



'''simple docstring'''







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


_UpperCamelCase  						=			logging.get_logger(__name__)

class      lowerCamelCase__     (     _A				):






	'''simple docstring'''

	def __init__(  self		:  Optional[Any]   ,		__A		:  Union["PreTrainedModel", nn.Module] = None   ,		__A		:  "TrainingArguments" = None   ,		__A		:  Optional["DataCollator"] = None   ,		__A		:  Optional[Dataset] = None   ,		__A		:  Optional[Union[Dataset, Dict[str, Dataset]]] = None   ,		__A		:  Optional["PreTrainedTokenizerBase"] = None   ,		__A		:  Optional[Callable[[], "PreTrainedModel"]] = None   ,		__A		:  Optional[Callable[["EvalPrediction"], Dict]] = None   ,		__A		:  Optional[List["TrainerCallback"]] = None   ,		__A		:  Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None)   ,		__A		:  Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None   ,		)     ->			Any:




								'''simple docstring'''





								super().__init__(
								    model=__A   ,		args=__A   ,		data_collator=__A   ,		train_dataset=__A   ,		eval_dataset=__A   ,		tokenizer=__A   ,		model_init=__A   ,		compute_metrics=__A   ,		callbacks=__A   ,		optimizers=__A   ,		preprocess_logits_for_metrics=__A   ,		)

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




	@staticmethod
	def      lowercase__							(  __A		:  Union[str, GenerationConfig]					)     ->			GenerationConfig:




								'''simple docstring'''





								if isinstance(__A   ,		__A					):
															return deepcopy(__A					)

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

								lowerCAmelCase__  =   GenerationConfig.from_pretrained(__A   ,		__A					)
								return gen_config




	def      lowercase__							(  self		:  Optional[int]   ,		__A		:  Optional[Dataset] = None   ,		__A		:  Optional[List[str]] = None   ,		__A		:  str = "eval"   ,		**__A		:  Any   ,		)     ->			Dict[str, float]:




								'''simple docstring'''





								lowerCAmelCase__  =   gen_kwargs.copy()
								if gen_kwargs.get("""max_length"""					) is None and gen_kwargs.get("""max_new_tokens"""					) is None:
															lowerCAmelCase__  =   self.args.generation_max_length
								lowerCAmelCase__  =   (
								    gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""					) is not None else self.args.generation_num_beams
								)
								lowerCAmelCase__  =   gen_kwargs

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




	def      lowercase__							(  self		:  Optional[Any]   ,		__A		:  Dataset   ,		__A		:  Optional[List[str]] = None   ,		__A		:  str = "test"   ,		**__A		:  List[Any]   ,		)     ->			"PredictionOutput":




								'''simple docstring'''





								lowerCAmelCase__  =   gen_kwargs.copy()
								if gen_kwargs.get("""max_length"""					) is None and gen_kwargs.get("""max_new_tokens"""					) is None:
															lowerCAmelCase__  =   self.args.generation_max_length
								lowerCAmelCase__  =   (
								    gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""					) is not None else self.args.generation_num_beams
								)
								lowerCAmelCase__  =   gen_kwargs

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




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




								'''simple docstring'''





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

								lowerCAmelCase__  =   """labels""" in inputs
								lowerCAmelCase__  =   self._prepare_inputs(__A					)

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

								if len(__A					) == 0 and hasattr(self   ,		"""_gen_kwargs"""					):
															lowerCAmelCase__  =   self._gen_kwargs.copy()

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

								# 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:
															lowerCAmelCase__  =   False

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

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

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

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

								return loss, generated_tokens, labels




	def      lowercase__							(  self		:  Optional[Any]   ,		__A		:  Any   ,		__A		:  Union[str, Any]					)     ->			List[Any]:




								'''simple docstring'''





								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
															lowerCAmelCase__  =   (
															    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:
																						lowerCAmelCase__  =   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"""					)

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










'''simple docstring'''







def 			_lowerCAmelCase(      UpperCAmelCase_      :	str						) ->							str:
							lowerCAmelCase__  =   word.upper()
							lowerCAmelCase__  =   0
							lowerCAmelCase__  =   {}
							lowerCAmelCase__  =   []

							for letter in word:
														if letter not in letter_nums:
																					lowerCAmelCase__  =   str(UpperCAmelCase_						)
																					next_num += 1
														word_pattern.append(letter_nums[letter]						)
							return ".".join(UpperCAmelCase_						)


if __name__ == "__main__":
		import pprint
		import time

		_UpperCamelCase  						=			time.time()
		with open("""dictionary.txt""") as in_file:
				_UpperCamelCase  						=			in_file.read().splitlines()

		_UpperCamelCase  						=			{}
		for word in word_list:
				_UpperCamelCase  						=			get_word_pattern(word)
				if pattern in all_patterns:
						all_patterns[pattern].append(word)
				else:
						_UpperCamelCase  						=			[word]

		with open("""word_patterns.txt""", """w""") as out_file:
				out_file.write(pprint.pformat(all_patterns))

		_UpperCamelCase  						=			round(time.time() - start_time, 2)
		print(f'Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.')
		# Done!  9,581 word patterns found in 0.58 seconds.





