


import unittest

from transformers import YosoConfig, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    YosoForMaskedLM,
			    YosoForMultipleChoice,
			    YosoForQuestionAnswering,
			    YosoForSequenceClassification,
			    YosoForTokenClassification,
			    YosoModel,
			)
			from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST



class     snake_case__  :

				'''simple docstring'''





				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase=13     ,      _UpperCAmelCase=7     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=99     ,      _UpperCAmelCase=32     ,      _UpperCAmelCase=5     ,      _UpperCAmelCase=4     ,      _UpperCAmelCase=37     ,      _UpperCAmelCase="gelu"     ,      _UpperCAmelCase=0.1     ,      _UpperCAmelCase=0.1     ,      _UpperCAmelCase=512     ,      _UpperCAmelCase=16     ,      _UpperCAmelCase=2     ,      _UpperCAmelCase=0.02     ,      _UpperCAmelCase=3     ,      _UpperCAmelCase=4     ,      _UpperCAmelCase=None     ,      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      parent
										__magic_name__				:							Tuple				=      batch_size
										__magic_name__				:							int				=      seq_length
										__magic_name__				:							Optional[int]				=      is_training
										__magic_name__				:							List[Any]				=      use_input_mask
										__magic_name__				:							Tuple				=      use_token_type_ids
										__magic_name__				:							Tuple				=      use_labels
										__magic_name__				:							str				=      vocab_size
										__magic_name__				:							Any				=      hidden_size
										__magic_name__				:							Any				=      num_hidden_layers
										__magic_name__				:							int				=      num_attention_heads
										__magic_name__				:							Any				=      intermediate_size
										__magic_name__				:							Union[str, Any]				=      hidden_act
										__magic_name__				:							List[str]				=      hidden_dropout_prob
										__magic_name__				:							List[Any]				=      attention_probs_dropout_prob
										__magic_name__				:							List[Any]				=      max_position_embeddings
										__magic_name__				:							str				=      type_vocab_size
										__magic_name__				:							int				=      type_sequence_label_size
										__magic_name__				:							Tuple				=      initializer_range
										__magic_name__				:							List[Any]				=      num_labels
										__magic_name__				:							Optional[int]				=      num_choices
										__magic_name__				:							Union[str, Any]				=      scope
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							str				=      ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size      )

										__magic_name__				:							str				=      None
										if self.use_input_mask:
																__magic_name__				:							Tuple				=      random_attention_mask([self.batch_size, self.seq_length]      )

										__magic_name__				:							List[str]				=      None
										if self.use_token_type_ids:
																__magic_name__				:							Tuple				=      ids_tensor([self.batch_size, self.seq_length]     ,      self.type_vocab_size      )

										__magic_name__				:							Optional[Any]				=      None
										__magic_name__				:							int				=      None
										__magic_name__				:							Any				=      None
										if self.use_labels:
																__magic_name__				:							Any				=      ids_tensor([self.batch_size]     ,      self.type_sequence_label_size      )
																__magic_name__				:							Dict				=      ids_tensor([self.batch_size, self.seq_length]     ,      self.num_labels      )
																__magic_name__				:							Optional[int]				=      ids_tensor([self.batch_size]     ,      self.num_choices      )

										__magic_name__				:							Optional[Any]				=      self.get_config()

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return YosoConfig(
										    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     ,      is_decoder=_UpperCAmelCase     ,      initializer_range=self.initializer_range     ,      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      self.get_config()
										__magic_name__				:							List[Any]				=      300
										return config
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										(
										    __magic_name__ 
										)				:							str				=      self.prepare_config_and_inputs()

										__magic_name__				:							List[Any]				=      True
										__magic_name__				:							Optional[int]				=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
										__magic_name__				:							Any				=      ids_tensor([self.batch_size, self.seq_length]     ,      vocab_size=2      )

