




"""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     :	str         =				logging.get_logger(__name__)


_lowerCamelCase     :	Optional[Any]         =				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 				lowercase					(			__a):







				@add_start_docstrings(__a				)
				def __call__(							self      :    Optional[Any]					,   _lowerCamelCase      :    torch.LongTensor					,   _lowerCamelCase      :    torch.FloatTensor					,   **_lowerCamelCase      :    Tuple				):



					"""simple docstring"""

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

class 				lowercase					(			__a):







				def __init__(							self      :    List[Any]					,   _lowerCamelCase      :    int					,   _lowerCamelCase      :    Optional[int] = None				):



					"""simple docstring"""

					A_     : str		      =						max_length
					A_     : Tuple		      =						max_position_embeddings







				@add_start_docstrings(__a				)
				def __call__(							self      :    Tuple					,   _lowerCamelCase      :    torch.LongTensor					,   _lowerCamelCase      :    torch.FloatTensor					,   **_lowerCamelCase      :    Any				):



					"""simple docstring"""

					A_     : Dict		      =						input_ids.shape[-1]
					A_     : Dict		      =						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					(			__a):







				def __init__(							self      :    List[str]					,   _lowerCamelCase      :    int					,   _lowerCamelCase      :    int				):



					"""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.'''					,   __a					,   )
					A_     : List[str]		      =						start_length
					A_     : List[Any]		      =						max_new_tokens
					A_     : List[str]		      =						start_length + max_new_tokens







				@add_start_docstrings(__a				)
				def __call__(							self      :    List[str]					,   _lowerCamelCase      :    torch.LongTensor					,   _lowerCamelCase      :    torch.FloatTensor					,   **_lowerCamelCase      :    List[Any]				):



					"""simple docstring"""

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

class 				lowercase					(			__a):







				def __init__(							self      :    Dict					,   _lowerCamelCase      :    float					,   _lowerCamelCase      :    Optional[float] = None				):



					"""simple docstring"""

					A_     : List[Any]		      =						max_time
					A_     : List[str]		      =						time.time() if initial_timestamp is None else initial_timestamp







				@add_start_docstrings(__a				)
				def __call__(							self      :    str					,   _lowerCamelCase      :    torch.LongTensor					,   _lowerCamelCase      :    torch.FloatTensor					,   **_lowerCamelCase      :    Any				):



					"""simple docstring"""

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

class 				lowercase					(			__a):







				@add_start_docstrings(__a				)
				def __call__(							self      :    Any					,   _lowerCamelCase      :    torch.LongTensor					,   _lowerCamelCase      :    torch.FloatTensor					,   **_lowerCamelCase      :    Tuple				):



					"""simple docstring"""

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







				@property
				def     a_     (							self      :    Union[str, Any]				):



					"""simple docstring"""

					for stopping_criterium in self:
						if isinstance(__a					,   __a				):
							return stopping_criterium.max_length
						elif isinstance(__a					,   __a				):
							return stopping_criterium.max_length
					return None

def 						lowercase_					(						_UpperCAmelCase     ,					_UpperCAmelCase		):




	"""simple docstring"""






	A_     : Tuple		      =						stopping_criteria.max_length
	A_     : int		      =						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













import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase       :       Any        =				get_tests_dir('''fixtures/test_sentencepiece.model''')
lowerCamelCase       :       Any        =				get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

lowerCamelCase       :       Optional[int]        =				'''pt''' if is_torch_available() else '''tf'''

@require_sentencepiece
@require_tokenizers
class 						lowerCAmelCase   (       __a					,				unittest.TestCase	):

					'''simple docstring'''

					_A  :	Dict     =					CamembertTokenizer
					_A  :	Union[str, Any]     =					CamembertTokenizerFast
					_A  :	Union[str, Any]     =					True
					_A  :	Tuple     =					True



					def 					lowerCAmelCase (     self :		Optional[int]							)	->     Optional[int]:


												"""simple docstring"""

												super().setUp()

