




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

from transformers import BigBirdTokenizerFast


_lowerCamelCase			 =						{0: 'null', 1: 'short', 2: 'long', 3: 'yes', 4: 'no'}
_lowerCamelCase			 =						set(''.join(['‘', '’', '´', '`', '.', ',', '-', '"']))




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Any       ,     __UpperCamelCase			:						Dict=0       ,     __UpperCamelCase			:						Optional[int]=None      )  ->							Any:
    return [" ".join(x.split(''' '''      )[begin:end]      ) for x in answers if len(x.split(''' '''      )      ) > 1]




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Dict       ,     __UpperCamelCase			:						Optional[int]=False      )  ->							Optional[int]:
    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"
        UpperCAmelCase_							 =     (
            given_answers + get_sub_answers(__UpperCamelCase       ,     begin=1      ) + get_sub_answers(__UpperCamelCase       ,     end=-1      )
        )
    UpperCAmelCase_							 =     []
    for answer in given_answers:
        UpperCAmelCase_							 =     answer.replace('''_'''       ,     ''' '''      ).lower()
        UpperCAmelCase_							 =     ''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias      )
        answers.append(''' '''.join(alias.split()      ).strip()      )
    return set(__UpperCamelCase      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Any       ,     __UpperCamelCase			:						List[Any]       ,     __UpperCamelCase			:						Union[str, Any]=1       ,     __UpperCamelCase			:						Tuple=100      )  ->							Optional[int]:
    UpperCAmelCase_ ,							UpperCAmelCase_							 =     jax.lax.top_k(__UpperCamelCase       ,     __UpperCamelCase      )
    UpperCAmelCase_ ,							UpperCAmelCase_							 =     jax.lax.top_k(__UpperCamelCase       ,     __UpperCamelCase      )

    UpperCAmelCase_							 =     best_end_idx[:, None] - best_start_idx[None, :]
    UpperCAmelCase_							 =     jnp.logical_or(widths < 0       ,     widths > max_size      )
    UpperCAmelCase_							 =     (best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
    UpperCAmelCase_							 =     jnp.argmax(__UpperCamelCase      ).item()

    return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Optional[Any]      )  ->							Tuple:
    UpperCAmelCase_							 =     sample['''question''']['''text''']
    UpperCAmelCase_							 =     sample['''document''']['''tokens''']['''token''']
    UpperCAmelCase_							 =     sample['''document''']['''tokens''']['''is_html''']
    UpperCAmelCase_							 =     sample['''annotations''']['''long_answer''']
    UpperCAmelCase_							 =     sample['''annotations''']['''short_answers''']

    UpperCAmelCase_							 =     ''' '''.join([context[i] for i in range(len(__UpperCamelCase      )      ) 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",
            }

    UpperCAmelCase_							 =     []
    for s in short_answers:
        short_targets.extend(s['''text''']      )
    UpperCAmelCase_							 =     list(set(__UpperCamelCase      )      )

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

    UpperCAmelCase_							 =     '''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 					SCREAMING_SNAKE_CASE	(       )  ->							Union[str, Any]:
    UpperCAmelCase_							 =     load_from_disk('''natural-questions-validation'''      )
    UpperCAmelCase_							 =     dataset.map(__UpperCamelCase      ).remove_columns(['''annotations''', '''document''', '''id''']      )
    print(__UpperCamelCase      )

    UpperCAmelCase_							 =     dataset.filter(lambda __UpperCamelCase      : (len(x['''question''']      ) + len(x['''context''']      )) < 4 * 4096      )
    UpperCAmelCase_							 =     short_validation_dataset.filter(lambda __UpperCamelCase      : x["category"] != "null"      )
    short_validation_dataset

    UpperCAmelCase_							 =     '''vasudevgupta/flax-bigbird-natural-questions'''
    UpperCAmelCase_							 =     FlaxBigBirdForNaturalQuestions.from_pretrained(__UpperCamelCase      )
    UpperCAmelCase_							 =     BigBirdTokenizerFast.from_pretrained(__UpperCamelCase      )

    @jax.jit
    def forward(*__UpperCamelCase			:						Dict       ,     **__UpperCamelCase			:						Union[str, Any]      ):
        UpperCAmelCase_ ,							UpperCAmelCase_ ,							UpperCAmelCase_							 =     model(*__UpperCamelCase       ,     **__UpperCamelCase      )
        return start_logits, end_logits, jnp.argmax(__UpperCamelCase       ,     axis=-1      )

    def evaluate(__UpperCamelCase			:						Tuple      ):
        # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
        UpperCAmelCase_							 =     tokenizer(
            example['''question''']       ,     example['''context''']       ,     return_tensors='''np'''       ,     max_length=4096       ,     padding='''max_length'''       ,     truncation=__UpperCamelCase       ,     )

        UpperCAmelCase_ ,							UpperCAmelCase_ ,							UpperCAmelCase_							 =     forward(**__UpperCamelCase      )

        UpperCAmelCase_							 =     CATEGORY_MAPPING[category.item()]

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

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

        UpperCAmelCase_							 =     38 if predicted_category == '''short''' else 1024
        UpperCAmelCase_ ,							UpperCAmelCase_							 =     get_best_valid_start_end_idx(
            start_scores[0]       ,     end_scores[0]       ,     top_k=8       ,     max_size=__UpperCamelCase      )

