






import unittest

from transformers import ErnieMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    ErnieMForInformationExtraction,
							    ErnieMForMultipleChoice,
							    ErnieMForQuestionAnswering,
							    ErnieMForSequenceClassification,
							    ErnieMForTokenClassification,
							    ErnieMModel,
							)
							from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST
class 				_UpperCAmelCase :

				"""simple docstring"""




				def __init__(					self						:   List[str],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Union[str, Any]=13,  lowerCamelCase						:   List[str]=7,  lowerCamelCase						:   str=True,  lowerCamelCase						:   List[Any]=True,  lowerCamelCase						:   int=True,  lowerCamelCase						:   List[str]=99,  lowerCamelCase						:   Union[str, Any]=32,  lowerCamelCase						:   Tuple=5,  lowerCamelCase						:   Dict=4,  lowerCamelCase						:   Dict=37,  lowerCamelCase						:   Union[str, Any]="gelu",  lowerCamelCase						:   Dict=0.1,  lowerCamelCase						:   Optional[int]=0.1,  lowerCamelCase						:   int=512,  lowerCamelCase						:   Dict=16,  lowerCamelCase						:   Optional[Any]=2,  lowerCamelCase						:   str=0.02,  lowerCamelCase						:   Dict=3,  lowerCamelCase						:   Optional[Any]=4,  lowerCamelCase						:   Dict=None,  ):

								'''simple docstring'''
								lowercase__       			=      parent
								lowercase__       			=      batch_size
								lowercase__       			=      seq_length
								lowercase__       			=      is_training
								lowercase__       			=      use_input_mask
								lowercase__       			=      use_labels
								lowercase__       			=      vocab_size
								lowercase__       			=      hidden_size
								lowercase__       			=      num_hidden_layers
								lowercase__       			=      num_attention_heads
								lowercase__       			=      intermediate_size
								lowercase__       			=      hidden_act
								lowercase__       			=      hidden_dropout_prob
								lowercase__       			=      attention_probs_dropout_prob
								lowercase__       			=      max_position_embeddings
								lowercase__       			=      type_vocab_size
								lowercase__       			=      type_sequence_label_size
								lowercase__       			=      initializer_range
								lowercase__       			=      num_labels
								lowercase__       			=      num_choices
								lowercase__       			=      scope




				def     lowercase__       (					self						:   Any  ):

								'''simple docstring'''
								lowercase__       			=      ids_tensor([self.batch_size, self.seq_length],  self.vocab_size  )

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

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

								lowercase__       			=      self.get_config()

								return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								lowercase__       			=      ids_tensor([self.batch_size, self.seq_length],  self.vocab_size  )

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

								return config, input_ids, input_mask




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								return ErnieMConfig(
								    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,  type_vocab_size=self.type_vocab_size,  initializer_range=self.initializer_range,  )




				def     lowercase__       (					self						:   Optional[Any],  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   List[Any],  lowerCamelCase						:   List[str],  lowerCamelCase						:   Union[str, Any]  ):

								'''simple docstring'''
								lowercase__       			=      ErnieMModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase,  return_dict=lowerCamelCase  )
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.hidden_size)  )




				def     lowercase__       (					self						:   str,  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   str,  lowerCamelCase						:   Dict  ):

								'''simple docstring'''
								lowercase__       			=      ErnieMForQuestionAnswering(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  start_positions=lowerCamelCase,  end_positions=lowerCamelCase,  )
								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     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   Dict,  lowerCamelCase						:   str,  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   List[Any],  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Optional[int]  ):

								'''simple docstring'''
								lowercase__       			=      ErnieMForInformationExtraction(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      torch.ones_like(lowerCamelCase,  dtype=torch.floataa  )
								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  start_positions=lowerCamelCase,  end_positions=lowerCamelCase,  )
								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     lowercase__       (					self						:   Optional[Any],  lowerCamelCase						:   str,  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   str,  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   Union[str, Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.num_labels
								lowercase__       			=      ErnieMForSequenceClassification(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase,  attention_mask=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)  )




				def     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   List[Any],  lowerCamelCase						:   str,  lowerCamelCase						:   int,  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   List[str]  ):

