


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  __A   (   lowerCAmelCase__			):

			'''simple docstring'''







			def __init__(   self	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = (None, None)	,  __lowerCAmelCase = None	,  ):



				'''simple docstring'''







				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:
					lowerCamelCase__        =		self.load_generation_config(self.args.generation_config							)
					lowerCamelCase__        =		gen_config





			@staticmethod
			def 		__lowerCamelCase    (   __lowerCAmelCase							):



				'''simple docstring'''







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

				# str or Path
				lowerCamelCase__        =		Path(UpperCamelCase__							) if isinstance(UpperCamelCase__	,  UpperCamelCase__							) 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(UpperCamelCase__	,  UpperCamelCase__							)
				return gen_config





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = "eval"	,  **__lowerCAmelCase	,  ):



				'''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(UpperCamelCase__	,  ignore_keys=UpperCamelCase__	,  metric_key_prefix=UpperCamelCase__							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase = None	,  __lowerCAmelCase = "test"	,  **__lowerCAmelCase	,  ):



				'''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(UpperCamelCase__	,  ignore_keys=UpperCamelCase__	,  metric_key_prefix=UpperCamelCase__							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase = None	,  **__lowerCAmelCase	,  ):



				'''simple docstring'''







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

				lowerCamelCase__        =		"labels" in inputs
				lowerCamelCase__        =		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'''							):
					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(**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:
					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(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:
					lowerCamelCase__        =		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():
							lowerCamelCase__        =		model(**UpperCamelCase__							)
						if self.label_smoother is not None:
							lowerCamelCase__        =		self.label_smoother(UpperCamelCase__	,  inputs['''labels''']							).mean().detach()
						else:
							lowerCamelCase__        =		(outputs["loss"] if isinstance(UpperCamelCase__	,  UpperCamelCase__							) 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(UpperCamelCase__	,  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(UpperCamelCase__	,  gen_config.max_new_tokens + 1							)
				else:
					lowerCamelCase__        =		None

				return loss, generated_tokens, labels





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''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










def lowerCAmelCase__(__snake_case							)    ->				int:







	'''simple docstring'''







	lowerCamelCase__        =		str(__snake_case							).strip()
	if not bin_string:
		raise ValueError('''Empty string was passed to the function'''							)
	lowerCamelCase__        =		bin_string[0] == '''-'''
	if is_negative:
		lowerCamelCase__        =		bin_string[1:]
	if not all(char in '''01''' for char in bin_string							):
		raise ValueError('''Non-binary value was passed to the function'''							)
	lowerCamelCase__        =		0
	for char in bin_string:
		lowerCamelCase__        =		2 * decimal_number + int(__snake_case							)
	return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
			from doctest import testmod

			testmod()



