

import dataclasses
import enum
import functools
import math
import re

# The following script is adapted from the script of TaPas.
# Original: https://github.com/google-research/tapas/master/wikisql_utils.py
from typing import Any, List, Text


_lowerCamelCase								="none"
_lowerCamelCase								="none"







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=value.split(lowerCAmelCase_      )
     return len(lowerCAmelCase_      ) > 1 and any((len(lowerCAmelCase_      ) == 3 for x in split)      )







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     if isinstance(lowerCAmelCase_,   lowerCAmelCase_      ):
          return value
     if isinstance(lowerCAmelCase_,   lowerCAmelCase_      ):
          return float(lowerCAmelCase_      )
     if not isinstance(lowerCAmelCase_,   lowerCAmelCase_      ):
          raise ValueError('Argument value is not a string. Can\'t parse it as float'      )
     SCREAMING_SNAKE_CASE										=value

     try:
          # Example: 1,000.7
          if "." in sanitized and "," in sanitized:
               return float(sanitized.replace(',',   ''      )      )
          # 1,000
          if "," in sanitized and _split_thousands(',',   lowerCAmelCase_      ):
               return float(sanitized.replace(',',   ''      )      )
          # 5,5556
          if "," in sanitized and sanitized.count(','      ) == 1 and not _split_thousands(',',   lowerCAmelCase_      ):
               return float(sanitized.replace(',',   '.'      )      )
          # 0.0.0.1
          if sanitized.count('.'      ) > 1:
               return float(sanitized.replace('.',   ''      )      )
          # 0,0,0,1
          if sanitized.count(','      ) > 1:
               return float(sanitized.replace(',',   ''      )      )
          return float(lowerCAmelCase_      )
     except ValueError:
          # Avoid adding the sanitized value in the error message.
          raise ValueError('Unable to convert value to float'      )







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     if answer is None:
          return None
     try:
          SCREAMING_SNAKE_CASE										=convert_to_float(lowerCAmelCase_      )
          if isinstance(lowerCAmelCase_,   lowerCAmelCase_      ) and math.isnan(lowerCAmelCase_      ):
               return None
          return value
     except ValueError:
          return answer.lower()


_lowerCamelCase								={
    "text": lambda x: x,
    "real": convert_to_float,
}



class 		a_	(							enum.Enum					):



     """simple docstring"""







     __UpperCAmelCase      		=				0
     __UpperCAmelCase      		=				1
     __UpperCAmelCase      		=				2
     __UpperCAmelCase      		=				3
     __UpperCAmelCase      		=				4
     __UpperCAmelCase      		=				5







class 		a_	(							enum.Enum					):



     """simple docstring"""







     __UpperCAmelCase      		=				0
     __UpperCAmelCase      		=				1
     __UpperCAmelCase      		=				2







@dataclasses.dataclass
class 		a_	:



     """simple docstring"""







     __UpperCAmelCase      		=				42
     __UpperCAmelCase      		=				42
     __UpperCAmelCase      		=				42







_lowerCamelCase								=re.compile(R"\w+|[^\w\s]+", re.UNICODE | re.MULTILINE | re.DOTALL)







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     return list(_TOKENIZER.findall(x.lower()      )      )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     if operator == _Operator.EQUALS:
          return src == tgt
     elif operator == _Operator.GREATER:
          return src > tgt
     elif operator == _Operator.LESSER:
          return src < tgt
     raise ValueError(F'Unknown operator: {operator}'      )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=table['types']
     return _TYPE_CONVERTER[types[column]](lowerCAmelCase_      )







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     return isinstance(lowerCAmelCase_,   lowerCAmelCase_      )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     for cond in conditions:
          SCREAMING_SNAKE_CASE										=row[cond.column]

          SCREAMING_SNAKE_CASE										=_parse_value(lowerCAmelCase_,   cond.column,   cond.cmp_value      )

          if _is_string(lowerCAmelCase_      ) and _is_string(lowerCAmelCase_      ):
               SCREAMING_SNAKE_CASE										=_normalize_for_match(lowerCAmelCase_      )
               SCREAMING_SNAKE_CASE										=_normalize_for_match(lowerCAmelCase_      )

          if not isinstance(lowerCAmelCase_,   type(lowerCAmelCase_      )      ):
               raise ValueError('Type difference {} != {}'.format(type(lowerCAmelCase_      ),   type(lowerCAmelCase_      )      )      )

          if not _compare(cond.operator,   lowerCAmelCase_,   lowerCAmelCase_      ):
               return False
     return True







