






'''simple docstring'''



import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						List[Any]     )	->      List[Any]:

   """simple docstring"""
   assert isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
   assert dataset.num_rows == 4
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_1", "col_2", "col_3"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('keep_in_memory',       [False, True]     )
def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						Any,       SCREAMING_SNAKE_CASE__:						List[Any]     )	->      str:

   """simple docstring"""
   __a					  =     tmp_path / """cache"""
   __a					  =     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      __a					  =     JsonDatasetReader(SCREAMING_SNAKE_CASE__,       cache_dir=SCREAMING_SNAKE_CASE__,       keep_in_memory=SCREAMING_SNAKE_CASE__     ).read()
   _check_json_dataset(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )



@pytest.mark.parametrize(
    'features',       [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ],       )
def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						Union[str, Any]     )	->      Optional[Any]:

   """simple docstring"""
   __a					  =     tmp_path / """cache"""
   __a					  =     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   __a					  =     features.copy() if features else default_expected_features
   __a					  =     (
       Features({feature: Value(SCREAMING_SNAKE_CASE__     ) for feature, dtype in features.items()}     ) if features is not None else None
   )
   __a					  =     JsonDatasetReader(SCREAMING_SNAKE_CASE__,       features=SCREAMING_SNAKE_CASE__,       cache_dir=SCREAMING_SNAKE_CASE__     ).read()
   _check_json_dataset(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )



@pytest.mark.parametrize(
    'features',       [
        None,
        {'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'},
    ],       )
def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[int],       SCREAMING_SNAKE_CASE__:						Dict,       SCREAMING_SNAKE_CASE__:						Optional[Any]     )	->      Optional[int]:

   """simple docstring"""
   __a					  =     tmp_path / """cache"""
   __a					  =     {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""}
   __a					  =     features.copy() if features else default_expected_features
   __a					  =     (
       Features({feature: Value(SCREAMING_SNAKE_CASE__     ) for feature, dtype in features.items()}     ) if features is not None else None
   )
   __a					  =     JsonDatasetReader(SCREAMING_SNAKE_CASE__,       features=SCREAMING_SNAKE_CASE__,       cache_dir=SCREAMING_SNAKE_CASE__     ).read()
   assert isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
   assert dataset.num_rows == 2
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_3", "col_1", "col_2"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Union[str, Any],       SCREAMING_SNAKE_CASE__:						Dict     )	->      Dict:

   """simple docstring"""
   # jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
   __a					  =     {"""col_2""": """int64""", """col_3""": """float64""", """col_1""": """string"""}
   __a					  =     features.copy()
   __a					  =     (
       Features({feature: Value(SCREAMING_SNAKE_CASE__     ) for feature, dtype in features.items()}     ) if features is not None else None
   )
   __a					  =     tmp_path / """cache"""
   __a					  =     JsonDatasetReader(SCREAMING_SNAKE_CASE__,       features=SCREAMING_SNAKE_CASE__,       cache_dir=SCREAMING_SNAKE_CASE__     ).read()
   assert isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
   assert dataset.num_rows == 2
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_2", "col_3", "col_1"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('split',       [None, NamedSplit('train'     ), 'train', 'test']     )
def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						Union[str, Any]     )	->      Dict:

   """simple docstring"""
   __a					  =     tmp_path / """cache"""
   __a					  =     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   __a					  =     JsonDatasetReader(SCREAMING_SNAKE_CASE__,       cache_dir=SCREAMING_SNAKE_CASE__,       split=SCREAMING_SNAKE_CASE__     ).read()
   _check_json_dataset(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
   assert dataset.split == split if split else "train"



@pytest.mark.parametrize('path_type',       [str, list]     )
def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Union[str, Any],       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						Union[str, Any]     )	->      Union[str, Any]:

   """simple docstring"""
   if issubclass(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     jsonl_path
   elif issubclass(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     [jsonl_path]
   __a					  =     tmp_path / """cache"""
   __a					  =     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   __a					  =     JsonDatasetReader(SCREAMING_SNAKE_CASE__,       cache_dir=SCREAMING_SNAKE_CASE__     ).read()
   _check_json_dataset(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[str],       SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						Optional[int]=("train",)     )	->      List[Any]:

   """simple docstring"""
   assert isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
   for split in splits:
      __a					  =     dataset_dict[split]
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
         assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('keep_in_memory',       [False, True]     )
def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[int],       SCREAMING_SNAKE_CASE__:						Any,       SCREAMING_SNAKE_CASE__:						List[str]     )	->      Optional[Any]:

