




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	:       List[Any]     =   logging.get_logger(__name__)


__lowerCAmelCase	:       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 					snake_case__							(_A     ):
				"""simple docstring"""



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




class 					snake_case__							(_A     ):
				"""simple docstring"""



				def __init__(   self       :  List[str]      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  Optional[int] = None							)      ->  List[Any]:
							a					=    max_length
							a					=    max_position_embeddings





				@add_start_docstrings(__lowerCamelCase							)
				def __call__(   self       :  List[str]      ,   __lowerCamelCase       :  torch.LongTensor      ,   __lowerCamelCase       :  torch.FloatTensor      ,   **__lowerCamelCase       :  Any							)      ->  bool:
							a					=    input_ids.shape[-1]
							a					=    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     ):
				"""simple docstring"""



				def __init__(   self       :  Optional[Any]      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  int							)      ->  List[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."      ,   __lowerCamelCase      ,   )
							a					=    start_length
							a					=    max_new_tokens
							a					=    start_length + max_new_tokens





				@add_start_docstrings(__lowerCamelCase							)
				def __call__(   self       :  int      ,   __lowerCamelCase       :  torch.LongTensor      ,   __lowerCamelCase       :  torch.FloatTensor      ,   **__lowerCamelCase       :  Tuple							)      ->  bool:
							return input_ids.shape[-1] >= self.max_length




class 					snake_case__							(_A     ):
				"""simple docstring"""



				def __init__(   self       :  Optional[int]      ,   __lowerCamelCase       :  float      ,   __lowerCamelCase       :  Optional[float] = None							)      ->  List[Any]:
							a					=    max_time
							a					=    time.time() if initial_timestamp is None else initial_timestamp





				@add_start_docstrings(__lowerCamelCase							)
				def __call__(   self       :  Any      ,   __lowerCamelCase       :  torch.LongTensor      ,   __lowerCamelCase       :  torch.FloatTensor      ,   **__lowerCamelCase       :  Tuple							)      ->  bool:
							return time.time() - self.initial_timestamp > self.max_time




class 					snake_case__							(_A     ):
				"""simple docstring"""



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





				@property
				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Optional[int]:
							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     __magic_name__       (				A	:      Optional[Any],						A	:      Optional[int]     ):






			'''simple docstring'''







			a					=    stopping_criteria.max_length
			a					=    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












from typing import TYPE_CHECKING

from ....utils import _LazyModule


__lowerCAmelCase	:       int     =	{'tokenization_tapex': ['TapexTokenizer']}


if TYPE_CHECKING:
		from .tokenization_tapex import TapexTokenizer


else:
		import sys

		__lowerCAmelCase	:       Tuple     =	_LazyModule(__name__, globals()['__file__'], _import_structure)






