



'''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 _A	(     snake_case_     ):





		def __init__(  self			,							__UpperCAmelCase = None			,							__UpperCAmelCase = None			,							__UpperCAmelCase = None			,							__UpperCAmelCase = None			,							__UpperCAmelCase = None			,							__UpperCAmelCase = None			,							__UpperCAmelCase = None			,							__UpperCAmelCase = None			,							__UpperCAmelCase = None			,							__UpperCAmelCase = (None, None)			,							__UpperCAmelCase = None			,							)							->  Union[str, Any]:

					'''simple docstring'''
					super().__init__(
					    model=lowercase_			,							args=lowercase_			,							data_collator=lowercase_			,							train_dataset=lowercase_			,							eval_dataset=lowercase_			,							tokenizer=lowercase_			,							model_init=lowercase_			,							compute_metrics=lowercase_			,							callbacks=lowercase_			,							optimizers=lowercase_			,							preprocess_logits_for_metrics=lowercase_			,							)

					# 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 :   Optional[int]    					=  self.load_generation_config(self.args.generation_config					)
								__UpperCAmelCase :   Optional[Any]    					=  gen_config





		@staticmethod
		def 				__A							(  __UpperCAmelCase					)							->  GenerationConfig:

					'''simple docstring'''

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

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

					__UpperCAmelCase :   Any    					=  GenerationConfig.from_pretrained(lowercase_			,							lowercase_					)
					return gen_config





		def 				__A							(  self			,							__UpperCAmelCase = None			,							__UpperCAmelCase = None			,							__UpperCAmelCase = "eval"			,							**__UpperCAmelCase			,							)							->  Dict[str, float]:

					'''simple docstring'''
					__UpperCAmelCase :   Optional[int]    					=  gen_kwargs.copy()
					if gen_kwargs.get("""max_length"""					) is None and gen_kwargs.get("""max_new_tokens"""					) is None:
								__UpperCAmelCase :   List[Any]    					=  self.args.generation_max_length
					__UpperCAmelCase :   Any    					=  (
					    gen_kwargs['num_beams'] if gen_kwargs.get("""num_beams"""					) is not None else self.args.generation_num_beams
					)
					__UpperCAmelCase :   List[Any]    					=  gen_kwargs

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





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase = None			,							__UpperCAmelCase = "test"			,							**__UpperCAmelCase			,							)							->  "PredictionOutput":

					'''simple docstring'''
					__UpperCAmelCase :   Dict    					=  gen_kwargs.copy()
					if gen_kwargs.get("""max_length"""					) is None and gen_kwargs.get("""max_new_tokens"""					) is None:
								__UpperCAmelCase :   List[Any]    					=  self.args.generation_max_length
					__UpperCAmelCase :   Union[str, Any]    					=  (
					    gen_kwargs['num_beams'] if gen_kwargs.get("""num_beams"""					) is not None else self.args.generation_num_beams
					)
					__UpperCAmelCase :   Optional[Any]    					=  gen_kwargs

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





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase = None			,							**__UpperCAmelCase			,							)							->  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(
								    lowercase_			,							lowercase_			,							prediction_loss_only=lowercase_			,							ignore_keys=lowercase_					)

					__UpperCAmelCase :   Tuple    					=  'labels' in inputs
					__UpperCAmelCase :   Any    					=  self._prepare_inputs(lowercase_					)

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

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

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

					# 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 :   Optional[int]    					=  False

					# Retrieves GenerationConfig from model.generation_config
					__UpperCAmelCase :   List[Any]    					=  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 :   Dict    					=  self._pad_tensors_to_max_len(lowercase_			,							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 :   str    					=  self._pad_tensors_to_max_len(lowercase_			,							gen_config.max_new_tokens + 1					)

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

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

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

					return loss, generated_tokens, labels





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase					)							->  List[str]:

					'''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
								__UpperCAmelCase :   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:
											__UpperCAmelCase :   Any    					=  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 :   Union[str, Any]    					=  pad_token_id * torch.ones(
					    (tensor.shape[0], max_length)			,							dtype=tensor.dtype			,							device=tensor.device					)
					__UpperCAmelCase :   Tuple    					=  tensor
					return padded_tensor












