

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')


class        _SCREAMING_SNAKE_CASE	:







						def __init__(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE = True			,		_SCREAMING_SNAKE_CASE = False				)->				Union[str, Any]:
													lowerCamelCase_				     =scheduler
													lowerCamelCase_				     =optimizers if isinstance(_SCREAMING_SNAKE_CASE			,		(list, tuple)				) else [optimizers]
													lowerCamelCase_				     =split_batches
													lowerCamelCase_				     =step_with_optimizer
													lowerCamelCase_				     =GradientState()







						def     _snake_case							(       self			,		*_SCREAMING_SNAKE_CASE			,		**_SCREAMING_SNAKE_CASE				)->				str:



													if not self.step_with_optimizer:
																				# No link between scheduler and optimizer -> just step
																				self.scheduler.step(*_SCREAMING_SNAKE_CASE			,		**_SCREAMING_SNAKE_CASE				)
																				return

													# Otherwise, first make sure the optimizer was stepped.
													if not self.gradient_state.sync_gradients:
																				if self.gradient_state.adjust_scheduler:
																											self.scheduler._step_count += 1
																				return

													for opt in self.optimizers:
																				if opt.step_was_skipped:
																											return
													if self.split_batches:
																				# Split batches -> the training dataloader batch size is not changed so one step per training step
																				self.scheduler.step(*_SCREAMING_SNAKE_CASE			,		**_SCREAMING_SNAKE_CASE				)
													else:
																				# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
																				# num_processes steps per training step
																				lowerCamelCase_				     =AcceleratorState().num_processes
																				for _ in range(_SCREAMING_SNAKE_CASE				):
																											# Special case when using OneCycle and `drop_last` was not used
																											if hasattr(self.scheduler			,		"""total_steps"""				):
																																		if self.scheduler._step_count <= self.scheduler.total_steps:
																																									self.scheduler.step(*_SCREAMING_SNAKE_CASE			,		**_SCREAMING_SNAKE_CASE				)
																											else:
																																		self.scheduler.step(*_SCREAMING_SNAKE_CASE			,		**_SCREAMING_SNAKE_CASE				)







						def     _snake_case							(       self				)->				Any:
													return self.scheduler.get_last_lr()







						def     _snake_case							(       self				)->				Optional[int]:
													return self.scheduler.state_dict()







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE				)->				Optional[int]:
													self.scheduler.load_state_dict(_SCREAMING_SNAKE_CASE				)







						def     _snake_case							(       self				)->				Any:
													return self.scheduler.get_lr()







						def     _snake_case							(       self			,		*_SCREAMING_SNAKE_CASE			,		**_SCREAMING_SNAKE_CASE				)->				str:
													return self.scheduler.print_lr(*_SCREAMING_SNAKE_CASE			,		**_SCREAMING_SNAKE_CASE				)





'''simple docstring'''





import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A):



     """simple docstring"""



     if isinstance(__A  ,	__A):
          _a      =  np.full((len(__A), sequence_length, 2)  ,	__A)
     else:
          _a      =  np.full((len(__A), sequence_length)  ,	__A)

     for i, tensor in enumerate(__A):
          if padding_side == "right":
               if isinstance(__A  ,	__A):
                    _a      =  tensor[:sequence_length]
               else:
                    _a      =  tensor[:sequence_length]
          else:
               if isinstance(__A  ,	__A):
                    _a      =  tensor[:sequence_length]
               else:
                    _a      =  tensor[:sequence_length]

     return out_tensor.tolist()




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  ord(__A)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
          return True
     _a      =  unicodedata.category(__A)
     if cat.startswith('''P'''):
          return True
     return False






@dataclass
class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       PreTrainedTokenizerBase
 __lowerCamelCase    :       Union[bool, str, PaddingStrategy]          =       True
 __lowerCamelCase    :       Optional[int]          =       None
 __lowerCamelCase    :       Optional[int]          =       None
 __lowerCamelCase    :       int          =       -100
 __lowerCamelCase    :       str          =       "pt"



 def 					a__  (self ,   A       )    ->     List[str]:


      """simple docstring"""



      import torch

      _a      =  '''label''' if '''label''' in features[0].keys() else '''labels'''
      _a      =  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
      _a      =  self.tokenizer.pad(
          A ,   padding=self.padding ,   max_length=self.max_length ,   pad_to_multiple_of=self.pad_to_multiple_of ,   return_tensors='''pt''' if labels is None else None ,   )

      if labels is None:
           return batch

      _a      =  torch.tensor(batch['''entity_ids''']       ).shape[1]
      _a      =  self.tokenizer.padding_side
      if padding_side == "right":
           _a      =  [
               list(A       ) + [self.label_pad_token_id] * (sequence_length - len(A       )) for label in labels
           ]
      else:
           _a      =  [
               [self.label_pad_token_id] * (sequence_length - len(A       )) + list(A       ) for label in labels
           ]

      _a      =  [feature['''ner_tags'''] for feature in features]
      _a      =  padding_tensor(A ,   -1 ,   A ,   A       )
      _a      =  [feature['''original_entity_spans'''] for feature in features]
      _a      =  padding_tensor(A ,   (-1, -1) ,   A ,   A       )
      _a      =  {k: torch.tensor(A ,   dtype=torch.intaa       ) for k, v in batch.items()}

      return batch



