





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       __A					(					lowerCAmelCase				):

			'''simple docstring'''







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



				'''simple docstring'''







				raise NotImplementedError('''StoppingCriteria needs to be subclassed'''							)






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







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



				'''simple docstring'''







				lowerCamelCase__              =	max_length
				lowerCamelCase__              =	max_position_embeddings





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



				'''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					(					lowerCAmelCase				):

			'''simple docstring'''







			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''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 ,						)
				lowerCamelCase__              =	start_length
				lowerCamelCase__              =	max_new_tokens
				lowerCamelCase__              =	start_length + max_new_tokens





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



				'''simple docstring'''







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






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







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



				'''simple docstring'''







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





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



				'''simple docstring'''







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






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







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



				'''simple docstring'''







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





			@property
			def 	__lowerCamelCase       (					self							):



				'''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__(__snake_case ,__snake_case							)       ->   StoppingCriteriaList:







	'''simple docstring'''







	lowerCamelCase__              =	stopping_criteria.max_length
	lowerCamelCase__              =	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 string
from math import logaa





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   int:







	'''simple docstring'''







	lowerCamelCase__              =	document.translate(
	    str.maketrans('''''' ,'''''' ,string.punctuation							)							).replace('''\n''' ,''''''							)
	lowerCamelCase__              =	document_without_punctuation.split(''' '''							)  # word tokenization
	return len([word for word in tokenize_document if word.lower() == term.lower()]							)





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   tuple[int, int]:







	'''simple docstring'''







	lowerCamelCase__              =	corpus.lower().translate(
	    str.maketrans('''''' ,'''''' ,string.punctuation							)							)  # strip all punctuation and replace it with ''
	lowerCamelCase__              =	corpus_without_punctuation.split('''\n'''							)
	lowerCamelCase__              =	term.lower()
	return (len([doc for doc in docs if term in doc]							), len(__snake_case							))





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case=False							)       ->   float:







	'''simple docstring'''







	if smoothing:
		if n == 0:
			raise ValueError('''log10(0) is undefined.'''							)
		return round(1 + logaa(n / (1 + df)							) ,3							)

	if df == 0:
		raise ZeroDivisionError('''df must be > 0'''							)
	elif n == 0:
		raise ValueError('''log10(0) is undefined.'''							)
	return round(logaa(n / df							) ,3							)





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   float:







	'''simple docstring'''







	return round(tf * idf ,3							)



