


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


A :	Tuple												=     logging.get_logger(__name__)

A :	Optional[Any]												=     {
    'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}



class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					A__    =				'''blenderbot'''
					A__    =				['''past_key_values''']
					A__    =				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

					def __init__(self					:		Optional[int]   ,      _UpperCAmelCase					:		Union[str, Any]=8008   ,      _UpperCAmelCase					:		Dict=128   ,      _UpperCAmelCase					:		Dict=2   ,      _UpperCAmelCase					:		Tuple=1_0240   ,      _UpperCAmelCase					:		List[Any]=32   ,      _UpperCAmelCase					:		Optional[Any]=24   ,      _UpperCAmelCase					:		List[str]=1_0240   ,      _UpperCAmelCase					:		int=32   ,      _UpperCAmelCase					:		Union[str, Any]=0.0   ,      _UpperCAmelCase					:		List[str]=0.0   ,      _UpperCAmelCase					:		List[str]=True   ,      _UpperCAmelCase					:		Optional[int]=True   ,      _UpperCAmelCase					:		Union[str, Any]="gelu"   ,      _UpperCAmelCase					:		List[Any]=2560   ,      _UpperCAmelCase					:		Tuple=0.1   ,      _UpperCAmelCase					:		Union[str, Any]=0.0   ,      _UpperCAmelCase					:		Optional[Any]=0.0   ,      _UpperCAmelCase					:		Dict=0.02   ,      _UpperCAmelCase					:		List[str]=1   ,      _UpperCAmelCase					:		List[str]=False   ,      _UpperCAmelCase					:		Optional[int]=0   ,      _UpperCAmelCase					:		str=1   ,      _UpperCAmelCase					:		Optional[int]=2   ,      _UpperCAmelCase					:		Tuple=3   ,      _UpperCAmelCase					:		Dict=2   ,      **_UpperCAmelCase					:		List[str]   ,      )		->     Tuple:




							"""simple docstring"""


							lowercase__      =	vocab_size
							lowercase__      =	max_position_embeddings
							lowercase__      =	d_model
							lowercase__      =	encoder_ffn_dim
							lowercase__      =	encoder_layers
							lowercase__      =	encoder_attention_heads
							lowercase__      =	decoder_ffn_dim
							lowercase__      =	decoder_layers
							lowercase__      =	decoder_attention_heads
							lowercase__      =	dropout
							lowercase__      =	attention_dropout
							lowercase__      =	activation_dropout
							lowercase__      =	activation_function
							lowercase__      =	init_std
							lowercase__      =	encoder_layerdrop
							lowercase__      =	decoder_layerdrop
							lowercase__      =	use_cache
							lowercase__      =	encoder_layers
							lowercase__      =	scale_embedding  # scale factor will be sqrt(d_model) if True

							super().__init__(
							    pad_token_id=_UpperCAmelCase   ,      bos_token_id=_UpperCAmelCase   ,      eos_token_id=_UpperCAmelCase   ,      is_encoder_decoder=_UpperCAmelCase   ,      decoder_start_token_id=_UpperCAmelCase   ,      encoder_no_repeat_ngram_size=_UpperCAmelCase   ,      forced_eos_token_id=_UpperCAmelCase   ,      **_UpperCAmelCase   ,      )






class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					@property
					def 						lowerCamelCase__  (self					:		str	)		->     Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							if self.task in ["default", "seq2seq-lm"]:
									lowercase__      =	OrderedDict(
									    [
									        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
									        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
									    ]	)
									if self.use_past:
											lowercase__      =	{0: """batch"""}
											lowercase__      =	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
									else:
											lowercase__      =	{0: """batch""", 1: """decoder_sequence"""}
											lowercase__      =	{0: """batch""", 1: """decoder_sequence"""}
									if self.use_past:
											self.fill_with_past_key_values_(_UpperCAmelCase   ,      direction="""inputs"""	)
							elif self.task == "causal-lm":
									lowercase__      =	OrderedDict(
									    [
									        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
									        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
									    ]	)
									if self.use_past:
											lowercase__    ,			lowercase__      =	self.num_layers
											for i in range(_UpperCAmelCase	):
													lowercase__      =	{0: """batch""", 2: """past_sequence + sequence"""}
													lowercase__      =	{0: """batch""", 2: """past_sequence + sequence"""}
							else:
									lowercase__      =	OrderedDict(
									    [
									        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
									        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
									        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
									        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
									    ]	)