								'''simple docstring'''
								lowercase__       			=      self.num_labels
								lowercase__       			=      ErnieMForTokenClassification(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								input_ids.to(lowerCamelCase  )
								input_mask.to(lowerCamelCase  )
								token_labels.to(lowerCamelCase  )

								lowercase__       			=      model(lowerCamelCase,  attention_mask=lowerCamelCase,  labels=lowerCamelCase  )

								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.num_labels)  )




				def     lowercase__       (					self						:   Optional[Any],  lowerCamelCase						:   str,  lowerCamelCase						:   int,  lowerCamelCase						:   str,  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Optional[int]  ):

								'''simple docstring'''
								lowercase__       			=      self.num_choices
								lowercase__       			=      ErnieMForMultipleChoice(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      input_ids.unsqueeze(1  ).expand(-1,  self.num_choices,  -1  ).contiguous()
								lowercase__       			=      input_mask.unsqueeze(1  ).expand(-1,  self.num_choices,  -1  ).contiguous()
								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  labels=lowerCamelCase,  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_choices)  )




				def     lowercase__       (					self						:   Optional[int]  ):

								'''simple docstring'''
								lowercase__       			=      self.prepare_config_and_inputs()
								(
								    (
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						
								)       			=      config_and_inputs
								lowercase__       			=      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
								return config, inputs_dict



@require_torch
class 				_UpperCAmelCase (	A__		,A__		,unittest.TestCase     ):

				"""simple docstring"""




				lowercase__    							=	(
				    (
				        ErnieMModel,
				        ErnieMForMultipleChoice,
				        ErnieMForQuestionAnswering,
				        ErnieMForSequenceClassification,
				        ErnieMForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				lowercase__    							=	()
				lowercase__    							=	(
				    {
				        """feature-extraction""": ErnieMModel,
				        """question-answering""": ErnieMForQuestionAnswering,
				        """text-classification""": ErnieMForSequenceClassification,
				        """token-classification""": ErnieMForTokenClassification,
				        """zero-shot""": ErnieMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				lowercase__    							=	False




				def     lowercase__       (					self						:   List[Any],  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Dict  ):

								'''simple docstring'''
								if pipeline_test_casse_name == "QAPipelineTests":
												return True

								return False




				def     lowercase__       (					self						:   Union[str, Any]  ):

								'''simple docstring'''
								lowercase__       			=      ErnieMModelTester(self  )
								lowercase__       			=      ConfigTester(self,  config_class=lowerCamelCase,  hidden_size=37  )




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								self.config_tester.run_common_tests()




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase  )




				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
												lowercase__       			=      type
												self.model_tester.create_and_check_model(*lowerCamelCase  )




				def     lowercase__       (					self						:   Any  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase  )




				def     lowercase__       (					self						:   Optional[int]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowerCamelCase  )




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase  )




				def     lowercase__       (					self						:   Any  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_information_extraction(*lowerCamelCase  )




				def     lowercase__       (					self						:   int  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowerCamelCase  )




				@slow
				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__       			=      ErnieMModel.from_pretrained(lowerCamelCase  )
												self.assertIsNotNone(lowerCamelCase  )



@require_torch
class 				_UpperCAmelCase (	unittest.TestCase     ):

				"""simple docstring"""




				@slow
				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								lowercase__       			=      ErnieMModel.from_pretrained('''susnato/ernie-m-base_pytorch'''  )
								model.eval()
								lowercase__       			=      torch.tensor([[0, 1, 2, 3, 4, 5]]  )
								lowercase__       			=      model(lowerCamelCase  )[0]

