






"""simple docstring"""





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


UpperCamelCase					=     logging.get_logger(__name__)

UpperCamelCase					=     {
    """facebook/musicgen-small""": """https://huggingface.co/facebook/musicgen-small/resolve/main/config.json""",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							"""musicgen_decoder"""
   UpperCamelCase__     	=							["""past_key_values"""]





   def __init__(       self					, UpperCAmelCase_=2_048					, UpperCAmelCase_=2_048					, UpperCAmelCase_=24					, UpperCAmelCase_=4_096					, UpperCAmelCase_=16					, UpperCAmelCase_=0.0					, UpperCAmelCase_=True					, UpperCAmelCase_="gelu"					, UpperCAmelCase_=1_024					, UpperCAmelCase_=0.1					, UpperCAmelCase_=0.0					, UpperCAmelCase_=0.0					, UpperCAmelCase_=0.02					, UpperCAmelCase_=False					, UpperCAmelCase_=4					, UpperCAmelCase_=2_048					, UpperCAmelCase_=2_048					, UpperCAmelCase_=None					, UpperCAmelCase_=False					, **UpperCAmelCase_					, )			->   int:




          '''simple docstring'''




          lowercase__:							Dict         =     vocab_size
          lowercase__:							List[Any]         =     max_position_embeddings
          lowercase__:							Optional[int]         =     hidden_size
          lowercase__:							List[Any]         =     ffn_dim
          lowercase__:							Tuple         =     num_hidden_layers
          lowercase__:							Tuple         =     num_attention_heads
          lowercase__:							List[Any]         =     dropout
          lowercase__:							Optional[int]         =     attention_dropout
          lowercase__:							Tuple         =     activation_dropout
          lowercase__:							Optional[int]         =     activation_function
          lowercase__:							int         =     initializer_factor
          lowercase__:							str         =     layerdrop
          lowercase__:							Optional[int]         =     use_cache
          lowercase__:							List[Any]         =     scale_embedding  # scale factor will be sqrt(d_model) if True
          lowercase__:							Tuple         =     num_codebooks
          super().__init__(
              pad_token_id=UpperCAmelCase_					, bos_token_id=UpperCAmelCase_					, eos_token_id=UpperCAmelCase_					, tie_word_embeddings=UpperCAmelCase_					, **UpperCAmelCase_					, )







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							"""musicgen"""
   UpperCamelCase__     	=							True





   def __init__(       self					, **UpperCAmelCase_)			->   Any:




          '''simple docstring'''




          super().__init__(**UpperCAmelCase_)
          if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
                 raise ValueError("Config has to be initialized with text_encoder, audio_encoder and decoder config")

          lowercase__:							Tuple         =     kwargs.pop("text_encoder")
          lowercase__:							List[Any]         =     text_encoder_config.pop("model_type")

          lowercase__:							Optional[int]         =     kwargs.pop("audio_encoder")
          lowercase__:							Dict         =     audio_encoder_config.pop("model_type")

          lowercase__:							Optional[Any]         =     kwargs.pop("decoder")

          lowercase__:							Tuple         =     AutoConfig.for_model(UpperCAmelCase_					, **UpperCAmelCase_)
          lowercase__:							Tuple         =     AutoConfig.for_model(UpperCAmelCase_					, **UpperCAmelCase_)
          lowercase__:							List[str]         =     MusicgenDecoderConfig(**UpperCAmelCase_)
          lowercase__:							int         =     True





   @classmethod
   def __lowercase							(       cls					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, **UpperCAmelCase_					, )			->   List[Any]:




          '''simple docstring'''




          return cls(
              text_encoder=text_encoder_config.to_dict()					, audio_encoder=audio_encoder_config.to_dict()					, decoder=decoder_config.to_dict()					, **UpperCAmelCase_					, )





   def __lowercase							(       self)			->   str:




          '''simple docstring'''




          lowercase__:							Optional[Any]         =     copy.deepcopy(self.__dict__)
          lowercase__:							Tuple         =     self.text_encoder.to_dict()
          lowercase__:							str         =     self.audio_encoder.to_dict()
          lowercase__:							int         =     self.decoder.to_dict()
          lowercase__:							str         =     self.__class__.model_type
          return output









'''simple docstring'''

import sacrebleu as scb
from packaging import version

import datasets


lowercase__            =     "\\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"

lowercase__            =     "\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n"

lowercase__            =     "\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:\n        - `'none'`: no smoothing\n        - `'floor'`: increment zero counts\n        - `'add-k'`: increment num/denom by k for n>1\n        - `'exp'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:\n        - `'none'`: No tokenization.\n        - `'zh'`: Chinese tokenization.\n        - `'13a'`: mimics the `mteval-v13a` script from Moses.\n        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `'char'`: Language-agnostic character-level tokenization.\n        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    'score': BLEU score,\n    'counts': Counts,\n    'totals': Totals,\n    'precisions': Precisions,\n    'bp': Brevity penalty,\n    'sys_len': predictions length,\n    'ref_len': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = [\"hello there general kenobi\",\n        ...                 \"on our way to ankh morpork\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"],\n        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        39.8\n"




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






     '''simple docstring'''



     def   UpperCAmelCase_			(						self					:	Tuple			)		->      Union[str, Any]:
      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'	,     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']	,     reference_urls=[
              'https://github.com/mjpost/sacreBLEU',
              'https://en.wikipedia.org/wiki/BLEU',
              'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
          ]	,     )




     def   UpperCAmelCase_			(						self					:	Union[str, Any]	,     lowercase_					:	Union[str, Any]	,     lowercase_					:	int	,     lowercase_					:	List[Any]="exp"	,     lowercase_					:	Optional[int]=None	,     lowercase_					:	List[str]=False	,     lowercase_					:	Tuple=False	,     lowercase_					:	int=None	,     lowercase_					:	str=False	,     )		->      Dict:
      UpperCAmelCase			:      Union[str, Any]		   =					len(references[0]			)
      if any(len(lowercase_			) != 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(lowercase_			)]
      UpperCAmelCase			:      List[Any]		   =					scb.corpus_bleu(
          lowercase_	,     lowercase_	,     smooth_method=lowercase_	,     smooth_value=lowercase_	,     force=lowercase_	,     lowercase=lowercase_	,     use_effective_order=lowercase_	,     **({'tokenize': tokenize} if tokenize else {})	,     )
      UpperCAmelCase			:      Union[str, Any]		   =					{
          'score': output.score,
          'counts': output.counts,
          'totals': output.totals,
          'precisions': output.precisions,
          'bp': output.bp,
          'sys_len': output.sys_len,
          'ref_len': output.ref_len,
      }
      return output_dict




