

import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
					a		="pt"
elif is_tf_available():
					a		="tf"
else:
					a		="jax"






class 	A_		(	__lowerCAmelCase      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  int										=				ByTaTokenizer
							_UpperCAmelCase   :  Optional[int]										=				False
							def    lowerCAmelCase					(		self		:      List[str]):
								super().setUp()
								__lowerCamelCase    :    List[Any] 	=			ByTaTokenizer()
								tokenizer.save_pretrained(self.tmpdirname)
							@cached_property
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								return ByTaTokenizer.from_pretrained('google/byt5-small')
							def    lowerCAmelCase					(		self		:      List[Any]       ,**SCREAMING_SNAKE_CASE__		:      int):
								return self.tokenizer_class.from_pretrained(self.tmpdirname       ,**lowerCamelCase__)
							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=False       ,SCREAMING_SNAKE_CASE__		:      str=2_0       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=5):
								__lowerCamelCase    :    Optional[Any] 	=			[]
								for i in range(len(lowerCamelCase__)):
									try:
										__lowerCamelCase    :    Union[str, Any] 	=			tokenizer.decode([i]       ,clean_up_tokenization_spaces=lowerCamelCase__)
									except UnicodeDecodeError:
										pass
									toks.append((i, tok))

								__lowerCamelCase    :    Optional[Any] 	=			list(filter(lambda SCREAMING_SNAKE_CASE__: re.match(R'^[ a-zA-Z]+$'       ,t[1])       ,lowerCamelCase__))
								__lowerCamelCase    :    str 	=			list(filter(lambda SCREAMING_SNAKE_CASE__: [t[0]] == tokenizer.encode(t[1]       ,add_special_tokens=lowerCamelCase__)       ,lowerCamelCase__))
								if max_length is not None and len(lowerCamelCase__) > max_length:
									__lowerCamelCase    :    Optional[int] 	=			toks[:max_length]
								if min_length is not None and len(lowerCamelCase__) < min_length and len(lowerCamelCase__) > 0:
									while len(lowerCamelCase__) < min_length:
										__lowerCamelCase    :    Optional[int] 	=			toks + toks
										# toks_str = [t[1] for t in toks]
								__lowerCamelCase    :    Any 	=			[t[0] for t in toks]

								# Ensure consistency
								__lowerCamelCase    :    Optional[int] 	=			tokenizer.decode(lowerCamelCase__       ,clean_up_tokenization_spaces=lowerCamelCase__)
								if " " not in output_txt and len(lowerCamelCase__) > 1:
									__lowerCamelCase    :    int 	=			(
									    tokenizer.decode([toks_ids[0]]       ,clean_up_tokenization_spaces=lowerCamelCase__)
									    + ''' '''
									    + tokenizer.decode(toks_ids[1:]       ,clean_up_tokenization_spaces=lowerCamelCase__)
									)
								if with_prefix_space:
									__lowerCamelCase    :    Union[str, Any] 	=			''' ''' + output_txt
								__lowerCamelCase    :    Any 	=			tokenizer.encode(lowerCamelCase__       ,add_special_tokens=lowerCamelCase__)
								return output_txt, output_ids
							def    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase    :    Union[str, Any] 	=			self.ta_base_tokenizer
								__lowerCamelCase    :    Optional[int] 	=			tokenizer(['hi</s>', 'I went to the gym</s>', '</s>'])
								__lowerCamelCase    :    List[Any] 	=			tokenizer(['hi', 'I went to the gym', ''])
								self.assertListEqual(batch_with_eos_added['input_ids']       ,batch_without_eos_added['input_ids'])
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    Optional[int] 	=			self.ta_base_tokenizer
								__lowerCamelCase    :    Dict 	=			'''Unicode €.'''
								__lowerCamelCase    :    Optional[Any] 	=			tokenizer(lowerCamelCase__)
								__lowerCamelCase    :    Tuple 	=			[8_8, 1_1_3, 1_0_8, 1_0_2, 1_1_4, 1_0_3, 1_0_4, 3_5, 2_2_9, 1_3_3, 1_7_5, 4_9, 1]
								self.assertEqual(encoded['input_ids']       ,lowerCamelCase__)

								# decoding
								__lowerCamelCase    :    Optional[Any] 	=			tokenizer.decode(lowerCamelCase__)
								self.assertEqual(lowerCamelCase__       ,'Unicode €.</s>')

