




'''simple docstring'''

from datetime import datetime

import matplotlib.pyplot as plt
import torch


def 						__magic_name__       (      __UpperCAmelCase      )						->   str:

					'''simple docstring'''

					for param in module.parameters():
										snake_case_		     =							False


def 						__magic_name__       (      )						->   str:

					'''simple docstring'''

					snake_case_		     =							'''cuda''' if torch.cuda.is_available() else '''cpu'''
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										snake_case_		     =							'''mps'''
					if device == "mps":
										print(
										    '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
										    ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
										    ''' with generations.'''      )
					return device


def 						__magic_name__       (      __UpperCAmelCase      )						->   List[Any]:

					'''simple docstring'''

					snake_case_		     =							plt.imshow(lowerCAmelCase__      )
					fig.axes.get_xaxis().set_visible(lowerCAmelCase__      )
					fig.axes.get_yaxis().set_visible(lowerCAmelCase__      )
					plt.show()


def 						__magic_name__       (      )						->   Optional[int]:

					'''simple docstring'''

					snake_case_		     =							datetime.now()
					snake_case_		     =							current_time.strftime('''%H:%M:%S'''      )
					return timestamp



"""simple docstring"""


import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


A_    							=					logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__			)
class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      bool          =    field(default=lowerCAmelCase						,	metadata={'help': 'Whether to use SortishSampler or not.'}			)
				a__:      bool          =    field(
				    default=lowerCAmelCase						,	metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}			)
				a__:      Optional[int]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': (
				            'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
				            'to the `max_length` value of the model configuration.'
				        )
				    }						,	)
				a__:      Optional[int]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': (
				            'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
				            'to the `num_beams` value of the model configuration.'
				        )
				    }						,	)
				a__:      Optional[Union[str, Path, GenerationConfig]]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
				    }						,	)




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  super().to_dict()
							for k, v in d.items():
										if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
													lowerCamelCase_					=  v.to_dict()
							return d



