



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


__A				    =						logging.get_logger(__name__)






class 		lowercase						( snake_case__):






	"""simple docstring"""


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

							# 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:
													UpperCAmelCase_=   self.load_generation_config(self.args.generation_config     )
													UpperCAmelCase_=   gen_config




	@staticmethod
	def   _SCREAMING_SNAKE_CASE		(	__UpperCAmelCase	:						Union[str, GenerationConfig]     )			-> GenerationConfig:

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

							# str or Path
							UpperCAmelCase_=   Path(__UpperCAmelCase     ) if isinstance(__UpperCAmelCase						,  __UpperCAmelCase     ) else gen_config_arg
							UpperCAmelCase_=   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():
													UpperCAmelCase_=   pretrained_model_name.name
													UpperCAmelCase_=   pretrained_model_name.parent
							# dir path
							elif pretrained_model_name.is_dir():
													pass
							# model id or URL
							else:
													UpperCAmelCase_=   gen_config_arg

							UpperCAmelCase_=   GenerationConfig.from_pretrained(__UpperCAmelCase						,  __UpperCAmelCase     )
							return gen_config




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[str]						,  __UpperCAmelCase	:						Optional[Dataset] = None						,  __UpperCAmelCase	:						Optional[List[str]] = None						,  __UpperCAmelCase	:						str = "eval"						,  **__UpperCAmelCase	:						Any						,  )			-> Dict[str, float]:
							UpperCAmelCase_=   gen_kwargs.copy()
							if gen_kwargs.get("""max_length"""     ) is None and gen_kwargs.get("""max_new_tokens"""     ) is None:
													UpperCAmelCase_=   self.args.generation_max_length
							UpperCAmelCase_=   (
							    gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""     ) is not None else self.args.generation_num_beams
							)
							UpperCAmelCase_=   gen_kwargs

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




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

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




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

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

							UpperCAmelCase_=   """labels""" in inputs
							UpperCAmelCase_=   self._prepare_inputs(__UpperCAmelCase     )

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

							if len(__UpperCAmelCase     ) == 0 and hasattr(self						,  """_gen_kwargs"""     ):
													UpperCAmelCase_=   self._gen_kwargs.copy()

							if gen_kwargs.get("""max_length"""     ) is None and gen_kwargs.get("""max_new_tokens"""     ) is None:
													UpperCAmelCase_=   self.model.config.max_length
							UpperCAmelCase_=   (
							    gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""     ) is not None else self.model.config.num_beams
							)
							UpperCAmelCase_=   True if is_deepspeed_zeroa_enabled() else False
							UpperCAmelCase_=   (
							    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
							):
													UpperCAmelCase_=   {k: v for k, v in inputs.items() if k != """decoder_input_ids"""}
							UpperCAmelCase_=   self.model.generate(**__UpperCAmelCase						,  **__UpperCAmelCase     )

							# 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:
													UpperCAmelCase_=   False

							# Retrieves GenerationConfig from model.generation_config
							UpperCAmelCase_=   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:
													UpperCAmelCase_=   self._pad_tensors_to_max_len(__UpperCAmelCase						,  gen_config.max_length     )
							elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
													UpperCAmelCase_=   self._pad_tensors_to_max_len(__UpperCAmelCase						,  gen_config.max_new_tokens + 1     )

							with torch.no_grad():
													if has_labels:
																			with self.compute_loss_context_manager():
																									UpperCAmelCase_=   model(**__UpperCAmelCase     )
																			if self.label_smoother is not None:
																									UpperCAmelCase_=   self.label_smoother(__UpperCAmelCase						,  inputs["""labels"""]     ).mean().detach()
																			else:
																									UpperCAmelCase_=   (outputs["""loss"""] if isinstance(__UpperCAmelCase						,  __UpperCAmelCase     ) else outputs[0]).mean().detach()
													else:
																			UpperCAmelCase_=   None

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

							if has_labels:
													UpperCAmelCase_=   inputs["""labels"""]
													if labels.shape[-1] < gen_config.max_length:
																			UpperCAmelCase_=   self._pad_tensors_to_max_len(__UpperCAmelCase						,  gen_config.max_length     )
													elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
																			UpperCAmelCase_=   self._pad_tensors_to_max_len(__UpperCAmelCase						,  gen_config.max_new_tokens + 1     )
							else:
													UpperCAmelCase_=   None

							return loss, generated_tokens, labels




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]						,  __UpperCAmelCase	:						Tuple						,  __UpperCAmelCase	:						str     )			-> Dict:
							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
													UpperCAmelCase_=   (
													    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:
																			UpperCAmelCase_=   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"""     )

							UpperCAmelCase_=   pad_token_id * torch.ones(
							    (tensor.shape[0], max_length)						,  dtype=tensor.dtype						,  device=tensor.device     )
							UpperCAmelCase_=   tensor
							return padded_tensor









def        __a     (    lowerCAmelCase_   :					list				)      -> bool:






						'''simple docstring'''



						if not isinstance(lowerCAmelCase_			,lowerCAmelCase_				):
												raise ValueError("""Input series is not valid, valid series - [1, 2/3, 2]"""				)
						if len(lowerCAmelCase_				) == 0:
												raise ValueError("""Input list must be a non empty list"""				)
						if len(lowerCAmelCase_				) == 1 and series[0] != 0:
												return True
						UpperCAmelCase_=   []
						UpperCAmelCase_=   len(lowerCAmelCase_				)
						for i in range(0			,lowerCAmelCase_				):
												if series[i] == 0:
																		raise ValueError("""Input series cannot have 0 as an element"""				)
												rec_series.append(1 / series[i]				)
						UpperCAmelCase_=   rec_series[1] - rec_series[0]
						for index in range(2			,lowerCAmelCase_				):
												if rec_series[index] - rec_series[index - 1] != common_diff:
																		return False
						return True



def        __a     (    lowerCAmelCase_   :					list				)      -> float:






						'''simple docstring'''



						if not isinstance(lowerCAmelCase_			,lowerCAmelCase_				):
												raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""				)
						if len(lowerCAmelCase_				) == 0:
												raise ValueError("""Input list must be a non empty list"""				)
						UpperCAmelCase_=   0
						for val in series:
												answer += 1 / val
						return len(lowerCAmelCase_				) / answer


if __name__ == "__main__":
	import doctest

	doctest.testmod()