								__lowerCamelCase    :    List[str] 	=			tokenizer('e è é ê ë')
								__lowerCamelCase    :    Optional[int] 	=			[1_0_4, 3_5, 1_9_8, 1_7_1, 3_5, 1_9_8, 1_7_2, 3_5, 1_9_8, 1_7_3, 3_5, 1_9_8, 1_7_4, 1]
								self.assertEqual(encoded['input_ids']       ,lowerCamelCase__)
								# decoding
								__lowerCamelCase    :    Any 	=			tokenizer.decode(lowerCamelCase__)
								self.assertEqual(lowerCamelCase__       ,'e è é ê ë</s>')

								# encode/decode, but with `encode` instead of `__call__`
								self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'))       ,'e è é ê ë</s>')
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    Dict 	=			self.ta_base_tokenizer
								__lowerCamelCase    :    Any 	=			['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								# fmt: off
								__lowerCamelCase    :    Optional[Any] 	=			[6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 1, 0]
								# fmt: on
								__lowerCamelCase    :    Tuple 	=			tokenizer(lowerCamelCase__       ,padding=lowerCamelCase__       ,return_tensors=lowerCamelCase__)
								self.assertIsInstance(lowerCamelCase__       ,lowerCamelCase__)

								if FRAMEWORK != "jax":
									__lowerCamelCase    :    Optional[int] 	=			list(batch.input_ids.numpy()[0])
								else:
									__lowerCamelCase    :    List[str] 	=			list(batch.input_ids.tolist()[0])

								self.assertListEqual(lowerCamelCase__       ,lowerCamelCase__)

								self.assertEqual((2, 3_7)       ,batch.input_ids.shape)
								self.assertEqual((2, 3_7)       ,batch.attention_mask.shape)
							def    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase    :    List[str] 	=			self.ta_base_tokenizer
								__lowerCamelCase    :    int 	=			['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								__lowerCamelCase    :    Tuple 	=			tokenizer(lowerCamelCase__       ,padding=lowerCamelCase__       ,return_tensors=lowerCamelCase__)
								# check if input_ids are returned and no decoder_input_ids
								self.assertIn('input_ids'       ,lowerCamelCase__)
								self.assertIn('attention_mask'       ,lowerCamelCase__)
								self.assertNotIn('decoder_input_ids'       ,lowerCamelCase__)
								self.assertNotIn('decoder_attention_mask'       ,lowerCamelCase__)
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Any 	=			self.ta_base_tokenizer
								__lowerCamelCase    :    Optional[int] 	=			[
								    '''Summary of the text.''',
								    '''Another summary.''',
								]
								__lowerCamelCase    :    str 	=			tokenizer(
								    text_target=lowerCamelCase__       ,max_length=3_2       ,padding='max_length'       ,truncation=lowerCamelCase__       ,return_tensors=lowerCamelCase__)
								self.assertEqual(3_2       ,targets['input_ids'].shape[1])
							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase    :    Dict 	=			self.ta_base_tokenizer
								__lowerCamelCase    :    str 	=			['''A long paragraph for summarization. </s>''']
								__lowerCamelCase    :    Union[str, Any] 	=			['''Summary of the text. </s>''']
								# fmt: off
								__lowerCamelCase    :    List[str] 	=			[6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 3_5, 1]
								__lowerCamelCase    :    Tuple 	=			[8_6, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_2_4, 3_5, 1_1_4, 1_0_5, 3_5, 1_1_9, 1_0_7, 1_0_4, 3_5, 1_1_9, 1_0_4, 1_2_3, 1_1_9, 4_9, 3_5, 1]
								# fmt: on

								__lowerCamelCase    :    Dict 	=			tokenizer(lowerCamelCase__       ,text_target=lowerCamelCase__)

								self.assertEqual(lowerCamelCase__       ,batch['input_ids'][0])
								self.assertEqual(lowerCamelCase__       ,batch['labels'][0])
							def    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase    :    Dict 	=			self.get_tokenizers()
								for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
										self.assertNotEqual(tokenizer.model_max_length       ,4_2)

