



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_   :int        =      ["gpt2"]
a_   :Union[str, Any]        =      "gpt2"

if is_tf_available():




 class 					snake_case__						(       tf.Module     ):






      """simple docstring"""


      def __init__(       self  :  List[str], _snake_case  :  Union[str, Any]					)   ->Optional[int]:
             super().__init__()
             snake_case__							:	Union[str, Any]   	=	tokenizer
             snake_case__							:	Union[str, Any]   	=	AutoConfig.from_pretrained(_snake_case					)
             snake_case__							:	str   	=	TFGPTaLMHeadModel.from_config(_snake_case					)






      @tf.function(input_signature=(tf.TensorSpec((None,), tf.string, name='text'					),)					)
      def    lowercase_   (       self  :  Optional[int], _snake_case  :  Any					)   ->Dict:
             snake_case__							:	Dict   	=	self.tokenizer(_snake_case					)
             snake_case__							:	int   	=	tokenized['input_ids'].to_tensor()

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

             snake_case__							:	Optional[int]   	=	self.model(input_ids=_snake_case, attention_mask=_snake_case					)['logits']

             return outputs




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






     """simple docstring"""


     def    lowercase_   (       self  :  Optional[Any]					)   ->List[Any]:
            super().setUp()

            snake_case__							:	Any   	=	[GPTaTokenizer.from_pretrained(_snake_case					) for checkpoint in (TOKENIZER_CHECKPOINTS)]
            snake_case__							:	Optional[int]   	=	[TFGPTaTokenizer.from_pretrained(_snake_case					) for checkpoint in TOKENIZER_CHECKPOINTS]
            assert len(self.tokenizers					) == len(self.tf_tokenizers					)

            snake_case__							:	Dict   	=	[
                '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',
            ]
            snake_case__							:	Union[str, Any]   	=	list(zip(self.test_sentences, self.test_sentences[::-1]					)					)






     def    lowercase_   (       self  :  Union[str, Any]					)   ->str:
            for tokenizer, tf_tokenizer in zip(self.tokenizers, self.tf_tokenizers					):
                   for test_inputs in self.test_sentences:
                          snake_case__							:	Any   	=	tokenizer([test_inputs], return_tensors='tf'					)
                          snake_case__							:	Dict   	=	tf_tokenizer([test_inputs]					)

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

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






     @slow
     def    lowercase_   (       self  :  Any					)   ->Union[str, Any]:
            for tf_tokenizer in self.tf_tokenizers:
                   snake_case__							:	Any   	=	tf.function(_snake_case					)
                   for test_inputs in self.test_sentences:
                          snake_case__							:	Union[str, Any]   	=	tf.constant(_snake_case					)
                          snake_case__							:	Tuple   	=	compiled_tokenizer(_snake_case					)
                          snake_case__							:	Tuple   	=	tf_tokenizer(_snake_case					)

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






     @slow
     def    lowercase_   (       self  :  List[str]					)   ->Union[str, Any]:
            for tf_tokenizer in self.tf_tokenizers:
                   snake_case__							:	str   	=	ModelToSave(tokenizer=_snake_case					)
                   snake_case__							:	Tuple   	=	tf.convert_to_tensor([self.test_sentences[0]]					)
                   snake_case__							:	str   	=	model.serving(_snake_case					)  # Build model with some sample inputs
                   with TemporaryDirectory() as tempdir:
                          snake_case__							:	Optional[Any]   	=	Path(_snake_case					) / 'saved.model'
                          tf.saved_model.save(_snake_case, _snake_case, signatures={'serving_default': model.serving}					)
                          snake_case__							:	Any   	=	tf.saved_model.load(_snake_case					)
                   snake_case__							:	Optional[Any]   	=	loaded_model.signatures['serving_default'](_snake_case					)['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  :  str					)   ->List[Any]:
            for tf_tokenizer in self.tf_tokenizers:
                   snake_case__							:	Optional[int]   	=	tf.convert_to_tensor([self.test_sentences[0]]					)
                   snake_case__							:	Optional[Any]   	=	tf_tokenizer(_snake_case					)  # Build model with some sample inputs

                   snake_case__							:	Optional[int]   	=	tf_tokenizer.get_config()
                   snake_case__							:	Optional[Any]   	=	TFGPTaTokenizer.from_config(_snake_case					)
                   snake_case__							:	Optional[Any]   	=	model_from_config(_snake_case					)

