



"""simple docstring"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    List[str]	      =	["""image_processor""", """tokenizer"""]
 _lowerCAmelCase     :    Union[str, Any]	      =	"""ChineseCLIPImageProcessor"""
 _lowerCAmelCase     :    int	      =	("""BertTokenizer""", """BertTokenizerFast""")

 def __init__(  self	: Optional[Any]						,  lowercase_	: int=None						,  lowercase_	: Dict=None						,  **lowercase_	: Union[str, Any]       ):
     snake_case_				:  str       =			None
     if "feature_extractor" in kwargs:
         warnings.warn(
             '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
             ''' instead.'''						,  lowercase_						,  )
         snake_case_				:  List[str]       =			kwargs.pop('''feature_extractor'''       )

     snake_case_				:  Any       =			image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError('''You need to specify an `image_processor`.'''       )
     if tokenizer is None:
         raise ValueError('''You need to specify a `tokenizer`.'''       )

     super().__init__(lowercase_						,  lowercase_       )
     snake_case_				:  List[str]       =			self.image_processor

 def __call__(  self	: Tuple						,  lowercase_	: Tuple=None						,  lowercase_	: int=None						,  lowercase_	: Optional[int]=None						,  **lowercase_	: Dict       ):

     if text is None and images is None:
         raise ValueError('''You have to specify either text or images. Both cannot be none.'''       )

     if text is not None:
         snake_case_				:  List[str]       =			self.tokenizer(lowercase_						,  return_tensors=lowercase_						,  **lowercase_       )

     if images is not None:
         snake_case_				:  Optional[int]       =			self.image_processor(lowercase_						,  return_tensors=lowercase_						,  **lowercase_       )

     if text is not None and images is not None:
         snake_case_				:  Tuple       =			image_features.pixel_values
         return encoding
     elif text is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**lowercase_       )						,  tensor_type=lowercase_       )

 def 		_snake_case      (  self	: int						,  *lowercase_	: str						,  **lowercase_	: Any       ):
     return self.tokenizer.batch_decode(*lowercase_						,  **lowercase_       )

 def 		_snake_case      (  self	: Any						,  *lowercase_	: List[str]						,  **lowercase_	: str       ):
     return self.tokenizer.decode(*lowercase_						,  **lowercase_       )

 @property
 def 		_snake_case      (  self	: Dict       ):
     snake_case_				:  Optional[Any]       =			self.tokenizer.model_input_names
     snake_case_				:  int       =			self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )






 @property
 def 		_snake_case      (  self	: Optional[int]       ):
     warnings.warn(
         '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''						,  lowercase_						,  )
     return self.image_processor_class










"""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


lowercase__					:						List[Any]												=			logging.get_logger(__name__)




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Tuple	      =	"""linear"""
 _lowerCAmelCase     :    Optional[int]	      =	"""cosine"""
 _lowerCAmelCase     :    Optional[int]	      =	"""cosine_with_restarts"""
 _lowerCAmelCase     :    Union[str, Any]	      =	"""polynomial"""
 _lowerCAmelCase     :    Optional[Any]	      =	"""constant"""
 _lowerCAmelCase     :    Optional[Any]	      =	"""constant_with_warmup"""
 _lowerCAmelCase     :    Union[str, Any]	      =	"""piecewise_constant"""





def __lowercase    (							_a	,    _a = -1					):
    return LambdaLR(_a	,    lambda _a					: 1	,    last_epoch=_a					)




def __lowercase    (							_a	,    _a	,    _a = -1					):

    def lr_lambda(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1.0	,    _a					)					)
        return 1.0

    return LambdaLR(_a	,    _a	,    last_epoch=_a					)




def __lowercase    (							_a	,    _a	,    _a = -1					):
    snake_case_				:  Dict       =			{}
    snake_case_				:  Optional[Any]       =			step_rules.split(''','''					)
    for rule_str in rule_list[:-1]:
        snake_case_,			snake_case_				:  Union[str, Any]       =			rule_str.split(''':'''					)
        snake_case_				:  Dict       =			int(_a					)
        snake_case_				:  int       =			float(_a					)
        snake_case_				:  Any       =			value
    snake_case_				:  Any       =			float(rule_list[-1]					)

    def create_rules_function(_a	,    _a					):
        def rule_func(_a					) -> float:
            snake_case_				:  Union[str, Any]       =			sorted(rules_dict.keys()					)
            for i, sorted_step in enumerate(_a					):
                if steps < sorted_step:
                    return rules_dict[sorted_steps[i]]
            return last_lr_multiple

        return rule_func

    snake_case_				:  List[str]       =			create_rules_function(_a	,    _a					)

    return LambdaLR(_a	,    _a	,    last_epoch=_a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a=-1					):

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

    return LambdaLR(_a	,    _a	,    _a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a = 0.5	,    _a = -1					):

    def lr_lambda(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1	,    _a					)					)
        snake_case_				:  str       =			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(_a					) * 2.0 * progress					))					)

    return LambdaLR(_a	,    _a	,    _a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a = 1	,    _a = -1					):

    def lr_lambda(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1	,    _a					)					)
        snake_case_				:  int       =			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(_a					) * progress) % 1.0)					))					)

    return LambdaLR(_a	,    _a	,    _a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a=1E-7	,    _a=1.0	,    _a=-1					):
    snake_case_				:  List[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(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1	,    _a					)					)
        elif current_step > num_training_steps:
            return lr_end / lr_init  # as LambdaLR multiplies by lr_init
        else:
            snake_case_				:  Tuple       =			lr_init - lr_end
            snake_case_				:  List[str]       =			num_training_steps - num_warmup_steps
            snake_case_				:  str       =			1 - (current_step - num_warmup_steps) / decay_steps
            snake_case_				:  Optional[int]       =			lr_range * pct_remaining**power + lr_end
            return decay / lr_init  # as LambdaLR multiplies by lr_init

    return LambdaLR(_a	,    _a	,    _a					)


lowercase__					:						Dict												=			{
    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 __lowercase    (							_a	,    _a	,    _a = None	,    _a = None	,    _a = None	,    _a = 1	,    _a = 1.0	,    _a = -1	,    ):
    snake_case_				:  Union[str, Any]       =			SchedulerType(_a					)
    snake_case_				:  Any       =			TYPE_TO_SCHEDULER_FUNCTION[name]
    if name == SchedulerType.CONSTANT:
        return schedule_func(_a	,    last_epoch=_a					)

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

    # 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(_a	,    num_warmup_steps=_a	,    last_epoch=_a					)

    # 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(
            _a	,    num_warmup_steps=_a	,    num_training_steps=_a	,    num_cycles=_a	,    last_epoch=_a	,    )

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

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





