"""simple docstring"""





import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


lowerCAmelCase_     =	'''\
@inproceedings{popovic-2015-chrf,
    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W15-3049",
    doi = "10.18653/v1/W15-3049",
    pages = "392--395",
}
@inproceedings{popovic-2017-chrf,
    title = "chr{F}++: words helping character n-grams",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-4770",
    doi = "10.18653/v1/W17-4770",
    pages = "612--618",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

lowerCAmelCase_     =	'''\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
'''

lowerCAmelCase_     =	'''
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    \'score\' (float): The chrF (chrF++) score,
    \'char_order\' (int): The character n-gram order,
    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    \'beta\' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}
'''




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,       _KWARGS_DESCRIPTION )
class  _snake_case		(    datasets.Metric ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											if version.parse(scb.__version__) < version.parse("""1.4.12"""):
																		raise ImportWarning(
																		    """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
																		    """You can install it with `pip install \"sacrebleu>=1.4.12\"`.""")
											return datasets.MetricInfo(
											    description=_DESCRIPTION      ,    citation=_CITATION      ,    homepage="""https://github.com/mjpost/sacreBLEU#chrf--chrf"""      ,    inputs_description=_KWARGS_DESCRIPTION      ,    features=datasets.Features(
											        {
											            """predictions""": datasets.Value("""string"""      ,    id="""sequence"""),
											            """references""": datasets.Sequence(datasets.Value("""string"""      ,    id="""sequence""")      ,    id="""references"""),
											        })      ,    codebase_urls=["""https://github.com/mjpost/sacreBLEU#chrf--chrf"""]      ,    reference_urls=[
											        """https://github.com/m-popovic/chrF""",
											    ]      ,    )





				def 		_lowerCAmelCase		(      self							:							List[str]      ,    _A							:							str      ,    _A							:							List[str]      ,    _A							:							int = CHRF.CHAR_ORDER      ,    _A							:							int = CHRF.WORD_ORDER      ,    _A							:							int = CHRF.BETA      ,    _A							:							bool = False      ,    _A							:							bool = False      ,    _A							:							bool = False      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    len(references[0])
											if any(len(_A) != references_per_prediction for refs in references):
																		raise ValueError("""Sacrebleu requires the same number of references for each prediction""")
											_SCREAMING_SNAKE_CASE		:						Any			  =    [[refs[i] for refs in references] for i in range(_A)]

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    CHRF(_A      ,    _A      ,    _A      ,    _A      ,    _A      ,    _A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    sb_chrf.corpus_score(_A      ,    _A)

											return {
											    "score": output.score,
											    "char_order": output.char_order,
											    "word_order": output.word_order,
											    "beta": output.beta,
											}



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



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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    MusicgenDecoderConfig:
							if checkpoint == "small":
														# default config values
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    1_024
														_SCREAMING_SNAKE_CASE		:						str			  =    24
														_SCREAMING_SNAKE_CASE		:						Any			  =    16
							elif checkpoint == "medium":
														_SCREAMING_SNAKE_CASE		:						Dict			  =    1_536
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    48
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    24
							elif checkpoint == "large":
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    2_048
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    48
														_SCREAMING_SNAKE_CASE		:						str			  =    32
							else:
														raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""  )
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE="cpu"  )->    str:
							_SCREAMING_SNAKE_CASE		:						str			  =    MusicGen.get_pretrained(__SCREAMING_SNAKE_CASE     ,	device=__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    decoder_config_from_checkpoint(__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    fairseq_model.lm.state_dict()
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[str]			  =    rename_state_dict(
							    __SCREAMING_SNAKE_CASE     ,	hidden_size=decoder_config.hidden_size  )

							_SCREAMING_SNAKE_CASE		:						Tuple			  =    TaEncoderModel.from_pretrained("""t5-base"""  )
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    EncodecModel.from_pretrained("""facebook/encodec_32khz"""  )
							_SCREAMING_SNAKE_CASE		:						str			  =    MusicgenForCausalLM(__SCREAMING_SNAKE_CASE  ).eval()

							# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						str			  =    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
							_SCREAMING_SNAKE_CASE		:						Dict			  =    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
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    torch.arange(0     ,	8     ,	dtype=torch.long  ).reshape(2     ,	-1  )
							_SCREAMING_SNAKE_CASE		:						Dict			  =    input_ids.reshape(2 * 4     ,	-1  )

							with torch.no_grad():
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    model(input_ids=__SCREAMING_SNAKE_CASE     ,	decoder_input_ids=__SCREAMING_SNAKE_CASE  ).logits

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

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

							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    MusicgenProcessor(feature_extractor=__SCREAMING_SNAKE_CASE     ,	tokenizer=__SCREAMING_SNAKE_CASE  )

							# set the appropriate bos/pad token ids
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    2_048
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    2_048

							# set other default generation config params
							_SCREAMING_SNAKE_CASE		:						Any			  =    int(30 * audio_encoder.config.frame_rate  )
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    True
							_SCREAMING_SNAKE_CASE		:						int			  =    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__":
			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)


