import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )
    from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
    import torch
class 				a_ :






 '''simple docstring'''






 def __init__(			self					:  str			,    lowercase__					:  List[str]			,    lowercase__					:  Any=14			,    lowercase__					:  Optional[int]=7			,    lowercase__					:  Union[str, Any]=True			,    lowercase__					:  List[str]=True			,    lowercase__					:  Union[str, Any]=False			,    lowercase__					:  int=True			,    lowercase__					:  List[str]=99			,    lowercase__					:  List[str]=32			,    lowercase__					:  Optional[Any]=4			,    lowercase__					:  Tuple=4			,    lowercase__					:  List[str]=[[["global", "local"], 2]]			,    lowercase__					:  Tuple=37			,    lowercase__					:  int="gelu"			,    lowercase__					:  int=0.1			,    lowercase__					:  int=0.1			,    lowercase__					:  Union[str, Any]=512			,    lowercase__					:  Tuple=7			,    lowercase__					:  List[Any]=0.02			,    ):







        '''simple docstring'''



        lowerCAmelCase__     	=					parent
        lowerCAmelCase__     	=					batch_size
        lowerCAmelCase__     	=					seq_length
        lowerCAmelCase__     	=					is_training
        lowerCAmelCase__     	=					use_input_mask
        lowerCAmelCase__     	=					use_token_type_ids
        lowerCAmelCase__     	=					use_labels
        lowerCAmelCase__     	=					vocab_size
        lowerCAmelCase__     	=					hidden_size
        lowerCAmelCase__     	=					num_hidden_layers
        lowerCAmelCase__     	=					num_attention_heads
        lowerCAmelCase__     	=					attention_types
        lowerCAmelCase__     	=					intermediate_size
        lowerCAmelCase__     	=					hidden_act
        lowerCAmelCase__     	=					hidden_dropout_prob
        lowerCAmelCase__     	=					attention_probs_dropout_prob
        lowerCAmelCase__     	=					max_position_embeddings
        lowerCAmelCase__     	=					window_size
        lowerCAmelCase__     	=					initializer_range
        lowerCAmelCase__     	=					None
        lowerCAmelCase__     	=					vocab_size - 1
        lowerCAmelCase__     	=					vocab_size - 1
        lowerCAmelCase__     	=					vocab_size - 1




 def 		__snake_case						(			self					:  str):







        '''simple docstring'''



        lowerCAmelCase__     	=					ids_tensor([self.batch_size, self.seq_length]			,    self.vocab_size)

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

        lowerCAmelCase__     	=					GPTNeoConfig(
            vocab_size=self.vocab_size			,    hidden_size=self.hidden_size			,    num_layers=self.num_hidden_layers			,    num_heads=self.num_attention_heads			,    max_position_embeddings=self.max_position_embeddings			,    use_cache=lowercase__			,    bos_token_id=self.bos_token_id			,    eos_token_id=self.eos_token_id			,    pad_token_id=self.pad_token_id			,    window_size=self.window_size			,    attention_types=self.attention_types			,    )

        return (config, input_ids, input_mask)




 def 		__snake_case						(			self					:  Any):







        '''simple docstring'''



        lowerCAmelCase__     	=					self.prepare_config_and_inputs()
        lowerCAmelCase__	,					lowerCAmelCase__	,					lowerCAmelCase__     	=					config_and_inputs
        lowerCAmelCase__     	=					{'input_ids': input_ids, 'attention_mask': attention_mask}
        return config, inputs_dict