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






     @slow
     def    lowercase_   (       self  :  Any					)   ->Tuple:
            for tf_tokenizer in self.tf_tokenizers:
                   # for the test to run
                   snake_case__							:	Optional[Any]   	=	1_2_3_1_2_3

                   for max_length in [3, 5, 1_0_2_4]:
                          snake_case__							:	Optional[int]   	=	tf.convert_to_tensor([self.test_sentences[0]]					)
                          snake_case__							:	int   	=	tf_tokenizer(_snake_case, max_length=_snake_case					)

                          snake_case__							:	List[Any]   	=	out['input_ids'].numpy().shape[1]

                          assert out_length == max_length











import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class 					snake_case__						(       lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	TransfoXLTokenizer
     _SCREAMING_SNAKE_CASE						=	False
     _SCREAMING_SNAKE_CASE						=	False






     def    lowercase_   (       self  :  Optional[int]					)   ->Any:
            super().setUp()

            snake_case__							:	Tuple   	=	[
                '<unk>',
                '[CLS]',
                '[SEP]',
                'want',
                'unwanted',
                'wa',
                'un',
                'running',
                ',',
                'low',
                'l',
            ]
            snake_case__							:	Any   	=	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    lowercase_   (       self  :  Union[str, Any], **_snake_case  :  List[Any]					)   ->Dict:
            snake_case__							:	str   	=	True
            return TransfoXLTokenizer.from_pretrained(self.tmpdirname, **_snake_case					)






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  str					)   ->Dict:
            snake_case__							:	List[Any]   	=	'<unk> UNwanted , running'
            snake_case__							:	List[Any]   	=	'<unk> unwanted, running'
            return input_text, output_text






     def    lowercase_   (       self  :  List[Any]					)   ->Tuple:
            snake_case__							:	Dict   	=	TransfoXLTokenizer(vocab_file=self.vocab_file, lower_case=_snake_case					)

            snake_case__							:	str   	=	tokenizer.tokenize('<unk> UNwanted , running'					)
            self.assertListEqual(_snake_case, ['<unk>', 'unwanted', ',', 'running']					)

            self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case					), [0, 4, 8, 7]					)






     def    lowercase_   (       self  :  List[str]					)   ->List[Any]:
            snake_case__							:	str   	=	TransfoXLTokenizer(lower_case=_snake_case					)

            self.assertListEqual(
                tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '					), ['hello', '!', 'how', 'are', 'you', '?']					)






     def    lowercase_   (       self  :  Optional[int]					)   ->Optional[Any]:
            snake_case__							:	Optional[int]   	=	TransfoXLTokenizer(lower_case=_snake_case					)

            self.assertListEqual(
                tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '					), ['HeLLo', '!', 'how', 'Are', 'yoU', '?']					)






     def    lowercase_   (       self  :  Optional[int]					)   ->Union[str, Any]:
            snake_case__							:	List[Any]   	=	TransfoXLTokenizer(lower_case=_snake_case					)
            snake_case__							:	Dict   	=	'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
            snake_case__							:	List[Any]   	=	[
                'Hello',
                '(',
                'bracket',
                ')',
                'and',
                'side',
                '@-@',
                'scrolled',
                '[',
                'and',
                ']',
                'Henry',
                '\'s',
                '$',
                '5',
                '@,@',
                '000',
                'with',
                '3',
                '@.@',
                '34',
                'm',
                '.',
                'What',
                '\'s',
                'up',
                '!',
                '?',
            ]

            self.assertListEqual(tokenizer.tokenize(_snake_case					), _snake_case					)

            self.assertEqual(tokenizer.convert_tokens_to_string(_snake_case					), _snake_case					)






     def    lowercase_   (       self  :  Dict					)   ->Any:
            snake_case__							:	Dict   	=	self.get_tokenizer()
            snake_case__							:	Optional[Any]   	=	len(_snake_case					)

            tokenizer.add_tokens(['new1', 'new2']					)
            tokenizer.move_added_token('new1', 1					)

            # Check that moved token is not copied (duplicate)
            self.assertEqual(len(_snake_case					), original_len + 2					)
            # Check that token is moved to specified id
            self.assertEqual(tokenizer.encode('new1'					), [1]					)
            self.assertEqual(tokenizer.decode([1]					), 'new1'					)






