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


a					:	int							=		['model.decoder.embed_positions.weights']


def  _UpperCamelCase	(						_A      )    ->  str:
    if "emb" in name:
        _UpperCAmelCase          =   name.replace("""emb"""					,      """model.decoder.embed_tokens"""      )
    if "transformer" in name:
        _UpperCAmelCase          =   name.replace("""transformer"""					,      """model.decoder"""      )
    if "cross_attention" in name:
        _UpperCAmelCase          =   name.replace("""cross_attention"""					,      """encoder_attn"""      )
    if "linear1" in name:
        _UpperCAmelCase          =   name.replace("""linear1"""					,      """fc1"""      )
    if "linear2" in name:
        _UpperCAmelCase          =   name.replace("""linear2"""					,      """fc2"""      )
    if "norm1" in name:
        _UpperCAmelCase          =   name.replace("""norm1"""					,      """self_attn_layer_norm"""      )
    if "norm_cross" in name:
        _UpperCAmelCase          =   name.replace("""norm_cross"""					,      """encoder_attn_layer_norm"""      )
    if "norm2" in name:
        _UpperCAmelCase          =   name.replace("""norm2"""					,      """final_layer_norm"""      )
    if "out_norm" in name:
        _UpperCAmelCase          =   name.replace("""out_norm"""					,      """model.decoder.layer_norm"""      )
    if "linears" in name:
        _UpperCAmelCase          =   name.replace("""linears"""					,      """lm_heads"""      )
    if "condition_provider.conditioners.description.output_proj" in name:
        _UpperCAmelCase          =   name.replace("""condition_provider.conditioners.description.output_proj"""					,      """enc_to_dec_proj"""      )
    return name


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


def  _UpperCamelCase	(						_A      )    ->  MusicgenDecoderConfig:
    if checkpoint == "small":
        # default config values
        _UpperCAmelCase          =   1_0_2_4
        _UpperCAmelCase          =   2_4
        _UpperCAmelCase          =   1_6
    elif checkpoint == "medium":
        _UpperCAmelCase          =   1_5_3_6
        _UpperCAmelCase          =   4_8
        _UpperCAmelCase          =   2_4
    elif checkpoint == "large":
        _UpperCAmelCase          =   2_0_4_8
        _UpperCAmelCase          =   4_8
        _UpperCAmelCase          =   3_2
    else:
        raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""      )
    _UpperCAmelCase          =   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	(						_A					,      _A=None					,      _A=None					,      _A="cpu"      )    ->  Tuple:
    _UpperCAmelCase          =   MusicGen.get_pretrained(_SCREAMING_SNAKE_CASE					,      device=_SCREAMING_SNAKE_CASE      )
    _UpperCAmelCase          =   decoder_config_from_checkpoint(_SCREAMING_SNAKE_CASE      )

    _UpperCAmelCase          =   fairseq_model.lm.state_dict()
    _UpperCAmelCase    ,_UpperCAmelCase          =   rename_state_dict(
        _SCREAMING_SNAKE_CASE					,      hidden_size=decoder_config.hidden_size      )

    _UpperCAmelCase          =   TaEncoderModel.from_pretrained("""t5-base"""      )
    _UpperCAmelCase          =   EncodecModel.from_pretrained("""facebook/encodec_32khz"""      )
    _UpperCAmelCase          =   MusicgenForCausalLM(_SCREAMING_SNAKE_CASE      ).eval()

    # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
    _UpperCAmelCase    ,_UpperCAmelCase          =   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          =   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          =   torch.arange(0					,      8					,      dtype=torch.long      ).reshape(2					,      -1      )
    _UpperCAmelCase          =   input_ids.reshape(2 * 4					,      -1      )

    with torch.no_grad():
        _UpperCAmelCase          =   model(input_ids=_SCREAMING_SNAKE_CASE					,      decoder_input_ids=_SCREAMING_SNAKE_CASE      ).logits

    if logits.shape != (8, 1, 2_0_4_8):
        raise ValueError("""Incorrect shape for logits"""      )

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

    _UpperCAmelCase          =   MusicgenProcessor(feature_extractor=_SCREAMING_SNAKE_CASE					,      tokenizer=_SCREAMING_SNAKE_CASE      )

    # set the appropriate bos/pad token ids
    _UpperCAmelCase          =   2_0_4_8
    _UpperCAmelCase          =   2_0_4_8

    # set other default generation config params
    _UpperCAmelCase          =   int(3_0 * audio_encoder.config.frame_rate      )
    _UpperCAmelCase          =   True
    _UpperCAmelCase          =   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__":
 a					:	Tuple							=		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					:	int							=		parser.parse_args()
 convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)

"""simple docstring"""



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 		a_						( _UpperCAmelCase	):
    a      :				Any     =				['image_processor', 'tokenizer']
    a      :				Optional[int]     =				'AutoImageProcessor'
    a      :				Any     =				'AutoTokenizer'







    def __init__(			self			:       List[str]     ,					__UpperCamelCase			:       List[Any]=None     ,					__UpperCamelCase			:       List[str]=None     ,					**__UpperCamelCase			:       List[Any]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   None
        if "feature_extractor" in kwargs:
            warnings.warn(
                """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                """ instead."""     ,					__UpperCamelCase     ,					)
            _UpperCAmelCase          =   kwargs.pop("""feature_extractor"""     )