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    input_mask,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							int				=      YosoModel(config=_UpperCAmelCase      )
										model.to(_UpperCAmelCase      )
										model.eval()
										__magic_name__				:							Optional[Any]				=      model(_UpperCAmelCase     ,      attention_mask=_UpperCAmelCase     ,      token_type_ids=_UpperCAmelCase      )
										__magic_name__				:							Optional[int]				=      model(_UpperCAmelCase     ,      token_type_ids=_UpperCAmelCase      )
										__magic_name__				:							List[Any]				=      model(_UpperCAmelCase      )
										self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, self.seq_length, self.hidden_size)      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      ):




										"""simple docstring"""




										__magic_name__				:							str				=      True
										__magic_name__				:							List[Any]				=      YosoModel(_UpperCAmelCase      )
										model.to(_UpperCAmelCase      )
										model.eval()
										__magic_name__				:							Union[str, Any]				=      model(
										    _UpperCAmelCase     ,      attention_mask=_UpperCAmelCase     ,      token_type_ids=_UpperCAmelCase     ,      encoder_hidden_states=_UpperCAmelCase     ,      encoder_attention_mask=_UpperCAmelCase     ,      )
										__magic_name__				:							str				=      model(
										    _UpperCAmelCase     ,      attention_mask=_UpperCAmelCase     ,      token_type_ids=_UpperCAmelCase     ,      encoder_hidden_states=_UpperCAmelCase     ,      )
										__magic_name__				:							Optional[int]				=      model(_UpperCAmelCase     ,      attention_mask=_UpperCAmelCase     ,      token_type_ids=_UpperCAmelCase      )
										self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, self.seq_length, self.hidden_size)      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      YosoForMaskedLM(config=_UpperCAmelCase      )
										model.to(_UpperCAmelCase      )
										model.eval()
										__magic_name__				:							str				=      model(_UpperCAmelCase     ,      attention_mask=_UpperCAmelCase     ,      token_type_ids=_UpperCAmelCase     ,      labels=_UpperCAmelCase      )
										self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.vocab_size)      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      YosoForQuestionAnswering(config=_UpperCAmelCase      )
										model.to(_UpperCAmelCase      )
										model.eval()
										__magic_name__				:							int				=      model(
										    _UpperCAmelCase     ,      attention_mask=_UpperCAmelCase     ,      token_type_ids=_UpperCAmelCase     ,      start_positions=_UpperCAmelCase     ,      end_positions=_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  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      self.num_labels
										__magic_name__				:							Optional[Any]				=      YosoForSequenceClassification(_UpperCAmelCase      )
										model.to(_UpperCAmelCase      )
										model.eval()
										__magic_name__				:							List[Any]				=      model(_UpperCAmelCase     ,      attention_mask=_UpperCAmelCase     ,      token_type_ids=_UpperCAmelCase     ,      labels=_UpperCAmelCase      )
										self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_labels)      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      self.num_labels
										__magic_name__				:							str				=      YosoForTokenClassification(config=_UpperCAmelCase      )
										model.to(_UpperCAmelCase      )
										model.eval()
										__magic_name__				:							Any				=      model(_UpperCAmelCase     ,      attention_mask=_UpperCAmelCase     ,      token_type_ids=_UpperCAmelCase     ,      labels=_UpperCAmelCase      )
										self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.num_labels)      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							int				=      self.num_choices
										__magic_name__				:							str				=      YosoForMultipleChoice(config=_UpperCAmelCase      )
										model.to(_UpperCAmelCase      )
										model.eval()
										__magic_name__				:							List[Any]				=      input_ids.unsqueeze(1      ).expand(-1     ,      self.num_choices     ,      -1      ).contiguous()
										__magic_name__				:							Dict				=      token_type_ids.unsqueeze(1      ).expand(-1     ,      self.num_choices     ,      -1      ).contiguous()
										__magic_name__				:							Union[str, Any]				=      input_mask.unsqueeze(1      ).expand(-1     ,      self.num_choices     ,      -1      ).contiguous()
										__magic_name__				:							Dict				=      model(
										    _UpperCAmelCase     ,      attention_mask=_UpperCAmelCase     ,      token_type_ids=_UpperCAmelCase     ,      labels=_UpperCAmelCase     ,      )
										self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_choices)      )





				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      self.prepare_config_and_inputs()
										(
										    __magic_name__ 
										)				:							List[str]				=      config_and_inputs
										__magic_name__				:							List[str]				=      {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
										return config, inputs_dict






