






"""simple docstring"""







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
	import tensorflow as tf

if is_keras_nlp_available():
	from transformers.models.gpta import TFGPTaTokenizer


__a       :	Dict             = ['gpt2']
__a       :	Dict             = 'gpt2'

if is_tf_available():







	class        _SCREAMING_SNAKE_CASE						(  tf.Module							):





								"""simple docstring"""





								def __init__(   self:     Optional[Any]				,		__A:     Any    ):

													'''simple docstring'''

													super().__init__()
													a__          =  tokenizer
													a__          =  AutoConfig.from_pretrained(__A    )
													a__          =  TFGPTaLMHeadModel.from_config(__A    )





								@tf.function(input_signature=(tf.TensorSpec((None,)				,		tf.string				,		name='''text'''    ),)    )
								def  lowercase		(   self:     Tuple				,		__A:     List[str]    ):

													'''simple docstring'''

													a__          =  self.tokenizer(__A    )
													a__          =  tokenized['''input_ids'''].to_tensor()

													a__          =  tf.cast(input_ids_dense > 0				,		tf.intaa    )
													# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

													a__          =  self.model(input_ids=__A				,		attention_mask=__A    )['''logits''']

													return outputs







@require_tf
@require_keras_nlp
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												super().setUp()

												a__          =  [GPTaTokenizer.from_pretrained(__A    ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
												a__          =  [TFGPTaTokenizer.from_pretrained(__A    ) for checkpoint in TOKENIZER_CHECKPOINTS]
												assert len(self.tokenizers    ) == len(self.tf_tokenizers    )

												a__          =  [
												    '''This is a straightforward English test sentence.''',
												    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
												    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
												    '''And some much more rare Chinese: 齉 堃 齉堃''',
												    '''Je vais aussi écrire en français pour tester les accents''',
												    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
												]
												a__          =  list(zip(self.test_sentences				,		self.test_sentences[::-1]    )    )




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												for tokenizer, tf_tokenizer in zip(self.tokenizers				,		self.tf_tokenizers    ):
																	for test_inputs in self.test_sentences:
																						a__          =  tokenizer([test_inputs]				,		return_tensors='''tf'''    )
																						a__          =  tf_tokenizer([test_inputs]    )

																						for key in python_outputs.keys():
																											# convert them to numpy to avoid messing with ragged tensors
																											a__          =  python_outputs[key].numpy()
																											a__          =  tf_outputs[key].numpy()

																											self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape    )    )
																											self.assertTrue(tf.reduce_all(tf.cast(__A				,		tf.intaa    ) == tf_outputs_values    )    )




							@slow
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												for tf_tokenizer in self.tf_tokenizers:
																	a__          =  tf.function(__A    )
																	for test_inputs in self.test_sentences:
																						a__          =  tf.constant(__A    )
																						a__          =  compiled_tokenizer(__A    )
																						a__          =  tf_tokenizer(__A    )

																						for key in eager_outputs.keys():
																											self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]    )    )




							@slow
							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												for tf_tokenizer in self.tf_tokenizers:
																	a__          =  ModelToSave(tokenizer=__A    )
																	a__          =  tf.convert_to_tensor([self.test_sentences[0]]    )
																	a__          =  model.serving(__A    )  # Build model with some sample inputs
																	with TemporaryDirectory() as tempdir:
																						a__          =  Path(__A    ) / '''saved.model'''
																						tf.saved_model.save(__A				,		__A				,		signatures={'''serving_default''': model.serving}    )
																						a__          =  tf.saved_model.load(__A    )
																	a__          =  loaded_model.signatures['''serving_default'''](__A    )['''output_0''']
																	# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																	self.assertTrue(tf.reduce_all(out == loaded_output    )    )




