


"""simple docstring"""






from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__=None       ,					snake_case__=None		)					->  int:
			if attention_mask is None:
						_UpperCamelCase					:Optional[int]					     =	tf.cast(tf.math.not_equal(snake_case__       ,					config.pad_token_id		)       ,					tf.inta		)
			return {"input_ids": input_ids, "attention_mask": attention_mask}






@require_tf
class      A:

		"""simple docstring"""





		A    =							OPTConfig
		A    =							{}
		A    =							"gelu"
		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=13					,				SCREAMING_SNAKE_CASE__=7					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=99					,				SCREAMING_SNAKE_CASE__=16					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=4					,				SCREAMING_SNAKE_CASE__=4					,				SCREAMING_SNAKE_CASE__="gelu"					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=20					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=1					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=16					,				SCREAMING_SNAKE_CASE__=16					,				)      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	parent
					_UpperCamelCase					:Optional[Any]					     =	batch_size
					_UpperCamelCase					:Optional[int]					     =	seq_length
					_UpperCamelCase					:Union[str, Any]					     =	is_training
					_UpperCamelCase					:Union[str, Any]					     =	use_labels
					_UpperCamelCase					:Any					     =	vocab_size
					_UpperCamelCase					:str					     =	hidden_size
					_UpperCamelCase					:Union[str, Any]					     =	num_hidden_layers
					_UpperCamelCase					:List[str]					     =	num_attention_heads
					_UpperCamelCase					:Any					     =	intermediate_size
					_UpperCamelCase					:Optional[Any]					     =	hidden_act
					_UpperCamelCase					:int					     =	hidden_dropout_prob
					_UpperCamelCase					:List[str]					     =	attention_probs_dropout_prob
					_UpperCamelCase					:Tuple					     =	max_position_embeddings
					_UpperCamelCase					:Dict					     =	eos_token_id
					_UpperCamelCase					:str					     =	pad_token_id
					_UpperCamelCase					:int					     =	bos_token_id
					_UpperCamelCase					:Dict					     =	embed_dim
					_UpperCamelCase					:int					     =	word_embed_proj_dim
					_UpperCamelCase					:Optional[Any]					     =	False
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	ids_tensor([self.batch_size, self.seq_length - 1]					,				self.vocab_size   )
					_UpperCamelCase					:List[Any]					     =	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )					,				1   )
					_UpperCamelCase					:List[str]					     =	tf.concat([input_ids, eos_tensor]					,				axis=1   )

					_UpperCamelCase					:Any					     =	self.config_cls(
					    vocab_size=self.vocab_size					,				hidden_size=self.hidden_size					,				num_hidden_layers=self.num_hidden_layers					,				num_attention_heads=self.num_attention_heads					,				ffn_dim=self.intermediate_size					,				dropout=self.hidden_dropout_prob					,				attention_dropout=self.attention_probs_dropout_prob					,				max_position_embeddings=self.max_position_embeddings					,				eos_token_id=self.eos_token_id					,				bos_token_id=self.bos_token_id					,				pad_token_id=self.pad_token_id					,				embed_dim=self.embed_dim					,				word_embed_proj_dim=self.word_embed_proj_dim					,				is_encoder_decoder=SCREAMING_SNAKE_CASE__					,				**self.config_updates					,				)
					_UpperCamelCase					:int					     =	prepare_opt_inputs_dict(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					return config, inputs_dict





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	TFOPTModel(config=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	inputs_dict['''input_ids''']

					_UpperCamelCase					:Optional[int]					     =	input_ids[:1, :]
					_UpperCamelCase					:int					     =	inputs_dict['''attention_mask'''][:1, :]
					_UpperCamelCase					:Any					     =	1

					# first forward pass
					_UpperCamelCase					:Union[str, Any]					     =	model(SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__					,				use_cache=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase     ,      _UpperCamelCase					:Optional[Any]					     =	outputs.to_tuple()

					# create hypothetical next token and extent to next_input_ids
					_UpperCamelCase					:Optional[Any]					     =	ids_tensor((self.batch_size, 3)					,				config.vocab_size   )
					_UpperCamelCase					:str					     =	tf.cast(ids_tensor((self.batch_size, 3)					,				2   )					,				tf.inta   )

