


from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
						from .data.data_collator import DataCollator
						from .modeling_utils import PreTrainedModel
						from .tokenization_utils_base import PreTrainedTokenizerBase
						from .trainer_callback import TrainerCallback
						from .trainer_utils import EvalPrediction, PredictionOutput
						from .training_args import TrainingArguments


__lowerCAmelCase     :			Optional[Any]      =logging.get_logger(__name__)
class 	_lowercase				(      A__	):






				'''simple docstring'''



				def __init__(	self      :Tuple      ,      lowerCAmelCase__      :Union["PreTrainedModel", nn.Module] = None      ,      lowerCAmelCase__      :"TrainingArguments" = None      ,      lowerCAmelCase__      :Optional["DataCollator"] = None      ,      lowerCAmelCase__      :Optional[Dataset] = None      ,      lowerCAmelCase__      :Optional[Union[Dataset, Dict[str, Dataset]]] = None      ,      lowerCAmelCase__      :Optional["PreTrainedTokenizerBase"] = None      ,      lowerCAmelCase__      :Optional[Callable[[], "PreTrainedModel"]] = None      ,      lowerCAmelCase__      :Optional[Callable[["EvalPrediction"], Dict]] = None      ,      lowerCAmelCase__      :Optional[List["TrainerCallback"]] = None      ,      lowerCAmelCase__      :Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None)      ,      lowerCAmelCase__      :Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None      ,      )		->      Optional[Any]:
										super().__init__(
										    model=lowerCAmelCase__      ,      args=lowerCAmelCase__      ,      data_collator=lowerCAmelCase__      ,      train_dataset=lowerCAmelCase__      ,      eval_dataset=lowerCAmelCase__      ,      tokenizer=lowerCAmelCase__      ,      model_init=lowerCAmelCase__      ,      compute_metrics=lowerCAmelCase__      ,      callbacks=lowerCAmelCase__      ,      optimizers=lowerCAmelCase__      ,      preprocess_logits_for_metrics=lowerCAmelCase__      ,      )

										# Override self.model.generation_config if a GenerationConfig is specified in args.
										# Priority: args.generation_config > model.generation_config > default GenerationConfig.
										if self.args.generation_config is not None:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.load_generation_config(self.args.generation_config     )
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		gen_config


				@staticmethod
				def 		__magic_name__(	lowerCAmelCase__      :Union[str, GenerationConfig]     )		->      GenerationConfig:

										# GenerationConfig provided, nothing to do
										if isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     ):
																return deepcopy(lowerCAmelCase__     )

										# str or Path
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		Path(lowerCAmelCase__     ) if isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     ) else gen_config_arg
										__SCREAMING_SNAKE_CASE						:					Dict		      =		None

										# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
										# This step is required in order to determine config_file_name
										if pretrained_model_name.is_file():
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		pretrained_model_name.name
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		pretrained_model_name.parent
										# dir path
										elif pretrained_model_name.is_dir():
																pass
										# model id or URL
										else:
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		gen_config_arg

										__SCREAMING_SNAKE_CASE						:					int		      =		GenerationConfig.from_pretrained(lowerCAmelCase__      ,      lowerCAmelCase__     )
										return gen_config


				def 		__magic_name__(	self      :str      ,      lowerCAmelCase__      :Optional[Dataset] = None      ,      lowerCAmelCase__      :Optional[List[str]] = None      ,      lowerCAmelCase__      :str = "eval"      ,      **lowerCAmelCase__      :Tuple      ,      )		->      Dict[str, float]:
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		gen_kwargs.copy()
										if gen_kwargs.get('''max_length'''     ) is None and gen_kwargs.get('''max_new_tokens'''     ) is None:
																__SCREAMING_SNAKE_CASE						:					int		      =		self.args.generation_max_length
										__SCREAMING_SNAKE_CASE						:					Dict		      =		(
										    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''     ) is not None else self.args.generation_num_beams
										)
										__SCREAMING_SNAKE_CASE						:					Any		      =		gen_kwargs

										return super().evaluate(lowerCAmelCase__      ,      ignore_keys=lowerCAmelCase__      ,      metric_key_prefix=lowerCAmelCase__     )


				def 		__magic_name__(	self      :str      ,      lowerCAmelCase__      :Dataset      ,      lowerCAmelCase__      :Optional[List[str]] = None      ,      lowerCAmelCase__      :str = "test"      ,      **lowerCAmelCase__      :Any      ,      )		->      "PredictionOutput":
										__SCREAMING_SNAKE_CASE						:					Dict		      =		gen_kwargs.copy()
										if gen_kwargs.get('''max_length'''     ) is None and gen_kwargs.get('''max_new_tokens'''     ) is None:
																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.args.generation_max_length
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		(
										    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''     ) is not None else self.args.generation_num_beams
										)
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		gen_kwargs

										return super().predict(lowerCAmelCase__      ,      ignore_keys=lowerCAmelCase__      ,      metric_key_prefix=lowerCAmelCase__     )


				def 		__magic_name__(	self      :Tuple      ,      lowerCAmelCase__      :nn.Module      ,      lowerCAmelCase__      :Dict[str, Union[torch.Tensor, Any]]      ,      lowerCAmelCase__      :bool      ,      lowerCAmelCase__      :Optional[List[str]] = None      ,      **lowerCAmelCase__      :Optional[Any]      ,      )		->      Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:

										if not self.args.predict_with_generate or prediction_loss_only:
																return super().prediction_step(
																    lowerCAmelCase__      ,      lowerCAmelCase__      ,      prediction_loss_only=lowerCAmelCase__      ,      ignore_keys=lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		'''labels''' in inputs
										__SCREAMING_SNAKE_CASE						:					Any		      =		self._prepare_inputs(lowerCAmelCase__     )