							return common_inputs

					@property
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
					def 						lowerCamelCase__  (self					:		Optional[int]	)		->     Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							if self.task in ["default", "seq2seq-lm"]:
									lowercase__      =	super().outputs
							else:
									lowercase__      =	super(_UpperCAmelCase   ,      self	).outputs
									if self.use_past:
											lowercase__    ,			lowercase__      =	self.num_layers
											for i in range(_UpperCAmelCase	):
													lowercase__      =	{0: """batch""", 2: """past_sequence + sequence"""}
													lowercase__      =	{0: """batch""", 2: """past_sequence + sequence"""}
							return common_outputs

					def 						lowerCamelCase__  (self					:		Any   ,      _UpperCAmelCase					:		PreTrainedTokenizer   ,      _UpperCAmelCase					:		int = -1   ,      _UpperCAmelCase					:		int = -1   ,      _UpperCAmelCase					:		bool = False   ,      _UpperCAmelCase					:		Optional[TensorType] = None   ,      )		->     Mapping[str, Any]:




							"""simple docstring"""


							lowercase__      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    _UpperCAmelCase   ,      _UpperCAmelCase   ,      _UpperCAmelCase   ,      _UpperCAmelCase   ,      _UpperCAmelCase	)
							# Generate decoder inputs
							lowercase__      =	seq_length if not self.use_past else 1
							lowercase__      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    _UpperCAmelCase   ,      _UpperCAmelCase   ,      _UpperCAmelCase   ,      _UpperCAmelCase   ,      _UpperCAmelCase	)
							lowercase__      =	{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
							lowercase__      =	dict(**_UpperCAmelCase   ,      **_UpperCAmelCase	)

							if self.use_past:
									if not is_torch_available():
											raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""	)
									else:
											import torch
									lowercase__    ,			lowercase__      =	common_inputs["""input_ids"""].shape
									lowercase__      =	common_inputs["""decoder_input_ids"""].shape[1]
									lowercase__    ,			lowercase__      =	self.num_attention_heads
									lowercase__      =	(
									    batch,
									    num_encoder_attention_heads,
									    encoder_seq_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)
									lowercase__      =	decoder_seq_length
									lowercase__      =	(
									    batch,
									    num_decoder_attention_heads,
									    decoder_past_length,
									    self._config.hidden_size // num_decoder_attention_heads,
									)
									lowercase__      =	torch.cat(
									    [common_inputs["""decoder_attention_mask"""], torch.ones(_UpperCAmelCase   ,      _UpperCAmelCase	)]   ,      dim=1	)
									lowercase__      =	[]
									lowercase__    ,			lowercase__      =	self.num_layers

									for _ in range(_UpperCAmelCase	):
											common_inputs["past_key_values"].append(
											    (
											        torch.zeros(_UpperCAmelCase	),
											        torch.zeros(_UpperCAmelCase	),
											        torch.zeros(_UpperCAmelCase	),
											        torch.zeros(_UpperCAmelCase	),
											    )	)
							return common_inputs

					def 						lowerCamelCase__  (self					:		List[str]   ,      _UpperCAmelCase					:		PreTrainedTokenizer   ,      _UpperCAmelCase					:		int = -1   ,      _UpperCAmelCase					:		int = -1   ,      _UpperCAmelCase					:		bool = False   ,      _UpperCAmelCase					:		Optional[TensorType] = None   ,      )		->     Mapping[str, Any]:




							"""simple docstring"""


							lowercase__      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    _UpperCAmelCase   ,      _UpperCAmelCase   ,      _UpperCAmelCase   ,      _UpperCAmelCase   ,      _UpperCAmelCase	)

							if self.use_past:
									if not is_torch_available():
											raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""	)
									else:
											import torch
									lowercase__    ,			lowercase__      =	common_inputs["""input_ids"""].shape
									lowercase__      =	seqlen
									lowercase__    ,			lowercase__      =	self.num_layers
									lowercase__    ,			lowercase__      =	self.num_attention_heads
									lowercase__      =	(
									    batch,
									    num_encoder_attention_heads,
									    past_key_values_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)
									lowercase__      =	common_inputs["""attention_mask"""].dtype
									lowercase__      =	torch.cat(
									    [common_inputs["""attention_mask"""], torch.ones(_UpperCAmelCase   ,      _UpperCAmelCase   ,      dtype=_UpperCAmelCase	)]   ,      dim=1	)
									lowercase__      =	[
									    (torch.zeros(_UpperCAmelCase	), torch.zeros(_UpperCAmelCase	)) for _ in range(_UpperCAmelCase	)
									]
							return common_inputs

					def 						lowerCamelCase__  (self					:		Union[str, Any]   ,      _UpperCAmelCase					:		PreTrainedTokenizer   ,      _UpperCAmelCase					:		int = -1   ,      _UpperCAmelCase					:		int = -1   ,      _UpperCAmelCase					:		bool = False   ,      _UpperCAmelCase					:		Optional[TensorType] = None   ,      )		->     Mapping[str, Any]:




							"""simple docstring"""


							lowercase__      =	compute_effective_axis_dimension(
							    _UpperCAmelCase   ,      fixed_dimension=OnnxConfig.default_fixed_batch   ,      num_token_to_add=0	)

							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							lowercase__      =	tokenizer.num_special_tokens_to_add(_UpperCAmelCase	)
							lowercase__      =	compute_effective_axis_dimension(
							    _UpperCAmelCase   ,      fixed_dimension=OnnxConfig.default_fixed_sequence   ,      num_token_to_add=_UpperCAmelCase	)

							# Generate dummy inputs according to compute batch and sequence
							lowercase__      =	[""" """.join([tokenizer.unk_token]	) * seq_length] * batch_size
							lowercase__      =	dict(tokenizer(_UpperCAmelCase   ,      return_tensors=_UpperCAmelCase	)	)
							return common_inputs

					def 						lowerCamelCase__  (self					:		Optional[int]   ,      _UpperCAmelCase					:		PreTrainedTokenizer   ,      _UpperCAmelCase					:		int = -1   ,      _UpperCAmelCase					:		int = -1   ,      _UpperCAmelCase					:		bool = False   ,      _UpperCAmelCase					:		Optional[TensorType] = None   ,      )		->     Mapping[str, Any]:




							"""simple docstring"""


							if self.task in ["default", "seq2seq-lm"]:
									lowercase__      =	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
									    _UpperCAmelCase   ,      batch_size=_UpperCAmelCase   ,      seq_length=_UpperCAmelCase   ,      is_pair=_UpperCAmelCase   ,      framework=_UpperCAmelCase	)

							elif self.task == "causal-lm":
									lowercase__      =	self._generate_dummy_inputs_for_causal_lm(
									    _UpperCAmelCase   ,      batch_size=_UpperCAmelCase   ,      seq_length=_UpperCAmelCase   ,      is_pair=_UpperCAmelCase   ,      framework=_UpperCAmelCase	)
							else:
									lowercase__      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _UpperCAmelCase   ,      batch_size=_UpperCAmelCase   ,      seq_length=_UpperCAmelCase   ,      is_pair=_UpperCAmelCase   ,      framework=_UpperCAmelCase	)

							return common_inputs

					def 						lowerCamelCase__  (self					:		List[Any]   ,      _UpperCAmelCase					:		int   ,      _UpperCAmelCase					:		Optional[Any]   ,      _UpperCAmelCase					:		Optional[Any]   ,      _UpperCAmelCase					:		Dict	)		->     Union[str, Any]:




							"""simple docstring"""


							if self.task in ["default", "seq2seq-lm"]:
									lowercase__      =	super()._flatten_past_key_values_(_UpperCAmelCase   ,      _UpperCAmelCase   ,      _UpperCAmelCase   ,      _UpperCAmelCase	)
							else:
									lowercase__      =	super(_UpperCAmelCase   ,      self	)._flatten_past_key_values_(
									    _UpperCAmelCase   ,      _UpperCAmelCase   ,      _UpperCAmelCase   ,      _UpperCAmelCase	)


					def 						lowerCamelCase__  (self					:		Tuple   ,      _UpperCAmelCase					:		Mapping[str, Mapping[int, str]]   ,      _UpperCAmelCase					:		str	)		->     List[str]:




							"""simple docstring"""


							if direction not in ["inputs", "outputs"]:
									raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given'''	)

							lowercase__      =	"""past_key_values""" if direction == """inputs""" else """present"""
							lowercase__    ,			lowercase__      =	self.num_layers

							lowercase__      =	"""past_encoder_sequence"""
							lowercase__      =	"""past_decoder_sequence""" if direction == """inputs""" else """past_decoder_sequence + sequence"""

							for i in range(_UpperCAmelCase	):
									lowercase__      =	{0: """batch""", 2: decoder_sequence}
									lowercase__      =	{0: """batch""", 2: decoder_sequence}
									lowercase__      =	{0: """batch""", 2: encoder_sequence}
									lowercase__      =	{0: """batch""", 2: encoder_sequence}





# 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					( UpperCAmelCase__							):
					'''simple docstring'''






					pass






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							Dict[str, Callable]					,					__magic_name__  :							List[Any]=None					,					__magic_name__  :							int=False  )     ->						int:
		"""simple docstring"""




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

		return result






def 				UpperCamelCase      (			__magic_name__  :							ast.AST					,					__magic_name__  :							Dict[str, Any]					,					__magic_name__  :							Dict[str, Callable]  )     ->						Tuple:
		"""simple docstring"""




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




		lowercase__      =	assign.targets
		lowercase__      =	evaluate_ast(assign.value					,					__magic_name__					,					__magic_name__  )

		if len(__magic_name__  ) == 1:
				lowercase__      =	result
		else:
				if len(__magic_name__  ) != len(__magic_name__  ):
						raise InterpretorError(f'''Expected {len(__magic_name__  )} values but got {len(__magic_name__  )}.'''  )
				for var_name, r in zip(__magic_name__					,					__magic_name__  ):
						lowercase__      =	r
		return result






def 				UpperCamelCase      (			__magic_name__  :							Any					,					__magic_name__  :							Dict					,					__magic_name__  :							Union[str, Any]  )     ->						int:
		"""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(__magic_name__					,					__magic_name__					,					__magic_name__  ) for arg in call.args]
		lowercase__      =	{keyword.arg: evaluate_ast(keyword.value					,					__magic_name__					,					__magic_name__  ) for keyword in call.keywords}
		return func(*__magic_name__					,					**__magic_name__  )






def 				UpperCamelCase      (			__magic_name__  :							Tuple					,					__magic_name__  :							Any					,					__magic_name__  :							Optional[Any]  )     ->						List[Any]:
		"""simple docstring"""




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

		raise InterpretorError(f'''Could not index {value} with \'{index}\'.'''  )






def 				UpperCamelCase      (			__magic_name__  :							Dict					,					__magic_name__  :							Dict					,					__magic_name__  :							int  )     ->						int:
		"""simple docstring"""




		if name.id in state:
				return state[name.id]
		lowercase__      =	difflib.get_close_matches(name.id					,					list(state.keys()  )  )
		if len(__magic_name__  ) > 0:
				return state[close_matches[0]]
		raise InterpretorError(f'''The variable `{name.id}` is not defined.'''  )






def 				UpperCamelCase      (			__magic_name__  :							Union[str, Any]					,					__magic_name__  :							List[str]					,					__magic_name__  :							Tuple  )     ->						List[str]:
		"""simple docstring"""




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

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

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






def 				UpperCamelCase      (			__magic_name__  :							Union[str, Any]					,					__magic_name__  :							str					,					__magic_name__  :							Dict  )     ->						Optional[int]:
		"""simple docstring"""




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







def 				UpperCamelCase      (			__magic_name__  :							Any					,					__magic_name__  :							List[Any]					,					__magic_name__  :							List[Any]  )     ->						List[str]:
		"""simple docstring"""




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

