




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 			a__    (					snake_case__      ):






						@add_start_docstrings(_A			)
						def __call__(   self		,			_A		,			_A		,			**_A			):




											"""simple docstring"""



											raise NotImplementedError("StoppingCriteria needs to be subclassed"			)






class 			a__    (					snake_case__      ):






						def __init__(   self		,			_A		,			_A = None			):




											"""simple docstring"""



											__lowerCAmelCase				   =       max_length
											__lowerCAmelCase				   =       max_position_embeddings






						@add_start_docstrings(_A			)
						def __call__(   self		,			_A		,			_A		,			**_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       input_ids.shape[-1]
											__lowerCAmelCase				   =       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 			a__    (					snake_case__      ):






						def __init__(   self		,			_A		,			_A			):




											"""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."		,			_A		,			)
											__lowerCAmelCase				   =       start_length
											__lowerCAmelCase				   =       max_new_tokens
											__lowerCAmelCase				   =       start_length + max_new_tokens






						@add_start_docstrings(_A			)
						def __call__(   self		,			_A		,			_A		,			**_A			):




											"""simple docstring"""



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






class 			a__    (					snake_case__      ):






						def __init__(   self		,			_A		,			_A = None			):




											"""simple docstring"""



											__lowerCAmelCase				   =       max_time
											__lowerCAmelCase				   =       time.time() if initial_timestamp is None else initial_timestamp






						@add_start_docstrings(_A			)
						def __call__(   self		,			_A		,			_A		,			**_A			):




											"""simple docstring"""



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






class 			a__    (					snake_case__      ):






						@add_start_docstrings(_A			)
						def __call__(   self		,			_A		,			_A		,			**_A			):




											"""simple docstring"""



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






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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







def 	_a      (						SCREAMING_SNAKE_CASE_							:				StoppingCriteriaList      ,      SCREAMING_SNAKE_CASE_							:				int     ):
					__lowerCAmelCase				   =       stopping_criteria.max_length
					__lowerCAmelCase				   =       deepcopy(SCREAMING_SNAKE_CASE_     )
					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"      ,      SCREAMING_SNAKE_CASE_     )
					elif stopping_max_length is None:
										new_stopping_criteria.append(MaxLengthCriteria(max_length=SCREAMING_SNAKE_CASE_     )     )
					return new_stopping_criteria










'''simple docstring'''

from collections import defaultdict


def 		__magic_name__    (	__UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_									=				1
					snake_case_									=				True
					for v in tree[start]:
										if v not in visited:
															ret += dfs(__UpperCAmelCase	)
					if ret % 2 == 0:
										cuts.append(__UpperCAmelCase	)
					return ret


def 		__magic_name__    (	)		->					Union[str, Any]:

					'''simple docstring'''

					dfs(1	)


if __name__ == "__main__":
							a		,a  :				Dict									=  10, 9
							a  :				Dict									=  defaultdict(list)
							a  :				dict[int, bool]									=  {}
							a  :				list[int]									=  []
							a  :				Tuple									=  0
							a  :				str									=  [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
							for u, v in edges:
														tree[u].append(v)
														tree[v].append(u)
							even_tree()
							print(len(cuts) - 1)

