"""simple docstring"""






import ast
import difflib
from collections.abc import Mapping
from typing import Any, Callable, Dict
class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					pass






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

				return result



def   a_						( 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
								UpperCAmelCase__					=							[evaluate_ast(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	) for k in expression.keys]
								UpperCAmelCase__					=							[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   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							assign.targets
				UpperCAmelCase__					=							evaluate_ast(assign.value ,      lowerCamelCase ,      lowerCamelCase	)

				if len(lowerCamelCase	) == 1:
								UpperCAmelCase__					=							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	):
												UpperCAmelCase__					=							r
				return result



def   a_						( 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	)}.'''	)
				UpperCAmelCase__					=							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}).'''	)

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



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							evaluate_ast(subscript.slice ,      lowerCamelCase ,      lowerCamelCase	)
				UpperCAmelCase__					=							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	):
								UpperCAmelCase__					=							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   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	):
				if name.id in state:
								return state[name.id]
				UpperCAmelCase__					=							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   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	):
				if len(condition.ops	) > 1:
								raise InterpretorError('Cannot evaluate conditions with multiple operators'	)

				UpperCAmelCase__					=							evaluate_ast(condition.left ,      lowerCamelCase ,      lowerCamelCase	)
				UpperCAmelCase__					=							condition.ops[0]
				UpperCAmelCase__					=							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   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							None
				if evaluate_condition(if_statement.test ,      lowerCamelCase ,      lowerCamelCase	):
								for line in if_statement.body:
												UpperCAmelCase__					=							evaluate_ast(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	)
												if line_result is not None:
																UpperCAmelCase__					=							line_result
				else:
								for line in if_statement.orelse:
												UpperCAmelCase__					=							evaluate_ast(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	)
												if line_result is not None:
																UpperCAmelCase__					=							line_result
				return result







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



"""simple docstring"""






import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
						import torch


lowerCAmelCase__  :  Optional[Any]											=     logging.get_logger(__name__)
class     snake_case	:




					"""simple docstring"""

					def __init__(    self       :     Optional[Any]   ,lowerCamelCase__       :     Dict   ,lowerCamelCase__       :     Optional[Any]=21   ,lowerCamelCase__       :     str=[]   ,lowerCamelCase__       :     Any=None      ):
									UpperCAmelCase__					=							model
									UpperCAmelCase__					=							trace_batch_nums
									UpperCAmelCase__					=							abort_after_batch_num

									# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
									UpperCAmelCase__					=							collections.deque([]   ,lowerCamelCase__      )
									UpperCAmelCase__					=							[]
									UpperCAmelCase__					=							0
									UpperCAmelCase__					=							0
									UpperCAmelCase__					=							False
									UpperCAmelCase__					=							'                 '

									self.analyse_model()

									self.register_forward_hook()







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]   ,lowerCamelCase__       :     Tuple=None      ):
									if frame is not None:
													self.expand_frame(lowerCamelCase__      )
									self.frames.append('\n'.join(self.frame      )      )
									UpperCAmelCase__					=							[]  # start a new frame







					def 		__lowerCAmelCase						(    self       :     int   ,lowerCamelCase__       :     int      ):
									self.frame.append(lowerCamelCase__      )







					def 		__lowerCAmelCase						(    self       :     List[str]      ):
									print('\n'.join(self.frames      )      )
									UpperCAmelCase__					=							[]







					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									UpperCAmelCase__					=							[]







					def 		__lowerCAmelCase						(    self       :     Dict      ):
									print(f'''\nDetected inf/nan during batch_number={self.batch_number}'''      )
									print(f'''Last {len(self.frames      )} forward frames:'''      )
									print(f'''{"abs min":8} {"abs max":8} metadata'''      )
									print('\n'.join(self.frames      )      )
									print('\n\n'      )
									UpperCAmelCase__					=							[]







					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									# extract the fully qualified module names, to be able to report at run time. e.g.:
									# encoder.block.2.layer.0.SelfAttention.o
									#
									# for shared weights only the first shared module name will be registered
									UpperCAmelCase__					=							{m: name for name, m in self.model.named_modules()}
									# self.longest_module_name = max(len(v) for v in self.module_names.values())







					def 		__lowerCAmelCase						(    self       :     Optional[Any]   ,lowerCamelCase__       :     int   ,lowerCamelCase__       :     List[Any]      ):
									if torch.is_tensor(lowerCamelCase__      ):
													self.expand_frame(get_abs_min_max(lowerCamelCase__   ,lowerCamelCase__      )      )
													if detect_overflow(lowerCamelCase__   ,lowerCamelCase__      ):
																	UpperCAmelCase__					=							True
									elif var is None:
													self.expand_frame(f'''{"None":>17} {ctx}'''      )
									else:
													self.expand_frame(f'''{"not a tensor":>17} {ctx}'''      )







