






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


__snake_case    :    Optional[int]         =     logging.get_logger(__name__)


__snake_case    :    str         =     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     __SCREAMING_SNAKE_CASE					(   __lowercase):






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






												"""simple docstring"""
												raise NotImplementedError('StoppingCriteria needs to be subclassed'   )




class     __SCREAMING_SNAKE_CASE					(   __lowercase):






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






												"""simple docstring"""
												lowerCAmelCase__    						=						max_length
												lowerCAmelCase__    						=						max_position_embeddings







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






												"""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     __SCREAMING_SNAKE_CASE					(   __lowercase):






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""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.'     ,						_UpperCamelCase     ,						)
												lowerCAmelCase__    						=						start_length
												lowerCAmelCase__    						=						max_new_tokens
												lowerCAmelCase__    						=						start_length + max_new_tokens







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






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




class     __SCREAMING_SNAKE_CASE					(   __lowercase):






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






												"""simple docstring"""
												lowerCAmelCase__    						=						max_time
												lowerCAmelCase__    						=						time.time() if initial_timestamp is None else initial_timestamp







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






												"""simple docstring"""
												return time.time() - self.initial_timestamp > self.max_time




class     __SCREAMING_SNAKE_CASE					(   __lowercase):






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






												"""simple docstring"""
												return any(criteria(_UpperCamelCase     ,						_UpperCamelCase   ) for criteria in self   )







						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												for stopping_criterium in self:
																		if isinstance(_UpperCamelCase     ,						_UpperCamelCase   ):
																								return stopping_criterium.max_length
																		elif isinstance(_UpperCamelCase     ,						_UpperCamelCase   ):
																								return stopping_criterium.max_length
												return None







def 	_UpperCamelCase   (    UpperCamelCase_       :					StoppingCriteriaList    ,       UpperCamelCase_       :					int		)  ->   StoppingCriteriaList:

						"""simple docstring"""



						lowerCAmelCase__    						=						stopping_criteria.max_length
						lowerCAmelCase__    						=						deepcopy(UpperCamelCase_		)
						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'    ,       UpperCamelCase_		)
						elif stopping_max_length is None:
												new_stopping_criteria.append(MaxLengthCriteria(max_length=UpperCamelCase_		)		)
						return new_stopping_criteria













from __future__ import annotations

__snake_case    :    Any         =     {
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}


class     __SCREAMING_SNAKE_CASE					:






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						graph
												# mapping node to its parent in resulting breadth first tree
												lowerCAmelCase__    						=						{}
												lowerCAmelCase__    						=						source_vertex






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						{self.source_vertex}
												lowerCAmelCase__    						=						None
												lowerCAmelCase__    						=						[self.source_vertex]  # first in first out queue

												while queue:
																		lowerCAmelCase__    						=						queue.pop(0   )
																		for adjacent_vertex in self.graph[vertex]:
																								if adjacent_vertex not in visited:
																														visited.add(_UpperCamelCase   )
																														lowerCAmelCase__    						=						vertex
																														queue.append(_UpperCamelCase   )







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												if target_vertex == self.source_vertex:
																		return self.source_vertex

												lowerCAmelCase__    						=						self.parent.get(_UpperCamelCase   )
												if target_vertex_parent is None:
																		lowerCAmelCase__    						=						(
																		    F"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
																		)
																		raise ValueError(_UpperCamelCase   )

												return self.shortest_path(_UpperCamelCase   ) + F"->{target_vertex}"


if __name__ == "__main__":
					__snake_case    :    Optional[Any]         =     Graph(graph, """G""")
					g.breath_first_search()
					print(g.shortest_path("""D"""))
					print(g.shortest_path("""G"""))
					print(g.shortest_path("""Foo"""))





