





'''simple docstring'''






import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


A_		:  Tuple		 =						logging.get_logger(__name__)
class 					__snake_case  (					_a							):






   '''simple docstring'''





   lowerCamelCase__    							=      '''linear'''
   lowerCamelCase__    							=      '''cosine'''
   lowerCamelCase__    							=      '''cosine_with_restarts'''
   lowerCamelCase__    							=      '''polynomial'''
   lowerCamelCase__    							=      '''constant'''
   lowerCamelCase__    							=      '''constant_with_warmup'''
   lowerCamelCase__    							=      '''piecewise_constant'''



def        UpperCamelCase__		(						__magic_name__  :   Optimizer		,       __magic_name__  :   int = -1      )				->			int:
 '''simple docstring'''






 return LambdaLR(_snake_case		,       lambda __magic_name__      : 1		,       last_epoch=_snake_case      )



def        UpperCamelCase__		(						__magic_name__  :   Optimizer		,       __magic_name__  :   int		,       __magic_name__  :   int = -1      )				->			str:
 '''simple docstring'''






 def lr_lambda(__magic_name__  :   int      ):
  if current_step < num_warmup_steps:
   return float(_snake_case      ) / float(max(1.0		,       _snake_case      )      )
  return 1.0

 return LambdaLR(_snake_case		,       _snake_case		,       last_epoch=_snake_case      )



def        UpperCamelCase__		(						__magic_name__  :   Optimizer		,       __magic_name__  :   str		,       __magic_name__  :   int = -1      )				->			int:
 '''simple docstring'''






 snake_case__     :					Optional[Any]    =  {}
 snake_case__     :					Optional[int]    =  step_rules.split(""","""      )
 for rule_str in rule_list[:-1]:
  snake_case__  ,							snake_case__     :					str    =  rule_str.split(""":"""      )
  snake_case__     :					List[str]    =  int(_snake_case      )
  snake_case__     :					Dict    =  float(_snake_case      )
  snake_case__     :					Optional[int]    =  value
 snake_case__     :					Optional[Any]    =  float(rule_list[-1]      )

 def create_rules_function(__magic_name__  :   Any		,       __magic_name__  :   Any      ):
  def rule_func(__magic_name__  :   int      ) -> float:
   snake_case__     :					int    =  sorted(rules_dict.keys()      )
   for i, sorted_step in enumerate(_snake_case      ):
    if steps < sorted_step:
     return rules_dict[sorted_steps[i]]
   return last_lr_multiple

  return rule_func

 snake_case__     :					Any    =  create_rules_function(_snake_case		,       _snake_case      )

 return LambdaLR(_snake_case		,       _snake_case		,       last_epoch=_snake_case      )



def        UpperCamelCase__		(						__magic_name__  :   List[str]		,       __magic_name__  :   List[Any]		,       __magic_name__  :   Optional[int]		,       __magic_name__  :   List[Any]=-1      )				->			Dict:
 '''simple docstring'''






 def lr_lambda(__magic_name__  :   int      ):
  if current_step < num_warmup_steps:
   return float(_snake_case      ) / float(max(1		,       _snake_case      )      )
  return max(
      0.0		,       float(num_training_steps - current_step      ) / float(max(1		,       num_training_steps - num_warmup_steps      )      )      )

 return LambdaLR(_snake_case		,       _snake_case		,       _snake_case      )



def        UpperCamelCase__		(						__magic_name__  :   Optimizer		,       __magic_name__  :   int		,       __magic_name__  :   int		,       __magic_name__  :   float = 0.5		,       __magic_name__  :   int = -1      )				->			Any:
 '''simple docstring'''






 def lr_lambda(__magic_name__  :   Optional[int]      ):
  if current_step < num_warmup_steps:
   return float(_snake_case      ) / float(max(1		,       _snake_case      )      )
  snake_case__     :					Tuple    =  float(current_step - num_warmup_steps      ) / float(max(1		,       num_training_steps - num_warmup_steps      )      )
  return max(0.0		,       0.5 * (1.0 + math.cos(math.pi * float(_snake_case      ) * 2.0 * progress      ))      )

 return LambdaLR(_snake_case		,       _snake_case		,       _snake_case      )



def        UpperCamelCase__		(						__magic_name__  :   Optimizer		,       __magic_name__  :   int		,       __magic_name__  :   int		,       __magic_name__  :   int = 1		,       __magic_name__  :   int = -1      )				->			List[Any]:
 '''simple docstring'''






 def lr_lambda(__magic_name__  :   int      ):
  if current_step < num_warmup_steps:
   return float(_snake_case      ) / float(max(1		,       _snake_case      )      )
  snake_case__     :					List[str]    =  float(current_step - num_warmup_steps      ) / float(max(1		,       num_training_steps - num_warmup_steps      )      )
  if progress >= 1.0:
   return 0.0
  return max(0.0		,       0.5 * (1.0 + math.cos(math.pi * ((float(_snake_case      ) * progress) % 1.0)      ))      )

 return LambdaLR(_snake_case		,       _snake_case		,       _snake_case      )



def        UpperCamelCase__		(						__magic_name__  :   List[Any]		,       __magic_name__  :   List[Any]		,       __magic_name__  :   Any		,       __magic_name__  :   List[str]=1E-7		,       __magic_name__  :   Tuple=1.0		,       __magic_name__  :   List[str]=-1      )				->			List[str]:
 '''simple docstring'''






 snake_case__     :					str    =  optimizer.defaults["""lr"""]
 if not (lr_init > lr_end):
  raise ValueError(f"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"      )

 def lr_lambda(__magic_name__  :   int      ):
  if current_step < num_warmup_steps:
   return float(_snake_case      ) / float(max(1		,       _snake_case      )      )
  elif current_step > num_training_steps:
   return lr_end / lr_init  # as LambdaLR multiplies by lr_init
  else:
   snake_case__     :					Any    =  lr_init - lr_end
   snake_case__     :					Dict    =  num_training_steps - num_warmup_steps
   snake_case__     :					Optional[int]    =  1 - (current_step - num_warmup_steps) / decay_steps
   snake_case__     :					Tuple    =  lr_range * pct_remaining**power + lr_end
   return decay / lr_init  # as LambdaLR multiplies by lr_init

 return LambdaLR(_snake_case		,       _snake_case		,       _snake_case      )


