





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_        =							get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')



@require_sentencepiece
@require_tokenizers
class    __lowerCAmelCase			(     _a,	unittest.TestCase	):
			lowerCamelCase_	:	List[Any]			     = GPTSwaTokenizer
			lowerCamelCase_	:	List[str]			     = False
			lowerCamelCase_	:	List[str]			     = True
			lowerCamelCase_	:	Union[str, Any]			     = False






			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_		:       List[Any]           =    GPTSwaTokenizer(__magic_name__   ,				eos_token='''<unk>'''   ,				bos_token='''<unk>'''   ,				pad_token='''<unk>'''       )

							tokenizer.save_pretrained(self.tmpdirname       )






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


							'''simple docstring'''



							snake_case_		:       Optional[int]           =    '''This is a test'''
							snake_case_		:       Tuple           =    '''This is a test'''
							return input_text, output_text






			def      lowerCamelCase    (self       )	->  int:


							'''simple docstring'''



							snake_case_		:       int           =    '''<s>'''
							snake_case_		:       int           =    1

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






			def      lowerCamelCase    (self       )	->  Any:


							'''simple docstring'''



							snake_case_		:       Dict           =    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(__magic_name__       )   ,				2000       )






			def      lowerCamelCase    (self       )	->  Tuple:


							'''simple docstring'''



							self.assertEqual(self.get_tokenizer().vocab_size   ,				2000       )






			def      lowerCamelCase    (self       )	->  int:


							'''simple docstring'''



							snake_case_		:       Union[str, Any]           =    GPTSwaTokenizer(__magic_name__       )

							snake_case_		:       Optional[int]           =    tokenizer.tokenize('''This is a test'''       )
							self.assertListEqual(__magic_name__   ,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

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

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

							snake_case_		:       Union[str, Any]           =    tokenizer.convert_tokens_to_ids(__magic_name__       )
							self.assertListEqual(
							    __magic_name__   ,				[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]   ,				)

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






			def      lowerCamelCase    (self       )	->  List[Any]:


							'''simple docstring'''



							snake_case_		:       List[str]           =    GPTSwaTokenizer(__magic_name__       )
							snake_case_		:       Tuple           =    ['''This is a test''', '''I was born in 92000, and this is falsé.''']
							snake_case_		:       str           =    [
							    [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(__magic_name__   ,				__magic_name__       ):
											self.assertListEqual(tokenizer.encode_fast(__magic_name__       )   ,				__magic_name__       )

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






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


							'''simple docstring'''



							snake_case_		:       int           =    [
							    '''<|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
							snake_case_		:       Optional[Any]           =    {'''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=__magic_name__   ,				model_name='''AI-Sweden/gpt-sw3-126m'''   ,				sequences=__magic_name__   ,				)


"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


A_    							=					{
    """configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxConfig"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """GPTNeoForCausalLM""",
						    """GPTNeoForQuestionAnswering""",
						    """GPTNeoForSequenceClassification""",
						    """GPTNeoForTokenClassification""",
						    """GPTNeoModel""",
						    """GPTNeoPreTrainedModel""",
						    """load_tf_weights_in_gpt_neo""",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """FlaxGPTNeoForCausalLM""",
						    """FlaxGPTNeoModel""",
						    """FlaxGPTNeoPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_gpt_neo import (
												    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GPTNeoForCausalLM,
												    GPTNeoForQuestionAnswering,
												    GPTNeoForSequenceClassification,
												    GPTNeoForTokenClassification,
												    GPTNeoModel,
												    GPTNeoPreTrainedModel,
												    load_tf_weights_in_gpt_neo,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
						import sys

						A_    							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



