



"""simple docstring"""

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

lowercase__					:						str												=			{
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    str	      =	"""pix2struct_text_model"""
 _lowerCAmelCase     :    List[Any]	      =	["""past_key_values"""]
 _lowerCAmelCase     :    Tuple	      =	{
     """hidden_size""": """hidden_size""",
     """num_attention_heads""": """num_heads""",
     """num_hidden_layers""": """num_layers""",
 }

 def __init__(  self	: List[Any]						,  lowercase_	: Dict=50244						,  lowercase_	: List[str]=768						,  lowercase_	: Optional[int]=64						,  lowercase_	: Optional[int]=2048						,  lowercase_	: Optional[Any]=12						,  lowercase_	: Optional[int]=12						,  lowercase_	: Optional[Any]=32						,  lowercase_	: List[Any]=128						,  lowercase_	: Optional[int]=0.1						,  lowercase_	: Optional[Any]=1E-6						,  lowercase_	: List[str]=1.0						,  lowercase_	: List[str]="gelu_new"						,  lowercase_	: Tuple=0						,  lowercase_	: List[str]=False						,  lowercase_	: Tuple=0						,  lowercase_	: Optional[int]=1						,  lowercase_	: Optional[int]=False						,  lowercase_	: Union[str, Any]=True						,  **lowercase_	: List[str]						,  ):
     snake_case_				:  Optional[int]       =			vocab_size
     snake_case_				:  Tuple       =			hidden_size
     snake_case_				:  List[Any]       =			d_kv
     snake_case_				:  List[Any]       =			d_ff
     snake_case_				:  List[Any]       =			num_layers
     snake_case_				:  Union[str, Any]       =			num_heads
     snake_case_				:  Optional[int]       =			relative_attention_num_buckets
     snake_case_				:  Optional[Any]       =			relative_attention_max_distance
     snake_case_				:  int       =			dropout_rate
     snake_case_				:  Optional[Any]       =			layer_norm_epsilon
     snake_case_				:  Union[str, Any]       =			initializer_factor
     snake_case_				:  List[str]       =			use_cache

     snake_case_				:  Dict       =			eos_token_id
     snake_case_				:  Dict       =			decoder_start_token_id

     # for backwards compatibility
     snake_case_				:  str       =			dense_act_fn

     super().__init__(
         pad_token_id=lowercase_						,  eos_token_id=lowercase_						,  decoder_start_token_id=lowercase_						,  tie_word_embeddings=lowercase_						,  is_decoder=lowercase_						,  **lowercase_						,  )






 @classmethod
 def 		_snake_case      (  cls	: Tuple						,  lowercase_	: Union[str, os.PathLike]						,  **lowercase_	: List[str]       ):
     cls._set_token_in_kwargs(lowercase_       )

     snake_case_,			snake_case_				:  Union[str, Any]       =			cls.get_config_dict(lowercase_						,  **lowercase_       )

     # get the text config dict if we are loading from Pix2StructConfig
     if config_dict.get('''model_type'''       ) == "pix2struct":
         snake_case_				:  List[Any]       =			config_dict['''text_config''']

     if "model_type" in config_dict and hasattr(cls						,  '''model_type'''       ) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
             f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."       )

     return cls.from_dict(lowercase_						,  **lowercase_       )





class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Dict	      =	"""pix2struct_vision_model"""

 def __init__(  self	: List[str]						,  lowercase_	: Tuple=768						,  lowercase_	: str=768						,  lowercase_	: Optional[Any]=2048						,  lowercase_	: int=64						,  lowercase_	: Optional[int]=12						,  lowercase_	: str=12						,  lowercase_	: str="gelu_new"						,  lowercase_	: Optional[int]=1E-6						,  lowercase_	: Tuple=0.0						,  lowercase_	: List[Any]=0.0						,  lowercase_	: Tuple=1E-10						,  lowercase_	: Optional[int]=1.0						,  lowercase_	: Tuple=4096						,  lowercase_	: Optional[Any]=32						,  lowercase_	: List[str]=128						,  **lowercase_	: Optional[Any]						,  ):
     super().__init__(**lowercase_       )

