

import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


a_      =     'true'

def 			lowerCamelCase__			(       _a				,      _a=82				,      _a=16):
	set_seed(42)
	SCREAMING_SNAKE_CASE :   List[Any]					  =							RegressionModel()
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							deepcopy(_a)
	SCREAMING_SNAKE_CASE :   Any					  =							RegressionDataset(length=_a)
	SCREAMING_SNAKE_CASE :   List[Any]					  =							DataLoader(_a				,      batch_size=_a)
	model.to(accelerator.device)
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							accelerator.prepare(_a				,      _a)
	return model, ddp_model, dataloader

def 			lowerCamelCase__			(       _a				,      _a=False):
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased")
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							load_dataset("glue"				,      "mrpc"				,      split="validation")

	def tokenize_function(_a):
		SCREAMING_SNAKE_CASE :   str					  =							tokenizer(examples["sentence1"]				,      examples["sentence2"]				,      truncation=_a				,      max_length=_a)
		return outputs

	with accelerator.main_process_first():
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							dataset.map(
		    _a				,      batched=_a				,      remove_columns=["idx", "sentence1", "sentence2"]				,      )

	SCREAMING_SNAKE_CASE :   Any					  =							tokenized_datasets.rename_column("label"				,      "labels")

	def collate_fn(_a):
		if use_longest:
			return tokenizer.pad(_a				,      padding="longest"				,      return_tensors="pt")
		return tokenizer.pad(_a				,      padding="max_length"				,      max_length=128				,      return_tensors="pt")

	return DataLoader(_a				,      shuffle=_a				,      collate_fn=_a				,      batch_size=16)

def 			lowerCamelCase__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   Any					  =							Accelerator(dispatch_batches=_a				,      split_batches=_a)
	SCREAMING_SNAKE_CASE :   str					  =							get_dataloader(_a				,      not dispatch_batches)
	SCREAMING_SNAKE_CASE :   Any					  =							AutoModelForSequenceClassification.from_pretrained(
	    "hf-internal-testing/mrpc-bert-base-cased"				,      return_dict=_a)
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Dict					  =							accelerator.prepare(_a				,      _a)
	return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator

def 			lowerCamelCase__			(       _a				,      _a				,      _a):
	SCREAMING_SNAKE_CASE :   int					  =							[]
	for batch in dataloader:
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							batch.values()
		with torch.no_grad():
			SCREAMING_SNAKE_CASE :   str					  =							model(_a)
			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Any					  =							accelerator.gather_for_metrics((logit, target))
			logits_and_targets.append((logit, target))
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							[], []
	for logit, targ in logits_and_targets:
		logits.append(_a)
		targs.append(_a)
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[str]					  =							torch.cat(_a), torch.cat(_a)
	return logits, targs

def 			lowerCamelCase__			(       _a				,      _a=82				,      _a=False				,      _a=False				,      _a=16):
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   int					  =							get_basic_setup(_a				,      _a				,      _a)
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							generate_predictions(_a				,      _a				,      _a)
	assert (
	    len(_a) == num_samples
	), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_a)}"

def 			lowerCamelCase__			(       _a = False				,      _a = False):
	SCREAMING_SNAKE_CASE :   List[str]					  =							evaluate.load("glue"				,      "mrpc")
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   int					  =							get_mrpc_setup(_a				,      _a)
	# First do baseline
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   int					  =							setup["no"]
	model.to(_a)
	model.eval()
	for batch in dataloader:
		batch.to(_a)
		with torch.inference_mode():
			SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model(**_a)
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							outputs.logits.argmax(dim=-1)
		metric.add_batch(predictions=_a				,      references=batch["labels"])
	SCREAMING_SNAKE_CASE :   Dict					  =							metric.compute()

	# Then do distributed
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[Any]					  =							setup["ddp"]
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			SCREAMING_SNAKE_CASE :   Optional[int]					  =							model(**_a)
		SCREAMING_SNAKE_CASE :   Tuple					  =							outputs.logits.argmax(dim=-1)
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							batch["labels"]
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							accelerator.gather_for_metrics((preds, references))
		metric.add_batch(predictions=_a				,      references=_a)
	SCREAMING_SNAKE_CASE :   Dict					  =							metric.compute()

	for key in "accuracy f1".split():
		assert math.isclose(
		    baseline[key]				,      distributed[key]), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"

def 			lowerCamelCase__			(       ):
	SCREAMING_SNAKE_CASE :   Tuple					  =							Accelerator(split_batches=_a				,      dispatch_batches=_a)
	if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_warning()
	else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	# These are a bit slower so they should only be ran on the GPU or TPU
	if torch.cuda.is_available() or is_tpu_available():
		if accelerator.is_local_main_process:
			print("**Testing gather_for_metrics**")
		for split_batches in [True, False]:
			for dispatch_batches in [True, False]:
				if accelerator.is_local_main_process:
					print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`")
				test_mrpc(_a				,      _a)
				accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print("**Test torch metrics**")
	for split_batches in [True, False]:
		for dispatch_batches in [True, False]:
			SCREAMING_SNAKE_CASE :   Tuple					  =							Accelerator(split_batches=_a				,      dispatch_batches=_a)
			if accelerator.is_local_main_process:
				print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99")
			test_torch_metrics(_a				,      99)
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print("**Test last batch is not dropped when perfectly divisible**")
	SCREAMING_SNAKE_CASE :   str					  =							Accelerator()
	test_torch_metrics(_a				,      512)
	accelerator.state._reset_state()

def 			lowerCamelCase__			(       _a):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
			main()




"""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)
