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()
SCREAMING_SNAKE_CASE		:     Dict         =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	["model.decoder.embed_positions.weights"]






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






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






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str			):
						if checkpoint == "small":
												# default config values
												UpperCamelCase_		:							List[Any]  =		1024
												UpperCamelCase_		:							Tuple  =		24
												UpperCamelCase_		:							List[str]  =		16
						elif checkpoint == "medium":
												UpperCamelCase_		:							Any  =		1536
												UpperCamelCase_		:							Tuple  =		48
												UpperCamelCase_		:							List[str]  =		24
						elif checkpoint == "large":
												UpperCamelCase_		:							Dict  =		2048
												UpperCamelCase_		:							int  =		48
												UpperCamelCase_		:							Union[str, Any]  =		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     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Any		, _SCREAMING_SNAKE_CASE     :				Tuple=None		, _SCREAMING_SNAKE_CASE     :				Tuple=None		, _SCREAMING_SNAKE_CASE     :				int="cpu"			):
						UpperCamelCase_		:							Any  =		MusicGen.get_pretrained(_SCREAMING_SNAKE_CASE		, device=_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Optional[int]  =		decoder_config_from_checkpoint(_SCREAMING_SNAKE_CASE			)

						UpperCamelCase_		:							Tuple  =		fairseq_model.lm.state_dict()
						UpperCamelCase_,UpperCamelCase_		:							str  =		rename_state_dict(
						    _SCREAMING_SNAKE_CASE		, hidden_size=decoder_config.hidden_size			)

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

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

						with torch.no_grad():
												UpperCamelCase_		:							Tuple  =		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_		:							int  =		AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""		, padding_side="""left"""			)

						UpperCamelCase_		:							Optional[Any]  =		MusicgenProcessor(feature_extractor=_SCREAMING_SNAKE_CASE		, tokenizer=_SCREAMING_SNAKE_CASE			)

						# set the appropriate bos/pad token ids
						UpperCamelCase_		:							List[Any]  =		2048
						UpperCamelCase_		:							Optional[Any]  =		2048

						# set other default generation config params
						UpperCamelCase_		:							Tuple  =		int(30 * audio_encoder.config.frame_rate			)
						UpperCamelCase_		:							List[str]  =		True
						UpperCamelCase_		:							Optional[Any]  =		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__":
	SCREAMING_SNAKE_CASE		:     int         =	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."
	)

	SCREAMING_SNAKE_CASE		:     Dict         =	parser.parse_args()
	convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)







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

import datasets


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

SCREAMING_SNAKE_CASE		:     Tuple         =	"\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"

SCREAMING_SNAKE_CASE		:     Optional[int]         =	"\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> 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.\"]\n        >>> 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.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> 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.\"]\n        >>> 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.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> 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.\"]\n        >>> 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.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"

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





						def        A_							(self						)				->				Dict:
												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        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase = CHRF.CHAR_ORDER			,     __UpperCamelCase = CHRF.WORD_ORDER			,     __UpperCamelCase = CHRF.BETA			,     __UpperCamelCase = False			,     __UpperCamelCase = False			,     __UpperCamelCase = False			,     )				->				List[Any]:
												UpperCamelCase_		:							List[str]  =		len(references[0]						)
												if any(len(__UpperCamelCase						) != references_per_prediction for refs in references						):
																		raise ValueError("""Sacrebleu requires the same number of references for each prediction"""						)
												UpperCamelCase_		:							Optional[Any]  =		[[refs[i] for refs in references] for i in range(__UpperCamelCase						)]

												UpperCamelCase_		:							List[Any]  =		CHRF(__UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						)
												UpperCamelCase_		:							int  =		sb_chrf.corpus_score(__UpperCamelCase			,     __UpperCamelCase						)

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






