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


lowerCAmelCase__  	=      get_tests_dir("fixtures/spiece.model")
@require_sentencepiece
@require_tokenizers
class 	_a  (							lowerCamelCase_							,		unittest.TestCase					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					ErnieMTokenizer
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					True
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False


	def __lowerCAmelCase      (   self       ):
		super().setUp()

		# We have a SentencePiece fixture for testing
		_lowercase   =ErnieMTokenizer(lowerCAmelCase_       ,  unk_token="<unk>"       ,  pad_token="<pad>"       )
		tokenizer.save_pretrained(self.tmpdirname       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   ="this is a test"
		_lowercase   ="this is a test"
		return input_text, output_text


	def __lowerCAmelCase      (   self       ):
		_lowercase   ="<pad>"
		_lowercase   =0

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =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_       )       ,  30000       )


	def __lowerCAmelCase      (   self       ):
		self.assertEqual(self.get_tokenizer().vocab_size       ,  30000       )


	def __lowerCAmelCase      (   self       ):
		if not self.test_rust_tokenizer:
			return

		_lowercase   =self.get_tokenizer()
		_lowercase   =self.get_rust_tokenizer()

		_lowercase   ="I was born in 92000, and this is falsé."

		_lowercase   =tokenizer.tokenize(lowerCAmelCase_       )
		_lowercase   =rust_tokenizer.tokenize(lowerCAmelCase_       )
		self.assertListEqual(lowerCAmelCase_       ,  lowerCAmelCase_       )

		_lowercase   =tokenizer.encode(lowerCAmelCase_       ,  add_special_tokens=lowerCAmelCase_       )
		_lowercase   =rust_tokenizer.encode(lowerCAmelCase_       ,  add_special_tokens=lowerCAmelCase_       )
		self.assertListEqual(lowerCAmelCase_       ,  lowerCAmelCase_       )

		_lowercase   =self.get_rust_tokenizer()
		_lowercase   =tokenizer.encode(lowerCAmelCase_       )
		_lowercase   =rust_tokenizer.encode(lowerCAmelCase_       )
		self.assertListEqual(lowerCAmelCase_       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =ErnieMTokenizer(lowerCAmelCase_       ,  do_lower_case=lowerCAmelCase_       ,  unk_token="<unk>"       ,  pad_token="<pad>"       )

		_lowercase   =tokenizer.tokenize("This is a test"       )
		self.assertListEqual(lowerCAmelCase_       ,  ["▁this", "▁is", "▁a", "▁test"]       )

		self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_       )       ,  [48, 25, 21, 1289]       )

		_lowercase   =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", "."]       )
		_lowercase   =tokenizer.convert_tokens_to_ids(lowerCAmelCase_       )
		self.assertListEqual(lowerCAmelCase_       ,  [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]       )

		_lowercase   =tokenizer.convert_ids_to_tokens(lowerCAmelCase_       )
		self.assertListEqual(
		    lowerCAmelCase_       ,  ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]       ,  )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =ErnieMTokenizer(lowerCAmelCase_       ,  unk_token="<unk>"       ,  pad_token="<pad>"       )

		_lowercase   =tokenizer.encode("sequence builders"       )
		_lowercase   =tokenizer.encode("multi-sequence build"       )

		_lowercase   =tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_       )
		_lowercase   =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 __lowerCAmelCase      (   self       ):
		# fmt: off
		_lowercase   ={"input_ids": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 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, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 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.",
		    ]       ,  )






'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase	=    logging.get_logger(__name__)

__lowercase	=    {
    '''google/pegasus-x-base''': '''https://huggingface.co/google/pegasus-x-base/resolve/main/config.json''',
    '''google/pegasus-x-large''': '''https://huggingface.co/google/pegasus-x-large/resolve/main/config.json''',
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}

class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  Dict								=		'''pegasus_x'''
	_UpperCamelCase							:  Optional[int]								=		['''past_key_values''']
	_UpperCamelCase							:  str								=		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




	def __init__(						self				: Any					,						UpperCamelCase_				: Tuple=96103					,						UpperCamelCase_				: List[str]=16384					,						UpperCamelCase_				: Any=16					,						UpperCamelCase_				: Dict=4096					,						UpperCamelCase_				: str=16					,						UpperCamelCase_				: Union[str, Any]=16					,						UpperCamelCase_				: str=4096					,						UpperCamelCase_				: Any=16					,						UpperCamelCase_				: str=0.0					,						UpperCamelCase_				: Dict=0.0					,						UpperCamelCase_				: Union[str, Any]=True					,						UpperCamelCase_				: Union[str, Any]=True					,						UpperCamelCase_				: List[str]="gelu"					,						UpperCamelCase_				: Dict=1024					,						UpperCamelCase_				: str=0.1					,						UpperCamelCase_				: str=0.0					,						UpperCamelCase_				: Optional[Any]=0.0					,						UpperCamelCase_				: str=0.0_2					,						UpperCamelCase_				: Optional[Any]=0					,						UpperCamelCase_				: Tuple=True					,						UpperCamelCase_				: Tuple=0					,						UpperCamelCase_				: Optional[int]=1					,						UpperCamelCase_				: List[Any]=1					,						UpperCamelCase_				: List[Any]=32					,						UpperCamelCase_				: Optional[int]=512					,						UpperCamelCase_				: Any=True					,						**UpperCamelCase_				: Dict					,						):
			lowerCAmelCase_   :       List[Any]    							=vocab_size
			lowerCAmelCase_   :       Any    							=max_position_embeddings
			lowerCAmelCase_   :       int    							=d_model
			lowerCAmelCase_   :       List[Any]    							=encoder_ffn_dim
			lowerCAmelCase_   :       Union[str, Any]    							=encoder_layers
			lowerCAmelCase_   :       Tuple    							=encoder_attention_heads
			lowerCAmelCase_   :       List[Any]    							=decoder_ffn_dim
			lowerCAmelCase_   :       List[Any]    							=decoder_layers
			lowerCAmelCase_   :       List[str]    							=decoder_attention_heads
			lowerCAmelCase_   :       str    							=dropout
			lowerCAmelCase_   :       Any    							=attention_dropout
			lowerCAmelCase_   :       Any    							=activation_dropout
			lowerCAmelCase_   :       Optional[int]    							=activation_function
			lowerCAmelCase_   :       List[Any]    							=init_std
			lowerCAmelCase_   :       List[str]    							=encoder_layerdrop
			lowerCAmelCase_   :       Optional[int]    							=decoder_layerdrop
			lowerCAmelCase_   :       int    							=use_cache
			lowerCAmelCase_   :       Union[str, Any]    							=encoder_layers
			lowerCAmelCase_   :       Tuple    							=scale_embedding  # scale factor will be sqrt(d_model) if True

			lowerCAmelCase_   :       Optional[int]    							=num_global_tokens
			lowerCAmelCase_   :       List[str]    							=block_size
			lowerCAmelCase_   :       Optional[int]    							=stagger_local_blocks

			super().__init__(
			    pad_token_id=UpperCamelCase_					,						eos_token_id=UpperCamelCase_					,						is_encoder_decoder=UpperCamelCase_					,						decoder_start_token_id=UpperCamelCase_					,						forced_eos_token_id=UpperCamelCase_					,						**UpperCamelCase_					,						)




	@property
	def 	__A      (						self				: List[str]      ):
			return self.encoder_attention_heads





	@property
	def 	__A      (						self				: int      ):
			return self.d_model