"""simple docstring"""


import math




def 					a			(  UpperCamelCase_				:					int		)	->		str:
 if number == 0:
  __A						:							Dict      =						'Zero'
  return words
 else:
  __A						:							List[str]      =						math.logaa(UpperCamelCase_		)
  __A						:							Union[str, Any]      =						digits + 1
  __A						:							List[str]      =						{}
  __A						:							int      =						''
  __A						:							Any      =						'One'
  __A						:							Tuple      =						'Two'
  __A						:							Any      =						'Three'
  __A						:							Any      =						'Four'
  __A						:							Optional[Any]      =						'Five'
  __A						:							List[Any]      =						'Six'
  __A						:							List[Any]      =						'Seven'
  __A						:							Dict      =						'Eight'
  __A						:							Union[str, Any]      =						'Nine'

  __A						:							List[Any]      =						{}
  __A						:							Tuple      =						''
  __A						:							List[Any]      =						'Twenty'
  __A						:							Optional[Any]      =						'Thirty'
  __A						:							Any      =						'Forty'
  __A						:							Optional[int]      =						'Fifty'
  __A						:							Dict      =						'Sixty'
  __A						:							Dict      =						'Seventy'
  __A						:							Optional[Any]      =						'Eighty'
  __A						:							Any      =						'Ninety'

  __A						:							int      =						{}
  __A						:							List[str]      =						'Ten'
  __A						:							Optional[Any]      =						'Eleven'
  __A						:							Dict      =						'Twelve'
  __A						:							Union[str, Any]      =						'Thirteen'
  __A						:							Union[str, Any]      =						'Fourteen'
  __A						:							Dict      =						'Fifteen'
  __A						:							str      =						'Sixteen'
  __A						:							str      =						'Seventeen'
  __A						:							Union[str, Any]      =						'Eighteen'
  __A						:							Union[str, Any]      =						'Nineteen'

  __A						:							Optional[Any]      =						{}
  __A						:							Optional[int]      =						'Hundred,'
  __A						:							Union[str, Any]      =						'Thousand,'
  __A						:							Union[str, Any]      =						'Lakh,'
  __A						:							Union[str, Any]      =						'Crore,'

  __A						:							Optional[Any]      =						number
  __A						:							Optional[Any]      =						''
  __A						:							List[str]      =						0
  __A						:							List[str]      =						int(UpperCamelCase_		)
  while counter < digits:
   __A						:							Any      =						temp_num % 1_0
   if counter % 2 == 0:
    __A						:							Optional[Any]      =						''
    if counter in placevalue and current != 0:
     __A						:							Union[str, Any]      =						placevalue[counter]
    if counter == 2:
     __A						:							Dict      =						singles[current] + addition + words
    elif counter == 0:
     if ((temp_num % 1_0_0) // 1_0) == 1:
      __A						:							Any      =						teens[current] + addition + words
      __A						:							int      =						temp_num // 1_0
      counter += 1
     else:
      __A						:							Optional[Any]      =						singles[current] + addition + words

    else:
     __A						:							str      =						doubles[current] + addition + words

   else:
    if counter == 1:
     if current == 1:
      __A						:							List[Any]      =						teens[number % 1_0] + words
     else:
      __A						:							Union[str, Any]      =						''
      if counter in placevalue:
       __A						:							int      =						placevalue[counter]
      __A						:							Optional[Any]      =						doubles[current] + addition + words
    else:
     __A						:							Any      =						''
     if counter in placevalue:
      if current == 0 and ((temp_num % 1_0_0) // 1_0) == 0:
       __A						:							Optional[int]      =						''
      else:
       __A						:							Optional[int]      =						placevalue[counter]
     if ((temp_num % 1_0_0) // 1_0) == 1:
      __A						:							Dict      =						teens[current] + addition + words
      __A						:							Any      =						temp_num // 1_0
      counter += 1
     else:
      __A						:							List[str]      =						singles[current] + addition + words
   counter += 1
   __A						:							Dict      =						temp_num // 1_0
 return words


if __name__ == "__main__":
   import doctest

   doctest.testmod()