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

        UpperCAmelCase_							 =     expand_to_aliases(example['''targets''']       ,     make_sub_answers=__UpperCamelCase      )
        UpperCAmelCase_							 =     expand_to_aliases(example['''output''']      )

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

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

        return example

    UpperCAmelCase_							 =     short_validation_dataset.map(__UpperCamelCase      )

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


if __name__ == "__main__":
 main()







import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


_A  					= logging.get_logger(__name__)







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Tuple           =		"semantic"





     def __init__(self      :    Dict				,    _A      :    Optional[Any]=1_0_0_0_0				,    _A      :    List[Any]=True				,    _A      :    Any=7_6_8				,    _A      :    Union[str, Any]=False				,    _A      :    List[Any]=False				,    _A      :    Union[str, Any]=False				,    _A      :    Union[str, Any]=False				,    _A      :    str=0.7				,    _A      :    Optional[int]=True				,    _A      :    Any=1_0_0_4_8				,    _A      :    Optional[int]=1_2_9_5_9_5				,    _A      :    Union[str, Any]=1_2_9_5_9_9				,    _A      :    Any=1_0_0_0_0				,    _A      :    str=2_5_6				,    _A      :    int=49.9				,    **_A      :    Union[str, Any]				,    )						->						Any:
         super().__init__(
             temperature=_A				,    do_sample=_A				,    eos_token_id=_A				,    renormalize_logits=_A				,    max_new_tokens=_A				,    output_scores=_A				,    return_dict_in_generate=_A				,    output_hidden_states=_A				,    output_attentions=_A				,    **_A				,    )

         snake_case      =					text_encoding_offset
         snake_case      =					text_pad_token
         snake_case      =					eos_token_id
         snake_case      =					semantic_infer_token
         snake_case      =					semantic_vocab_size
         snake_case      =					max_input_semantic_length
         snake_case      =					semantic_rate_hz







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							List[Any]           =		"coarse_acoustics"





     def __init__(self      :    Any				,    _A      :    Union[str, Any]=True				,    _A      :    List[Any]=False				,    _A      :    Any=False				,    _A      :    Union[str, Any]=False				,    _A      :    Optional[Any]=False				,    _A      :    str=0.7				,    _A      :    int=True				,    _A      :    List[str]=1_2_0_4_8				,    _A      :    Optional[int]=7_5				,    _A      :    Any=2				,    _A      :    Optional[Any]=1_2_0_5_0				,    _A      :    Tuple=2_5_6				,    _A      :    int = 6_3_0				,    _A      :    int = 6_0				,    **_A      :    int				,    )						->						Union[str, Any]:
         super().__init__(
             temperature=_A				,    do_sample=_A				,    renormalize_logits=_A				,    output_scores=_A				,    return_dict_in_generate=_A				,    output_hidden_states=_A				,    output_attentions=_A				,    **_A				,    )

         snake_case      =					coarse_semantic_pad_token
         snake_case      =					coarse_rate_hz
         snake_case      =					n_coarse_codebooks
         snake_case      =					coarse_infer_token
         snake_case      =					max_coarse_input_length
         snake_case      =					max_coarse_history
         snake_case      =					sliding_window_len







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							int           =		"fine_acoustics"





     def __init__(self      :    Tuple				,    _A      :    Optional[int]=0.5				,    _A      :    List[str]=5_1_2				,    _A      :    Union[str, Any]=1_0_2_4				,    _A      :    Tuple=8				,    **_A      :    Tuple				,    )						->						Tuple:
         super().__init__(temperature=_A   )

         snake_case      =					max_fine_history_length
         snake_case      =					max_fine_input_length
         snake_case      =					n_fine_codebooks







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Optional[Any]           =		"bark"
     UpperCAmelCase__				:							Union[str, Any]           =		True





     def __init__(self      :    Union[str, Any]				,    _A      :    Dict = None				,    _A      :    Dict = None				,    _A      :    Dict = None				,    _A      :    Optional[int]=2_4_0_0_0				,    _A      :    Any=1_0_2_4				,    **_A      :    List[str]				,    )						->						Optional[Any]:
         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."   )

         snake_case      =					BarkSemanticGenerationConfig(**_A   )
         snake_case      =					BarkCoarseGenerationConfig(**_A   )
         snake_case      =					BarkFineGenerationConfig(**_A   )

         snake_case      =					sample_rate
         snake_case      =					codebook_size





     @classmethod
     def 		UpperCAmelCase(cls      :    List[Any]				,    _A      :    BarkSemanticGenerationConfig				,    _A      :    BarkCoarseGenerationConfig				,    _A      :    BarkFineGenerationConfig				,    **_A      :    Optional[int]				,    )						->						Union[str, Any]:
         return cls(
             semantic_config=semantic_config.to_dict()				,    coarse_acoustics_config=coarse_acoustics_config.to_dict()				,    fine_acoustics_config=fine_acoustics_config.to_dict()				,    **_A				,    )





     def 		UpperCAmelCase(self      :    List[str]   )						->						int:
         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.__class__.model_type
         return output