												# We have a SentencePiece fixture for testing
												__lowercase					:							Union[str, Any]			=   CamembertTokenizer(__a							)
												tokenizer.save_pretrained(self.tmpdirname							)



					def 					lowerCAmelCase (     self :		Optional[int]							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Dict			=   """<pad>"""
												__lowercase					:							Dict			=   1

												self.assertEqual(self.get_tokenizer()._convert_token_to_id(__a							)						,   __a							)
												self.assertEqual(self.get_tokenizer()._convert_id_to_token(__a							)						,   __a							)



					def 					lowerCAmelCase (     self :		Optional[Any]							)	->     Tuple:


												"""simple docstring"""

												__lowercase					:							int			=   list(self.get_tokenizer().get_vocab().keys()							)

												self.assertEqual(vocab_keys[0]						,   """<s>NOTUSED"""							)
												self.assertEqual(vocab_keys[1]						,   """<pad>"""							)
												self.assertEqual(vocab_keys[-1]						,   """<mask>"""							)
												self.assertEqual(len(__a							)						,   1004							)



					def 					lowerCAmelCase (     self :		str							)	->     List[Any]:


												"""simple docstring"""

												self.assertEqual(self.get_tokenizer().vocab_size						,   1005							)



					def 					lowerCAmelCase (     self :		Any							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Tuple			=   CamembertTokenizer(__a							)
												tokenizer.save_pretrained(self.tmpdirname							)
												__lowercase					:							Tuple			=   CamembertTokenizerFast.from_pretrained(self.tmpdirname							)

												__lowercase					:							List[str]			=   """I was born in 92000, and this is falsé."""

												__lowercase					:							Optional[Any]			=   tokenizer.encode(__a							)
												__lowercase					:							List[Any]			=   rust_tokenizer.encode(__a							)
												self.assertListEqual(__a						,   __a							)

												__lowercase					:							Tuple			=   tokenizer.encode(__a						,   add_special_tokens=__a							)
												__lowercase					:							Union[str, Any]			=   rust_tokenizer.encode(__a						,   add_special_tokens=__a							)
												self.assertListEqual(__a						,   __a							)

												# <unk> tokens are not the same for `rust` than for `slow`.
												# Because spm gives back raw token instead of `unk` in EncodeAsPieces
												# tokens = tokenizer.tokenize(sequence)
												__lowercase					:							Dict			=   tokenizer.convert_ids_to_tokens(__a							)
												__lowercase					:							Any			=   rust_tokenizer.tokenize(__a							)
												self.assertListEqual(__a						,   __a							)



					def 					lowerCAmelCase (     self :		Any							)	->     Dict:


												"""simple docstring"""

												if not self.test_rust_tokenizer:
																			return

												__lowercase					:							List[str]			=   self.get_tokenizer()
												__lowercase					:							Any			=   self.get_rust_tokenizer()

												__lowercase					:							Any			=   """I was born in 92000, and this is falsé."""

												__lowercase					:							Tuple			=   tokenizer.tokenize(__a							)
												__lowercase					:							Optional[Any]			=   rust_tokenizer.tokenize(__a							)
												self.assertListEqual(__a						,   __a							)

												__lowercase					:							Dict			=   tokenizer.encode(__a						,   add_special_tokens=__a							)
												__lowercase					:							Dict			=   rust_tokenizer.encode(__a						,   add_special_tokens=__a							)
												self.assertListEqual(__a						,   __a							)

												__lowercase					:							Any			=   self.get_rust_tokenizer()
												__lowercase					:							str			=   tokenizer.encode(__a							)
												__lowercase					:							Union[str, Any]			=   rust_tokenizer.encode(__a							)
												self.assertListEqual(__a						,   __a							)



					@slow
					def 					lowerCAmelCase (     self :		Optional[int]							)	->     List[Any]:


												"""simple docstring"""

												__lowercase					:							str			=   {"""input_ids""": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
												# fmt: on

												# camembert is a french model. So we also use french texts.
												__lowercase					:							List[str]			=   [
												    """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
												    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
												    """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
												    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
												    """telles que la traduction et la synthèse de texte.""",
												]

												self.tokenizer_integration_test_util(
												    expected_encoding=__a						,   model_name="""camembert-base"""						,   revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""						,   sequences=__a						,   )