
'''simple docstring'''





import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


A           =   logging.get_logger(__name__)


A           =   {
    "suno/bark-small": "https://huggingface.co/suno/bark-small/resolve/main/config.json",
    "suno/bark": "https://huggingface.co/suno/bark/resolve/main/config.json",
}

A           =   "\n    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model\n    according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)\n    architecture.\n\n    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n    documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        block_size (`int`, *optional*, defaults to 1024):\n            The maximum sequence length that this model might ever be used with. Typically set this to something large\n            just in case (e.g., 512 or 1024 or 2048).\n        input_vocab_size (`int`, *optional*, defaults to 10_048):\n            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the\n            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with\n            regards to the chosen sub-model.\n        output_vocab_size (`int`, *optional*, defaults to 10_048):\n            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented\n            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought\n            with regards to the chosen sub-model.\n        num_layers (`int`, *optional*, defaults to 12):\n            Number of hidden layers in the given sub-model.\n        num_heads (`int`, *optional*, defaults to 12):\n            Number of attention heads for each attention layer in the Transformer architecture.\n        hidden_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the \"intermediate\" (often named feed-forward) layer in the architecture.\n        dropout (`float`, *optional*, defaults to 0.0):\n            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.\n        bias (`bool`, *optional*, defaults to `True`):\n            Whether or not to use bias in the linear layers and layer norm layers.\n        initializer_range (`float`, *optional*, defaults to 0.02):\n            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n"



class  lowerCAmelCase__   (       UpperCamelCase       ):



  '''simple docstring'''






  _UpperCamelCase						= 'bark_module'
  _UpperCamelCase						= ['past_key_values']

  _UpperCamelCase						= {
      'num_attention_heads': 'num_heads',
      'num_hidden_layers': 'num_layers',
      'vocab_size': 'input_vocab_size',
      'window_size': 'block_size',
  }

  def __init__(self  : List[str]	, lowerCAmelCase_  : Optional[int]=10_24	, lowerCAmelCase_  : Dict=1_00_48	, lowerCAmelCase_  : Any=1_00_48	, lowerCAmelCase_  : List[str]=12	, lowerCAmelCase_  : Optional[Any]=12	, lowerCAmelCase_  : Optional[Any]=7_68	, lowerCAmelCase_  : str=0.0	, lowerCAmelCase_  : Dict=True	, lowerCAmelCase_  : Union[str, Any]=0.02	, lowerCAmelCase_  : Any=True	, **lowerCAmelCase_  : str	, )   ->       List[Any]:
        snake_case_						       =							block_size
        snake_case_						       =							input_vocab_size
        snake_case_						       =							output_vocab_size
        snake_case_						       =							num_layers
        snake_case_						       =							num_heads
        snake_case_						       =							hidden_size
        snake_case_						       =							dropout
        snake_case_						       =							bias
        snake_case_						       =							use_cache
        snake_case_						       =							initializer_range

        super().__init__(**lowerCAmelCase_    )


  @classmethod
  def 		_lowerCamelCase	(cls  : Optional[Any]	, lowerCAmelCase_  : Union[str, os.PathLike]	, lowerCAmelCase_  : Optional[Union[str, os.PathLike]] = None	, lowerCAmelCase_  : bool = False	, lowerCAmelCase_  : bool = False	, lowerCAmelCase_  : Optional[Union[str, bool]] = None	, lowerCAmelCase_  : str = "main"	, **lowerCAmelCase_  : Tuple	, )   ->       "PretrainedConfig":
        snake_case_						       =							cache_dir
        snake_case_						       =							force_download
        snake_case_						       =							local_files_only
        snake_case_						       =							revision

        cls._set_token_in_kwargs(lowerCAmelCase_	, lowerCAmelCase_    )

        snake_case_       ,	snake_case_						       =							cls.get_config_dict(lowerCAmelCase_	, **lowerCAmelCase_    )

