


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   :					int	=      logging.get_logger(__name__)


A   :					Dict	=      ["model.decoder.embed_positions.weights"]

def    lowercase_	(	_A    :					Optional[Any]		):

      """simple docstring"""




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

def    lowercase_	(	_A    :					OrderedDict					,      _A    :					int		):

      """simple docstring"""




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

def    lowercase_	(	_A    :					str		):

      """simple docstring"""




      if checkpoint == "small":
            # default config values
            lowerCamelCase__	:       Union[str, Any]					=						1024
            lowerCamelCase__	:       List[Any]					=						24
            lowerCamelCase__	:       int					=						16
      elif checkpoint == "medium":
            lowerCamelCase__	:       Tuple					=						1536
            lowerCamelCase__	:       Union[str, Any]					=						48
            lowerCamelCase__	:       Optional[Any]					=						24
      elif checkpoint == "large":
            lowerCamelCase__	:       Any					=						2048
            lowerCamelCase__	:       Optional[Any]					=						48
            lowerCamelCase__	:       Tuple					=						32
      else:
            raise ValueError(F"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."		)
      lowerCamelCase__	:       Tuple					=						MusicgenDecoderConfig(
          hidden_size=_A					,      ffn_dim=hidden_size * 4					,      num_hidden_layers=_A					,      num_attention_heads=_A					,      )
      return config

@torch.no_grad()
def    lowercase_	(	_A    :					List[str]					,      _A    :					Any=None					,      _A    :					Dict=None					,      _A    :					str="cpu"		):

      """simple docstring"""




      lowerCamelCase__	:       Dict					=						MusicGen.get_pretrained(_A					,      device=_A		)
      lowerCamelCase__	:       Optional[Any]					=						decoder_config_from_checkpoint(_A		)

      lowerCamelCase__	:       str					=						fairseq_model.lm.state_dict()
      lowerCamelCase__	:       Optional[Any]					=						rename_state_dict(
          _A					,      hidden_size=decoder_config.hidden_size		)

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

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

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

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

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

      # init the composite model
      lowerCamelCase__	:       Optional[int]					=						MusicgenForConditionalGeneration(text_encoder=_A					,      audio_encoder=_A					,      decoder=_A		)

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

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

      with torch.no_grad():
            lowerCamelCase__	:       List[Any]					=						model(input_ids=_A					,      decoder_input_ids=_A		).logits

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

      # now construct the processor
      lowerCamelCase__	:       Union[str, Any]					=						AutoTokenizer.from_pretrained("t5-base"		)
      lowerCamelCase__	:       Dict					=						AutoFeatureExtractor.from_pretrained("facebook/encodec_32khz"					,      padding_side="left"		)

      lowerCamelCase__	:       List[str]					=						MusicgenProcessor(feature_extractor=_A					,      tokenizer=_A		)

      # set the appropriate bos/pad token ids
      lowerCamelCase__	:       Union[str, Any]					=						2048
      lowerCamelCase__	:       Dict					=						2048

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

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

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


if __name__ == "__main__":
    A   :					List[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   :					Optional[Any]	=      parser.parse_args()
    convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)






import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


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

A   :					Union[str, Any]	=      list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
A   :					Optional[Any]	=      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class     _lowercase				:


       """simple docstring"""




       A__      		=    field(
           default=lowercase__							,						metadata={"help": "Model type selected in the list: " + ", ".join(lowercase__)})
       A__      		=    field(
           default=lowercase__							,						metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."})
       A__      		=    field(
           default=1_28							,						metadata={
               "help": (
                   "The maximum total input sequence length after tokenization. Sequences longer "
                   "than this will be truncated, sequences shorter will be padded."
               )
           }							,						)
       A__      		=    field(
           default=1_28							,						metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."}							,						)
       A__      		=    field(
           default=64							,						metadata={
               "help": (
                   "The maximum number of tokens for the question. Questions longer than this will "
                   "be truncated to this length."
               )
           }							,						)
       A__      		=    field(
           default=30							,						metadata={
               "help": (
                   "The maximum length of an answer that can be generated. This is needed because the start "
                   "and end predictions are not conditioned on one another."
               )
           }							,						)
       A__      		=    field(
           default=lowercase__							,						metadata={"help": "Overwrite the cached training and evaluation sets"})
       A__      		=    field(
           default=lowercase__							,						metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."})
       A__      		=    field(
           default=0.0							,						metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."})
       A__      		=    field(
           default=20							,						metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."})
       A__      		=    field(
           default=0							,						metadata={
               "help": (
                   "language id of input for language-specific xlm models (see"
                   " tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
               )
           }							,						)
       A__      		=    field(default=1							,						metadata={"help": "multiple threads for converting example to features"})







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "train"
       A__      		=    "dev"







