

"""simple docstring"""


import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
lowerCamelCase__				  =   pytest.mark.integration


lowerCamelCase__				  =   {"comet"}
lowerCamelCase__				  =   importlib.util.find_spec("fairseq") is not None

lowerCamelCase__				  =   {"code_eval"}
lowerCamelCase__				  =   os.name == "nt"

lowerCamelCase__				  =   {"bertscore", "frugalscore", "perplexity"}
lowerCamelCase__				  =   importlib.util.find_spec("transformers") is not None




def 					lowercase__    (					lowercase_			)					->		Tuple:




      """simple docstring"""

      @wraps(lowercase_			)
      def wrapper(self		,lowercase_			):
            if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
                  self.skipTest("\"test requires Fairseq\""			)
            else:
                  test_case(self		,lowercase_			)

      return wrapper




def 					lowercase__    (					lowercase_			)					->		Dict:




      """simple docstring"""

      @wraps(lowercase_			)
      def wrapper(self		,lowercase_			):
            if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
                  self.skipTest("\"test requires transformers\""			)
            else:
                  test_case(self		,lowercase_			)

      return wrapper




def 					lowercase__    (					lowercase_			)					->		int:




      """simple docstring"""

      @wraps(lowercase_			)
      def wrapper(self		,lowercase_			):
            if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
                  self.skipTest("\"test not supported on Windows\""			)
            else:
                  test_case(self		,lowercase_			)

      return wrapper




def 					lowercase__    (					)					->		List[Any]:




      """simple docstring"""

      _UpperCamelCase      :	str						=       [metric_dir.split(os.sep			)[-2] for metric_dir in glob.glob("./metrics/*/"			)]
      return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished



@parameterized.named_parameters(get_local_metric_names()   )
@for_all_test_methods(
    snake_case__		,  snake_case__		,  snake_case__   )
@local
class      __SCREAMING_SNAKE_CASE    (						parameterized.TestCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Optional[int]	     =					{}
  SCREAMING_SNAKE_CASE__							:Union[str, Any]	     =					None

  @pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"	)
  @pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning"	)
  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]					,   __a   :       List[str]	)		->				Dict:
        _UpperCamelCase      :	List[Any]						=       "[...]"
        _UpperCamelCase      :	List[Any]						=       importlib.import_module(
            datasets.load.metric_module_factory(os.path.join("metrics"					,   _SCREAMING_SNAKE_CASE	)	).module_path	)
        _UpperCamelCase      :	Optional[int]						=       datasets.load.import_main_class(metric_module.__name__					,   dataset=_SCREAMING_SNAKE_CASE	)
        # check parameters
        _UpperCamelCase      :	Tuple						=       inspect.signature(metric._compute	).parameters
        self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()	)	)  # no **kwargs
        # run doctest
        with self.patch_intensive_calls(_SCREAMING_SNAKE_CASE					,   metric_module.__name__	):
              with self.use_local_metrics():
                    try:
                          _UpperCamelCase      :	Tuple						=       doctest.testmod(_SCREAMING_SNAKE_CASE					,   verbose=_SCREAMING_SNAKE_CASE					,   raise_on_error=_SCREAMING_SNAKE_CASE	)
                    except doctest.UnexpectedException as e:
                          raise e.exc_info[1]  # raise the exception that doctest caught
        self.assertEqual(results.failed					,   0	)
        self.assertGreater(results.attempted					,   1	)

  @slow
  def    __SCREAMING_SNAKE_CASE				(		self   :       int					,   __a   :       Tuple	)		->				Tuple:
        _UpperCamelCase      :	Dict						=       "[...]"
        _UpperCamelCase      :	str						=       importlib.import_module(
            datasets.load.metric_module_factory(os.path.join("metrics"					,   _SCREAMING_SNAKE_CASE	)	).module_path	)
        # run doctest
        with self.use_local_metrics():
              _UpperCamelCase      :	Tuple						=       doctest.testmod(_SCREAMING_SNAKE_CASE					,   verbose=_SCREAMING_SNAKE_CASE					,   raise_on_error=_SCREAMING_SNAKE_CASE	)
        self.assertEqual(results.failed					,   0	)
        self.assertGreater(results.attempted					,   1	)

  @contextmanager
  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]					,   __a   :       Optional[int]					,   __a   :       int	)		->				Optional[int]:
        if metric_name in self.INTENSIVE_CALLS_PATCHER:
              with self.INTENSIVE_CALLS_PATCHER[metric_name](_SCREAMING_SNAKE_CASE	):
                    yield
        else:
              yield

  @contextmanager
  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				List[Any]:
        def load_local_metric(__a   :       List[str]					,   *__a   :       List[str]					,   **__a   :       Dict	):
              return load_metric(os.path.join("metrics"					,   _SCREAMING_SNAKE_CASE	)					,   *_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE	)

        with patch("datasets.load_metric"	) as mock_load_metric:
              _UpperCamelCase      :	List[Any]						=       load_local_metric
              yield


  @classmethod
  def    __SCREAMING_SNAKE_CASE				(		cls   :       List[str]					,   __a   :       Union[str, Any]	)		->				Any:
        def wrapper(__a   :       str	):
              _UpperCamelCase      :	int						=       contextmanager(_SCREAMING_SNAKE_CASE	)
              _UpperCamelCase      :	List[str]						=       patcher
              return patcher

        return wrapper





@LocalMetricTest.register_intensive_calls_patcher("bleurt"			)
def 					lowercase__    (					lowercase_			)					->		List[Any]:




