'''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()
__UpperCAmelCase					=logging.get_logger(__name__)


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






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Union[str, Any]:
    if "emb" in name:
        __lowerCamelCase            =    name.replace('''emb'''							,    '''model.decoder.embed_tokens'''							)
    if "transformer" in name:
        __lowerCamelCase            =    name.replace('''transformer'''							,    '''model.decoder'''							)
    if "cross_attention" in name:
        __lowerCamelCase            =    name.replace('''cross_attention'''							,    '''encoder_attn'''							)
    if "linear1" in name:
        __lowerCamelCase            =    name.replace('''linear1'''							,    '''fc1'''							)
    if "linear2" in name:
        __lowerCamelCase            =    name.replace('''linear2'''							,    '''fc2'''							)
    if "norm1" in name:
        __lowerCamelCase            =    name.replace('''norm1'''							,    '''self_attn_layer_norm'''							)
    if "norm_cross" in name:
        __lowerCamelCase            =    name.replace('''norm_cross'''							,    '''encoder_attn_layer_norm'''							)
    if "norm2" in name:
        __lowerCamelCase            =    name.replace('''norm2'''							,    '''final_layer_norm'''							)
    if "out_norm" in name:
        __lowerCamelCase            =    name.replace('''out_norm'''							,    '''model.decoder.layer_norm'''							)
    if "linears" in name:
        __lowerCamelCase            =    name.replace('''linears'''							,    '''lm_heads'''							)
    if "condition_provider.conditioners.description.output_proj" in name:
        __lowerCamelCase            =    name.replace('''condition_provider.conditioners.description.output_proj'''							,    '''enc_to_dec_proj'''							)
    return name






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       Tuple[Dict, Dict]:
    __lowerCamelCase            =    list(state_dict.keys()							)
    __lowerCamelCase            =    {}
    for key in keys:
        __lowerCamelCase            =    state_dict.pop(__lowerCAmelCase							)
        __lowerCamelCase            =    rename_keys(__lowerCAmelCase							)
        if "in_proj_weight" in key:
            # split fused qkv proj
            __lowerCamelCase            =    val[:hidden_size, :]
            __lowerCamelCase            =    val[hidden_size : 2 * hidden_size, :]
            __lowerCamelCase            =    val[-hidden_size:, :]
        elif "enc_to_dec_proj" in key:
            __lowerCamelCase            =    val
        else:
            __lowerCamelCase            =    val
    return state_dict, enc_dec_proj_state_dict






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       MusicgenDecoderConfig:
    if checkpoint == "small":
        # default config values
        __lowerCamelCase            =    10_24
        __lowerCamelCase            =    24
        __lowerCamelCase            =    16
    elif checkpoint == "medium":
        __lowerCamelCase            =    15_36
        __lowerCamelCase            =    48
        __lowerCamelCase            =    24
    elif checkpoint == "large":
        __lowerCamelCase            =    20_48
        __lowerCamelCase            =    48
        __lowerCamelCase            =    32
    else:
        raise ValueError(f"""Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}."""							)
    __lowerCamelCase            =    MusicgenDecoderConfig(
        hidden_size=__lowerCAmelCase							,    ffn_dim=hidden_size * 4							,    num_hidden_layers=__lowerCAmelCase							,    num_attention_heads=__lowerCAmelCase							,    )
    return config






@torch.no_grad()
def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__=None							,    UpperCamelCase__=None							,    UpperCamelCase__="cpu"							)      ->       Optional[int]:
    __lowerCamelCase            =    MusicGen.get_pretrained(__lowerCAmelCase							,    device=__lowerCAmelCase							)
    __lowerCamelCase            =    decoder_config_from_checkpoint(__lowerCAmelCase							)

    __lowerCamelCase            =    fairseq_model.lm.state_dict()
    __lowerCamelCase            =    rename_state_dict(
        __lowerCAmelCase							,    hidden_size=decoder_config.hidden_size							)

    __lowerCamelCase            =    TaEncoderModel.from_pretrained('''t5-base'''							)
    __lowerCamelCase            =    EncodecModel.from_pretrained('''facebook/encodec_32khz'''							)
    __lowerCamelCase            =    MusicgenForCausalLM(__lowerCAmelCase							).eval()

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

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

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

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

    # init the composite model
    __lowerCamelCase            =    MusicgenForConditionalGeneration(text_encoder=__lowerCAmelCase							,    audio_encoder=__lowerCAmelCase							,    decoder=__lowerCAmelCase							)

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

    # check we can do a forward pass
    __lowerCamelCase            =    torch.arange(0							,    8							,    dtype=torch.long							).reshape(2							,    -1							)
    __lowerCamelCase            =    input_ids.reshape(2 * 4							,    -1							)

    with torch.no_grad():
        __lowerCamelCase            =    model(input_ids=__lowerCAmelCase							,    decoder_input_ids=__lowerCAmelCase							).logits

    if logits.shape != (8, 1, 20_48):
        raise ValueError('''Incorrect shape for logits'''							)

    # now construct the processor
    __lowerCamelCase            =    AutoTokenizer.from_pretrained('''t5-base'''							)
    __lowerCamelCase            =    AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz'''							,    padding_side='''left'''							)

    __lowerCamelCase            =    MusicgenProcessor(feature_extractor=__lowerCAmelCase							,    tokenizer=__lowerCAmelCase							)

    # set the appropriate bos/pad token ids
    __lowerCamelCase            =    20_48
    __lowerCamelCase            =    20_48

    # set other default generation config params
    __lowerCamelCase            =    int(30 * audio_encoder.config.frame_rate							)
    __lowerCamelCase            =    True
    __lowerCamelCase            =    3.0

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

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


if __name__ == "__main__":
  __UpperCAmelCase					=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."
  )

  __UpperCAmelCase					=parser.parse_args()
  convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)








import os

import pytest

from transformers.dynamic_module_utils import get_imports


UpperCamelCase    							=      '\nimport os\n'

UpperCamelCase    							=      '\ndef foo():\n    import os\n    return False\n'

UpperCamelCase    							=      '\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n'

UpperCamelCase    							=      [
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]


@pytest.mark.parametrize("""case"""					,							__lowerCAmelCase							)
def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[Any]					,							__lowerCAmelCase							:					Optional[Any]							)     ->  str:
   __UpperCamelCase	:			str					=					os.path.join(__lowerCAmelCase					,							"""test_file.py"""							)
   with open(__lowerCAmelCase					,							"""w"""							) as _tmp_file:
      _tmp_file.write(__lowerCAmelCase							)

   __UpperCamelCase	:			Optional[int]					=					get_imports(__lowerCAmelCase							)
   assert parsed_imports == ["os"]