        # get the config dict if we are loading from Bark
        if config_dict.get("model_type"    ) == "bark":
              snake_case_						       =							config_dict[f'''{cls.model_type}_config''']

        if "model_type" in config_dict and hasattr(cls	, "model_type"    ) and config_dict["model_type"] != cls.model_type:
              logger.warning(
                  f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                  f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''    )

        return cls.from_dict(lowerCAmelCase_	, **lowerCAmelCase_    )



@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='BarkSemanticConfig'					,							model='BarkSemanticModel'       )					,							'\n    Example:\n\n    ```python\n    >>> from transformers import BarkSemanticConfig, BarkSemanticModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkSemanticConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkSemanticModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```'					,							)
class  lowerCAmelCase__   (       UpperCamelCase       ):



  '''simple docstring'''






  _UpperCamelCase						= 'semantic'



@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='BarkCoarseConfig'					,							model='BarkCoarseModel'       )					,							'\n    Example:\n\n    ```python\n    >>> from transformers import BarkCoarseConfig, BarkCoarseModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkCoarseConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkCoarseModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```'					,							)
class  lowerCAmelCase__   (       UpperCamelCase       ):



  '''simple docstring'''






  _UpperCamelCase						= 'coarse_acoustics'



@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='BarkFineConfig'					,							model='BarkFineModel'       )					,							'\n        n_codes_total (`int`, *optional*, defaults to 8):\n            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.\n        n_codes_given (`int`, *optional*, defaults to 1):\n            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics\n            sub-models.\n    Example:\n\n    ```python\n    >>> from transformers import BarkFineConfig, BarkFineModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkFineConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkFineModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```'					,							)
class  lowerCAmelCase__   (       UpperCamelCase       ):



  '''simple docstring'''






  _UpperCamelCase						= 'fine_acoustics'

  def __init__(self  : Tuple	, lowerCAmelCase_  : int=True	, lowerCAmelCase_  : Optional[Any]=8	, lowerCAmelCase_  : Optional[int]=1	, **lowerCAmelCase_  : List[str]    )   ->       Optional[Any]:
        snake_case_						       =							n_codes_total
        snake_case_						       =							n_codes_given

        super().__init__(tie_word_embeddings=lowerCAmelCase_	, **lowerCAmelCase_    )



class  lowerCAmelCase__   (       UpperCamelCase       ):



  '''simple docstring'''






  _UpperCamelCase						= 'bark'
  _UpperCamelCase						= True

  def __init__(self  : Optional[int]	, lowerCAmelCase_  : Dict = None	, lowerCAmelCase_  : Dict = None	, lowerCAmelCase_  : Dict = None	, lowerCAmelCase_  : Dict = None	, lowerCAmelCase_  : Union[str, Any]=0.02	, **lowerCAmelCase_  : Tuple	, )   ->       List[str]:
        if semantic_config is None:
              snake_case_						       =							{}
              logger.info("semantic_config is None. initializing the semantic model with default values."    )

        if coarse_acoustics_config is None:
              snake_case_						       =							{}
              logger.info("coarse_acoustics_config is None. initializing the coarse model with default values."    )

        if fine_acoustics_config is None:
              snake_case_						       =							{}
              logger.info("fine_acoustics_config is None. initializing the fine model with default values."    )

        if codec_config is None:
              snake_case_						       =							{}
              logger.info("codec_config is None. initializing the codec model with default values."    )

        snake_case_						       =							BarkSemanticConfig(**lowerCAmelCase_    )
        snake_case_						       =							BarkCoarseConfig(**lowerCAmelCase_    )
        snake_case_						       =							BarkFineConfig(**lowerCAmelCase_    )
        snake_case_						       =							AutoConfig.for_model(**lowerCAmelCase_    )

        snake_case_						       =							initializer_range

        super().__init__(**lowerCAmelCase_    )

