import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


__UpperCamelCase		:       Any									=     logging.get_logger(__name__)





class 					lowercase__						(   __A):
						UpperCamelCase_     =		"upernet"


						def __init__(       self	:				List[str]    ,		UpperCamelCase__	:				List[Any]=None    ,		UpperCamelCase__	:				Optional[int]=512    ,		UpperCamelCase__	:				str=0.02    ,		UpperCamelCase__	:				Optional[int]=[1, 2, 3, 6]    ,		UpperCamelCase__	:				List[str]=True    ,		UpperCamelCase__	:				Dict=0.4    ,		UpperCamelCase__	:				Optional[Any]=384    ,		UpperCamelCase__	:				Optional[int]=256    ,		UpperCamelCase__	:				int=1    ,		UpperCamelCase__	:				str=False    ,		UpperCamelCase__	:				Optional[int]=255    ,		**UpperCamelCase__	:				Union[str, Any]    ,		):





												'''simple docstring'''





												super().__init__(**lowercase_						)

												if backbone_config is None:
																		logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''						)
																		SCREAMING_SNAKE_CASE							:    str							=							CONFIG_MAPPING["resnet"](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']						)
												elif isinstance(lowercase_    ,		lowercase_						):
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							backbone_config.get('''model_type'''						)
																		SCREAMING_SNAKE_CASE							:    List[str]							=							CONFIG_MAPPING[backbone_model_type]
																		SCREAMING_SNAKE_CASE							:    Tuple							=							config_class.from_dict(lowercase_						)

												SCREAMING_SNAKE_CASE							:    int							=							backbone_config
												SCREAMING_SNAKE_CASE							:    str							=							hidden_size
												SCREAMING_SNAKE_CASE							:    Dict							=							initializer_range
												SCREAMING_SNAKE_CASE							:    int							=							pool_scales
												SCREAMING_SNAKE_CASE							:    int							=							use_auxiliary_head
												SCREAMING_SNAKE_CASE							:    List[str]							=							auxiliary_loss_weight
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							auxiliary_in_channels
												SCREAMING_SNAKE_CASE							:    Tuple							=							auxiliary_channels
												SCREAMING_SNAKE_CASE							:    List[Any]							=							auxiliary_num_convs
												SCREAMING_SNAKE_CASE							:    Tuple							=							auxiliary_concat_input
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							loss_ignore_index



						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							copy.deepcopy(self.__dict__						)
												SCREAMING_SNAKE_CASE							:    Dict							=							self.backbone_config.to_dict()
												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.__class__.model_type
												return output






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


UpperCamelCase  							=						logging.get_logger(__name__)


UpperCamelCase  							=						R'''
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

'''



class   snake_case_       (		__A       ):






   @add_start_docstrings(lowercase_  )
   def __call__(      self				:     Optional[Any]	,						lowercase_				:     torch.LongTensor	,						lowercase_				:     torch.FloatTensor	,						**lowercase_				:     List[str]  )					->						bool:
       raise NotImplementedError("StoppingCriteria needs to be subclassed"  )



class   snake_case_       (		__A       ):






   def __init__(      self				:     Dict	,						lowercase_				:     int	,						lowercase_				:     Optional[int] = None  )					->						List[str]:
       lowercase__ :   str    					=				max_length
       lowercase__ :   Optional[int]    					=				max_position_embeddings







   @add_start_docstrings(lowercase_  )
   def __call__(      self				:     Tuple	,						lowercase_				:     torch.LongTensor	,						lowercase_				:     torch.FloatTensor	,						**lowercase_				:     Union[str, Any]  )					->						bool:
       lowercase__ :   str    					=				input_ids.shape[-1]
       lowercase__ :   Any    					=				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   snake_case_       (		__A       ):






   def __init__(      self				:     Tuple	,						lowercase_				:     int	,						lowercase_				:     int  )					->						List[str]:
       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."	,						lowercase_	,						)
       lowercase__ :   Optional[int]    					=				start_length
       lowercase__ :   str    					=				max_new_tokens
       lowercase__ :   Tuple    					=				start_length + max_new_tokens







   @add_start_docstrings(lowercase_  )
   def __call__(      self				:     List[Any]	,						lowercase_				:     torch.LongTensor	,						lowercase_				:     torch.FloatTensor	,						**lowercase_				:     Dict  )					->						bool:
       return input_ids.shape[-1] >= self.max_length



class   snake_case_       (		__A       ):






   def __init__(      self				:     Tuple	,						lowercase_				:     float	,						lowercase_				:     Optional[float] = None  )					->						Dict:
       lowercase__ :   List[str]    					=				max_time
       lowercase__ :   Tuple    					=				time.time() if initial_timestamp is None else initial_timestamp







   @add_start_docstrings(lowercase_  )
   def __call__(      self				:     int	,						lowercase_				:     torch.LongTensor	,						lowercase_				:     torch.FloatTensor	,						**lowercase_				:     Union[str, Any]  )					->						bool:
       return time.time() - self.initial_timestamp > self.max_time



class   snake_case_       (		__A       ):






   @add_start_docstrings(lowercase_  )
   def __call__(      self				:     str	,						lowercase_				:     torch.LongTensor	,						lowercase_				:     torch.FloatTensor	,						**lowercase_				:     List[str]  )					->						bool:
       return any(criteria(lowercase_	,						lowercase_  ) for criteria in self  )







   @property
   def       __UpperCamelCase     (      self				:     Optional[Any]  )					->						Optional[int]:
       for stopping_criterium in self:
           if isinstance(lowercase_	,						lowercase_  ):
               return stopping_criterium.max_length
           elif isinstance(lowercase_	,						lowercase_  ):
               return stopping_criterium.max_length
       return None



def 			lowercase_				(			_lowerCamelCase							:       StoppingCriteriaList		,	_lowerCamelCase							:       int):
    lowercase__ :   Optional[int]    					=				stopping_criteria.max_length
    lowercase__ :   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



