




'''simple docstring'''

import copy
from typing import Dict

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


a__         =     logging.get_logger(__name__)





class __magic_name__(						lowerCamelCase_      ):
						UpperCAmelCase_		:					Union[str, Any]    =  '''semantic'''






						def __init__(   self						:						Tuple					,   __UpperCamelCase						:						List[str]=1_0_0_0_0					,   __UpperCamelCase						:						Optional[Any]=True					,   __UpperCamelCase						:						Optional[Any]=7_6_8					,   __UpperCamelCase						:						int=False					,   __UpperCamelCase						:						Any=False					,   __UpperCamelCase						:						Dict=False					,   __UpperCamelCase						:						int=False					,   __UpperCamelCase						:						Any=0.7					,   __UpperCamelCase						:						Union[str, Any]=True					,   __UpperCamelCase						:						Optional[Any]=1_0_0_4_8					,   __UpperCamelCase						:						Union[str, Any]=1_2_9_5_9_5					,   __UpperCamelCase						:						Tuple=1_2_9_5_9_9					,   __UpperCamelCase						:						Optional[int]=1_0_0_0_0					,   __UpperCamelCase						:						Union[str, Any]=2_5_6					,   __UpperCamelCase						:						Optional[Any]=49.9					,   **__UpperCamelCase						:						int					,   ):



									'''simple docstring'''






									super().__init__(
									    temperature=snake_case_					,   do_sample=snake_case_					,   eos_token_id=snake_case_					,   renormalize_logits=snake_case_					,   max_new_tokens=snake_case_					,   output_scores=snake_case_					,   return_dict_in_generate=snake_case_					,   output_hidden_states=snake_case_					,   output_attentions=snake_case_					,   **snake_case_					,   )

									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 __magic_name__(						lowerCamelCase_      ):
						UpperCAmelCase_		:					Optional[Any]    =  '''coarse_acoustics'''






						def __init__(   self						:						Optional[Any]					,   __UpperCamelCase						:						List[Any]=True					,   __UpperCamelCase						:						str=False					,   __UpperCamelCase						:						List[str]=False					,   __UpperCamelCase						:						Tuple=False					,   __UpperCamelCase						:						str=False					,   __UpperCamelCase						:						Tuple=0.7					,   __UpperCamelCase						:						Any=True					,   __UpperCamelCase						:						Union[str, Any]=1_2_0_4_8					,   __UpperCamelCase						:						Tuple=7_5					,   __UpperCamelCase						:						Any=2					,   __UpperCamelCase						:						List[Any]=1_2_0_5_0					,   __UpperCamelCase						:						List[Any]=2_5_6					,   __UpperCamelCase						:						Dict = 6_3_0					,   __UpperCamelCase						:						str = 6_0					,   **__UpperCamelCase						:						Union[str, Any]					,   ):



									'''simple docstring'''






									super().__init__(
									    temperature=snake_case_					,   do_sample=snake_case_					,   renormalize_logits=snake_case_					,   output_scores=snake_case_					,   return_dict_in_generate=snake_case_					,   output_hidden_states=snake_case_					,   output_attentions=snake_case_					,   **snake_case_					,   )

									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 __magic_name__(						lowerCamelCase_      ):
						UpperCAmelCase_		:					List[str]    =  '''fine_acoustics'''






						def __init__(   self						:						Optional[int]					,   __UpperCamelCase						:						List[Any]=0.5					,   __UpperCamelCase						:						List[str]=5_1_2					,   __UpperCamelCase						:						Optional[Any]=1_0_2_4					,   __UpperCamelCase						:						Any=8					,   **__UpperCamelCase						:						Tuple					,   ):



									'''simple docstring'''






									super().__init__(temperature=snake_case_       )

									snake_case__          =						max_fine_history_length
									snake_case__          =						max_fine_input_length
									snake_case__          =						n_fine_codebooks







class __magic_name__(						lowerCamelCase_      ):
						UpperCAmelCase_		:					Optional[int]    =  '''bark'''
						UpperCAmelCase_		:					Dict    =  True






						def __init__(   self						:						int					,   __UpperCamelCase						:						str = None					,   __UpperCamelCase						:						List[Any] = None					,   __UpperCamelCase						:						List[str] = None					,   __UpperCamelCase						:						Optional[Any]=2_4_0_0_0					,   __UpperCamelCase						:						Optional[int]=1_0_2_4					,   **__UpperCamelCase						:						int					,   ):