					# append to next input_ids and
					_UpperCamelCase					:Any					     =	tf.concat([input_ids, next_tokens]					,				axis=-1   )
					_UpperCamelCase					:int					     =	tf.concat([attention_mask, next_attn_mask]					,				axis=-1   )

					_UpperCamelCase					:int					     =	model(SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__   )[0]
					_UpperCamelCase					:Optional[Any]					     =	model(SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__					,				past_key_values=SCREAMING_SNAKE_CASE__   )[0]

					self.parent.assertEqual(next_tokens.shape[1]					,				output_from_past.shape[1]   )

					# select random slice
					_UpperCamelCase					:int					     =	int(ids_tensor((1,)					,				output_from_past.shape[-1]   )   )
					_UpperCamelCase					:Tuple					     =	output_from_no_past[:, -3:, random_slice_idx]
					_UpperCamelCase					:int					     =	output_from_past[:, :, random_slice_idx]

					# test that outputs are equal for slice
					tf.debugging.assert_near(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				rtol=1E-3   )






@require_tf
class      A(  lowerCamelCase__				,							lowerCamelCase__				,							unittest.TestCase		):

		"""simple docstring"""





		A    =							(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
		A    =							(TFOPTForCausalLM,) if is_tf_available() else ()
		A    =							(
		    {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
		)
		A    =							False
		A    =							False
		A    =							False
		A    =							10
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	TFOPTModelTester(self   )
					_UpperCamelCase					:List[Any]					     =	ConfigTester(self					,				config_class=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					self.config_tester.run_common_tests()
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE__   )





		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase     ,      _UpperCamelCase					:Any					     =	self.model_tester.prepare_config_and_inputs_for_common()

					def _get_word_embedding_weight(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ):
								if hasattr(SCREAMING_SNAKE_CASE__					,				'''weight'''   ):
											return embedding_layer.weight
								else:
											# Here we build the word embeddings weights if not exists.
											# And then we retry to get the attribute once built.
											model.build()
											if hasattr(SCREAMING_SNAKE_CASE__					,				'''weight'''   ):
														return embedding_layer.weight
											else:
														return None

					for model_class in self.all_model_classes:
								for size in [config.vocab_size - 10, config.vocab_size + 10]:
											# build the embeddings
											_UpperCamelCase					:str					     =	model_class(config=SCREAMING_SNAKE_CASE__   )
											_UpperCamelCase					:Optional[int]					     =	_get_word_embedding_weight(SCREAMING_SNAKE_CASE__					,				model.get_input_embeddings()   )
											_UpperCamelCase					:int					     =	_get_word_embedding_weight(SCREAMING_SNAKE_CASE__					,				model.get_output_embeddings()   )

											# reshape the embeddings
											model.resize_token_embeddings(SCREAMING_SNAKE_CASE__   )
											_UpperCamelCase					:Tuple					     =	_get_word_embedding_weight(SCREAMING_SNAKE_CASE__					,				model.get_input_embeddings()   )
											_UpperCamelCase					:List[Any]					     =	_get_word_embedding_weight(SCREAMING_SNAKE_CASE__					,				model.get_output_embeddings()   )

											# check that the resized embeddings size matches the desired size.
											_UpperCamelCase					:List[Any]					     =	size if size is not None else config.vocab_size

											self.assertEqual(new_input_embeddings.shape[0]					,				SCREAMING_SNAKE_CASE__   )

											# check that weights remain the same after resizing
											_UpperCamelCase					:str					     =	True
											for pa, pa in zip(old_input_embeddings.value()					,				new_input_embeddings.value()   ):
														if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
																	_UpperCamelCase					:Union[str, Any]					     =	False
											self.assertTrue(SCREAMING_SNAKE_CASE__   )

											if old_output_embeddings is not None and new_output_embeddings is not None:
														self.assertEqual(new_output_embeddings.shape[0]					,				SCREAMING_SNAKE_CASE__   )