   """simple docstring"""
   __a					  =     tmp_path / """cache"""
   __a					  =     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      __a					  =     JsonDatasetReader({'train': jsonl_path},       cache_dir=SCREAMING_SNAKE_CASE__,       keep_in_memory=SCREAMING_SNAKE_CASE__     ).read()
   _check_json_datasetdict(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )



@pytest.mark.parametrize(
    'features',       [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ],       )
def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[int],       SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						List[Any]     )	->      int:

   """simple docstring"""
   __a					  =     tmp_path / """cache"""
   __a					  =     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   __a					  =     features.copy() if features else default_expected_features
   __a					  =     (
       Features({feature: Value(SCREAMING_SNAKE_CASE__     ) for feature, dtype in features.items()}     ) if features is not None else None
   )
   __a					  =     JsonDatasetReader({'train': jsonl_path},       features=SCREAMING_SNAKE_CASE__,       cache_dir=SCREAMING_SNAKE_CASE__     ).read()
   _check_json_datasetdict(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )



@pytest.mark.parametrize('split',       [None, NamedSplit('train'     ), 'train', 'test']     )
def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[Any],       SCREAMING_SNAKE_CASE__:						List[Any],       SCREAMING_SNAKE_CASE__:						int     )	->      int:

   """simple docstring"""
   if split:
      __a					  =     {split: jsonl_path}
   else:
      __a					  =     """train"""
      __a					  =     {"""train""": jsonl_path, """test""": jsonl_path}
   __a					  =     tmp_path / """cache"""
   __a					  =     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   __a					  =     JsonDatasetReader(SCREAMING_SNAKE_CASE__,       cache_dir=SCREAMING_SNAKE_CASE__     ).read()
   _check_json_datasetdict(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       splits=list(path.keys()     )     )
   assert all(dataset[split].split == split for split in path.keys()     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Any     )	->      List[str]:

   """simple docstring"""
   return json.load(SCREAMING_SNAKE_CASE__     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[str]     )	->      str:

   """simple docstring"""
   return [json.loads(SCREAMING_SNAKE_CASE__     ) for line in buffer]



class 				__SCREAMING_SNAKE_CASE							:




      @pytest.mark.parametrize('lines, load_json_function'			, [(True, load_json_lines), (False, load_json)]   )
      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->Union[str, Any]:



         '''simple docstring'''







         with io.BytesIO() as buffer:
            JsonDatasetWriter(_A			, _A			, lines=_A   ).write()
            buffer.seek(0   )
            __a					  =     load_json_function(_A   )
         assert isinstance(_A			, _A   )
         assert isinstance(exported_content[0]			, _A   )
         assert len(_A   ) == 10




      @pytest.mark.parametrize(
          'orient, container, keys, len_at'			, [
              ('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
              ('split', dict, {'columns', 'data'}, 'data'),
              ('index', dict, set('0123456789'   ), None),
              ('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
              ('values', list, None, None),
              ('table', dict, {'schema', 'data'}, 'data'),
          ]			, )
      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->Optional[int]:



         '''simple docstring'''







         with io.BytesIO() as buffer:
            JsonDatasetWriter(_A			, _A			, lines=_A			, orient=_A   ).write()
            buffer.seek(0   )
            __a					  =     load_json(_A   )
         assert isinstance(_A			, _A   )
         if keys:
            if container is dict:
               assert exported_content.keys() == keys
            else:
               assert exported_content[0].keys() == keys
         else:
            assert not hasattr(_A			, 'keys'   ) and not hasattr(exported_content[0]			, 'keys'   )
         if len_at:
            assert len(exported_content[len_at]   ) == 10
         else:
            assert len(_A   ) == 10




      @pytest.mark.parametrize('lines, load_json_function'			, [(True, load_json_lines), (False, load_json)]   )
      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->Tuple:



         '''simple docstring'''







         with io.BytesIO() as buffer:
            JsonDatasetWriter(_A			, _A			, lines=_A			, num_proc=2   ).write()
            buffer.seek(0   )
            __a					  =     load_json_function(_A   )
         assert isinstance(_A			, _A   )
         assert isinstance(exported_content[0]			, _A   )
         assert len(_A   ) == 10