							@slow
							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												for tf_tokenizer in self.tf_tokenizers:
																	a__          =  tf.convert_to_tensor([self.test_sentences[0]]    )
																	a__          =  tf_tokenizer(__A    )  # Build model with some sample inputs

																	a__          =  tf_tokenizer.get_config()
																	a__          =  TFGPTaTokenizer.from_config(__A    )
																	a__          =  model_from_config(__A    )

																	for key in from_config_output.keys():
																						self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]    )    )





							@slow
							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												for tf_tokenizer in self.tf_tokenizers:
																	# for the test to run
																	a__          =  123123

																	for max_length in [3, 5, 1024]:
																						a__          =  tf.convert_to_tensor([self.test_sentences[0]]    )
																						a__          =  tf_tokenizer(__A				,		max_length=__A    )

																						a__          =  out['''input_ids'''].numpy().shape[1]

																						assert out_length == max_length










"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_albert import AlbertTokenizer
else:
	__a       :	int             = None

__a       :	List[str]             = logging.get_logger(__name__)
__a       :	Union[str, Any]             = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__a       :	str             = {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

__a       :	Optional[int]             = {
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

__a       :	Union[str, Any]             = '▁'







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =VOCAB_FILES_NAMES
							_SCREAMING_SNAKE_CASE    =PRETRAINED_VOCAB_FILES_MAP
							_SCREAMING_SNAKE_CASE    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_SCREAMING_SNAKE_CASE    =AlbertTokenizer




							def __init__(   self:     List[Any]				,		__A:     List[Any]=None				,		__A:     Tuple=None				,		__A:     Union[str, Any]=True				,		__A:     int=True				,		__A:     Optional[int]=False				,		__A:     List[Any]="[CLS]"				,		__A:     Optional[int]="[SEP]"				,		__A:     Union[str, Any]="<unk>"				,		__A:     int="[SEP]"				,		__A:     Optional[Any]="<pad>"				,		__A:     Optional[int]="[CLS]"				,		__A:     str="[MASK]"				,		**__A:     List[Any]				,		):

												'''simple docstring'''

												a__          =  (
												    AddedToken(__A				,		lstrip=__A				,		rstrip=__A				,		normalized=__A    )
												    if isinstance(__A				,		__A    )
												    else mask_token
												)

												super().__init__(
												    __A				,		tokenizer_file=__A				,		do_lower_case=__A				,		remove_space=__A				,		keep_accents=__A				,		bos_token=__A				,		eos_token=__A				,		unk_token=__A				,		sep_token=__A				,		pad_token=__A				,		cls_token=__A				,		mask_token=__A				,		**__A				,		)

												a__          =  do_lower_case
												a__          =  remove_space
												a__          =  keep_accents
												a__          =  vocab_file
												a__          =  False if not self.vocab_file else True




							def  lowercase		(   self:     Union[str, Any]				,		__A:     List[int]				,		__A:     Optional[List[int]] = None    ):

												'''simple docstring'''

												a__          =  [self.sep_token_id]
												a__          =  [self.cls_token_id]
												if token_ids_a is None:
																	return cls + token_ids_a + sep
												return cls + token_ids_a + sep + token_ids_a + sep




							def  lowercase		(   self:     List[Any]				,		__A:     List[int]				,		__A:     Optional[List[int]] = None    ):

												'''simple docstring'''

												a__          =  [self.sep_token_id]
												a__          =  [self.cls_token_id]

												if token_ids_a is None:
																	return len(cls + token_ids_a + sep    ) * [0]
												return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]





							def  lowercase		(   self:     Union[str, Any]				,		__A:     str				,		__A:     Optional[str] = None    ):

												'''simple docstring'''

												if not self.can_save_slow_tokenizer:
																	raise ValueError(
																	    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																	    '''tokenizer.'''    )

												if not os.path.isdir(__A    ):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
																	return
												a__          =  os.path.join(
												    __A				,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

												if os.path.abspath(self.vocab_file    ) != os.path.abspath(__A    ):
																	copyfile(self.vocab_file				,		__A    )

												return (out_vocab_file,)