  @classmethod
  def 		_lowerCamelCase	(cls  : Dict	, lowerCAmelCase_  : BarkSemanticConfig	, lowerCAmelCase_  : BarkCoarseConfig	, lowerCAmelCase_  : BarkFineConfig	, lowerCAmelCase_  : AutoConfig	, **lowerCAmelCase_  : str	, )   ->       Dict:
        return cls(
            semantic_config=semantic_config.to_dict()	, coarse_acoustics_config=coarse_acoustics_config.to_dict()	, fine_acoustics_config=fine_acoustics_config.to_dict()	, codec_config=codec_config.to_dict()	, **lowerCAmelCase_	, )


  def 		_lowerCamelCase	(self  : Tuple    )   ->       List[Any]:
        snake_case_						       =							copy.deepcopy(self.__dict__    )

        snake_case_						       =							self.semantic_config.to_dict()
        snake_case_						       =							self.coarse_acoustics_config.to_dict()
        snake_case_						       =							self.fine_acoustics_config.to_dict()
        snake_case_						       =							self.codec_config.to_dict()

        snake_case_						       =							self.__class__.model_type
        return output



'''simple docstring'''





import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


A           =   {0: "null", 1: "short", 2: "long", 3: "yes", 4: "no"}
A           =   set("".join(["‘", "’", "´", "`", ".", ",", "-", "\""]))
def   snake_case_       (							_a     :							Any ,					_a     :							Union[str, Any]=0 ,					_a     :							Optional[int]=None			):
      return [" ".join(x.split(" "			)[begin:end]			) for x in answers if len(x.split(" "			)			) > 1]
def   snake_case_       (							_a     :							Optional[int] ,					_a     :							str=False			):
      if make_sub_answers:
            # if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
            # *e.g.* if the correct answer contains a prefix such as "the", or "a"
            snake_case_						       =							(
                given_answers + get_sub_answers(_a ,					begin=1			) + get_sub_answers(_a ,					end=-1			)
            )
      snake_case_						       =							[]
      for answer in given_answers:
            snake_case_						       =							answer.replace("_" ,					" "			).lower()
            snake_case_						       =							"".join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else " " for c in alias			)
            answers.append(" ".join(alias.split()			).strip()			)
      return set(_a			)
def   snake_case_       (							_a     :							Optional[Any] ,					_a     :							int ,					_a     :							Optional[Any]=1 ,					_a     :							List[Any]=100			):
      snake_case_       ,	snake_case_						       =							jax.lax.top_k(_a ,					_a			)
      snake_case_       ,	snake_case_						       =							jax.lax.top_k(_a ,					_a			)

      snake_case_						       =							best_end_idx[:, None] - best_start_idx[None, :]
      snake_case_						       =							jnp.logical_or(widths < 0 ,					widths > max_size			)
      snake_case_						       =							(best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
      snake_case_						       =							jnp.argmax(_a			).item()

      return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]
def   snake_case_       (							_a     :							Any			):
      snake_case_						       =							sample["question"]["text"]
      snake_case_						       =							sample["document"]["tokens"]["token"]
      snake_case_						       =							sample["document"]["tokens"]["is_html"]
      snake_case_						       =							sample["annotations"]["long_answer"]
      snake_case_						       =							sample["annotations"]["short_answers"]

      snake_case_						       =							" ".join([context[i] for i in range(len(_a			)			) if not is_html[i]]			)

      # 0 - No ; 1 - Yes
      for answer in sample["annotations"]["yes_no_answer"]:
            if answer == 0 or answer == 1:
                  return {
                      "question": question,
                      "context": context_string,
                      "short": [],
                      "long": [],
                      "category": "no" if answer == 0 else "yes",
                  }

      snake_case_						       =							[]
      for s in short_answers:
            short_targets.extend(s["text"]			)
      snake_case_						       =							list(set(_a			)			)