      @pytest.mark.parametrize(
          'orient, container, keys, len_at'			, [
              ('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
              ('split', dict, {'columns', 'data'}, 'data'),
              ('index', dict, set('0123456789'   ), None),
              ('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
              ('values', list, None, None),
              ('table', dict, {'schema', 'data'}, 'data'),
          ]			, )
      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->Any:



         '''simple docstring'''







         with io.BytesIO() as buffer:
            JsonDatasetWriter(_A			, _A			, lines=_A			, orient=_A			, num_proc=2   ).write()
            buffer.seek(0   )
            __a					  =     load_json(_A   )
         assert isinstance(_A			, _A   )
         if keys:
            if container is dict:
               assert exported_content.keys() == keys
            else:
               assert exported_content[0].keys() == keys
         else:
            assert not hasattr(_A			, 'keys'   ) and not hasattr(exported_content[0]			, 'keys'   )
         if len_at:
            assert len(exported_content[len_at]   ) == 10
         else:
            assert len(_A   ) == 10




      def 			__UpperCamelCase				(	self			, lowerCamelCase   )				->int:



         '''simple docstring'''







         with pytest.raises(_A   ):
            with io.BytesIO() as buffer:
               JsonDatasetWriter(_A			, _A			, num_proc=0   )






      @pytest.mark.parametrize('compression, extension'			, [('gzip', 'gz'), ('bz2', 'bz2'), ('xz', 'xz')]   )
      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->int:



         '''simple docstring'''
         __a					  =     tmp_path_factory.mktemp('data'   ) / F"""test.json.{extension}"""
         __a					  =     str(shared_datadir / F"""test_file.json.{extension}"""   )
         JsonDatasetWriter(_A			, _A			, compression=_A   ).write()

         with fsspec.open(_A			, 'rb'			, compression='infer'   ) as f:
            __a					  =     f.read()
         with fsspec.open(_A			, 'rb'			, compression='infer'   ) as f:
            __a					  =     f.read()
         assert exported_content == original_content






"""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()
__magic_name__       :					List[Any]						=			logging.get_logger(__name__)


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

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

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

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						if checkpoint == "small":
												# default config values
												UpperCamelCase				:		Tuple				   =			1024
												UpperCamelCase				:		Dict				   =			24
												UpperCamelCase				:		Optional[int]				   =			16
						elif checkpoint == "medium":
												UpperCamelCase				:		Optional[Any]				   =			1536
												UpperCamelCase				:		int				   =			48
												UpperCamelCase				:		int				   =			24
						elif checkpoint == "large":
												UpperCamelCase				:		Any				   =			2048
												UpperCamelCase				:		List[Any]				   =			48
												UpperCamelCase				:		str				   =			32
						else:
												raise ValueError(f"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""     )
						UpperCamelCase				:		Optional[int]				   =			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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=None							,     SCREAMING_SNAKE_CASE=None							,     SCREAMING_SNAKE_CASE="cpu"     ):
						UpperCamelCase				:		Optional[Any]				   =			MusicGen.get_pretrained(SCREAMING_SNAKE_CASE							,     device=SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Tuple				   =			decoder_config_from_checkpoint(SCREAMING_SNAKE_CASE     )

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

						UpperCamelCase				:		int				   =			TaEncoderModel.from_pretrained("""t5-base"""     )
						UpperCamelCase				:		List[str]				   =			EncodecModel.from_pretrained("""facebook/encodec_32khz"""     )
						UpperCamelCase				:		Optional[int]				   =			MusicgenForCausalLM(SCREAMING_SNAKE_CASE     ).eval()

						# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
						UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			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				:		Tuple				   =			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				:		Tuple				   =			input_ids.reshape(2 * 4							,     -1     )

						with torch.no_grad():
												UpperCamelCase				:		Any				   =			model(input_ids=SCREAMING_SNAKE_CASE							,     decoder_input_ids=SCREAMING_SNAKE_CASE     ).logits

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

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

						UpperCamelCase				:		int				   =			MusicgenProcessor(feature_extractor=SCREAMING_SNAKE_CASE							,     tokenizer=SCREAMING_SNAKE_CASE     )

						# set the appropriate bos/pad token ids
						UpperCamelCase				:		Optional[int]				   =			2048
						UpperCamelCase				:		Tuple				   =			2048

						# set other default generation config params
						UpperCamelCase				:		Optional[int]				   =			int(30 * audio_encoder.config.frame_rate     )
						UpperCamelCase				:		int				   =			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__":
		__magic_name__       :					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."""
		)

		__magic_name__       :					Optional[Any]						=			parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)