										# XXX: adapt synced_gpus for fairscale as well
										# Priority (handled in generate):
										# gen_kwargs > model.generation_config > default GenerationConfig()

										if len(lowerCAmelCase__     ) == 0 and hasattr(self      ,      '''_gen_kwargs'''     ):
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		self._gen_kwargs.copy()

										if gen_kwargs.get('''max_length'''     ) is None and gen_kwargs.get('''max_new_tokens'''     ) is None:
																__SCREAMING_SNAKE_CASE						:					Any		      =		self.model.config.max_length
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		(
										    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''     ) is not None else self.model.config.num_beams
										)
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		True if is_deepspeed_zeroa_enabled() else False
										__SCREAMING_SNAKE_CASE						:					int		      =		(
										    gen_kwargs['''synced_gpus'''] if gen_kwargs.get('''synced_gpus'''     ) is not None else default_synced_gpus
										)

										# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
										# (otherwise, it would continue generating from the padded `decoder_input_ids`)
										if (
										    "labels" in inputs
										    and "decoder_input_ids" in inputs
										    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
										):
																__SCREAMING_SNAKE_CASE						:					str		      =		{k: v for k, v in inputs.items() if k != '''decoder_input_ids'''}
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.model.generate(**lowerCAmelCase__      ,      **lowerCAmelCase__     )

										# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
										# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
										# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
										if self.model.generation_config._from_model_config:
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		False

										# Retrieves GenerationConfig from model.generation_config
										__SCREAMING_SNAKE_CASE						:					str		      =		self.model.generation_config
										# in case the batch is shorter than max length, the output should be padded
										if generated_tokens.shape[-1] < gen_config.max_length:
																__SCREAMING_SNAKE_CASE						:					int		      =		self._pad_tensors_to_max_len(lowerCAmelCase__      ,      gen_config.max_length     )
										elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
																__SCREAMING_SNAKE_CASE						:					int		      =		self._pad_tensors_to_max_len(lowerCAmelCase__      ,      gen_config.max_new_tokens + 1     )

										with torch.no_grad():
																if has_labels:
																						with self.compute_loss_context_manager():
																												__SCREAMING_SNAKE_CASE						:					Dict		      =		model(**lowerCAmelCase__     )
																						if self.label_smoother is not None:
																												__SCREAMING_SNAKE_CASE						:					Any		      =		self.label_smoother(lowerCAmelCase__      ,      inputs['''labels''']     ).mean().detach()
																						else:
																												__SCREAMING_SNAKE_CASE						:					List[str]		      =		(outputs['''loss'''] if isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     ) else outputs[0]).mean().detach()
																else:
																						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		None

										if self.args.prediction_loss_only:
																return loss, None, None

										if has_labels:
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		inputs['''labels''']
																if labels.shape[-1] < gen_config.max_length:
																						__SCREAMING_SNAKE_CASE						:					List[str]		      =		self._pad_tensors_to_max_len(lowerCAmelCase__      ,      gen_config.max_length     )
																elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
																						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self._pad_tensors_to_max_len(lowerCAmelCase__      ,      gen_config.max_new_tokens + 1     )
										else:
																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		None

										return loss, generated_tokens, labels



				def 		__magic_name__(	self      :Optional[Any]      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :int     )		->      List[str]:
										if self.tokenizer is not None and hasattr(self.tokenizer      ,      '''pad_token_id'''     ):
																# If PAD token is not defined at least EOS token has to be defined
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		(
																    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
																)
										else:
																if self.model.config.pad_token_id is not None:
																						__SCREAMING_SNAKE_CASE						:					str		      =		self.model.config.pad_token_id
																else:
																						raise ValueError('''Pad_token_id must be set in the configuration of the model, in order to pad tensors'''     )

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		pad_token_id * torch.ones(
										    (tensor.shape[0], max_length)      ,      dtype=tensor.dtype      ,      device=tensor.device     )
										__SCREAMING_SNAKE_CASE						:					Dict		      =		tensor
										return padded_tensor




def 	_UpperCamelCase							(				lowercase__				):
						barcode //= 10  # exclude the last digit
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		False
						__SCREAMING_SNAKE_CASE						:					Dict		      =		0

						# extract and check each digit
						while barcode != 0:
												__SCREAMING_SNAKE_CASE						:					int		      =		1 if checker else 3
												s += mult * (barcode % 10)
												barcode //= 10
												__SCREAMING_SNAKE_CASE						:					Dict		      =		not checker

						return (10 - (s % 10)) % 10


def 	_UpperCamelCase							(				lowercase__				):
						return len(str(lowercase__				)				) == 13 and get_check_digit(lowercase__				) == barcode % 10


def 	_UpperCamelCase							(				lowercase__				):
						if str(lowercase__				).isalpha():
												__SCREAMING_SNAKE_CASE						:					str		      =		F'''Barcode \'{barcode}\' has alphabetic characters.'''
												raise ValueError(lowercase__				)
						elif int(lowercase__				) < 0:
												raise ValueError('''The entered barcode has a negative value. Try again.'''				)
						else:
												return int(lowercase__				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__lowerCAmelCase     :			Union[str, Any]      =get_barcode(input('Barcode: ').strip())

						if is_valid(barcode):
												print(f"""'{barcode}' is a valid barcode.""")
						else:
												print(f"""'{barcode}' is NOT a valid barcode.""")