								# TODO Replace vocab size
								lowercase__       			=      768

								lowercase__       			=      torch.Size((1, 6, hidden_size)  )
								self.assertEqual(output.shape,  lowerCamelCase  )

								lowercase__       			=      torch.tensor(
								    [[[-0.0012, 0.1245, -0.0214], [-0.0742, 0.0244, -0.0771], [-0.0333, 0.1164, -0.1554]]]  )

								self.assertTrue(torch.allclose(output[:, :3, :3],  lowerCamelCase,  atol=1E-3  )  )












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


A__     :      Dict             =			'none'
A__     :      Optional[Any]             =			'none'






def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      value.split(lowerCamelCase_    )
				return len(lowerCamelCase_    ) > 1 and any((len(lowerCamelCase_    ) == 3 for x in split)    )






def   a      (						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'''    )
				lowercase__       			=      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   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				if answer is None:
								return None
				try:
								lowercase__       			=      convert_to_float(lowerCamelCase_    )
								if isinstance(lowerCamelCase_		,       lowerCamelCase_    ) and math.isnan(lowerCamelCase_    ):
												return None
								return value
				except ValueError:
								return answer.lower()


A__     :      Tuple             =			{
    'text': lambda x: x,
    'real': convert_to_float,
}
class 				_UpperCAmelCase (	enum.Enum     ):

				"""simple docstring"""




				lowercase__    							=	0
				lowercase__    							=	1
				lowercase__    							=	2
				lowercase__    							=	3
				lowercase__    							=	4
				lowercase__    							=	5



class 				_UpperCAmelCase (	enum.Enum     ):

				"""simple docstring"""




				lowercase__    							=	0
				lowercase__    							=	1
				lowercase__    							=	2



@dataclasses.dataclass
class 				_UpperCAmelCase :

				"""simple docstring"""




				lowercase__    							=	42
				lowercase__    							=	42
				lowercase__    							=	42



A__     :      str             =			re.compile(r'\w+|[^\w\s]+', re.UNICODE | re.MULTILINE | re.DOTALL)






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				return list(_TOKENIZER.findall(x.lower()    )    )






def   a      (						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   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      table['''types''']
				return _TYPE_CONVERTER[types[column]](lowerCamelCase_    )






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				return isinstance(lowerCamelCase_		,       lowerCamelCase_    )






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				for cond in conditions:
								lowercase__       			=      row[cond.column]

								lowercase__       			=      _parse_value(lowerCamelCase_		,       cond.column		,       cond.cmp_value    )

								if _is_string(lowerCamelCase_    ) and _is_string(lowerCamelCase_    ):
												lowercase__       			=      _normalize_for_match(lowerCamelCase_    )
												lowercase__       			=      _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   a      (						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.
				lowercase__       			=      [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   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      sql_query['''agg''']
				if aggregation_op_index >= 3:
								lowercase__       			=      _Aggregation(lowerCamelCase_    )
				else:
								lowercase__       			=      _Aggregation.NONE

				lowercase__       			=      sql_query['''sel''']
				lowercase__       			=      [
				    _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''']    )
				]

				lowercase__       			=      []
				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):
								lowercase__       			=      {2: min, 1: max}
								lowercase__       			=      [(table['''rows'''][i][j], index) for index, (i, j) in enumerate(lowerCamelCase_    )]
								lowercase__       			=      functools.reduce(operators[sql_query['''agg''']]		,       lowerCamelCase_    )

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

				return indices, aggregation_op






def   a      (						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   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__			,						lowercase__       			=      _get_answer_coordinates(lowerCamelCase_		,       lowerCamelCase_    )
				lowercase__       			=      _get_float_answer(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    )
				lowercase__       			=      _get_answer_text(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    )
				# keep the original data the same with TaPas
				if len(lowerCamelCase_    ) == 0:
								lowercase__       			=      [EMPTY_ANSWER]
				return answer_text