									'''simple docstring'''






									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(**snake_case_       )
									snake_case__          =						BarkCoarseGenerationConfig(**snake_case_       )
									snake_case__          =						BarkFineGenerationConfig(**snake_case_       )

									snake_case__          =						sample_rate
									snake_case__          =						codebook_size






						@classmethod
						def       __lowerCAmelCase(   cls						:						List[Any]					,   __UpperCamelCase						:						str					,   __UpperCamelCase						:						List[Any]					,   __UpperCamelCase						:						Optional[Any]					,   **__UpperCamelCase						:						int					,   ):



									'''simple docstring'''






									return cls(
									    semantic_config=semantic_config.to_dict()					,   coarse_acoustics_config=coarse_acoustics_config.to_dict()					,   fine_acoustics_config=fine_acoustics_config.to_dict()					,   **snake_case_					,   )







						def       __lowerCAmelCase(   self						:						Any       ):



									'''simple docstring'''






									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
'''simple docstring'''


import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


snake_case__	:     int      =    '''\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
'''

snake_case__	:     List[str]      =    '''
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
'''

snake_case__	:     int      =    '''
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer \'id\' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - \'id\': id of the question-answer pair (see above),
            - \'answers\': a list of Dict {\'text\': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    \'exact\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
    \'total\': Number of score considered
    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer
    \'HasAns_total\': Number of score considered
    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer
    \'NoAns_total\': Number of score considered
    \'best_exact\': Best exact match (with varying threshold)
    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match
    \'best_f1\': Best F1 (with varying threshold)
    \'best_f1_thresh\': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_v2_metric = datasets.load_metric("squad_v2")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}
'''






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







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return datasets.MetricInfo(
           description=_DESCRIPTION ,						citation=_CITATION ,						inputs_description=_KWARGS_DESCRIPTION ,						features=datasets.Features(
               {
                   'predictions': {
                       'id': datasets.Value('string'		),
                       'prediction_text': datasets.Value('string'		),
                       'no_answer_probability': datasets.Value('float32'		),
                   },
                   'references': {
                       'id': datasets.Value('string'		),
                       'answers': datasets.features.Sequence(
                           {'text': datasets.Value('string'		), 'answer_start': datasets.Value('int32'		)}		),
                   },
               }		) ,						codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/'] ,						reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/'] ,						)






  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_=1.0		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = {p['id']: p['no_answer_probability'] for p in predictions}
       UpperCAmelCase_				:  Dict  = [{'paragraphs': [{'qas': references}]}]
       UpperCAmelCase_				:  Optional[int]  = {p['id']: p['prediction_text'] for p in predictions}

       UpperCAmelCase_				:  Optional[int]  = make_qid_to_has_ans(snake_case_		)  # maps qid to True/False
       UpperCAmelCase_				:  List[Any]  = [k for k, v in qid_to_has_ans.items() if v]
       UpperCAmelCase_				:  Union[str, Any]  = [k for k, v in qid_to_has_ans.items() if not v]

       UpperCAmelCase_  ,     UpperCAmelCase_				:  Optional[int]  = get_raw_scores(snake_case_ ,						snake_case_		)
       UpperCAmelCase_				:  Tuple  = apply_no_ans_threshold(snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_		)
       UpperCAmelCase_				:  Dict  = apply_no_ans_threshold(snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_		)
       UpperCAmelCase_				:  str  = make_eval_dict(snake_case_ ,						snake_case_		)

       if has_ans_qids:
            UpperCAmelCase_				:  Dict  = make_eval_dict(snake_case_ ,						snake_case_ ,						qid_list=snake_case_		)
            merge_eval(snake_case_ ,						snake_case_ ,						'HasAns'		)
       if no_ans_qids:
            UpperCAmelCase_				:  List[str]  = make_eval_dict(snake_case_ ,						snake_case_ ,						qid_list=snake_case_		)
            merge_eval(snake_case_ ,						snake_case_ ,						'NoAns'		)
       find_all_best_thresh(snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_		)
       return dict(snake_case_		)






