






"""simple docstring"""
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         =							logging.get_logger(__name__)


_lowerCAmelCase         =							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  (				SCREAMING_SNAKE_CASE__						):





	"""simple docstring"""




	@add_start_docstrings(snake_case__							)
	def __call__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							):
		raise NotImplementedError("""StoppingCriteria needs to be subclassed"""							)






class      SCREAMING_SNAKE_CASE  (				SCREAMING_SNAKE_CASE__						):





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):
		A_		:						List[str]	    =   max_length
		A_		:						Optional[int]	    =   max_position_embeddings





	@add_start_docstrings(snake_case__							)
	def __call__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							):
		A_		:						Optional[int]	    =   input_ids.shape[-1]
		A_		:						List[Any]	    =   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  (				SCREAMING_SNAKE_CASE__						):





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		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."""     ,     snake_case__     ,     )
		A_		:						Tuple	    =   start_length
		A_		:						Any	    =   max_new_tokens
		A_		:						Any	    =   start_length + max_new_tokens





	@add_start_docstrings(snake_case__							)
	def __call__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return input_ids.shape[-1] >= self.max_length






class      SCREAMING_SNAKE_CASE  (				SCREAMING_SNAKE_CASE__						):





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):
		A_		:						Dict	    =   max_time
		A_		:						List[Any]	    =   time.time() if initial_timestamp is None else initial_timestamp





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






class      SCREAMING_SNAKE_CASE  (				SCREAMING_SNAKE_CASE__						):





	"""simple docstring"""




	@add_start_docstrings(snake_case__							)
	def __call__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return any(criteria(snake_case__     ,     snake_case__							) for criteria in self							)





	@property
	def    lowerCamelCase(self							):
		for stopping_criterium in self:
			if isinstance(snake_case__     ,     snake_case__							):
				return stopping_criterium.max_length
			elif isinstance(snake_case__     ,     snake_case__							):
				return stopping_criterium.max_length
		return None






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						Any	    =   stopping_criteria.max_length
	A_		:						str	    =   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









"""simple docstring"""
import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				Dict    =				FunnelTokenizer
	_A       :				Union[str, Any]    =				FunnelTokenizerFast
	_A       :				List[str]    =				True
	_A       :				List[Any]    =				True





	def    lowerCamelCase(self							):
		super().setUp()

		A_		:						Tuple	    =   [
		    """<unk>""",
		    """<cls>""",
		    """<sep>""",
		    """want""",
		    """##want""",
		    """##ed""",
		    """wa""",
		    """un""",
		    """runn""",
		    """##ing""",
		    """,""",
		    """low""",
		    """lowest""",
		]
		A_		:						str	    =   os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""vocab_file"""]							)
		with open(self.vocab_file     ,     """w"""     ,     encoding="""utf-8"""							) as vocab_writer:
			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]							)							)





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		return FunnelTokenizer.from_pretrained(self.tmpdirname     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		return FunnelTokenizerFast.from_pretrained(self.tmpdirname     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   """UNwant\u00E9d,running"""
		A_		:						int	    =   """unwanted, running"""
		return input_text, output_text





	def    lowerCamelCase(self							):
		A_		:						Optional[Any]	    =   self.tokenizer_class(self.vocab_file							)

		A_		:						Any	    =   tokenizer.tokenize("""UNwant\u00E9d,running"""							)
		self.assertListEqual(lowerCAmelCase_     ,     ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]							)
		self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_							)     ,     [7, 4, 5, 10, 8, 9]							)





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   self.get_tokenizers(do_lower_case=lowerCAmelCase_							)
		for tokenizer in tokenizers:
			A_		:						Any	    =   tokenizer("""UNwant\u00E9d,running"""							)
			A_		:						Dict	    =   len(inputs["""input_ids"""]							) - 1
			self.assertListEqual(inputs["""token_type_ids"""]     ,     [2] + [0] * sentence_len							)

			A_		:						Tuple	    =   tokenizer("""UNwant\u00E9d,running"""     ,     """UNwant\u00E9d,running"""							)
			self.assertListEqual(inputs["""token_type_ids"""]     ,     [2] + [0] * sentence_len + [1] * sentence_len							)