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     if not answer_coordinates:
          if aggregation_op == _Aggregation.COUNT:
               return 0.0
          else:
               return EMPTY_ANSWER_AGG

    # Count can support non numeric answers.
     if aggregation_op == _Aggregation.COUNT:
          return float(len(lowerCAmelCase_      )      )

     # If we have just one answer, if float returns it or try a conversion.
     SCREAMING_SNAKE_CASE										=[table['rows'][i][j] for (i, j) in answer_coordinates]
     if len(lowerCAmelCase_      ) == 1:
          try:
               return convert_to_float(values[0]      )
          except ValueError as e:
               if aggregation_op != _Aggregation.NONE:
                    raise e

     if aggregation_op == _Aggregation.NONE:
          return None

     # Other aggregation only support numeric values. Bail out if we have strings.
     if not all((isinstance(lowerCAmelCase_,   (int, float)      ) for v in values)      ):
          return None

     if aggregation_op == _Aggregation.SUM:
          return float(sum(lowerCAmelCase_      )      )
     elif aggregation_op == _Aggregation.AVERAGE:
          return sum(lowerCAmelCase_      ) / len(lowerCAmelCase_      )
     else:
          raise ValueError(F'Unknown aggregation: {aggregation_op}'      )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=sql_query['agg']
     if aggregation_op_index >= 3:
          SCREAMING_SNAKE_CASE										=_Aggregation(lowerCAmelCase_      )
     else:
          SCREAMING_SNAKE_CASE										=_Aggregation.NONE

     SCREAMING_SNAKE_CASE										=sql_query['sel']
     SCREAMING_SNAKE_CASE										=[
         _Condition(lowerCAmelCase_,   _Operator(lowerCAmelCase_      ),   lowerCAmelCase_      )
         for column, operator, cmp_value in zip(
             sql_query['conds']['column_index'],   sql_query['conds']['operator_index'],   sql_query['conds']['condition']      )
     ]

     SCREAMING_SNAKE_CASE										=[]
     for row in range(len(table['rows']      )      ):
          if _respect_conditions(lowerCAmelCase_,   table['rows'][row],   lowerCAmelCase_      ):
               indices.append((row, target_column)      )

     if not indices:
          return [], aggregation_op

     if len(lowerCAmelCase_      ) == 1:
          return indices, aggregation_op

     # Parsing of MIN/MAX.
     if aggregation_op_index in (1, 2):
          SCREAMING_SNAKE_CASE										={2: min, 1: max}
          SCREAMING_SNAKE_CASE										=[(table['rows'][i][j], index) for index, (i, j) in enumerate(lowerCAmelCase_      )]
          SCREAMING_SNAKE_CASE										=functools.reduce(operators[sql_query['agg']],   lowerCAmelCase_      )

          SCREAMING_SNAKE_CASE										=[indices[reduced[1]]]
          return ret, _Aggregation.NONE

     return indices, aggregation_op







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     if float_answer is not None:
          return [str(lowerCAmelCase_      )]
     return [str(table['real_rows'][r][c]      ) for r, c in answer_coordinates]







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=_get_answer_coordinates(lowerCAmelCase_,   lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=_get_float_answer(lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=_get_answer_text(lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      )
     # keep the original data the same with TaPas
     if len(lowerCAmelCase_      ) == 0:
          SCREAMING_SNAKE_CASE										=[EMPTY_ANSWER]
     return answer_text




from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers.models.mpnet.modeling_tf_mpnet import (
     TFMPNetForMaskedLM,
     TFMPNetForMultipleChoice,
     TFMPNetForQuestionAnswering,
     TFMPNetForSequenceClassification,
     TFMPNetForTokenClassification,
     TFMPNetModel,
 )



class 		a_	:



