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


_lowerCamelCase					:   str							=		logging.get_logger(__name__)


_lowerCamelCase					:   List[str]							=		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 						lowercase    (     __lowerCAmelCase     ):




							@add_start_docstrings(lowerCAmelCase_      )
							def __call__(						self   :					Optional[Any]	,							_UpperCamelCase   :					Optional[int]	,							_UpperCamelCase   :					Dict	,							**_UpperCamelCase   :					Union[str, Any]      )					->				bool:



												'''simple docstring'''

												raise NotImplementedError("StoppingCriteria needs to be subclassed"      )



class 						lowercase    (     __lowerCAmelCase     ):




							def __init__(						self   :					Tuple	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					str = None      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       max_length
												SCREAMING_SNAKE_CASE    			=       max_position_embeddings





							@add_start_docstrings(lowerCAmelCase_      )
							def __call__(						self   :					Any	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					str	,							**_UpperCamelCase   :					Optional[Any]      )					->				bool:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       input_ids.shape[-1]
												SCREAMING_SNAKE_CASE    			=       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 						lowercase    (     __lowerCAmelCase     ):




							def __init__(						self   :					Optional[Any]	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Tuple      )					->				Dict:



												'''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_	,							)
												SCREAMING_SNAKE_CASE    			=       start_length
												SCREAMING_SNAKE_CASE    			=       max_new_tokens
												SCREAMING_SNAKE_CASE    			=       start_length + max_new_tokens





							@add_start_docstrings(lowerCAmelCase_      )
							def __call__(						self   :					Tuple	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					int	,							**_UpperCamelCase   :					Optional[int]      )					->				bool:



												'''simple docstring'''

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



class 						lowercase    (     __lowerCAmelCase     ):




							def __init__(						self   :					int	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Optional[int] = None      )					->				Any:



												'''simple docstring'''

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





							@add_start_docstrings(lowerCAmelCase_      )
							def __call__(						self   :					int	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					Optional[int]	,							**_UpperCamelCase   :					List[Any]      )					->				bool:



												'''simple docstring'''

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



class 						lowercase    (     __lowerCAmelCase     ):




							@add_start_docstrings(lowerCAmelCase_      )
							def __call__(						self   :					str	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					List[str]	,							**_UpperCamelCase   :					Any      )					->				bool:



												'''simple docstring'''

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





							@property
							def 	__snake_case(						self   :					Optional[int]      )					->				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 	__lowerCamelCase							(UpperCAmelCase__     :							StoppingCriteriaList      , UpperCAmelCase__     :							int  ):
					SCREAMING_SNAKE_CASE    			=       stopping_criteria.max_length
					SCREAMING_SNAKE_CASE    			=       deepcopy(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"      , snake_case__  )
					elif stopping_max_length is None:
										new_stopping_criteria.append(MaxLengthCriteria(max_length=snake_case__  )  )
					return new_stopping_criteria





import functools





def 	__lowerCamelCase							(UpperCAmelCase__     :							list[int]      , UpperCAmelCase__     :							list[int]  ):

					# Validation
					if not isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) or not all(isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) for day in days  ):
										raise ValueError("The parameter days should be a list of integers"  )

					if len(UpperCAmelCase__  ) != 3 or not all(isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) for cost in costs  ):
										raise ValueError("The parameter costs should be a list of three integers"  )

					if len(UpperCAmelCase__  ) == 0:
										return 0

					if min(UpperCAmelCase__  ) <= 0:
										raise ValueError("All days elements should be greater than 0"  )

					if max(UpperCAmelCase__  ) >= 3_6_6:
										raise ValueError("All days elements should be less than 366"  )

					SCREAMING_SNAKE_CASE    			=       set(UpperCAmelCase__  )

					@functools.cache
					def dynamic_programming(UpperCAmelCase__     :							int  ) -> int:
										if index > 3_6_5:
															return 0

										if index not in days_set:
															return dynamic_programming(index + 1  )

										return min(
										    costs[0] + dynamic_programming(index + 1  )      , costs[1] + dynamic_programming(index + 7  )      , costs[2] + dynamic_programming(index + 3_0  )      , )

					return dynamic_programming(1  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