A_		:  List[Any]		 =						{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}





def        UpperCamelCase__		(						__magic_name__  :   Union[str, SchedulerType]		,       __magic_name__  :   Optimizer		,       __magic_name__  :   Optional[str] = None		,       __magic_name__  :   Optional[int] = None		,       __magic_name__  :   Optional[int] = None		,       __magic_name__  :   int = 1		,       __magic_name__  :   float = 1.0		,       __magic_name__  :   int = -1		,       )				->			Dict:
 '''simple docstring'''






 snake_case__     :					Any    =  SchedulerType(_snake_case      )
 snake_case__     :					int    =  TYPE_TO_SCHEDULER_FUNCTION[name]
 if name == SchedulerType.CONSTANT:
  return schedule_func(_snake_case		,       last_epoch=_snake_case      )

 if name == SchedulerType.PIECEWISE_CONSTANT:
  return schedule_func(_snake_case		,       step_rules=_snake_case		,       last_epoch=_snake_case      )

 # All other schedulers require `num_warmup_steps`
 if num_warmup_steps is None:
  raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument."      )

 if name == SchedulerType.CONSTANT_WITH_WARMUP:
  return schedule_func(_snake_case		,       num_warmup_steps=_snake_case		,       last_epoch=_snake_case      )

 # All other schedulers require `num_training_steps`
 if num_training_steps is None:
  raise ValueError(f"{name} requires `num_training_steps`, please provide that argument."      )

 if name == SchedulerType.COSINE_WITH_RESTARTS:
  return schedule_func(
      _snake_case		,       num_warmup_steps=_snake_case		,       num_training_steps=_snake_case		,       num_cycles=_snake_case		,       last_epoch=_snake_case		,       )

 if name == SchedulerType.POLYNOMIAL:
  return schedule_func(
      _snake_case		,       num_warmup_steps=_snake_case		,       num_training_steps=_snake_case		,       power=_snake_case		,       last_epoch=_snake_case		,       )

 return schedule_func(
     _snake_case		,       num_warmup_steps=_snake_case		,       num_training_steps=_snake_case		,       last_epoch=_snake_case      )












from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Dict					=['''image_processor''', '''tokenizer''']
       __lowerCAmelCase    :		List[Any]					='''BlipImageProcessor'''
       __lowerCAmelCase    :		Union[str, Any]					=('''BertTokenizer''', '''BertTokenizerFast''')





       def __init__(				self	:Optional[int],      snake_case	:int,      snake_case	:Optional[int]):



          """simple docstring"""

          _lowercase              =False
          super().__init__(snake_case,      snake_case)
          _lowercase              =self.image_processor





       def __call__(				self	:Dict,      snake_case	:ImageInput = None,      snake_case	:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,      snake_case	:bool = True,      snake_case	:Union[bool, str, PaddingStrategy] = False,      snake_case	:Union[bool, str, TruncationStrategy] = None,      snake_case	:Optional[int] = None,      snake_case	:int = 0,      snake_case	:Optional[int] = None,      snake_case	:Optional[bool] = None,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = True,      snake_case	:Optional[Union[str, TensorType]] = None,      **snake_case	:str,      ):



          """simple docstring"""

          if images is None and text is None:
             raise ValueError('You have to specify either images or text.')

          # Get only text
          if images is None:
             _lowercase              =self.tokenizer
             _lowercase              =self.tokenizer(
                 text=snake_case,      add_special_tokens=snake_case,      padding=snake_case,      truncation=snake_case,      max_length=snake_case,      stride=snake_case,      pad_to_multiple_of=snake_case,      return_attention_mask=snake_case,      return_overflowing_tokens=snake_case,      return_special_tokens_mask=snake_case,      return_offsets_mapping=snake_case,      return_token_type_ids=snake_case,      return_length=snake_case,      verbose=snake_case,      return_tensors=snake_case,      **snake_case,      )
             return text_encoding

          # add pixel_values
          _lowercase              =self.image_processor(snake_case,      return_tensors=snake_case)

          if text is not None:
             _lowercase              =self.tokenizer(
                 text=snake_case,      add_special_tokens=snake_case,      padding=snake_case,      truncation=snake_case,      max_length=snake_case,      stride=snake_case,      pad_to_multiple_of=snake_case,      return_attention_mask=snake_case,      return_overflowing_tokens=snake_case,      return_special_tokens_mask=snake_case,      return_offsets_mapping=snake_case,      return_token_type_ids=snake_case,      return_length=snake_case,      verbose=snake_case,      return_tensors=snake_case,      **snake_case,      )
          else:
             _lowercase              =None

          if text_encoding is not None:
             encoding_image_processor.update(snake_case)

          return encoding_image_processor





       def 				UpperCamelCase__						(				self	:List[Any],      *snake_case	:int,      **snake_case	:str):



          """simple docstring"""

          return self.tokenizer.batch_decode(*snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:Tuple,      *snake_case	:List[str],      **snake_case	:str):



          """simple docstring"""

          return self.tokenizer.decode(*snake_case,      **snake_case)





       @property
       def 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

          _lowercase              =self.tokenizer.model_input_names
          _lowercase              =self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


