]														=       MusicgenProcessor(feature_extractor=SCREAMING_SNAKE_CASE__		,    tokenizer=SCREAMING_SNAKE_CASE__							)

     # set the appropriate bos/pad token ids
     _UpperCAmelCase     :      Optional[int]														=       2_048
     _UpperCAmelCase     :      Union[str, Any]														=       2_048

     # set other default generation config params
     _UpperCAmelCase     :      Optional[int]														=       int(30 * audio_encoder.config.frame_rate							)
     _UpperCAmelCase     :      Union[str, Any]														=       True
     _UpperCAmelCase     :      Dict														=       3.0

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

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


if __name__ == "__main__":
 _lowerCAmelCase							:   int				=				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."
 )

 _lowerCAmelCase							:   List[str]				=				parser.parse_args()
 convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
