


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import ast
import difflib
from collections.abc import Mapping
from typing import Any, Callable, Dict







class  lowerCamelCase__  (			lowerCAmelCase):
     pass



def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case=None	,		__snake_case=False		)	->     List[Any]:







 """simple docstring"""


 try:
  _lowercase				 =ast.parse(__snake_case		)
 except SyntaxError as e:
  print('''The code generated by the agent is not valid.\n'''	,		__snake_case		)
  return
 if state is None:
  _lowercase				 ={}
 _lowercase				 =None
 for idx, node in enumerate(expression.body		):
  try:
   _lowercase				 =evaluate_ast(__snake_case	,		__snake_case	,		__snake_case		)
  except InterpretorError as e:
   _lowercase				 =F"Evaluation of the code stopped at line {idx} before the end because of the following error"
   if chat_mode:
    msg += (
        F". Copy paste the following error message and send it back to the agent:\nI get an error: '{e}'"
    )
   else:
    msg += F":\n{e}"
   print(__snake_case		)
   break
  if line_result is not None:
   _lowercase				 =line_result

 return result
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case		)	->     List[str]:







 """simple docstring"""


 if isinstance(__snake_case	,		ast.Assign		):
  # Assignement -> we evaluate the assignement which should update the state
  # We return the variable assigned as it may be used to determine the final result.
  return evaluate_assign(__snake_case	,		__snake_case	,		__snake_case		)
 elif isinstance(__snake_case	,		ast.Call		):
  # Function call -> we return the value of the function call
  return evaluate_call(__snake_case	,		__snake_case	,		__snake_case		)
 elif isinstance(__snake_case	,		ast.Constant		):
  # Constant -> just return the value
  return expression.value
 elif isinstance(__snake_case	,		ast.Dict		):
  # Dict -> evaluate all keys and values
  _lowercase				 =[evaluate_ast(__snake_case	,		__snake_case	,		__snake_case		) for k in expression.keys]
  _lowercase				 =[evaluate_ast(__snake_case	,		__snake_case	,		__snake_case		) for v in expression.values]
  return dict(zip(__snake_case	,		__snake_case		)		)
 elif isinstance(__snake_case	,		ast.Expr		):
  # Expression -> evaluate the content
  return evaluate_ast(expression.value	,		__snake_case	,		__snake_case		)
 elif isinstance(__snake_case	,		ast.For		):
  # For loop -> execute the loop
  return evaluate_for(__snake_case	,		__snake_case	,		__snake_case		)
 elif isinstance(__snake_case	,		ast.FormattedValue		):
  # Formatted value (part of f-string) -> evaluate the content and return
  return evaluate_ast(expression.value	,		__snake_case	,		__snake_case		)
 elif isinstance(__snake_case	,		ast.If		):
  # If -> execute the right branch
  return evaluate_if(__snake_case	,		__snake_case	,		__snake_case		)
 elif hasattr(__snake_case	,		'''Index'''		) and isinstance(__snake_case	,		ast.Index		):
  return evaluate_ast(expression.value	,		__snake_case	,		__snake_case		)
 elif isinstance(__snake_case	,		ast.JoinedStr		):
  return "".join([str(evaluate_ast(__snake_case	,		__snake_case	,		__snake_case		)		) for v in expression.values]		)
 elif isinstance(__snake_case	,		ast.List		):
  # List -> evaluate all elements
  return [evaluate_ast(__snake_case	,		__snake_case	,		__snake_case		) for elt in expression.elts]
 elif isinstance(__snake_case	,		ast.Name		):
  # Name -> pick up the value in the state
  return evaluate_name(__snake_case	,		__snake_case	,		__snake_case		)
 elif isinstance(__snake_case	,		ast.Subscript		):
  # Subscript -> return the value of the indexing
  return evaluate_subscript(__snake_case	,		__snake_case	,		__snake_case		)
 else:
  # For now we refuse anything else. Let's add things as we need them.
  raise InterpretorError(F"{expression.__class__.__name__} is not supported."		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case		)	->     Union[str, Any]:







 """simple docstring"""


 _lowercase				 =assign.targets
 _lowercase				 =evaluate_ast(assign.value	,		__snake_case	,		__snake_case		)

 if len(__snake_case		) == 1:
  _lowercase				 =result
 else:
  if len(__snake_case		) != len(__snake_case		):
   raise InterpretorError(F"Expected {len(__snake_case		)} values but got {len(__snake_case		)}."		)
  for var_name, r in zip(__snake_case	,		__snake_case		):
   _lowercase				 =r
 return result
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case		)	->     Optional[Any]:







 """simple docstring"""


 if not isinstance(call.func	,		ast.Name		):
  raise InterpretorError(
      F"It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func} of "
      F"type {type(call.func		)}."		)
 _lowercase				 =call.func.id
 if func_name not in tools:
  raise InterpretorError(
      F"It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func.id})."		)