     snake_case_				:  Any       =			hidden_size
     snake_case_				:  List[str]       =			patch_embed_hidden_size
     snake_case_				:  List[Any]       =			d_ff
     snake_case_				:  int       =			dropout_rate
     snake_case_				:  int       =			num_hidden_layers
     snake_case_				:  List[Any]       =			num_attention_heads
     snake_case_				:  int       =			initializer_range
     snake_case_				:  Union[str, Any]       =			initializer_factor
     snake_case_				:  Tuple       =			attention_dropout
     snake_case_				:  Optional[Any]       =			layer_norm_eps
     snake_case_				:  str       =			dense_act_fn
     snake_case_				:  Union[str, Any]       =			seq_len
     snake_case_				:  str       =			relative_attention_num_buckets
     snake_case_				:  Optional[Any]       =			relative_attention_max_distance
     snake_case_				:  int       =			d_kv






 @classmethod
 def 		_snake_case      (  cls	: Any						,  lowercase_	: Union[str, os.PathLike]						,  **lowercase_	: List[str]       ):
     cls._set_token_in_kwargs(lowercase_       )

     snake_case_,			snake_case_				:  int       =			cls.get_config_dict(lowercase_						,  **lowercase_       )

     # get the vision config dict if we are loading from Pix2StructConfig
     if config_dict.get('''model_type'''       ) == "pix2struct":
         snake_case_				:  List[str]       =			config_dict['''vision_config''']

     if "model_type" in config_dict and hasattr(cls						,  '''model_type'''       ) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
             f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."       )

     return cls.from_dict(lowercase_						,  **lowercase_       )





class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Any	      =	"""pix2struct"""
 _lowerCAmelCase     :    int	      =	True

 def __init__(  self	: List[Any]						,  lowercase_	: Dict=None						,  lowercase_	: Optional[int]=None						,  lowercase_	: Dict=1.0						,  lowercase_	: List[Any]=0.02						,  lowercase_	: List[str]=False						,  lowercase_	: Tuple=False						,  lowercase_	: Any=True						,  **lowercase_	: Optional[Any]						,  ):
     super().__init__(tie_word_embeddings=lowercase_						,  is_encoder_decoder=lowercase_						,  **lowercase_       )

     if text_config is None:
         snake_case_				:  Any       =			{}
         logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.'''       )

     if vision_config is None:
         snake_case_				:  Dict       =			{}
         logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.'''       )

     snake_case_				:  str       =			PixaStructTextConfig(**lowercase_       )
     snake_case_				:  int       =			PixaStructVisionConfig(**lowercase_       )

     snake_case_				:  List[str]       =			self.text_config.decoder_start_token_id
     snake_case_				:  int       =			self.text_config.pad_token_id
     snake_case_				:  Optional[int]       =			self.text_config.eos_token_id

     snake_case_				:  Any       =			initializer_factor
     snake_case_				:  Union[str, Any]       =			initializer_range

     snake_case_				:  List[Any]       =			self.initializer_range
     snake_case_				:  List[str]       =			self.initializer_range

     snake_case_				:  Tuple       =			is_vqa

 @classmethod
 def 		_snake_case      (  cls	: Optional[int]						,  lowercase_	: PixaStructTextConfig						,  lowercase_	: PixaStructVisionConfig						,  **lowercase_	: Union[str, Any]       ):
     return cls(text_config=text_config.to_dict()						,  vision_config=vision_config.to_dict()						,  **lowercase_       )






 def 		_snake_case      (  self	: int       ):
     snake_case_				:  List[str]       =			copy.deepcopy(self.__dict__       )
     snake_case_				:  int       =			self.text_config.to_dict()
     snake_case_				:  List[Any]       =			self.vision_config.to_dict()
     snake_case_				:  Any       =			self.__class__.model_type
     return output










"""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					)