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







        '''simple docstring'''



        lowerCAmelCase__     	=					20
        lowerCAmelCase__     	=					model_class_name(lowercase__)

        lowerCAmelCase__     	=					model.init_cache(input_ids.shape[0]			,    lowercase__)
        lowerCAmelCase__     	=					jnp.ones((input_ids.shape[0], max_decoder_length)			,    dtype='i4')

        lowerCAmelCase__     	=					jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1)[None, :]			,    (input_ids.shape[0], input_ids.shape[-1] - 1))
        lowerCAmelCase__     	=					model(
            input_ids[:, :-1]			,    attention_mask=lowercase__			,    past_key_values=lowercase__			,    position_ids=lowercase__			,    )

        lowerCAmelCase__     	=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,    dtype='i4')
        lowerCAmelCase__     	=					model(
            input_ids[:, -1:]			,    attention_mask=lowercase__			,    past_key_values=outputs_cache.past_key_values			,    position_ids=lowercase__			,    )

        lowerCAmelCase__     	=					model(lowercase__)

        lowerCAmelCase__     	=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
        self.parent.assertTrue(diff < 1e-3			,    msg=F"""Max diff is {diff}""")




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







        '''simple docstring'''



        lowerCAmelCase__     	=					20
        lowerCAmelCase__     	=					model_class_name(lowercase__)

        lowerCAmelCase__     	=					jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))]			,    axis=-1			,    )

        lowerCAmelCase__     	=					model.init_cache(input_ids.shape[0]			,    lowercase__)
        lowerCAmelCase__     	=					jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1)[None, :]			,    (input_ids.shape[0], input_ids.shape[-1] - 1))

        lowerCAmelCase__     	=					model(
            input_ids[:, :-1]			,    attention_mask=lowercase__			,    past_key_values=lowercase__			,    position_ids=lowercase__			,    )
        lowerCAmelCase__     	=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,    dtype='i4')
        lowerCAmelCase__     	=					model(
            input_ids[:, -1:]			,    past_key_values=outputs_cache.past_key_values			,    attention_mask=lowercase__			,    position_ids=lowercase__			,    )

        lowerCAmelCase__     	=					model(lowercase__			,    attention_mask=lowercase__)

        lowerCAmelCase__     	=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
        self.parent.assertTrue(diff < 1e-3			,    msg=F"""Max diff is {diff}""")






@require_flax
class 				a_ (						SCREAMING_SNAKE_CASE       ,     SCREAMING_SNAKE_CASE       ,     unittest.TestCase						):






 '''simple docstring'''






 UpperCAmelCase_	 =					(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
 UpperCAmelCase_	 =					(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




 def 		__snake_case						(			self					:  List[str]):







        '''simple docstring'''



        lowerCAmelCase__     	=					FlaxGPTNeoModelTester(self)




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







        '''simple docstring'''



        for model_class_name in self.all_model_classes:
               lowerCAmelCase__	,					lowerCAmelCase__	,					lowerCAmelCase__     	=					self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward(lowercase__			,    lowercase__			,    lowercase__			,    lowercase__)




 def 		__snake_case						(			self					:  List[str]):







        '''simple docstring'''



        for model_class_name in self.all_model_classes:
               lowerCAmelCase__	,					lowerCAmelCase__	,					lowerCAmelCase__     	=					self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward_with_attn_mask(
                   lowercase__			,    lowercase__			,    lowercase__			,    lowercase__)




 @slow
 def 		__snake_case						(			self					:  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__     	=					GPTaTokenizer.from_pretrained('gpt2'			,    pad_token='<|endoftext|>'			,    padding_side='left')
        lowerCAmelCase__     	=					tokenizer(['Hello this is a long string', 'Hey']			,    return_tensors='np'			,    padding=lowercase__			,    truncation=lowercase__)

        lowerCAmelCase__     	=					FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M')
        lowerCAmelCase__     	=					False
        lowerCAmelCase__     	=					model.config.eos_token_id

        lowerCAmelCase__     	=					jax.jit(model.generate)

        lowerCAmelCase__     	=					jit_generate(
            inputs['input_ids']			,    attention_mask=inputs['attention_mask']			,    pad_token_id=tokenizer.pad_token_id).sequences

        lowerCAmelCase__     	=					tokenizer.batch_decode(lowercase__			,    skip_special_tokens=lowercase__)

        lowerCAmelCase__     	=					[
            'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
            'Hey, I\'m a little late to the party. I\'m going to',
        ]

        self.assertListEqual(lowercase__			,    lowercase__)




