





import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


UpperCAmelCase_           =	logging.get_logger(__name__)

UpperCAmelCase_           =	{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_           =	{
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
UpperCAmelCase_           =	{
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
UpperCAmelCase_           =	{
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase_           =	{
    "facebook/dpr-ctx_encoder-single-nq-base": 5_12,
    "facebook/dpr-ctx_encoder-multiset-base": 5_12,
}
UpperCAmelCase_           =	{
    "facebook/dpr-question_encoder-single-nq-base": 5_12,
    "facebook/dpr-question_encoder-multiset-base": 5_12,
}
UpperCAmelCase_           =	{
    "facebook/dpr-reader-single-nq-base": 5_12,
    "facebook/dpr-reader-multiset-base": 5_12,
}


UpperCAmelCase_           =	{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
UpperCAmelCase_           =	{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
UpperCAmelCase_           =	{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}




class 			__UpperCamelCase    (							A__    ):
     __A	:  Optional[int]							  =   VOCAB_FILES_NAMES
     __A	:  int							  =   CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     __A	:  List[str]							  =   CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A	:  Optional[int]							  =   CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION




class 			__UpperCamelCase    (							A__    ):
     __A	:  Dict							  =   VOCAB_FILES_NAMES
     __A	:  List[str]							  =   QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     __A	:  Any							  =   QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A	:  str							  =   QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION




UpperCAmelCase_           =	collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

UpperCAmelCase_           =	collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


UpperCAmelCase_           =	r"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "




@add_start_docstrings(A__    )
class 			__UpperCamelCase    :





     def __call__(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        if titles is None and texts is None:
           return super().__call__(
               _UpperCamelCase				,							padding=_UpperCamelCase				,							truncation=_UpperCamelCase				,							max_length=_UpperCamelCase				,							return_tensors=_UpperCamelCase				,							return_attention_mask=_UpperCamelCase				,							**_UpperCamelCase				,							)
        elif titles is None or texts is None:
           _UpperCAmelCase           =   titles if texts is None else texts
           return super().__call__(
               _UpperCamelCase				,							_UpperCamelCase				,							padding=_UpperCamelCase				,							truncation=_UpperCamelCase				,							max_length=_UpperCamelCase				,							return_tensors=_UpperCamelCase				,							return_attention_mask=_UpperCamelCase				,							**_UpperCamelCase				,							)
        _UpperCAmelCase           =   titles if not isinstance(_UpperCamelCase				,							_UpperCamelCase					) else [titles]
        _UpperCAmelCase           =   texts if not isinstance(_UpperCamelCase				,							_UpperCamelCase					) else [texts]
        _UpperCAmelCase           =   len(_UpperCamelCase					)
        _UpperCAmelCase           =   questions if not isinstance(_UpperCamelCase				,							_UpperCamelCase					) else [questions] * n_passages
        if len(_UpperCamelCase					) != len(_UpperCamelCase					):
           raise ValueError(
               f'''There should be as many titles than texts but got {len(_UpperCamelCase					)} titles and {len(_UpperCamelCase					)} texts.'''					)
        _UpperCAmelCase           =   super().__call__(_UpperCamelCase				,							_UpperCamelCase				,							padding=_UpperCamelCase				,							truncation=_UpperCamelCase					)['''input_ids''']
        _UpperCAmelCase           =   super().__call__(_UpperCamelCase				,							add_special_tokens=_UpperCamelCase				,							padding=_UpperCamelCase				,							truncation=_UpperCamelCase					)['''input_ids''']
        _UpperCAmelCase           =   {
            '''input_ids''': [
                (encoded_question_and_title + encoded_text)[:max_length]
                if max_length is not None and truncation
                else encoded_question_and_title + encoded_text
                for encoded_question_and_title, encoded_text in zip(_UpperCamelCase				,							_UpperCamelCase					)
            ]
        }
        if return_attention_mask is not False:
           _UpperCAmelCase           =   []
           for input_ids in encoded_inputs["input_ids"]:
              attention_mask.append([int(input_id != self.pad_token_id					) for input_id in input_ids]					)
           _UpperCAmelCase           =   attention_mask
        return self.pad(_UpperCamelCase				,							padding=_UpperCamelCase				,							max_length=_UpperCamelCase				,							return_tensors=_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = 16				,							_UpperCamelCase = 64				,							_UpperCamelCase = 4				,							):
        _UpperCAmelCase           =   reader_input['''input_ids''']
        _UpperCAmelCase       ,      _UpperCAmelCase       ,      _UpperCAmelCase           =   reader_output[:3]
        _UpperCAmelCase           =   len(_UpperCamelCase					)
        _UpperCAmelCase           =   sorted(range(_UpperCamelCase					)				,							reverse=_UpperCamelCase				,							key=relevance_logits.__getitem__					)
        _UpperCAmelCase           =   []
        for doc_id in sorted_docs:
           _UpperCAmelCase           =   list(input_ids[doc_id]					)
           # assuming question & title information is at the beginning of the sequence
           _UpperCAmelCase           =   sequence_ids.index(self.sep_token_id				,							2					) + 1  # second sep id
           if sequence_ids[-1] == self.pad_token_id:
              _UpperCAmelCase           =   sequence_ids.index(self.pad_token_id					)
           else:
              _UpperCAmelCase           =   len(_UpperCamelCase					)