      snake_case_						       =							[]
      for s in long_answers:
            if s["start_token"] == -1:
                  continue
            snake_case_						       =							context[s["start_token"] : s["end_token"]]
            snake_case_						       =							is_html[s["start_token"] : s["end_token"]]
            snake_case_						       =							" ".join([answer[i] for i in range(len(_a			)			) if not html[i]]			)
            if new_answer not in long_targets:
                  long_targets.append(_a			)

      snake_case_						       =							"long_short" if len(short_targets + long_targets			) > 0 else "null"

      return {
          "question": question,
          "context": context_string,
          "short": short_targets,
          "long": long_targets,
          "category": category,
      }
def   snake_case_       (							):
      snake_case_						       =							load_from_disk("natural-questions-validation"			)
      snake_case_						       =							dataset.map(_a			).remove_columns(["annotations", "document", "id"]			)
      print(_a			)

      snake_case_						       =							dataset.filter(lambda _a			: (len(x["question"]			) + len(x["context"]			)) < 4 * 4_096			)
      snake_case_						       =							short_validation_dataset.filter(lambda _a			: x["category"] != "null"			)
      short_validation_dataset

      snake_case_						       =							"vasudevgupta/flax-bigbird-natural-questions"
      snake_case_						       =							FlaxBigBirdForNaturalQuestions.from_pretrained(_a			)
      snake_case_						       =							BigBirdTokenizerFast.from_pretrained(_a			)

      @jax.jit
      def forward(*_a     :							List[Any] ,					**_a     :							int			):
            snake_case_       ,	snake_case_       ,	snake_case_						       =							model(*_a ,					**_a			)
            return start_logits, end_logits, jnp.argmax(_a ,					axis=-1			)

      def evaluate(_a     :							List[Any]			):
            # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
            snake_case_						       =							tokenizer(
                example["question"] ,					example["context"] ,					return_tensors="np" ,					max_length=4_096 ,					padding="max_length" ,					truncation=_a ,					)

            snake_case_       ,	snake_case_       ,	snake_case_						       =							forward(**_a			)

            snake_case_						       =							CATEGORY_MAPPING[category.item()]

            snake_case_						       =							example["long"] + example["short"]
            if example["category"] in ["yes", "no", "null"]:
                  snake_case_						       =							[example["category"]]
            snake_case_						       =							example["category"] != "null"
            # Now target can be: "yes", "no", "null", "list of long & short answers"

            if predicted_category in ["yes", "no", "null"]:
                  snake_case_						       =							[predicted_category]
                  snake_case_						       =							example["output"] == example["targets"]
                  snake_case_						       =							predicted_category != "null"
                  return example

            snake_case_						       =							38 if predicted_category == "short" else 1_024
            snake_case_       ,	snake_case_						       =							get_best_valid_start_end_idx(
                start_scores[0] ,					end_scores[0] ,					top_k=8 ,					max_size=_a			)

            snake_case_						       =							inputs["input_ids"][0].tolist()
            snake_case_						       =							[tokenizer.decode(input_ids[start_score : end_score + 1]			)]

            snake_case_						       =							expand_to_aliases(example["targets"] ,					make_sub_answers=_a			)
            snake_case_						       =							expand_to_aliases(example["output"]			)

            # some preprocessing to both prediction and answer
            snake_case_						       =							{"".join(a.split()			) for a in answers}
            snake_case_						       =							{"".join(p.split()			) for p in predictions}
            snake_case_						       =							{s for s in predictions if s not in ["``", "''", "`", "'"]}

            # if there is a common element, it's a exact match
            snake_case_						       =							len(list(answers & predictions			)			) > 0
            snake_case_						       =							predicted_category != "null" and len(_a			) > 0

            return example

      snake_case_						       =							short_validation_dataset.map(_a			)

      snake_case_						       =							len(_a			)
      snake_case_						       =							len(short_validation_dataset.filter(lambda _a			: x["match"] == 1			)			)
      print("EM score:" ,					(matched / total) * 100 ,					"%"			)


if __name__ == "__main__":
     main()