 @is_pt_flax_cross_test
 def 		__snake_case						(			self					:  Dict):







        '''simple docstring'''



        lowerCAmelCase__	,					lowerCAmelCase__     	=					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__):
                      # prepare inputs
                      lowerCAmelCase__     	=					self._prepare_for_class(lowercase__			,    lowercase__)
                      lowerCAmelCase__     	=					{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

                      # load corresponding PyTorch class
                      lowerCAmelCase__     	=					model_class.__name__[4:]  # Skip the "Flax" at the beginning
                      lowerCAmelCase__     	=					getattr(lowercase__			,    lowercase__)

                      lowerCAmelCase__	,					lowerCAmelCase__     	=					pt_inputs['input_ids'].shape
                      lowerCAmelCase__     	=					np.random.randint(0			,    seq_length - 1			,    size=(batch_size,))
                      for batch_idx, start_index in enumerate(lowercase__):
                             lowerCAmelCase__     	=					0
                             lowerCAmelCase__     	=					1
                             lowerCAmelCase__     	=					0
                             lowerCAmelCase__     	=					1
                      lowerCAmelCase__     	=					pt_model_class(lowercase__).eval()
                      lowerCAmelCase__     	=					model_class(lowercase__			,    dtype=jnp.floataa)

                      lowerCAmelCase__     	=					convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,    lowercase__)
                      lowerCAmelCase__     	=					fx_state

                      with torch.no_grad():
                             lowerCAmelCase__     	=					pt_model(**lowercase__).to_tuple()

                      lowerCAmelCase__     	=					fx_model(**lowercase__).to_tuple()
                      self.assertEqual(len(lowercase__)			,    len(lowercase__)			,    'Output lengths differ between Flax and PyTorch')
                      for fx_output, pt_output in zip(lowercase__			,    lowercase__):
                             self.assert_almost_equals(fx_output[:, -1]			,    pt_output[:, -1].numpy()			,    4e-2)

                      with tempfile.TemporaryDirectory() as tmpdirname:
                             pt_model.save_pretrained(lowercase__)
                             lowerCAmelCase__     	=					model_class.from_pretrained(lowercase__			,    from_pt=lowercase__)

                      lowerCAmelCase__     	=					fx_model_loaded(**lowercase__).to_tuple()
                      self.assertEqual(
                          len(lowercase__)			,    len(lowercase__)			,    'Output lengths differ between Flax and PyTorch')
                      for fx_output_loaded, pt_output in zip(lowercase__			,    lowercase__):
                             self.assert_almost_equals(fx_output_loaded[:, -1]			,    pt_output[:, -1].numpy()			,    4e-2)




 @is_pt_flax_cross_test
 def 		__snake_case						(			self					:  Dict):







        '''simple docstring'''



        lowerCAmelCase__	,					lowerCAmelCase__     	=					self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__):
                      # prepare inputs
                      lowerCAmelCase__     	=					self._prepare_for_class(lowercase__			,    lowercase__)
                      lowerCAmelCase__     	=					{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

                      # load corresponding PyTorch class
                      lowerCAmelCase__     	=					model_class.__name__[4:]  # Skip the "Flax" at the beginning
                      lowerCAmelCase__     	=					getattr(lowercase__			,    lowercase__)

                      lowerCAmelCase__     	=					pt_model_class(lowercase__).eval()
                      lowerCAmelCase__     	=					model_class(lowercase__			,    dtype=jnp.floataa)

                      lowerCAmelCase__     	=					load_flax_weights_in_pytorch_model(lowercase__			,    fx_model.params)
                      lowerCAmelCase__	,					lowerCAmelCase__     	=					pt_inputs['input_ids'].shape
                      lowerCAmelCase__     	=					np.random.randint(0			,    seq_length - 1			,    size=(batch_size,))
                      for batch_idx, start_index in enumerate(lowercase__):
                             lowerCAmelCase__     	=					0
                             lowerCAmelCase__     	=					1
                             lowerCAmelCase__     	=					0
                             lowerCAmelCase__     	=					1