      """simple docstring"""

      import tensorflow.compat.va as tf
      from bleurt.score import Predictor

      tf.flags.DEFINE_string("sv"		,""		,""			)  # handle pytest cli flags



      class      __SCREAMING_SNAKE_CASE    (						snake_case__   ):







        '''simple docstring'''


        def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]					,   __a   :       Optional[int]	)		->				List[str]:
              assert len(input_dict["input_ids"]	) == 2
              return np.array([1.03, 1.04]	)

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
      with patch("bleurt.score._create_predictor"			) as mock_create_predictor:
            _UpperCamelCase      :	Optional[Any]						=       MockedPredictor()
            yield




@LocalMetricTest.register_intensive_calls_patcher("bertscore"			)
def 					lowercase__    (					lowercase_			)					->		List[Any]:




      """simple docstring"""

      import torch

      def bert_cos_score_idf(lowercase_		,lowercase_		,*lowercase_		,**lowercase_			):
            return torch.tensor([[1.0, 1.0, 1.0]] * len(lowercase_			)			)

      # mock get_model which is supposed to do download a bert model
      # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
      with patch("bert_score.scorer.get_model"			), patch(
          "bert_score.scorer.bert_cos_score_idf"			) as mock_bert_cos_score_idf:
            _UpperCamelCase      :	Optional[int]						=       bert_cos_score_idf
            yield




@LocalMetricTest.register_intensive_calls_patcher("comet"			)
def 					lowercase__    (					lowercase_			)					->		str:




      """simple docstring"""

      def load_from_checkpoint(lowercase_			):



            class      __SCREAMING_SNAKE_CASE    :







              '''simple docstring'''


              def    __SCREAMING_SNAKE_CASE				(		self   :       Dict					,   __a   :       List[str]					,   *__a   :       Tuple					,   **__a   :       List[Any]	)		->				Dict:
                    assert len(_SCREAMING_SNAKE_CASE	) == 2
                    _UpperCamelCase      :	Any						=       [0.19, 0.92]
                    return scores, sum(_SCREAMING_SNAKE_CASE	) / len(_SCREAMING_SNAKE_CASE	)





            return Model()

      # mock load_from_checkpoint which is supposed to do download a bert model
      # mock load_from_checkpoint which is supposed to do download a bert model
      with patch("comet.download_model"			) as mock_download_model:
            _UpperCamelCase      :	str						=       None
            with patch("comet.load_from_checkpoint"			) as mock_load_from_checkpoint:
                  _UpperCamelCase      :	Any						=       load_from_checkpoint
                  yield




def 					lowercase__    (					)					->		str:




      """simple docstring"""

      _UpperCamelCase      :	Dict						=       load_metric(os.path.join("metrics"		,"seqeval"			)			)
      _UpperCamelCase      :	List[str]						=       "ERROR"
      _UpperCamelCase      :	str						=       F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
      with pytest.raises(lowercase_		,match=re.escape(lowercase_			)			):
            metric.compute(predictions=[]		,references=[]		,scheme=lowercase_			)







"""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__				  =   logging.get_logger(__name__)


lowerCamelCase__				  =   ["model.decoder.embed_positions.weights"]




def 					lowercase__    (					lowercase_			)					->		Optional[Any]:




      """simple docstring"""

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




def 					lowercase__    (					lowercase_		,lowercase_			)					->		Tuple[Dict, Dict]:




      """simple docstring"""

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




def 					lowercase__    (					lowercase_			)					->		MusicgenDecoderConfig:




      """simple docstring"""

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




@torch.no_grad()
def 					lowercase__    (					lowercase_		,lowercase_=None		,lowercase_=None		,lowercase_="cpu"			)					->		List[str]:




      """simple docstring"""

      _UpperCamelCase      :	str						=       MusicGen.get_pretrained(lowercase_		,device=lowercase_			)
      _UpperCamelCase      :	Union[str, Any]						=       decoder_config_from_checkpoint(lowercase_			)

      _UpperCamelCase      :	Optional[int]						=       fairseq_model.lm.state_dict()
      _UpperCamelCase,  _UpperCamelCase      :	Optional[Any]						=       rename_state_dict(
          lowercase_		,hidden_size=decoder_config.hidden_size			)

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

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

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

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

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

      # init the composite model
      _UpperCamelCase      :	str						=       MusicgenForConditionalGeneration(text_encoder=lowercase_		,audio_encoder=lowercase_		,decoder=lowercase_			)

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

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

      with torch.no_grad():
            _UpperCamelCase      :	Tuple						=       model(input_ids=lowercase_		,decoder_input_ids=lowercase_			).logits

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

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

      _UpperCamelCase      :	Optional[int]						=       MusicgenProcessor(feature_extractor=lowercase_		,tokenizer=lowercase_			)

      # set the appropriate bos/pad token ids
      _UpperCamelCase      :	str						=       2_048
      _UpperCamelCase      :	str						=       2_048

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

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

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


if __name__ == "__main__":
  lowerCamelCase__				  =   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__				  =   parser.parse_args()
  convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
