
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin


__SCREAMING_SNAKE_CASE			:   Any         =     get_tests_dir('fixtures/spiece.model')


@require_sentencepiece
@require_tokenizers
class 		__magic_name__   (						snake_case,				unittest.TestCase    ):
		_lowerCAmelCase		=	ErnieMTokenizer
		_lowerCAmelCase		=	False
		_lowerCAmelCase		=	True
		_lowerCAmelCase		=	False
		_lowerCAmelCase		=	False





		def       _A							(							self    :	Tuple				):
				super().setUp()

				# We have a SentencePiece fixture for testing
				lowerCAmelCase		:					Optional[Any]  	=     ErnieMTokenizer(lowerCamelCase__						,  unk_token='''<unk>'''						,  pad_token='''<pad>'''				)
				tokenizer.save_pretrained(self.tmpdirname				)





		def       _A							(							self    :	List[str]						,  lowerCamelCase__    :	List[str]				):
				lowerCAmelCase		:					int  	=     '''this is a test'''
				lowerCAmelCase		:					Optional[Any]  	=     '''this is a test'''
				return input_text, output_text





		def       _A							(							self    :	Dict				):
				lowerCAmelCase		:					int  	=     '''<pad>'''
				lowerCAmelCase		:					Dict  	=     0

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





		def       _A							(							self    :	Union[str, Any]				):
				lowerCAmelCase		:					Tuple  	=     list(self.get_tokenizer().get_vocab().keys()				)

				self.assertEqual(vocab_keys[0]						,  '''<pad>'''				)
				self.assertEqual(vocab_keys[1]						,  '''<unk>'''				)
				self.assertEqual(vocab_keys[-1]						,  '''▁eloquent'''				)
				self.assertEqual(len(lowerCamelCase__				)						,  3_0_0_0_0				)





		def       _A							(							self    :	Union[str, Any]				):
				self.assertEqual(self.get_tokenizer().vocab_size						,  3_0_0_0_0				)





		def       _A							(							self    :	Optional[Any]				):
				if not self.test_rust_tokenizer:
						return

				lowerCAmelCase		:					List[str]  	=     self.get_tokenizer()
				lowerCAmelCase		:					Optional[int]  	=     self.get_rust_tokenizer()

				lowerCAmelCase		:					Any  	=     '''I was born in 92000, and this is falsé.'''

				lowerCAmelCase		:					Optional[Any]  	=     tokenizer.tokenize(lowerCamelCase__				)
				lowerCAmelCase		:					List[Any]  	=     rust_tokenizer.tokenize(lowerCamelCase__				)
				self.assertListEqual(lowerCamelCase__						,  lowerCamelCase__				)

				lowerCAmelCase		:					List[str]  	=     tokenizer.encode(lowerCamelCase__						,  add_special_tokens=lowerCamelCase__				)
				lowerCAmelCase		:					Dict  	=     rust_tokenizer.encode(lowerCamelCase__						,  add_special_tokens=lowerCamelCase__				)
				self.assertListEqual(lowerCamelCase__						,  lowerCamelCase__				)

				lowerCAmelCase		:					Tuple  	=     self.get_rust_tokenizer()
				lowerCAmelCase		:					Dict  	=     tokenizer.encode(lowerCamelCase__				)
				lowerCAmelCase		:					Optional[int]  	=     rust_tokenizer.encode(lowerCamelCase__				)
				self.assertListEqual(lowerCamelCase__						,  lowerCamelCase__				)





		def       _A							(							self    :	str				):
				lowerCAmelCase		:					List[str]  	=     ErnieMTokenizer(lowerCamelCase__						,  do_lower_case=lowerCamelCase__						,  unk_token='''<unk>'''						,  pad_token='''<pad>'''				)

				lowerCAmelCase		:					Dict  	=     tokenizer.tokenize('''This is a test'''				)
				self.assertListEqual(lowerCamelCase__						,  ['''▁this''', '''▁is''', '''▁a''', '''▁test''']				)

				self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__				)						,  [4_8, 2_5, 2_1, 1_2_8_9]				)

				lowerCAmelCase		:					List[str]  	=     tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
				# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
				self.assertListEqual(
				    lowerCamelCase__						,  ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']				)
				lowerCAmelCase		:					Union[str, Any]  	=     tokenizer.convert_tokens_to_ids(lowerCamelCase__				)
				self.assertListEqual(lowerCamelCase__						,  [3_1, 2_3, 3_8_6, 1_9, 5_1_8, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]				)

				lowerCAmelCase		:					Dict  	=     tokenizer.convert_ids_to_tokens(lowerCamelCase__				)
				self.assertListEqual(
				    lowerCamelCase__						,  ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']						,  )





		def       _A							(							self    :	Optional[int]				):
				lowerCAmelCase		:					int  	=     ErnieMTokenizer(lowerCamelCase__						,  unk_token='''<unk>'''						,  pad_token='''<pad>'''				)

				lowerCAmelCase		:					Any  	=     tokenizer.encode('''sequence builders'''				)
				lowerCAmelCase		:					int  	=     tokenizer.encode('''multi-sequence build'''				)

				lowerCAmelCase		:					Union[str, Any]  	=     tokenizer.build_inputs_with_special_tokens(lowerCamelCase__				)
				lowerCAmelCase		:					Dict  	=     tokenizer.build_inputs_with_special_tokens(lowerCamelCase__						,  lowerCamelCase__				)

				assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
				assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
				    tokenizer.sep_token_id
				] + text_a + [tokenizer.sep_token_id]





		@slow
		def       _A							(							self    :	Union[str, Any]				):
				# fmt: off
				lowerCAmelCase		:					Optional[Any]  	=     {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 9, 3_0_4, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 5, 5, 5, 1_6, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 6_4_6_0, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 3_5_5_9, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 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, 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, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
				# fmt: on

				self.tokenizer_integration_test_util(
				    expected_encoding=lowerCamelCase__						,  model_name='''susnato/ernie-m-base_pytorch'''						,  sequences=[
				        '''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
				        '''general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
				        '''Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '''
				        '''models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
				        '''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
				        '''conditioning on both left and right context in all layers.''',
				        '''The quick brown fox jumps over the lazy dog.''',
				    ]						,  )


from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE			:   str         =     {
    'configuration_deta': ['DETA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetaConfig'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   Optional[int]         =     ['DetaImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   str         =     [
		    'DETA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DetaForObjectDetection',
		    'DetaModel',
		    'DetaPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_deta import DetaImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deta import (
				    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DetaForObjectDetection,
				    DetaModel,
				    DetaPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE			:   Dict         =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