										# Now let's start the test
								__lowerCamelCase    :    Union[str, Any] 	=			self.get_tokenizers()
								for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
										# Isolate this from the other tests because we save additional tokens/etc
										__lowerCamelCase    :    Union[str, Any] 	=			tempfile.mkdtemp()

										__lowerCamelCase    :    Dict 	=			''' He is very happy, UNwant\u00E9d,running'''
										__lowerCamelCase    :    str 	=			tokenizer.encode(lowerCamelCase__       ,add_special_tokens=lowerCamelCase__)
										tokenizer.save_pretrained(lowerCamelCase__)

										__lowerCamelCase    :    List[str] 	=			tokenizer.__class__.from_pretrained(lowerCamelCase__)
										__lowerCamelCase    :    Optional[Any] 	=			after_tokenizer.encode(lowerCamelCase__       ,add_special_tokens=lowerCamelCase__)
										self.assertListEqual(lowerCamelCase__       ,lowerCamelCase__)

										shutil.rmtree(lowerCamelCase__)

								__lowerCamelCase    :    Dict 	=			self.get_tokenizers(model_max_length=4_2)
								for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
										# Isolate this from the other tests because we save additional tokens/etc
										__lowerCamelCase    :    Dict 	=			tempfile.mkdtemp()

										__lowerCamelCase    :    List[Any] 	=			''' He is very happy, UNwant\u00E9d,running'''
										tokenizer.add_tokens(['bim', 'bambam'])
										__lowerCamelCase    :    int 	=			tokenizer.additional_special_tokens
										additional_special_tokens.append('new_additional_special_token')
										tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens})
										__lowerCamelCase    :    Union[str, Any] 	=			tokenizer.encode(lowerCamelCase__       ,add_special_tokens=lowerCamelCase__)
										tokenizer.save_pretrained(lowerCamelCase__)

										__lowerCamelCase    :    Tuple 	=			tokenizer.__class__.from_pretrained(lowerCamelCase__)
										__lowerCamelCase    :    int 	=			after_tokenizer.encode(lowerCamelCase__       ,add_special_tokens=lowerCamelCase__)
										self.assertListEqual(lowerCamelCase__       ,lowerCamelCase__)
										self.assertIn('new_additional_special_token'       ,after_tokenizer.additional_special_tokens)
										self.assertEqual(after_tokenizer.model_max_length       ,4_2)

										__lowerCamelCase    :    Tuple 	=			tokenizer.__class__.from_pretrained(lowerCamelCase__       ,model_max_length=4_3)
										self.assertEqual(tokenizer.model_max_length       ,4_3)

										shutil.rmtree(lowerCamelCase__)
							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    Optional[int] 	=			[]
								if self.test_slow_tokenizer:
									tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

								if self.test_rust_tokenizer:
									tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

								for tokenizer_class, tokenizer_utils in tokenizer_list:
									with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer_utils.save_pretrained(lowerCamelCase__)

										with open(os.path.join(lowerCamelCase__       ,'special_tokens_map.json')       ,encoding='utf-8') as json_file:
											__lowerCamelCase    :    Any 	=			json.load(lowerCamelCase__)

										with open(os.path.join(lowerCamelCase__       ,'tokenizer_config.json')       ,encoding='utf-8') as json_file:
											__lowerCamelCase    :    Tuple 	=			json.load(lowerCamelCase__)

										__lowerCamelCase    :    List[Any] 	=			[F"<extra_id_{i}>" for i in range(1_2_5)]

										__lowerCamelCase    :    Tuple 	=			added_tokens_extra_ids + [
										    '''an_additional_special_token'''
										]
										__lowerCamelCase    :    Any 	=			added_tokens_extra_ids + [
										    '''an_additional_special_token'''
										]

										with open(os.path.join(lowerCamelCase__       ,'special_tokens_map.json')       ,'w'       ,encoding='utf-8') as outfile:
											json.dump(lowerCamelCase__       ,lowerCamelCase__)
										with open(os.path.join(lowerCamelCase__       ,'tokenizer_config.json')       ,'w'       ,encoding='utf-8') as outfile:
											json.dump(lowerCamelCase__       ,lowerCamelCase__)

										# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
										# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
										# "special_tokens_map.json" files
										__lowerCamelCase    :    List[str] 	=			tokenizer_class.from_pretrained(
										    lowerCamelCase__       ,)
										self.assertIn(
										    'an_additional_special_token'       ,tokenizer_without_change_in_init.additional_special_tokens)
										# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
										self.assertEqual(
										    ['an_additional_special_token']       ,tokenizer_without_change_in_init.convert_ids_to_tokens(
										        tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']))       ,)

										# Now we test that we can change the value of additional_special_tokens in the from_pretrained
										__lowerCamelCase    :    Dict 	=			added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'       ,lstrip=lowerCamelCase__)]
										__lowerCamelCase    :    Tuple 	=			tokenizer_class.from_pretrained(
										    lowerCamelCase__       ,additional_special_tokens=lowerCamelCase__       ,)

										self.assertIn('a_new_additional_special_token'       ,tokenizer.additional_special_tokens)
										self.assertEqual(
										    ['a_new_additional_special_token']       ,tokenizer.convert_ids_to_tokens(
										        tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']))       ,)
							def    lowerCAmelCase					(		self		:      List[str]):
								__lowerCamelCase    :    Dict 	=			[]
								if self.test_slow_tokenizer:
									tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

								if self.test_rust_tokenizer:
									tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

								for tokenizer_class, tokenizer_utils in tokenizer_list:
									with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer_utils.save_pretrained(lowerCamelCase__)

										__lowerCamelCase    :    List[str] 	=			tokenizer_class.from_pretrained(lowerCamelCase__)

										self.assertTrue(tokenizer.decode([2_5_5]) == '')
							def    lowerCAmelCase					(		self		:      Optional[int]):
								pass
							def    lowerCAmelCase					(		self		:      int):
								pass
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								pass
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								pass
							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase    :    str 	=			self.get_tokenizers(fast=lowerCamelCase__       ,do_lower_case=lowerCamelCase__)
								for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
										__lowerCamelCase    :    Dict 	=			['''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''x''', '''t''', '''</s>''']
										__lowerCamelCase    :    int 	=			tokenizer.convert_tokens_to_string(lowerCamelCase__)

										self.assertIsInstance(lowerCamelCase__       ,lowerCamelCase__)


							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    Dict 	=			self.get_tokenizers()
								for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
										__lowerCamelCase    :    int 	=			[
										    '''bos_token''',
										    '''eos_token''',
										    '''unk_token''',
										    '''sep_token''',
										    '''pad_token''',
										    '''cls_token''',
										    '''mask_token''',
										]

										__lowerCamelCase    :    Optional[int] 	=			0
										__lowerCamelCase    :    Any 	=			tokenizer.convert_ids_to_tokens(
										    lowerCamelCase__       ,skip_special_tokens=lowerCamelCase__)

										for attr in attributes_list:
											setattr(lowerCamelCase__       ,attr + '_id'       ,lowerCamelCase__)
											self.assertEqual(getattr(lowerCamelCase__       ,lowerCamelCase__)       ,lowerCamelCase__)
											self.assertEqual(getattr(lowerCamelCase__       ,attr + '_id')       ,lowerCamelCase__)

											setattr(lowerCamelCase__       ,attr + '_id'       ,lowerCamelCase__)
											self.assertEqual(getattr(lowerCamelCase__       ,lowerCamelCase__)       ,lowerCamelCase__)
											self.assertEqual(getattr(lowerCamelCase__       ,attr + '_id')       ,lowerCamelCase__)

										setattr(lowerCamelCase__       ,'additional_special_tokens_ids'       ,[])
										self.assertListEqual(getattr(lowerCamelCase__       ,'additional_special_tokens')       ,[])
										self.assertListEqual(getattr(lowerCamelCase__       ,'additional_special_tokens_ids')       ,[])

										setattr(lowerCamelCase__       ,'additional_special_tokens_ids'       ,[token_id_to_test_setters])
										self.assertListEqual(getattr(lowerCamelCase__       ,'additional_special_tokens')       ,[token_to_test_setters])
										self.assertListEqual(getattr(lowerCamelCase__       ,'additional_special_tokens_ids')       ,[token_id_to_test_setters])







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		=["""gpt2"""]
a		="""gpt2"""

if is_tf_available():






					class 	A_		(	tf.Module    ):
												def __init__(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Tuple):
													super().__init__()
													__lowerCamelCase    :    List[Any] 	=			tokenizer
													__lowerCamelCase    :    str 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
													__lowerCamelCase    :    Tuple 	=			TFGPTaLMHeadModel.from_config(SCREAMING_SNAKE_CASE__)