                      # make sure weights are tied in PyTorch
                      pt_model.tie_weights()

                      with torch.no_grad():
                             lowerCAmelCase__     	=					pt_model(**lowercase__).to_tuple()

                      lowerCAmelCase__     	=					fx_model(**lowercase__).to_tuple()
                      self.assertEqual(len(lowercase__)			,    len(lowercase__)			,    'Output lengths differ between Flax and PyTorch')
                      for fx_output, pt_output in zip(lowercase__			,    lowercase__):
                             self.assert_almost_equals(fx_output[:, -1]			,    pt_output[:, -1].numpy()			,    4e-2)

                      with tempfile.TemporaryDirectory() as tmpdirname:
                             fx_model.save_pretrained(lowercase__)
                             lowerCAmelCase__     	=					pt_model_class.from_pretrained(lowercase__			,    from_flax=lowercase__)

                      with torch.no_grad():
                             lowerCAmelCase__     	=					pt_model_loaded(**lowercase__).to_tuple()

                      self.assertEqual(
                          len(lowercase__)			,    len(lowercase__)			,    'Output lengths differ between Flax and PyTorch')
                      for fx_output, pt_output in zip(lowercase__			,    lowercase__):
                             self.assert_almost_equals(fx_output[:, -1]			,    pt_output[:, -1].numpy()			,    4e-2)




 @slow
 def 		__snake_case						(			self					:  Optional[int]):







        '''simple docstring'''



        for model_class_name in self.all_model_classes:
               lowerCAmelCase__     	=					model_class_name.from_pretrained('EleutherAI/gpt-neo-125M')
               lowerCAmelCase__     	=					model(np.ones((1, 1)))
               self.assertIsNotNone(lowercase__)

# 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 				a_ (						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 pass






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__=None	,  lowerCAmelCase__=False	):
       try:
              lowerCAmelCase__     	=					ast.parse(lowerCAmelCase__	)
       except SyntaxError as e:
              print('The code generated by the agent is not valid.\n'	,  lowerCAmelCase__	)
              return
       if state is None:
              lowerCAmelCase__     	=					{}
       lowerCAmelCase__     	=					None
       for idx, node in enumerate(expression.body	):
              try:
                     lowerCAmelCase__     	=					evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
              except InterpretorError as e:
                     lowerCAmelCase__     	=					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(lowerCAmelCase__	)
                     break
              if line_result is not None:
                     lowerCAmelCase__     	=					line_result

       return result






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

       if len(lowerCAmelCase__	) == 1:
              lowerCAmelCase__     	=					result
       else:
              if len(lowerCAmelCase__	) != len(lowerCAmelCase__	):
                     raise InterpretorError(F"""Expected {len(lowerCAmelCase__	)} values but got {len(lowerCAmelCase__	)}."""	)
              for var_name, r in zip(lowerCAmelCase__	,  lowerCAmelCase__	):
                     lowerCAmelCase__     	=					r
       return result






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       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	)}."""	)
       lowerCAmelCase__     	=					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})."""	)

       lowerCAmelCase__     	=					tools[func_name]
       # Todo deal with args
       lowerCAmelCase__     	=					[evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	) for arg in call.args]
       lowerCAmelCase__     	=					{keyword.arg: evaluate_ast(keyword.value	,  lowerCAmelCase__	,  lowerCAmelCase__	) for keyword in call.keywords}
       return func(*lowerCAmelCase__	,  **lowerCAmelCase__	)






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

       raise InterpretorError(F"""Could not index {value} with '{index}'."""	)






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






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

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

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






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






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