
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     =  logging.get_logger(__name__)


_A     =  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 			_lowerCAmelCase      (			__a ):




				@add_start_docstrings(_UpperCamelCase  )
				def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       **_UpperCamelCase  )       ->   bool:
							raise NotImplementedError("StoppingCriteria needs to be subclassed"  )





class 			_lowerCAmelCase      (			__a ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None  )       ->   Any:
							lowerCAmelCase_      			=			max_length
							lowerCAmelCase_      			=			max_position_embeddings




				@add_start_docstrings(_UpperCamelCase  )
				def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       **_UpperCamelCase  )       ->   bool:
							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 			_lowerCAmelCase      (			__a ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Optional[Any]:
							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  )       ->   bool:
							return input_ids.shape[-1] >= self.max_length





class 			_lowerCAmelCase      (			__a ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None  )       ->   Tuple:
							lowerCAmelCase_      			=			max_time
							lowerCAmelCase_      			=			time.time() if initial_timestamp is None else initial_timestamp




				@add_start_docstrings(_UpperCamelCase  )
				def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       **_UpperCamelCase  )       ->   bool:
							return time.time() - self.initial_timestamp > self.max_time





class 			_lowerCAmelCase      (			__a ):




				@add_start_docstrings(_UpperCamelCase  )
				def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       **_UpperCamelCase  )       ->   bool:
							return any(criteria(_UpperCamelCase       ,       _UpperCamelCase  ) for criteria in self  )




				@property
				def   __a		(      self  )       ->   Optional[int]:
							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      lowerCamelCase__       (       __lowerCAmelCase						:						StoppingCriteriaList					,							__lowerCAmelCase						:						int       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			stopping_criteria.max_length
			lowerCAmelCase_      			=			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





def      lowerCamelCase__       (       __lowerCAmelCase						:						list[list[int]]					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						set       ):




			"""simple docstring"""


			lowerCAmelCase_			,						lowerCAmelCase_      			=			len(__lowerCAmelCase       ), len(grid[0]       )
			if (
			    min(__lowerCAmelCase					,							__lowerCAmelCase       ) < 0
			    or row == row_length
			    or col == col_length
			    or (row, col) in visit
			    or grid[row][col] == 1
			):
						return 0
			if row == row_length - 1 and col == col_length - 1:
						return 1

			visit.add((row, col)       )

			lowerCAmelCase_      			=			0
			count += depth_first_search(__lowerCAmelCase					,							row + 1					,							__lowerCAmelCase					,							__lowerCAmelCase       )
			count += depth_first_search(__lowerCAmelCase					,							row - 1					,							__lowerCAmelCase					,							__lowerCAmelCase       )
			count += depth_first_search(__lowerCAmelCase					,							__lowerCAmelCase					,							col + 1					,							__lowerCAmelCase       )
			count += depth_first_search(__lowerCAmelCase					,							__lowerCAmelCase					,							col - 1					,							__lowerCAmelCase       )

			visit.remove((row, col)       )
			return count


if __name__ == "__main__":
			import doctest

			doctest.testmod()



