

from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


UpperCamelCase__		   =    logging.get_logger(__name__)

UpperCamelCase__		   =    {
    '''microsoft/layoutlm-base-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'''
    ),
    '''microsoft/layoutlm-large-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'''
    ),
}

class 						__snake_case						(							snake_case__     ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       "layoutlm"


       def __init__(		self			,   _UpperCamelCase=3_05_22			,   _UpperCamelCase=7_68			,   _UpperCamelCase=12			,   _UpperCamelCase=12			,   _UpperCamelCase=30_72			,   _UpperCamelCase="gelu"			,   _UpperCamelCase=0.1			,   _UpperCamelCase=0.1			,   _UpperCamelCase=5_12			,   _UpperCamelCase=2			,   _UpperCamelCase=0.02			,   _UpperCamelCase=1E-12			,   _UpperCamelCase=0			,   _UpperCamelCase="absolute"			,   _UpperCamelCase=True			,   _UpperCamelCase=10_24			,   **_UpperCamelCase			,   )			->       Any:






              """simple docstring"""


              super().__init__(pad_token_id=_UpperCamelCase			,   **_UpperCamelCase      )
              __snake_case					  =       vocab_size
              __snake_case					  =       hidden_size
              __snake_case					  =       num_hidden_layers
              __snake_case					  =       num_attention_heads
              __snake_case					  =       hidden_act
              __snake_case					  =       intermediate_size
              __snake_case					  =       hidden_dropout_prob
              __snake_case					  =       attention_probs_dropout_prob
              __snake_case					  =       max_position_embeddings
              __snake_case					  =       type_vocab_size
              __snake_case					  =       initializer_range
              __snake_case					  =       layer_norm_eps
              __snake_case					  =       position_embedding_type
              __snake_case					  =       use_cache
              __snake_case					  =       max_ad_position_embeddings






class 						__snake_case						(							snake_case__     ):
       """simple docstring"""







       def __init__(		self			,   _UpperCamelCase			,   _UpperCamelCase = "default"			,   _UpperCamelCase = None			,   )			->       int:






              """simple docstring"""


              super().__init__(_UpperCamelCase			,   task=_UpperCamelCase			,   patching_specs=_UpperCamelCase      )
              __snake_case					  =       config.max_ad_position_embeddings - 1


       @property
       def  a		(		self      )			->       Mapping[str, Mapping[int, str]]:






              """simple docstring"""


              return OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                      ("""bbox""", {0: """batch""", 1: """sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
                      ("""token_type_ids""", {0: """batch""", 1: """sequence"""}),
                  ]      )






       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase = -1			,   _UpperCamelCase = -1			,   _UpperCamelCase = False			,   _UpperCamelCase = None			,   )			->       Mapping[str, Any]:






              """simple docstring"""


              __snake_case					  =       super().generate_dummy_inputs(
                  _UpperCamelCase			,   batch_size=_UpperCamelCase			,   seq_length=_UpperCamelCase			,   is_pair=_UpperCamelCase			,   framework=_UpperCamelCase      )

              # Generate a dummy bbox
              __snake_case					  =       [48, 84, 73, 1_28]

              if not framework == TensorType.PYTORCH:
                     raise NotImplementedError("""Exporting LayoutLM to ONNX is currently only supported for PyTorch."""      )

              if not is_torch_available():
                     raise ValueError("""Cannot generate dummy inputs without PyTorch installed."""      )
              import torch

              __snake_case  ,						__snake_case					  =       input_dict["""input_ids"""].shape
              __snake_case					  =       torch.tensor([*[box] * seq_length]      ).tile(_UpperCamelCase			,   1			,   1      )
              return input_dict




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,
)


UpperCamelCase__		   =    '''\
@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}
}
'''

UpperCamelCase__		   =    '''
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.
'''

UpperCamelCase__		   =    '''
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 						__snake_case						(							datasets.Metric     ):
       """simple docstring"""







       def  a		(		self      )			->       Tuple:






              """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  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase=1.0      )			->       Dict:






              """simple docstring"""


              __snake_case					  =       {p["""id"""]: p["""no_answer_probability"""] for p in predictions}
              __snake_case					  =       [{"""paragraphs""": [{"""qas""": references}]}]
              __snake_case					  =       {p["""id"""]: p["""prediction_text"""] for p in predictions}

              __snake_case					  =       make_qid_to_has_ans(_UpperCamelCase      )  # maps qid to True/False
              __snake_case					  =       [k for k, v in qid_to_has_ans.items() if v]
              __snake_case					  =       [k for k, v in qid_to_has_ans.items() if not v]

              __snake_case  ,						__snake_case					  =       get_raw_scores(_UpperCamelCase			,   _UpperCamelCase      )
              __snake_case					  =       apply_no_ans_threshold(_UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase      )
              __snake_case					  =       apply_no_ans_threshold(_UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase      )
              __snake_case					  =       make_eval_dict(_UpperCamelCase			,   _UpperCamelCase      )

              if has_ans_qids:
                     __snake_case					  =       make_eval_dict(_UpperCamelCase			,   _UpperCamelCase			,   qid_list=_UpperCamelCase      )
                     merge_eval(_UpperCamelCase			,   _UpperCamelCase			,   """HasAns"""      )
              if no_ans_qids:
                     __snake_case					  =       make_eval_dict(_UpperCamelCase			,   _UpperCamelCase			,   qid_list=_UpperCamelCase      )
                     merge_eval(_UpperCamelCase			,   _UpperCamelCase			,   """NoAns"""      )
              find_all_best_thresh(_UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase      )
              return dict(_UpperCamelCase      )