@require_torch
class     snake_case__  (      _A						,       _A						,       unittest.TestCase  ):

				'''simple docstring'''





				__lowerCAmelCase		=      (
				    (
				        YosoModel,
				        YosoForMaskedLM,
				        YosoForMultipleChoice,
				        YosoForQuestionAnswering,
				        YosoForSequenceClassification,
				        YosoForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				__lowerCAmelCase		=      False
				__lowerCAmelCase		=      False
				__lowerCAmelCase		=      False

				__lowerCAmelCase		=      ()
				__lowerCAmelCase		=      (
				    {
				        '''feature-extraction''': YosoModel,
				        '''fill-mask''': YosoForMaskedLM,
				        '''question-answering''': YosoForQuestionAnswering,
				        '''text-classification''': YosoForSequenceClassification,
				        '''token-classification''': YosoForTokenClassification,
				        '''zero-shot''': YosoForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      YosoModelTester(self      )
										__magic_name__				:							Tuple				=      ConfigTester(self     ,      config_class=_UpperCAmelCase     ,      hidden_size=37      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										self.config_tester.run_common_tests()
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Dict				=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							int				=      self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																__magic_name__				:							List[str]				=      type
																self.model_tester.create_and_check_model(*_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							List[Any]				=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase      )
				@slow
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__magic_name__				:							str				=      YosoModel.from_pretrained(_UpperCAmelCase      )
																self.assertIsNotNone(_UpperCAmelCase      )





				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return






@require_torch
class     snake_case__  (      unittest.TestCase  ):

				'''simple docstring'''





				@slow
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							int				=      YosoModel.from_pretrained('uw-madison/yoso-4096'      )
										__magic_name__				:							Dict				=      torch.tensor([[0, 1, 2, 3, 4, 5]]      )

										with torch.no_grad():
																__magic_name__				:							Optional[Any]				=      model(_UpperCAmelCase      )[0]

										__magic_name__				:							str				=      torch.Size((1, 6, 768)      )
										self.assertEqual(output.shape     ,      _UpperCAmelCase      )

										__magic_name__				:							Dict				=      torch.tensor(
										    [[[-0.0611, 0.1242, 0.0840], [0.0280, -0.0048, 0.1125], [0.0106, 0.0226, 0.0751]]]      )

										self.assertTrue(torch.allclose(output[:, :3, :3]     ,      _UpperCAmelCase     ,      atol=1E-4      )      )
				@slow
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      YosoForMaskedLM.from_pretrained('uw-madison/yoso-4096'      )
										__magic_name__				:							List[str]				=      torch.tensor([[0, 1, 2, 3, 4, 5]]      )

										with torch.no_grad():
																__magic_name__				:							Tuple				=      model(_UpperCAmelCase      )[0]

										__magic_name__				:							Any				=      5_0265

										__magic_name__				:							Any				=      torch.Size((1, 6, vocab_size)      )
										self.assertEqual(output.shape     ,      _UpperCAmelCase      )

										__magic_name__				:							Union[str, Any]				=      torch.tensor(
										    [[[-2.1313, -3.7285, -2.2407], [-2.7047, -3.3314, -2.6408], [0.0629, -2.5166, -0.3356]]]      )

										self.assertTrue(torch.allclose(output[:, :3, :3]     ,      _UpperCAmelCase     ,      atol=1E-4      )      )





				@slow
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      YosoForMaskedLM.from_pretrained('uw-madison/yoso-4096'      )
										__magic_name__				:							Dict				=      torch.arange(4096      ).unsqueeze(0      )

										with torch.no_grad():
																__magic_name__				:							int				=      model(_UpperCAmelCase      )[0]

										__magic_name__				:							int				=      5_0265

										__magic_name__				:							List[Any]				=      torch.Size((1, 4096, vocab_size)      )
										self.assertEqual(output.shape     ,      _UpperCAmelCase      )

										__magic_name__				:							Any				=      torch.tensor(
										    [[[-2.3914, -4.3742, -5.0956], [-4.0988, -4.2384, -7.0406], [-3.1427, -3.7192, -6.6800]]]      )

										self.assertTrue(torch.allclose(output[:, :3, :3]     ,      _UpperCAmelCase     ,      atol=1E-4      )      )






