




import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
UpperCAmelCase_    =logging.getLogger(__name__)



def    UpperCAmelCase							(  _snake_case				):
							np.random.seed(args.seed				)
							torch.manual_seed(args.seed				)
							if args.n_gpu > 0:
														torch.cuda.manual_seed_all(args.seed				)



def    UpperCAmelCase							(  ):
							lowerCAmelCase =		argparse.ArgumentParser()
							parser.add_argument(
							    '''--model_name_or_path'''    ,			default=_snake_case    ,			type=_snake_case    ,			required=_snake_case    ,			)
							parser.add_argument('''--prompt'''    ,			type=_snake_case    ,			default=''''''				)
							parser.add_argument('''--length'''    ,			type=_snake_case    ,			default=20				)
							parser.add_argument('''--stop_token'''    ,			type=_snake_case    ,			default=_snake_case    ,			help='''Token at which text generation is stopped'''				)
							parser.add_argument(
							    '''--temperature'''    ,			type=_snake_case    ,			default=1.0    ,			help='''temperature of 1.0 has no effect, lower tend toward greedy sampling'''    ,			)
							parser.add_argument(
							    '''--repetition_penalty'''    ,			type=_snake_case    ,			default=1.0    ,			help='''primarily useful for CTRL model; in that case, use 1.2'''				)
							parser.add_argument('''--k'''    ,			type=_snake_case    ,			default=0				)
							parser.add_argument('''--penalty_alpha'''    ,			type=_snake_case    ,			default=0.0				)
							parser.add_argument('''--p'''    ,			type=_snake_case    ,			default=0.9				)

							parser.add_argument('''--prefix'''    ,			type=_snake_case    ,			default=''''''    ,			help='''Text added prior to input.'''				)
							parser.add_argument('''--padding_text'''    ,			type=_snake_case    ,			default=''''''    ,			help='''Deprecated, the use of `--prefix` is preferred.'''				)
							parser.add_argument('''--xlm_language'''    ,			type=_snake_case    ,			default=''''''    ,			help='''Optional language when used with the XLM model.'''				)

							parser.add_argument('''--seed'''    ,			type=_snake_case    ,			default=42    ,			help='''random seed for initialization'''				)
							parser.add_argument('''--no_cuda'''    ,			action='''store_true'''    ,			help='''Avoid using CUDA when available'''				)
							parser.add_argument(
							    '''--fp16'''    ,			action='''store_true'''    ,			help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''    ,			)
							lowerCAmelCase =		parser.parse_args()

							lowerCAmelCase =		torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''				)
							lowerCAmelCase =		0 if args.no_cuda else torch.cuda.device_count()

							logger.warning(F"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"""				)

							set_seed(_snake_case				)

							# Initialize the model and tokenizer
							lowerCAmelCase =		AutoTokenizer.from_pretrained(args.model_name_or_path				)
							lowerCAmelCase =		AutoModelForCausalLM.from_pretrained(args.model_name_or_path				)

							# tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
							# model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
							model.to(args.device				)

							if args.fpaa:
														model.half()

							logger.info(_snake_case				)
							lowerCAmelCase =		args.prompt if args.prompt else input('''Model prompt >>> '''				)

							lowerCAmelCase =		tokenizer(_snake_case    ,			return_tensors='''pt'''    ,			add_special_tokens=_snake_case				)
							lowerCAmelCase =		{key: value.to(args.device				) for key, value in inputs.items()}

							lowerCAmelCase =		model.generate(
							    **_snake_case    ,			max_length=args.length + len(inputs['''input_ids'''][0]				)    ,			penalty_alpha=args.penalty_alpha    ,			top_k=args.k    ,			)

							lowerCAmelCase =		[]
							for generated_sequence_idx, generated_sequence in enumerate(_snake_case				):
														print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""				)
														lowerCAmelCase =		generated_sequence.tolist()

														# Decode text
														lowerCAmelCase =		tokenizer.decode(_snake_case    ,			clean_up_tokenization_spaces=_snake_case    ,			add_special_tokens=_snake_case				)

														# Remove all text after the stop token
														lowerCAmelCase =		text[: text.find(args.stop_token				) if args.stop_token else None]

														# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
														lowerCAmelCase =		(
														    prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0]    ,			clean_up_tokenization_spaces=_snake_case				)				) :]
														)

														generated_sequences.append(_snake_case				)
														print(_snake_case				)

							return generated_sequences


if __name__ == "__main__":
		main()









import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
		import torch


UpperCAmelCase_    =logging.get_logger(__name__)


class 				__UpperCamelCase      :





				'''simple docstring'''



				def __init__(			self							,			UpperCAmelCase_							,			UpperCAmelCase_=21							,			UpperCAmelCase_=[]							,			UpperCAmelCase_=None    ):
											lowerCAmelCase =		model
											lowerCAmelCase =		trace_batch_nums
											lowerCAmelCase =		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
											lowerCAmelCase =		collections.deque([]							,			UpperCAmelCase_    )
											lowerCAmelCase =		[]
											lowerCAmelCase =		0
											lowerCAmelCase =		0
											lowerCAmelCase =		False
											lowerCAmelCase =		'''                 '''

											self.analyse_model()

											self.register_forward_hook()



				def    __snake_case		(			self							,			UpperCAmelCase_=None    ):
											if frame is not None:
																		self.expand_frame(UpperCAmelCase_    )
											self.frames.append('''\n'''.join(self.frame    )    )
											lowerCAmelCase =		[]  # start a new frame



				def    __snake_case		(			self							,			UpperCAmelCase_    ):
											self.frame.append(UpperCAmelCase_    )



				def    __snake_case		(			self    ):
											print('''\n'''.join(self.frames    )    )
											lowerCAmelCase =		[]



				def    __snake_case		(			self    ):
											lowerCAmelCase =		[]



				def    __snake_case		(			self    ):
											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'''    )
											lowerCAmelCase =		[]



				def    __snake_case		(			self    ):
											# 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
											lowerCAmelCase =		{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    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											if torch.is_tensor(UpperCAmelCase_    ):
																		self.expand_frame(get_abs_min_max(UpperCAmelCase_							,			UpperCAmelCase_    )    )
																		if detect_overflow(UpperCAmelCase_							,			UpperCAmelCase_    ):
																									lowerCAmelCase =		True
											elif var is None:
																		self.expand_frame(F"""{"None":>17} {ctx}"""    )
											else:
																		self.expand_frame(F"""{"not a tensor":>17} {ctx}"""    )



				def    __snake_case		(			self    ):
											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    __snake_case		(			self    ):
											self.expand_frame(F"""{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"""    )



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											self.expand_frame(F"""{self.prefix} {self.module_names[module]} {module.__class__.__name__}"""    )

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

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

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

											self.save_frame()



				def    __snake_case		(			self    ):
											self.model.apply(self._register_forward_hook    )



				def    __snake_case		(			self							,			UpperCAmelCase_    ):
											module.register_forward_hook(self.forward_hook    )






				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											# - input is a tuple of packed inputs (could be non-Tensors)
											# - output could be a Tensor or a tuple of Tensors and non-Tensors

											lowerCAmelCase =		False

											lowerCAmelCase =		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
																		lowerCAmelCase =		True

											self.create_frame(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

											# 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    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							lowerCAmelCase =		var.abs()
							return F"""{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"""



def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							lowerCAmelCase =		False
							if torch.isnan(_snake_case				).any().item():
														lowerCAmelCase =		True
														print(F"""{ctx} has nans"""				)
							if torch.isinf(_snake_case				).any().item():
														lowerCAmelCase =		True
														print(F"""{ctx} has infs"""				)

							# if needed to monitor large elements can enable the following
							if 0:  # and detected:
														lowerCAmelCase =		var[torch.ge(var.abs()    ,			100				)]
														if naaa.numel() > 0:
																					print(F"""{ctx}:  n100={naaa.numel()}"""				)
														lowerCAmelCase =		var[torch.ge(var.abs()    ,			1000				)]
														if naaaa.numel() > 0:
																					print(F"""{ctx}: n1000={naaaa.numel()}"""				)
														lowerCAmelCase =		var[torch.ge(var.abs()    ,			10000				)]
														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 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			Tuple					    ="""underflow_overflow"""
				__a				:			Dict					    ="""tpu_metrics_debug"""