												@tf.function(input_signature=(tf.TensorSpec((None,)       ,tf.string       ,name='text'),))
												def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
													__lowerCamelCase    :    str 	=			self.tokenizer(SCREAMING_SNAKE_CASE__)
													__lowerCamelCase    :    Dict 	=			tokenized['input_ids'].to_tensor()

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

													__lowerCamelCase    :    Union[str, Any] 	=			self.model(input_ids=SCREAMING_SNAKE_CASE__       ,attention_mask=SCREAMING_SNAKE_CASE__)['logits']

													return outputs






@require_tf
@require_keras_nlp
class 	A_		(	unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      List[str]):
								super().setUp()

								__lowerCamelCase    :    str 	=			[GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__) for checkpoint in (TOKENIZER_CHECKPOINTS)]
								__lowerCamelCase    :    List[Any] 	=			[TFGPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__) for checkpoint in TOKENIZER_CHECKPOINTS]
								assert len(self.tokenizers) == len(self.tf_tokenizers)

								__lowerCamelCase    :    Optional[int] 	=			[
								    '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',
								]
								__lowerCamelCase    :    List[str] 	=			list(zip(self.test_sentences       ,self.test_sentences[::-1]))
							def    lowerCAmelCase					(		self		:      Optional[int]):
								for tokenizer, tf_tokenizer in zip(self.tokenizers       ,self.tf_tokenizers):
									for test_inputs in self.test_sentences:
										__lowerCamelCase    :    Union[str, Any] 	=			tokenizer([test_inputs]       ,return_tensors='tf')
										__lowerCamelCase    :    Dict 	=			tf_tokenizer([test_inputs])

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

											self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape))
											self.assertTrue(tf.reduce_all(tf.cast(SCREAMING_SNAKE_CASE__       ,tf.intaa) == tf_outputs_values))
							@slow
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								for tf_tokenizer in self.tf_tokenizers:
									__lowerCamelCase    :    Dict 	=			tf.function(SCREAMING_SNAKE_CASE__)
									for test_inputs in self.test_sentences:
										__lowerCamelCase    :    Any 	=			tf.constant(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Dict 	=			compiled_tokenizer(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Dict 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__)

										for key in eager_outputs.keys():
											self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))
							@slow
							def    lowerCAmelCase					(		self		:      str):
								for tf_tokenizer in self.tf_tokenizers:
									__lowerCamelCase    :    Any 	=			ModelToSave(tokenizer=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Tuple 	=			tf.convert_to_tensor([self.test_sentences[0]])
									__lowerCamelCase    :    str 	=			model.serving(SCREAMING_SNAKE_CASE__)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
										__lowerCamelCase    :    Optional[Any] 	=			Path(SCREAMING_SNAKE_CASE__) / 'saved.model'
										tf.saved_model.save(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,signatures={'serving_default': model.serving})
										__lowerCamelCase    :    Tuple 	=			tf.saved_model.load(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[int] 	=			loaded_model.signatures['serving_default'](SCREAMING_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    lowerCAmelCase					(		self		:      int):
								for tf_tokenizer in self.tf_tokenizers:
									__lowerCamelCase    :    Optional[int] 	=			tf.convert_to_tensor([self.test_sentences[0]])
									__lowerCamelCase    :    Optional[Any] 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__)  # Build model with some sample inputs

									__lowerCamelCase    :    str 	=			tf_tokenizer.get_config()
									__lowerCamelCase    :    List[str] 	=			TFGPTaTokenizer.from_config(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[int] 	=			model_from_config(SCREAMING_SNAKE_CASE__)

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


							@slow
							def    lowerCAmelCase					(		self		:      Dict):
								for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									__lowerCamelCase    :    List[Any] 	=			1_2_3_1_2_3

									for max_length in [3, 5, 1_0_2_4]:
										__lowerCamelCase    :    Dict 	=			tf.convert_to_tensor([self.test_sentences[0]])
										__lowerCamelCase    :    Union[str, Any] 	=			tf_tokenizer(SCREAMING_SNAKE_CASE__       ,max_length=SCREAMING_SNAKE_CASE__)

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

										assert out_length == max_length




