



"""simple docstring"""
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


A:   Union[str, Any]								=		logging.get_logger(__name__)


A:   Any								=		R'\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n'



class       SCREAMING_SNAKE_CASE__  (					_UpperCamelCase ):

   @add_start_docstrings(lowerCAmelCase_						)
   def __call__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      bool:





    '''simple docstring'''




    raise NotImplementedError("""StoppingCriteria needs to be subclassed"""						)



class       SCREAMING_SNAKE_CASE__  (					_UpperCamelCase ):

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	max_length
    UpperCAmelCase	:					List[str]	=	max_position_embeddings




   @add_start_docstrings(lowerCAmelCase_						)
   def __call__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      bool:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	input_ids.shape[-1]
    UpperCAmelCase	:					Optional[int]	=	cur_len >= self.max_length
    if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
     logger.warning_once(
         """This is a friendly reminder - the current text generation call will exceed the model\'s predefined """
         F"maximum length ({self.max_position_embeddings}). Depending on the model, you may observe "
         """exceptions, performance degradation, or nothing at all."""						)
    return is_done



class       SCREAMING_SNAKE_CASE__  (					_UpperCamelCase ):

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      str:





    '''simple docstring'''




    warnings.warn(
        """The class `MaxNewTokensCriteria` is deprecated. """
        F"Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` "
        """with `max_length = start_length + max_new_tokens` instead."""      ,     lowerCAmelCase_      ,     )
    UpperCAmelCase	:					Dict	=	start_length
    UpperCAmelCase	:					Tuple	=	max_new_tokens
    UpperCAmelCase	:					Any	=	start_length + max_new_tokens




   @add_start_docstrings(lowerCAmelCase_						)
   def __call__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      bool:





    '''simple docstring'''




    return input_ids.shape[-1] >= self.max_length



class       SCREAMING_SNAKE_CASE__  (					_UpperCamelCase ):

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	max_time
    UpperCAmelCase	:					List[Any]	=	time.time() if initial_timestamp is None else initial_timestamp




   @add_start_docstrings(lowerCAmelCase_						)
   def __call__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      bool:





    '''simple docstring'''




    return time.time() - self.initial_timestamp > self.max_time



class       SCREAMING_SNAKE_CASE__  (					_UpperCamelCase ):

   @add_start_docstrings(lowerCAmelCase_						)
   def __call__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      bool:





    '''simple docstring'''




    return any(criteria(lowerCAmelCase_      ,     lowerCAmelCase_						) for criteria in self						)




   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[int]:





    '''simple docstring'''




    for stopping_criterium in self:
     if isinstance(lowerCAmelCase_      ,     lowerCAmelCase_						):
      return stopping_criterium.max_length
     elif isinstance(lowerCAmelCase_      ,     lowerCAmelCase_						):
      return stopping_criterium.max_length
    return None

def       _snake_case						(						UpperCamelCase						:		StoppingCriteriaList			,		UpperCamelCase						:		int						):
 UpperCAmelCase	:					List[str]	=	stopping_criteria.max_length
 UpperCAmelCase	:					str	=	deepcopy(lowerCAmelCase_						)
 if stopping_max_length is not None and stopping_max_length != max_length:
  warnings.warn("""You set different `max_length` for stopping criteria and `max_length` parameter"""			,		lowerCAmelCase_						)
 elif stopping_max_length is None:
  new_stopping_criteria.append(MaxLengthCriteria(max_length=lowerCAmelCase_						)						)
 return new_stopping_criteria







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

_snake_case  :    List[Any]						    =  {
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = """beit"""






  def __init__(				self :		List[Any]					,  lowerCAmelCase_ :		Tuple=8_1_9_2					,  lowerCAmelCase_ :		Optional[int]=7_6_8					,  lowerCAmelCase_ :		int=1_2					,  lowerCAmelCase_ :		Optional[int]=1_2					,  lowerCAmelCase_ :		Any=3_0_7_2					,  lowerCAmelCase_ :		Optional[int]="gelu"					,  lowerCAmelCase_ :		Any=0.0					,  lowerCAmelCase_ :		Any=0.0					,  lowerCAmelCase_ :		Any=0.02					,  lowerCAmelCase_ :		int=1e-12					,  lowerCAmelCase_ :		int=2_2_4					,  lowerCAmelCase_ :		str=1_6					,  lowerCAmelCase_ :		int=3					,  lowerCAmelCase_ :		Dict=False					,  lowerCAmelCase_ :		int=False					,  lowerCAmelCase_ :		List[Any]=False					,  lowerCAmelCase_ :		int=False					,  lowerCAmelCase_ :		List[str]=0.1					,  lowerCAmelCase_ :		Union[str, Any]=0.1					,  lowerCAmelCase_ :		List[str]=True					,  lowerCAmelCase_ :		List[Any]=[3, 5, 7, 1_1]					,  lowerCAmelCase_ :		Optional[Any]=[1, 2, 3, 6]					,  lowerCAmelCase_ :		Tuple=True					,  lowerCAmelCase_ :		Dict=0.4					,  lowerCAmelCase_ :		Tuple=2_5_6					,  lowerCAmelCase_ :		Any=1					,  lowerCAmelCase_ :		Any=False					,  lowerCAmelCase_ :		Optional[int]=2_5_5					,  **lowerCAmelCase_ :		Any					,  )							->						Dict:
     super().__init__(**lowerCAmelCase_     )

     __lowerCAmelCase								=	vocab_size
     __lowerCAmelCase								=	hidden_size
     __lowerCAmelCase								=	num_hidden_layers
     __lowerCAmelCase								=	num_attention_heads
     __lowerCAmelCase								=	intermediate_size
     __lowerCAmelCase								=	hidden_act
     __lowerCAmelCase								=	hidden_dropout_prob
     __lowerCAmelCase								=	attention_probs_dropout_prob
     __lowerCAmelCase								=	initializer_range
     __lowerCAmelCase								=	layer_norm_eps

     __lowerCAmelCase								=	image_size
     __lowerCAmelCase								=	patch_size
     __lowerCAmelCase								=	num_channels
     __lowerCAmelCase								=	use_mask_token
     __lowerCAmelCase								=	use_absolute_position_embeddings
     __lowerCAmelCase								=	use_relative_position_bias
     __lowerCAmelCase								=	use_shared_relative_position_bias
     __lowerCAmelCase								=	layer_scale_init_value
     __lowerCAmelCase								=	drop_path_rate
     __lowerCAmelCase								=	use_mean_pooling
     # decode head attributes (semantic segmentation)
     __lowerCAmelCase								=	out_indices
     __lowerCAmelCase								=	pool_scales
     # auxiliary head attributes (semantic segmentation)
     __lowerCAmelCase								=	use_auxiliary_head
     __lowerCAmelCase								=	auxiliary_loss_weight
     __lowerCAmelCase								=	auxiliary_channels
     __lowerCAmelCase								=	auxiliary_num_convs
     __lowerCAmelCase								=	auxiliary_concat_input
     __lowerCAmelCase								=	semantic_loss_ignore_index





class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = version.parse("""1.11"""    )






  @property
  def 				lowercase		(				self :		Tuple     )							->						Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
         ]     )






  @property
  def 				lowercase		(				self :		Optional[Any]     )							->						float:
     return 1e-4