      """simple docstring"""







      def __init__(				self					:							Any      ,snake_case					:							List[Any]      ,snake_case					:							Optional[Any]=13      ,snake_case					:							str=7      ,snake_case					:							List[Any]=True      ,snake_case					:							Optional[Any]=True      ,snake_case					:							int=False      ,snake_case					:							List[str]=True      ,snake_case					:							Optional[int]=99      ,snake_case					:							Any=64      ,snake_case					:							int=2      ,snake_case					:							str=4      ,snake_case					:							str=64      ,snake_case					:							List[Any]="gelu"      ,snake_case					:							Dict=0.1      ,snake_case					:							int=0.1      ,snake_case					:							List[Any]=512      ,snake_case					:							int=16      ,snake_case					:							List[Any]=2      ,snake_case					:							Union[str, Any]=0.02      ,snake_case					:							List[str]=3      ,snake_case					:							Any=4      ,snake_case					:							Tuple=None      ,):
           SCREAMING_SNAKE_CASE										=parent
           SCREAMING_SNAKE_CASE										=batch_size
           SCREAMING_SNAKE_CASE										=seq_length
           SCREAMING_SNAKE_CASE										=is_training
           SCREAMING_SNAKE_CASE										=use_input_mask
           SCREAMING_SNAKE_CASE										=use_token_type_ids
           SCREAMING_SNAKE_CASE										=use_labels
           SCREAMING_SNAKE_CASE										=vocab_size
           SCREAMING_SNAKE_CASE										=hidden_size
           SCREAMING_SNAKE_CASE										=num_hidden_layers
           SCREAMING_SNAKE_CASE										=num_attention_heads
           SCREAMING_SNAKE_CASE										=intermediate_size
           SCREAMING_SNAKE_CASE										=hidden_act
           SCREAMING_SNAKE_CASE										=hidden_dropout_prob
           SCREAMING_SNAKE_CASE										=attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE										=max_position_embeddings
           SCREAMING_SNAKE_CASE										=type_vocab_size
           SCREAMING_SNAKE_CASE										=type_sequence_label_size
           SCREAMING_SNAKE_CASE										=initializer_range
           SCREAMING_SNAKE_CASE										=num_labels
           SCREAMING_SNAKE_CASE										=num_choices
           SCREAMING_SNAKE_CASE										=scope
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size    )

           SCREAMING_SNAKE_CASE										=None
           if self.use_input_mask:
                SCREAMING_SNAKE_CASE										=random_attention_mask([self.batch_size, self.seq_length]    )

           SCREAMING_SNAKE_CASE										=None
           SCREAMING_SNAKE_CASE										=None
           SCREAMING_SNAKE_CASE										=None
           if self.use_labels:
                SCREAMING_SNAKE_CASE										=ids_tensor([self.batch_size]      ,self.type_sequence_label_size    )
                SCREAMING_SNAKE_CASE										=ids_tensor([self.batch_size, self.seq_length]      ,self.num_labels    )
                SCREAMING_SNAKE_CASE										=ids_tensor([self.batch_size]      ,self.num_choices    )

           SCREAMING_SNAKE_CASE										=MPNetConfig(
               vocab_size=self.vocab_size      ,hidden_size=self.hidden_size      ,num_hidden_layers=self.num_hidden_layers      ,num_attention_heads=self.num_attention_heads      ,intermediate_size=self.intermediate_size      ,hidden_act=self.hidden_act      ,hidden_dropout_prob=self.hidden_dropout_prob      ,attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,max_position_embeddings=self.max_position_embeddings      ,initializer_range=self.initializer_range      ,)
           return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
      def 	_lowerCAmelCase					(				self					:							Dict      ,snake_case					:							Any      ,snake_case					:							Optional[int]      ,snake_case					:							str      ,snake_case					:							Union[str, Any]      ,snake_case					:							Optional[Any]      ,snake_case					:							Any    ):
           SCREAMING_SNAKE_CASE										=TFMPNetModel(config=snake_case    )
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           SCREAMING_SNAKE_CASE										=[input_ids, input_mask]
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)    )
      def 	_lowerCAmelCase					(				self					:							List[Any]      ,snake_case					:							List[str]      ,snake_case					:							Tuple      ,snake_case					:							Dict      ,snake_case					:							Optional[Any]      ,snake_case					:							Optional[int]      ,snake_case					:							Dict    ):
           SCREAMING_SNAKE_CASE										=TFMPNetForMaskedLM(config=snake_case    )
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size)    )
      def 	_lowerCAmelCase					(				self					:							int      ,snake_case					:							List[str]      ,snake_case					:							Dict      ,snake_case					:							List[Any]      ,snake_case					:							Optional[int]      ,snake_case					:							Optional[int]      ,snake_case					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=TFMPNetForQuestionAnswering(config=snake_case    )
           SCREAMING_SNAKE_CASE										={
               'input_ids': input_ids,
               'attention_mask': input_mask,
           }
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.start_logits.shape      ,(self.batch_size, self.seq_length)    )
           self.parent.assertEqual(result.end_logits.shape      ,(self.batch_size, self.seq_length)    )
      def 	_lowerCAmelCase					(				self					:							List[Any]      ,snake_case					:							Union[str, Any]      ,snake_case					:							str      ,snake_case					:							str      ,snake_case					:							Any      ,snake_case					:							Tuple      ,snake_case					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										=self.num_labels
           SCREAMING_SNAKE_CASE										=TFMPNetForSequenceClassification(snake_case    )
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)    )
      def 	_lowerCAmelCase					(				self					:							int      ,snake_case					:							Any      ,snake_case					:							Union[str, Any]      ,snake_case					:							int      ,snake_case					:							List[str]      ,snake_case					:							Tuple      ,snake_case					:							Union[str, Any]    ):
           SCREAMING_SNAKE_CASE										=self.num_choices
           SCREAMING_SNAKE_CASE										=TFMPNetForMultipleChoice(snake_case    )
           SCREAMING_SNAKE_CASE										=tf.tile(tf.expand_dims(snake_case      ,1    )      ,(1, self.num_choices, 1)    )
           SCREAMING_SNAKE_CASE										=tf.tile(tf.expand_dims(snake_case      ,1    )      ,(1, self.num_choices, 1)    )
           SCREAMING_SNAKE_CASE										={
               'input_ids': multiple_choice_inputs_ids,
               'attention_mask': multiple_choice_input_mask,
           }
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_choices)    )
      def 	_lowerCAmelCase					(				self					:							Any      ,snake_case					:							Tuple      ,snake_case					:							Dict      ,snake_case					:							Tuple      ,snake_case					:							Union[str, Any]      ,snake_case					:							Optional[int]      ,snake_case					:							Dict    ):
           SCREAMING_SNAKE_CASE										=self.num_labels
           SCREAMING_SNAKE_CASE										=TFMPNetForTokenClassification(snake_case    )
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.num_labels)    )