 _lowercase				 =tools[func_name]
 # Todo deal with args
 _lowercase				 =[evaluate_ast(__snake_case	,		__snake_case	,		__snake_case		) for arg in call.args]
 _lowercase				 ={keyword.arg: evaluate_ast(keyword.value	,		__snake_case	,		__snake_case		) for keyword in call.keywords}
 return func(*__snake_case	,		**__snake_case		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case		)	->     Union[str, Any]:







 """simple docstring"""


 _lowercase				 =evaluate_ast(subscript.slice	,		__snake_case	,		__snake_case		)
 _lowercase				 =evaluate_ast(subscript.value	,		__snake_case	,		__snake_case		)
 if isinstance(__snake_case	,		(list, tuple)		):
  return value[int(__snake_case		)]
 if index in value:
  return value[index]
 if isinstance(__snake_case	,		__snake_case		) and isinstance(__snake_case	,		__snake_case		):
  _lowercase				 =difflib.get_close_matches(__snake_case	,		list(value.keys()		)		)
  if len(__snake_case		) > 0:
   return value[close_matches[0]]

 raise InterpretorError(F"Could not index {value} with '{index}'."		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case		)	->     Union[str, Any]:







 """simple docstring"""


 if name.id in state:
  return state[name.id]
 _lowercase				 =difflib.get_close_matches(name.id	,		list(state.keys()		)		)
 if len(__snake_case		) > 0:
  return state[close_matches[0]]
 raise InterpretorError(F"The variable `{name.id}` is not defined."		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case		)	->     int:







 """simple docstring"""


 if len(condition.ops		) > 1:
  raise InterpretorError('''Cannot evaluate conditions with multiple operators'''		)

 _lowercase				 =evaluate_ast(condition.left	,		__snake_case	,		__snake_case		)
 _lowercase				 =condition.ops[0]
 _lowercase				 =evaluate_ast(condition.comparators[0]	,		__snake_case	,		__snake_case		)

 if isinstance(__snake_case	,		ast.Eq		):
  return left == right
 elif isinstance(__snake_case	,		ast.NotEq		):
  return left != right
 elif isinstance(__snake_case	,		ast.Lt		):
  return left < right
 elif isinstance(__snake_case	,		ast.LtE		):
  return left <= right
 elif isinstance(__snake_case	,		ast.Gt		):
  return left > right
 elif isinstance(__snake_case	,		ast.GtE		):
  return left >= right
 elif isinstance(__snake_case	,		ast.Is		):
  return left is right
 elif isinstance(__snake_case	,		ast.IsNot		):
  return left is not right
 elif isinstance(__snake_case	,		ast.In		):
  return left in right
 elif isinstance(__snake_case	,		ast.NotIn		):
  return left not in right
 else:
  raise InterpretorError(F"Operator not supported: {comparator}"		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case		)	->     Dict:







 """simple docstring"""


 _lowercase				 =None
 if evaluate_condition(if_statement.test	,		__snake_case	,		__snake_case		):
  for line in if_statement.body:
   _lowercase				 =evaluate_ast(__snake_case	,		__snake_case	,		__snake_case		)
   if line_result is not None:
    _lowercase				 =line_result
 else:
  for line in if_statement.orelse:
   _lowercase				 =evaluate_ast(__snake_case	,		__snake_case	,		__snake_case		)
   if line_result is not None:
    _lowercase				 =line_result
 return result



def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case		)	->     Dict:







 """simple docstring"""


 _lowercase				 =None
 _lowercase				 =evaluate_ast(for_loop.iter	,		__snake_case	,		__snake_case		)
 for counter in iterator:
  _lowercase				 =counter
  for expression in for_loop.body:
   _lowercase				 =evaluate_ast(__snake_case	,		__snake_case	,		__snake_case		)
   if line_result is not None:
    _lowercase				 =line_result
 return result





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  lowerCamelCase__  :





