

import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						from transformers.models.bert.modeling_flax_bert import (
						    FlaxBertForMaskedLM,
						    FlaxBertForMultipleChoice,
						    FlaxBertForNextSentencePrediction,
						    FlaxBertForPreTraining,
						    FlaxBertForQuestionAnswering,
						    FlaxBertForSequenceClassification,
						    FlaxBertForTokenClassification,
						    FlaxBertModel,
						)






class 	__magic_name__    (			unittest.TestCase      ):





			def __init__(		self					,  __snake_case					,  __snake_case=13					,  __snake_case=7					,  __snake_case=True					,  __snake_case=True					,  __snake_case=True					,  __snake_case=True					,  __snake_case=99					,  __snake_case=32					,  __snake_case=5					,  __snake_case=4					,  __snake_case=37					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=512					,  __snake_case=16					,  __snake_case=2					,  __snake_case=0.02					,  __snake_case=4					,  )   -> Any:




						'''simple docstring'''
						__a        =parent
						__a        =batch_size
						__a        =seq_length
						__a        =is_training
						__a        =use_attention_mask
						__a        =use_token_type_ids
						__a        =use_labels
						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =intermediate_size
						__a        =hidden_act
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =type_vocab_size
						__a        =type_sequence_label_size
						__a        =initializer_range
						__a        =num_choices





			def 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						__a        =ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size					)

						__a        =None
						if self.use_attention_mask:
									__a        =random_attention_mask([self.batch_size, self.seq_length]					)

						__a        =None
						if self.use_token_type_ids:
									__a        =ids_tensor([self.batch_size, self.seq_length]					,  self.type_vocab_size					)

						__a        =BertConfig(
						    vocab_size=self.vocab_size					,  hidden_size=self.hidden_size					,  num_hidden_layers=self.num_hidden_layers					,  num_attention_heads=self.num_attention_heads					,  intermediate_size=self.intermediate_size					,  hidden_act=self.hidden_act					,  hidden_dropout_prob=self.hidden_dropout_prob					,  attention_probs_dropout_prob=self.attention_probs_dropout_prob					,  max_position_embeddings=self.max_position_embeddings					,  type_vocab_size=self.type_vocab_size					,  is_decoder=__snake_case					,  initializer_range=self.initializer_range					,  )

						return config, input_ids, token_type_ids, attention_mask





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




						'''simple docstring'''
						__a        =self.prepare_config_and_inputs()
						__a      ,    __a      ,    __a      ,    __a        =config_and_inputs
						__a        ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
						return config, inputs_dict





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




						'''simple docstring'''
						__a        =self.prepare_config_and_inputs()
						__a      ,    __a      ,    __a      ,    __a        =config_and_inputs

						__a        =True
						__a        =floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
						__a        =ids_tensor([self.batch_size, self.seq_length]					,  vocab_size=2					)

						return (
						    config,
						    input_ids,
						    attention_mask,
						    encoder_hidden_states,
						    encoder_attention_mask,
						)






@require_flax
class 	__magic_name__    (			lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						True

			SCREAMING_SNAKE_CASE							   =						(
			    (
			        FlaxBertModel,
			        FlaxBertForPreTraining,
			        FlaxBertForMaskedLM,
			        FlaxBertForMultipleChoice,
			        FlaxBertForQuestionAnswering,
			        FlaxBertForNextSentencePrediction,
			        FlaxBertForSequenceClassification,
			        FlaxBertForTokenClassification,
			        FlaxBertForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						__a        =FlaxBertModelTester(self					)





			@slow
			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						# Only check this for base model, not necessary for all model classes.
						# This will also help speed-up tests.
						__a        =FlaxBertModel.from_pretrained('bert-base-cased'					)
						__a        =model(np.ones((1, 1)					)					)
						self.assertIsNotNone(__snake_case					)









import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase					:			List[Any]  				=			get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")






@require_sentencepiece
@require_tokenizers
class 	__magic_name__    (			lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						GPTSwaTokenizer
			SCREAMING_SNAKE_CASE							   =						False
			SCREAMING_SNAKE_CASE							   =						True
			SCREAMING_SNAKE_CASE							   =						False





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						super().setUp()

						# We have a SentencePiece fixture for testing
						__a        =GPTSwaTokenizer(__snake_case					,  eos_token='<unk>'					,  bos_token='<unk>'					,  pad_token='<unk>'					)

						tokenizer.save_pretrained(self.tmpdirname					)





			def 		__magic_name__    (		self					,  __snake_case					)   -> Tuple:




						'''simple docstring'''
						__a        ='This is a test'
						__a        ='This is a test'
						return input_text, output_text





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




						'''simple docstring'''
						__a        ='<s>'
						__a        =1

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





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						__a        =list(self.get_tokenizer().get_vocab().keys()					)

						self.assertEqual(vocab_keys[0]					,  '<unk>'					)
						self.assertEqual(vocab_keys[1]					,  '<s>'					)
						self.assertEqual(vocab_keys[-1]					,  'j'					)
						self.assertEqual(len(__snake_case					)					,  2000					)





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




						'''simple docstring'''
						self.assertEqual(self.get_tokenizer().vocab_size					,  2000					)





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        =GPTSwaTokenizer(__snake_case					)

						__a        =tokenizer.tokenize('This is a test'					)
						self.assertListEqual(__snake_case					,  ['▁This', '▁is', '▁a', '▁t', 'est']					)

						self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case					)					,  [465, 287, 265, 631, 842]					)

						__a        =tokenizer.tokenize('I was born in 92000, and this is falsé.'					)
						# fmt: off
						self.assertListEqual(
						    __snake_case					,  ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']					,  )
						# fmt: on

						__a        =tokenizer.convert_tokens_to_ids(__snake_case					)
						self.assertListEqual(
						    __snake_case					,  [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]					,  )

						__a        =tokenizer.convert_ids_to_tokens(__snake_case					)
						# fmt: off
						self.assertListEqual(
						    __snake_case					,  ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']					)
						# fmt: on





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




						'''simple docstring'''
						__a        =GPTSwaTokenizer(__snake_case					)
						__a        =['This is a test', 'I was born in 92000, and this is falsé.']
						__a        =[
						    [465, 287, 265, 631, 842],
						    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
						]

						# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
						for text, expected_ids in zip(__snake_case					,  __snake_case					):
									self.assertListEqual(tokenizer.encode_fast(__snake_case					)					,  __snake_case					)

						# Test that decode_fast returns the input text
						for text, token_ids in zip(__snake_case					,  __snake_case					):
									self.assertEqual(tokenizer.decode_fast(__snake_case					)					,  __snake_case					)





			@slow
			def 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						__a        =[
						    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
						    'Hey there, how are you doing this fine day?',
						    'This is a text with a trailing spaces followed by a dot     .',
						    'Häj sväjs lillebrör! =)',
						    'Det är inget fel på Mr. Cool',
						]

						# fmt: off
						__a        ={'input_ids': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
						# fmt: on
						self.tokenizer_integration_test_util(
						    expected_encoding=__snake_case					,  model_name='AI-Sweden/gpt-sw3-126m'					,  sequences=__snake_case					,  )