      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           SCREAMING_SNAKE_CASE										=self.prepare_config_and_inputs()
           ((SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE))										=config_and_inputs
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           return config, inputs_dict







@require_tf
class 		a_	(							lowerCamelCase_			,      lowerCamelCase_			,      unittest.TestCase					):



      """simple docstring"""







      __UpperCAmelCase      		=				(
          (
              TFMPNetForMaskedLM,
              TFMPNetForMultipleChoice,
              TFMPNetForQuestionAnswering,
              TFMPNetForSequenceClassification,
              TFMPNetForTokenClassification,
              TFMPNetModel,
          )
          if is_tf_available()
          else ()
      )
      __UpperCAmelCase      		=				(
          {
              'feature-extraction': TFMPNetModel,
              'fill-mask': TFMPNetForMaskedLM,
              'question-answering': TFMPNetForQuestionAnswering,
              'text-classification': TFMPNetForSequenceClassification,
              'token-classification': TFMPNetForTokenClassification,
              'zero-shot': TFMPNetForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				False
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           SCREAMING_SNAKE_CASE										=TFMPNetModelTester(self    )
           SCREAMING_SNAKE_CASE										=ConfigTester(self      ,config_class=snake_case      ,hidden_size=37    )
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           self.config_tester.run_common_tests()
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_model(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_masked_lm(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_question_answering(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							str    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_sequence_classification(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_multiple_choice(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_token_classification(*snake_case    )


      @slow
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           for model_name in ["microsoft/mpnet-base"]:
                SCREAMING_SNAKE_CASE										=TFMPNetModel.from_pretrained(snake_case    )
                self.assertIsNotNone(snake_case    )







@require_tf
class 		a_	(							unittest.TestCase					):



      """simple docstring"""







      @slow
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										=TFMPNetModel.from_pretrained('microsoft/mpnet-base'    )
           SCREAMING_SNAKE_CASE										=tf.constant([[0, 1, 2, 3, 4, 5]]    )
           SCREAMING_SNAKE_CASE										=model(snake_case    )[0]

           SCREAMING_SNAKE_CASE										=[1, 6, 768]
           self.assertEqual(output.shape      ,snake_case    )

           SCREAMING_SNAKE_CASE										=tf.constant(
               [
                   [
                       [-0.1_067_172, 0.08_216_473, 0.0_024_543],
                       [-0.03_465_879, 0.8_354_118, -0.03_252_288],
                       [-0.06_569_476, -0.12_424_111, -0.0_494_436],
                   ]
               ]    )
           tf.debugging.assert_near(output[:, :3, :3]      ,snake_case      ,atol=1e-4    )