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase=1_3						,	UpperCAmelCase=7						,	UpperCAmelCase=True						,	UpperCAmelCase=True						,	UpperCAmelCase=False						,	UpperCAmelCase=True						,	UpperCAmelCase=9_9						,	UpperCAmelCase=6_4						,	UpperCAmelCase=2						,	UpperCAmelCase=4						,	UpperCAmelCase=6_4						,	UpperCAmelCase="gelu"						,	UpperCAmelCase=0.1						,	UpperCAmelCase=0.1						,	UpperCAmelCase=5_1_2						,	UpperCAmelCase=1_6						,	UpperCAmelCase=2						,	UpperCAmelCase=0.02						,	UpperCAmelCase=3						,	UpperCAmelCase=4						,	UpperCAmelCase=None						,	)	->  Optional[Any]:
      _lowercase				 =parent
      _lowercase				 =batch_size
      _lowercase				 =seq_length
      _lowercase				 =is_training
      _lowercase				 =use_input_mask
      _lowercase				 =use_token_type_ids
      _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 	__A   (self			)	->  Union[str, Any]:
      _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				 =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 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Tuple:
      _lowercase				 =TFMPNetModel(config=UpperCAmelCase			)
      _lowercase				 ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
      _lowercase				 =model(UpperCAmelCase			)
      _lowercase				 =[input_ids, input_mask]
      _lowercase				 =model(UpperCAmelCase			)
      self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  int:
      _lowercase				 =TFMPNetForMaskedLM(config=UpperCAmelCase			)
      _lowercase				 ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
      _lowercase				 =model(UpperCAmelCase			)
      self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Union[str, Any]:
      _lowercase				 =TFMPNetForQuestionAnswering(config=UpperCAmelCase			)
      _lowercase				 ={
          '''input_ids''': input_ids,
          '''attention_mask''': input_mask,
      }
      _lowercase				 =model(UpperCAmelCase			)
      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 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Dict:
      _lowercase				 =self.num_labels
      _lowercase				 =TFMPNetForSequenceClassification(UpperCAmelCase			)
      _lowercase				 ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
      _lowercase				 =model(UpperCAmelCase			)
      self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  List[Any]:
      _lowercase				 =self.num_choices
      _lowercase				 =TFMPNetForMultipleChoice(UpperCAmelCase			)
      _lowercase				 =tf.tile(tf.expand_dims(UpperCAmelCase						,	1			)						,	(1, self.num_choices, 1)			)
      _lowercase				 =tf.tile(tf.expand_dims(UpperCAmelCase						,	1			)						,	(1, self.num_choices, 1)			)
      _lowercase				 ={
          '''input_ids''': multiple_choice_inputs_ids,
          '''attention_mask''': multiple_choice_input_mask,
      }
      _lowercase				 =model(UpperCAmelCase			)
      self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices)			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Dict:
      _lowercase				 =self.num_labels
      _lowercase				 =TFMPNetForTokenClassification(UpperCAmelCase			)
      _lowercase				 ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
      _lowercase				 =model(UpperCAmelCase			)
      self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)			)





     def 	__A   (self			)	->  List[Any]:
      _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_tf
class  lowerCamelCase__  (			lowerCAmelCase  ,				lowerCAmelCase  ,				unittest.TestCase):
     SCREAMING_SNAKE_CASE__      =     (
         (
             TFMPNetForMaskedLM,
             TFMPNetForMultipleChoice,
             TFMPNetForQuestionAnswering,
             TFMPNetForSequenceClassification,
             TFMPNetForTokenClassification,
             TFMPNetModel,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE__      =     (
         {
             '''feature-extraction''': TFMPNetModel,
             '''fill-mask''': TFMPNetForMaskedLM,
             '''question-answering''': TFMPNetForQuestionAnswering,
             '''text-classification''': TFMPNetForSequenceClassification,
             '''token-classification''': TFMPNetForTokenClassification,
             '''zero-shot''': TFMPNetForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     SCREAMING_SNAKE_CASE__      =     False
     SCREAMING_SNAKE_CASE__      =     False





     def 	__A   (self			)	->  Union[str, Any]:
      _lowercase				 =TFMPNetModelTester(self			)
      _lowercase				 =ConfigTester(self						,	config_class=UpperCAmelCase						,	hidden_size=3_7			)





     def 	__A   (self			)	->  Optional[Any]:
      self.config_tester.run_common_tests()





     def 	__A   (self			)	->  Tuple:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_model(*UpperCAmelCase			)





     def 	__A   (self			)	->  Tuple:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_masked_lm(*UpperCAmelCase			)





     def 	__A   (self			)	->  str:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_question_answering(*UpperCAmelCase			)





     def 	__A   (self			)	->  List[str]:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_sequence_classification(*UpperCAmelCase			)





     def 	__A   (self			)	->  Any:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_multiple_choice(*UpperCAmelCase			)





     def 	__A   (self			)	->  List[str]:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_token_classification(*UpperCAmelCase			)





     @slow
     def 	__A   (self			)	->  Tuple:
      for model_name in ["microsoft/mpnet-base"]:
       _lowercase				 =TFMPNetModel.from_pretrained(UpperCAmelCase			)
       self.assertIsNotNone(UpperCAmelCase			)







@require_tf
class  lowerCamelCase__  (			unittest.TestCase):





     @slow
     def 	__A   (self			)	->  Dict:
      _lowercase				 =TFMPNetModel.from_pretrained('''microsoft/mpnet-base'''			)
      _lowercase				 =tf.constant([[0, 1, 2, 3, 4, 5]]			)
      _lowercase				 =model(UpperCAmelCase			)[0]

      _lowercase				 =[1, 6, 7_6_8]
      self.assertEqual(output.shape						,	UpperCAmelCase			)

      _lowercase				 =tf.constant(
          [
              [
                  [-0.106_7172, 0.0821_6473, 0.002_4543],
                  [-0.0346_5879, 0.835_4118, -0.0325_2288],
                  [-0.0656_9476, -0.1242_4111, -0.049_4436],
              ]
          ]			)
      tf.debugging.assert_near(output[:, :3, :3]						,	UpperCAmelCase						,	atol=1e-4			)