# 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     snake_case__  (      _A  ):
				pass






def  __SCREAMING_SNAKE_CASE       (				lowercase__  :str      ,						lowercase__  :Dict[str, Callable]      ,						lowercase__  :Union[str, Any]=None      ,						lowercase__  :str=False				)       ->  List[str]:
						try:
												__magic_name__				:							str				=      ast.parse(lowercase__				)
						except SyntaxError as e:
												print('The code generated by the agent is not valid.\n'      ,						lowercase__				)
												return
						if state is None:
												__magic_name__				:							Optional[Any]				=      {}
						__magic_name__				:							Dict				=      None
						for idx, node in enumerate(expression.body				):
												try:
																		__magic_name__				:							Any				=      evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				)
												except InterpretorError as e:
																		__magic_name__				:							Tuple				=      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(lowercase__				)
																		break
												if line_result is not None:
																		__magic_name__				:							List[str]				=      line_result

						return result
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :ast.AST      ,						lowercase__  :Dict[str, Any]      ,						lowercase__  :Dict[str, Callable]				)       ->  Dict:
						if isinstance(lowercase__      ,						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(lowercase__      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.Call				):
												# Function call -> we return the value of the function call
												return evaluate_call(lowercase__      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.Constant				):
												# Constant -> just return the value
												return expression.value
						elif isinstance(lowercase__      ,						ast.Dict				):
												# Dict -> evaluate all keys and values
												__magic_name__				:							Any				=      [evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				) for k in expression.keys]
												__magic_name__				:							Dict				=      [evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				) for v in expression.values]
												return dict(zip(lowercase__      ,						lowercase__				)				)
						elif isinstance(lowercase__      ,						ast.Expr				):
												# Expression -> evaluate the content
												return evaluate_ast(expression.value      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.For				):
												# For loop -> execute the loop
												return evaluate_for(lowercase__      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.FormattedValue				):
												# Formatted value (part of f-string) -> evaluate the content and return
												return evaluate_ast(expression.value      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.If				):
												# If -> execute the right branch
												return evaluate_if(lowercase__      ,						lowercase__      ,						lowercase__				)
						elif hasattr(lowercase__      ,						'Index'				) and isinstance(lowercase__      ,						ast.Index				):
												return evaluate_ast(expression.value      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.JoinedStr				):
												return "".join([str(evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				)				) for v in expression.values]				)
						elif isinstance(lowercase__      ,						ast.List				):
												# List -> evaluate all elements
												return [evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				) for elt in expression.elts]
						elif isinstance(lowercase__      ,						ast.Name				):
												# Name -> pick up the value in the state
												return evaluate_name(lowercase__      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.Subscript				):
												# Subscript -> return the value of the indexing
												return evaluate_subscript(lowercase__      ,						lowercase__      ,						lowercase__				)
						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  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[Any]      ,						lowercase__  :Optional[Any]      ,						lowercase__  :Union[str, Any]				)       ->  List[Any]:
						__magic_name__				:							Union[str, Any]				=      assign.targets
						__magic_name__				:							int				=      evaluate_ast(assign.value      ,						lowercase__      ,						lowercase__				)

						if len(lowercase__				) == 1:
												__magic_name__				:							Any				=      result
						else:
												if len(lowercase__				) != len(lowercase__				):
																		raise InterpretorError(f"Expected {len(lowercase__				)} values but got {len(lowercase__				)}."				)
												for var_name, r in zip(lowercase__      ,						lowercase__				):
																		__magic_name__				:							str				=      r
						return result
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[str]      ,						lowercase__  :List[str]      ,						lowercase__  :Tuple				)       ->  Optional[Any]:
						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				)}."				)
						__magic_name__				:							Dict				=      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})."				)