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    42
       A__      		=    42
       A__      		=    42
       A__      		=    42





       def __init__(     self							:    Optional[int]      ,   __lowerCamelCase							:    SquadDataTrainingArguments      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    Optional[int] = None      ,   __lowerCamelCase							:    Union[str, Split] = Split.train      ,   __lowerCamelCase							:    Optional[bool] = False      ,   __lowerCamelCase							:    Optional[str] = None      ,   __lowerCamelCase							:    Optional[str] = "pt"      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						args
             lowerCamelCase__	:       Tuple					=						is_language_sensitive
             lowerCamelCase__	:       int					=						SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
             if isinstance(__lowerCamelCase      ,   __lowerCamelCase ):
                   try:
                         lowerCamelCase__	:       List[str]					=						Split[mode]
                   except KeyError:
                         raise KeyError("mode is not a valid split name" )
             lowerCamelCase__	:       str					=						mode
             # Load data features from cache or dataset file
             lowerCamelCase__	:       Any					=						"v2" if args.version_2_with_negative else "v1"
             lowerCamelCase__	:       List[str]					=						os.path.join(
                 cache_dir if cache_dir is not None else args.data_dir      ,   f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}"      ,   )

             # Make sure only the first process in distributed training processes the dataset,
             # and the others will use the cache.
             lowerCamelCase__	:       List[str]					=						cached_features_file + ".lock"
             with FileLock(__lowerCamelCase ):
                   if os.path.exists(__lowerCamelCase ) and not args.overwrite_cache:
                         lowerCamelCase__	:       str					=						time.time()
                         lowerCamelCase__	:       Tuple					=						torch.load(__lowerCamelCase )

                         # Legacy cache files have only features, while new cache files
                         # will have dataset and examples also.
                         lowerCamelCase__	:       Optional[Any]					=						self.old_features["features"]
                         lowerCamelCase__	:       Optional[int]					=						self.old_features.get("dataset"      ,   __lowerCamelCase )
                         lowerCamelCase__	:       Optional[Any]					=						self.old_features.get("examples"      ,   __lowerCamelCase )
                         logger.info(
                             f"Loading features from cached file {cached_features_file} [took %.3f s]"      ,   time.time() - start )

                         if self.dataset is None or self.examples is None:
                               logger.warning(
                                   f"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"
                                   " future run" )
                   else:
                         if mode == Split.dev:
                               lowerCamelCase__	:       List[Any]					=						self.processor.get_dev_examples(args.data_dir )
                         else:
                               lowerCamelCase__	:       str					=						self.processor.get_train_examples(args.data_dir )

                         lowerCamelCase__					,     lowerCamelCase__	:       Tuple					=						squad_convert_examples_to_features(
                             examples=self.examples      ,   tokenizer=__lowerCamelCase      ,   max_seq_length=args.max_seq_length      ,   doc_stride=args.doc_stride      ,   max_query_length=args.max_query_length      ,   is_training=mode == Split.train      ,   threads=args.threads      ,   return_dataset=__lowerCamelCase      ,   )

                         lowerCamelCase__	:       int					=						time.time()
                         torch.save(
                             {"features": self.features, "dataset": self.dataset, "examples": self.examples}      ,   __lowerCamelCase      ,   )
                         # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                         logger.info(
                             f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]" )





       def __len__(     self							:    List[Any] ):


             '''simple docstring'''



             return len(self.features )





       def __getitem__(     self							:    List[str]      ,   __lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						self.features[i]

             lowerCamelCase__	:       Tuple					=						torch.tensor(feature.input_ids      ,   dtype=torch.long )
             lowerCamelCase__	:       List[Any]					=						torch.tensor(feature.attention_mask      ,   dtype=torch.long )
             lowerCamelCase__	:       Tuple					=						torch.tensor(feature.token_type_ids      ,   dtype=torch.long )
             lowerCamelCase__	:       Any					=						torch.tensor(feature.cls_index      ,   dtype=torch.long )
             lowerCamelCase__	:       Any					=						torch.tensor(feature.p_mask      ,   dtype=torch.float )
             lowerCamelCase__	:       Union[str, Any]					=						torch.tensor(feature.is_impossible      ,   dtype=torch.float )

             lowerCamelCase__	:       List[str]					=						{
                 "input_ids": input_ids,
                 "attention_mask": attention_mask,
                 "token_type_ids": token_type_ids,
             }

             if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
                   del inputs["token_type_ids"]

             if self.args.model_type in ["xlnet", "xlm"]:
                   inputs.update({"cls_index": cls_index, "p_mask": p_mask} )
                   if self.args.version_2_with_negative:
                         inputs.update({"is_impossible": is_impossible} )
                   if self.is_language_sensitive:
                         inputs.update({"langs": (torch.ones(input_ids.shape      ,   dtype=torch.intaa ) * self.args.lang_id)} )

             if self.mode == Split.train:
                   lowerCamelCase__	:       List[Any]					=						torch.tensor(feature.start_position      ,   dtype=torch.long )
                   lowerCamelCase__	:       List[Any]					=						torch.tensor(feature.end_position      ,   dtype=torch.long )
                   inputs.update({"start_positions": start_positions, "end_positions": end_positions} )

             return inputs