           _UpperCAmelCase           =   self._get_best_spans(
               start_logits=start_logits[doc_id][passage_offset:sequence_len]				,							end_logits=end_logits[doc_id][passage_offset:sequence_len]				,							max_answer_length=_UpperCamelCase				,							top_spans=_UpperCamelCase				,							)
           for start_index, end_index in best_spans:
              start_index += passage_offset
              end_index += passage_offset
              nbest_spans_predictions.append(
                  DPRSpanPrediction(
                      span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]				,							relevance_score=relevance_logits[doc_id]				,							doc_id=_UpperCamelCase				,							start_index=_UpperCamelCase				,							end_index=_UpperCamelCase				,							text=self.decode(sequence_ids[start_index : end_index + 1]					)				,							)					)
           if len(_UpperCamelCase					) >= num_spans:
              break
        return nbest_spans_predictions[:num_spans]





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							):
        _UpperCAmelCase           =   []
        for start_index, start_score in enumerate(_UpperCamelCase					):
           for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]					):
              scores.append(((start_index, start_index + answer_length), start_score + end_score)					)
        _UpperCAmelCase           =   sorted(_UpperCamelCase				,							key=lambda _UpperCamelCase					: x[1]				,							reverse=_UpperCamelCase					)
        _UpperCAmelCase           =   []
        for (start_index, end_index), score in scores:
           if start_index > end_index:
              raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''					)
           _UpperCAmelCase           =   end_index - start_index + 1
           if length > max_answer_length:
              raise ValueError(f'''Span is too long: {length} > {max_answer_length}'''					)
           if any(
               start_index <= prev_start_index <= prev_end_index <= end_index
               or prev_start_index <= start_index <= end_index <= prev_end_index
               for (prev_start_index, prev_end_index) in chosen_span_intervals					):
              continue
           chosen_span_intervals.append((start_index, end_index)					)

           if len(_UpperCamelCase					) == top_spans:
              break
        return chosen_span_intervals




@add_end_docstrings(A__    )
class 			__UpperCamelCase    (							A__	, A__    ):
     __A	:  Dict							  =   VOCAB_FILES_NAMES
     __A	:  Dict							  =   READER_PRETRAINED_VOCAB_FILES_MAP
     __A	:  int							  =   READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A	:  Optional[Any]							  =   READER_PRETRAINED_INIT_CONFIGURATION
     __A	:  Tuple							  =   ["""input_ids""", """attention_mask"""]






"""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)