						__magic_name__				:							Tuple				=      tools[func_name]
						# Todo deal with args
						__magic_name__				:							List[str]				=      [evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				) for arg in call.args]
						__magic_name__				:							int				=      {keyword.arg: evaluate_ast(keyword.value      ,						lowercase__      ,						lowercase__				) for keyword in call.keywords}
						return func(*lowercase__      ,						**lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Union[str, Any]      ,						lowercase__  :int      ,						lowercase__  :int				)       ->  List[Any]:
						__magic_name__				:							Optional[int]				=      evaluate_ast(subscript.slice      ,						lowercase__      ,						lowercase__				)
						__magic_name__				:							Union[str, Any]				=      evaluate_ast(subscript.value      ,						lowercase__      ,						lowercase__				)
						if isinstance(lowercase__      ,						(list, tuple)				):
												return value[int(lowercase__				)]
						if index in value:
												return value[index]
						if isinstance(lowercase__      ,						lowercase__				) and isinstance(lowercase__      ,						lowercase__				):
												__magic_name__				:							Any				=      difflib.get_close_matches(lowercase__      ,						list(value.keys()				)				)
												if len(lowercase__				) > 0:
																		return value[close_matches[0]]

						raise InterpretorError(f"Could not index {value} with '{index}'."				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[str]      ,						lowercase__  :Optional[Any]      ,						lowercase__  :Optional[int]				)       ->  int:
						if name.id in state:
												return state[name.id]
						__magic_name__				:							Union[str, Any]				=      difflib.get_close_matches(name.id      ,						list(state.keys()				)				)
						if len(lowercase__				) > 0:
												return state[close_matches[0]]
						raise InterpretorError(f"The variable `{name.id}` is not defined."				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Any      ,						lowercase__  :Union[str, Any]      ,						lowercase__  :Optional[Any]				)       ->  List[str]:
						if len(condition.ops				) > 1:
												raise InterpretorError('Cannot evaluate conditions with multiple operators'				)

						__magic_name__				:							int				=      evaluate_ast(condition.left      ,						lowercase__      ,						lowercase__				)
						__magic_name__				:							Optional[int]				=      condition.ops[0]
						__magic_name__				:							Union[str, Any]				=      evaluate_ast(condition.comparators[0]      ,						lowercase__      ,						lowercase__				)

						if isinstance(lowercase__      ,						ast.Eq				):
												return left == right
						elif isinstance(lowercase__      ,						ast.NotEq				):
												return left != right
						elif isinstance(lowercase__      ,						ast.Lt				):
												return left < right
						elif isinstance(lowercase__      ,						ast.LtE				):
												return left <= right
						elif isinstance(lowercase__      ,						ast.Gt				):
												return left > right
						elif isinstance(lowercase__      ,						ast.GtE				):
												return left >= right
						elif isinstance(lowercase__      ,						ast.Is				):
												return left is right
						elif isinstance(lowercase__      ,						ast.IsNot				):
												return left is not right
						elif isinstance(lowercase__      ,						ast.In				):
												return left in right
						elif isinstance(lowercase__      ,						ast.NotIn				):
												return left not in right
						else:
												raise InterpretorError(f"Operator not supported: {comparator}"				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :str      ,						lowercase__  :Tuple				)       ->  int:
						__magic_name__				:							Optional[int]				=      None
						if evaluate_condition(if_statement.test      ,						lowercase__      ,						lowercase__				):
												for line in if_statement.body:
																		__magic_name__				:							List[Any]				=      evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				)
																		if line_result is not None:
																								__magic_name__				:							Tuple				=      line_result
						else:
												for line in if_statement.orelse:
																		__magic_name__				:							Optional[int]				=      evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				)
																		if line_result is not None:
																								__magic_name__				:							List[Any]				=      line_result
						return result

def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[Any]      ,						lowercase__  :Union[str, Any]      ,						lowercase__  :List[str]				)       ->  Dict:
						__magic_name__				:							int				=      None
						__magic_name__				:							List[Any]				=      evaluate_ast(for_loop.iter      ,						lowercase__      ,						lowercase__				)
						for counter in iterator:
												__magic_name__				:							Tuple				=      counter
												for expression in for_loop.body:
																		__magic_name__				:							Dict				=      evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				)
																		if line_result is not None:
																								__magic_name__				:							Dict				=      line_result
						return result