        _UpperCAmelCase          =   image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
            raise ValueError("""You need to specify an `image_processor`."""     )
        if tokenizer is None:
            raise ValueError("""You need to specify a `tokenizer`."""     )

        super().__init__(__UpperCamelCase     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   self.image_processor
        _UpperCAmelCase          =   False







    def __call__(			self			:       Union[str, Any]     ,					*__UpperCamelCase			:       str     ,					**__UpperCamelCase			:       Optional[Any]     )		->List[str]:






        '''simple docstring'''






        if self._in_target_context_manager:
            return self.current_processor(*__UpperCamelCase     ,					**__UpperCamelCase     )

        _UpperCAmelCase          =   kwargs.pop("""images"""     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   kwargs.pop("""text"""     ,					__UpperCamelCase     )
        if len(__UpperCamelCase     ) > 0:
            _UpperCAmelCase          =   args[0]
            _UpperCAmelCase          =   args[1:]

        if images is None and text is None:
            raise ValueError("""You need to specify either an `images` or `text` input to process."""     )

        if images is not None:
            _UpperCAmelCase          =   self.image_processor(__UpperCamelCase     ,					*__UpperCamelCase     ,					**__UpperCamelCase     )
        if text is not None:
            _UpperCAmelCase          =   self.tokenizer(__UpperCamelCase     ,					**__UpperCamelCase     )

        if text is None:
            return inputs
        elif images is None:
            return encodings
        else:
            _UpperCAmelCase          =   encodings["""input_ids"""]
            return inputs







    def      _snake_case					(			self			:       Union[str, Any]     ,					*__UpperCamelCase			:       int     ,					**__UpperCamelCase			:       Tuple     )		->Tuple:






        '''simple docstring'''






        return self.tokenizer.batch_decode(*__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Tuple     ,					*__UpperCamelCase			:       int     ,					**__UpperCamelCase			:       Union[str, Any]     )		->int:






        '''simple docstring'''






        return self.tokenizer.decode(*__UpperCamelCase     ,					**__UpperCamelCase     )







    @contextmanager
    def      _snake_case					(			self			:       Tuple     )		->Union[str, Any]:






        '''simple docstring'''






        warnings.warn(
            """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
            """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
            """your images inputs, or in a separate call."""     )
        _UpperCAmelCase          =   True
        _UpperCAmelCase          =   self.tokenizer
        yield
        _UpperCAmelCase          =   self.image_processor
        _UpperCAmelCase          =   False







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[Any]=False     ,					__UpperCamelCase			:       Union[str, Any]=None     )		->List[str]:






        '''simple docstring'''






        if added_vocab is None:
            _UpperCAmelCase          =   self.tokenizer.get_added_vocab()

        _UpperCAmelCase          =   {}

        while tokens:
            _UpperCAmelCase          =   re.search(r"""<s_(.*?)>"""     ,					__UpperCamelCase     ,					re.IGNORECASE     )
            if start_token is None:
                break
            _UpperCAmelCase          =   start_token.group(1     )
            _UpperCAmelCase          =   re.search(rf"""</s_{key}>"""     ,					__UpperCamelCase     ,					re.IGNORECASE     )
            _UpperCAmelCase          =   start_token.group()
            if end_token is None:
                _UpperCAmelCase          =   tokens.replace(__UpperCamelCase     ,					""""""     )
            else:
                _UpperCAmelCase          =   end_token.group()
                _UpperCAmelCase          =   re.escape(__UpperCamelCase     )
                _UpperCAmelCase          =   re.escape(__UpperCamelCase     )
                _UpperCAmelCase          =   re.search(f"""{start_token_escaped}(.*?){end_token_escaped}"""     ,					__UpperCamelCase     ,					re.IGNORECASE     )
                if content is not None:
                    _UpperCAmelCase          =   content.group(1     ).strip()
                    if r"<s_" in content and r"</s_" in content:  # non-leaf node
                        _UpperCAmelCase          =   self.tokenajson(__UpperCamelCase     ,					is_inner_value=__UpperCamelCase     ,					added_vocab=__UpperCamelCase     )
                        if value:
                            if len(__UpperCamelCase     ) == 1:
                                _UpperCAmelCase          =   value[0]
                            _UpperCAmelCase          =   value
                    else:  # leaf nodes
                        _UpperCAmelCase          =   []
                        for leaf in content.split(r"""<sep/>"""     ):
                            _UpperCAmelCase          =   leaf.strip()
                            if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                _UpperCAmelCase          =   leaf[1:-2]  # for categorical special tokens
                            output[key].append(__UpperCamelCase     )
                        if len(output[key]     ) == 1:
                            _UpperCAmelCase          =   output[key][0]

                _UpperCAmelCase          =   tokens[tokens.find(__UpperCamelCase     ) + len(__UpperCamelCase     ) :].strip()
                if tokens[:6] == r"<sep/>":  # non-leaf nodes
                    return [output] + self.tokenajson(tokens[6:]     ,					is_inner_value=__UpperCamelCase     ,					added_vocab=__UpperCamelCase     )

        if len(__UpperCamelCase     ):
            return [output] if is_inner_value else output
        else:
            return [] if is_inner_value else {"text_sequence": tokens}







    @property
    def      _snake_case					(			self			:       Tuple     )		->Optional[int]:






        '''simple docstring'''






        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,					__UpperCamelCase     ,					)
        return self.image_processor_class







    @property
    def      _snake_case					(			self			:       List[str]     )		->Dict:






        '''simple docstring'''






        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,					__UpperCamelCase     ,					)
        return self.image_processor