					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									self.expand_frame(f'''\n\n{self.prefix} *** Starting batch number={self.batch_number} ***'''      )
									self.expand_frame(f'''{"abs min":8} {"abs max":8} metadata'''      )







					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''      )







					def 		__lowerCAmelCase						(    self       :     List[str]   ,lowerCamelCase__       :     List[str]   ,lowerCamelCase__       :     List[Any]   ,lowerCamelCase__       :     Optional[Any]      ):
									self.expand_frame(f'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''      )

									# params
									for name, p in module.named_parameters(recurse=lowerCamelCase__      ):
													self.analyse_variable(lowerCamelCase__   ,lowerCamelCase__      )

									# inputs
									if isinstance(lowerCamelCase__   ,lowerCamelCase__      ):
													for i, x in enumerate(lowerCamelCase__      ):
																	self.analyse_variable(lowerCamelCase__   ,f'''input[{i}]'''      )
									else:
													self.analyse_variable(lowerCamelCase__   ,'input'      )

									# outputs
									if isinstance(lowerCamelCase__   ,lowerCamelCase__      ):
													for i, x in enumerate(lowerCamelCase__      ):
																	# possibly a tuple of tuples
																	if isinstance(lowerCamelCase__   ,lowerCamelCase__      ):
																					for j, y in enumerate(lowerCamelCase__      ):
																									self.analyse_variable(lowerCamelCase__   ,f'''output[{i}][{j}]'''      )
																	else:
																					self.analyse_variable(lowerCamelCase__   ,f'''output[{i}]'''      )
									else:
													self.analyse_variable(lowerCamelCase__   ,'output'      )

									self.save_frame()







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									self.model.apply(self._register_forward_hook      )







					def 		__lowerCAmelCase						(    self       :     List[Any]   ,lowerCamelCase__       :     str      ):
									module.register_forward_hook(self.forward_hook      )







					def 		__lowerCAmelCase						(    self       :     Optional[Any]   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     str   ,lowerCamelCase__       :     Union[str, Any]      ):
									# - input is a tuple of packed inputs (could be non-Tensors)
									# - output could be a Tensor or a tuple of Tensors and non-Tensors

									UpperCAmelCase__					=							False

									UpperCAmelCase__					=							True if self.batch_number in self.trace_batch_nums else False
									if trace_mode:
													self.reset_saved_frames()

									if self.total_calls == 0:
													self.batch_start_frame()
									self.total_calls += 1

									# count batch numbers - the very first forward hook of the batch will be called when the
									# batch completes - i.e. it gets called very last - we know this batch has finished
									if module == self.model:
													self.batch_number += 1
													UpperCAmelCase__					=							True

									self.create_frame(lowerCamelCase__   ,lowerCamelCase__   ,lowerCamelCase__      )

									# if last_frame_of_batch:
									#     self.batch_end_frame()

									if trace_mode:
													self.trace_frames()

									if last_frame_of_batch:
													self.batch_start_frame()

									if self.detected_overflow and not trace_mode:
													self.dump_saved_frames()

													# now we can abort, as it's pointless to continue running
													raise ValueError(
													    'DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '
													    'Please scroll up above this traceback to see the activation values prior to this event.'      )

									# abort after certain batch if requested to do so
									if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
													raise ValueError(
													    f'''DebugUnderflowOverflow: aborting after {self.batch_number} batches due to'''
													    f''' `abort_after_batch_num={self.abort_after_batch_num}` arg'''      )






def   a_						( lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							var.abs()
				return f'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							False
				if torch.isnan(lowerCamelCase	).any().item():
								UpperCAmelCase__					=							True
								print(f'''{ctx} has nans'''	)
				if torch.isinf(lowerCamelCase	).any().item():
								UpperCAmelCase__					=							True
								print(f'''{ctx} has infs'''	)

				# if needed to monitor large elements can enable the following
				if 0:  # and detected:
								UpperCAmelCase__					=							var[torch.ge(var.abs() ,      1_0_0	)]
								if naaa.numel() > 0:
												print(f'''{ctx}:  n100={naaa.numel()}'''	)
								UpperCAmelCase__					=							var[torch.ge(var.abs() ,      1_0_0_0	)]
								if naaaa.numel() > 0:
												print(f'''{ctx}: n1000={naaaa.numel()}'''	)
								UpperCAmelCase__					=							var[torch.ge(var.abs() ,      1_0_0_0_0	)]
								if naaaaa.numel() > 0:
												print(f'''{ctx}: n10000={naaaaa.numel()}'''	)

				if 0:
								print(f'''min={var.min():9.2e} max={var.max():9.2e}'''	)

				if 0:
								print(f'''min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})'''	)

				return detected







class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"underflow_overflow"
					snake_case__       =			"tpu_metrics_debug"