														_UpperCamelCase					:Union[str, Any]					     =	True
														for pa, pa in zip(old_output_embeddings.value()					,				new_output_embeddings.value()   ):
																	if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
																				_UpperCamelCase					:str					     =	False
														self.assertTrue(SCREAMING_SNAKE_CASE__   )





def 				A_       ( snake_case__		)					->  Tuple:
			return tf.constant(snake_case__       ,					dtype=tf.intaa		)






@require_tf
class      A(  unittest.TestCase		):

		"""simple docstring"""





		A    =							99
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	tf.ones((4, 1)					,				dtype=tf.intaa   ) * 2
					_UpperCamelCase					:List[str]					     =	tf.concat([ids_tensor((4, 6)					,				self.vocab_size - 3   ) + 3, eos_column_vector]					,				axis=1   )
					_UpperCamelCase					:List[Any]					     =	input_ids.shape[0]
					_UpperCamelCase					:Union[str, Any]					     =	OPTConfig(
					    vocab_size=self.vocab_size					,				hidden_size=24					,				num_hidden_layers=2					,				num_attention_heads=2					,				ffn_dim=32					,				max_position_embeddings=48					,				eos_token_id=2					,				pad_token_id=1					,				bos_token_id=0					,				)
					return config, input_ids, batch_size






@require_sentencepiece
@require_tf
class      A(  unittest.TestCase		):

		"""simple docstring"""





		@slow
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	TFOPTModel.from_pretrained('''facebook/opt-350m'''   )
					_UpperCamelCase					:Dict					     =	_long_tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]   )
					_UpperCamelCase					:Optional[Any]					     =	tf.not_equal(SCREAMING_SNAKE_CASE__					,				model.config.pad_token_id   )
					with tf.GradientTape():
								_UpperCamelCase					:List[Any]					     =	model(input_ids=SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__   ).last_hidden_state
					_UpperCamelCase					:List[str]					     =	(1, 11, 5_12)
					self.assertEqual(output.shape					,				SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Dict					     =	tf.constant(
					    [[-0.2_8_7_3, -1.9_2_1_8, -0.3_0_3_3], [-1.2_7_1_0, -0.1_3_3_8, -0.1_9_0_2], [0.4_0_9_5, 0.1_2_1_4, -1.3_1_2_1]]   )
					self.assertTrue(np.allclose(output[:, :3, :3]					,				SCREAMING_SNAKE_CASE__					,				atol=4E-3   )   )

					_UpperCamelCase					:int					     =	tf.function(SCREAMING_SNAKE_CASE__					,				jit_compile=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Union[str, Any]					     =	xla_generate(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )[0]
					self.assertTrue(np.allclose(output[:, :3, :3]					,				SCREAMING_SNAKE_CASE__					,				atol=4E-2   )   )






@require_tf
@slow
class      A(  unittest.TestCase		):

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					super().setUp()
					_UpperCamelCase					:int					     =	'''facebook/opt-350m'''





		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	TFOPTForCausalLM.from_pretrained(self.path_model   )
					_UpperCamelCase					:str					     =	GPTaTokenizer.from_pretrained(self.path_model   )

					_UpperCamelCase					:Union[str, Any]					     =	[
					    '''Today is a beautiful day and I want to''',
					    '''In the city of''',
					    '''Paris is the capital of France and''',
					    '''Computers and mobile phones have taken''',
					]
					# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
					_UpperCamelCase					:List[str]					     =	tokenizer(SCREAMING_SNAKE_CASE__					,				return_tensors='''tf'''					,				padding=SCREAMING_SNAKE_CASE__					,				add_special_tokens=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[Any]					     =	tf.math.reduce_mean(model(inputs.input_ids					,				attention_mask=inputs.attention_mask   )[0]					,				axis=-1   )
					_UpperCamelCase					:Any					     =	tf.constant(
					    [
					        [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
					        [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
					        [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
					        [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
					    ]   )
					self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				atol=1E-4   )   )

					_UpperCamelCase					:List[str]					     =	tf.function(SCREAMING_SNAKE_CASE__					,				jit_compile=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[Any]					     =	tf.math.reduce_mean(xla_generate(inputs.input_ids					,				attention_mask=inputs.attention_mask   )[0]					,				axis=-1   )
					self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				atol=1E-4   )   )






@require_tf
@slow
class      A(  unittest.TestCase		):

		"""simple docstring"""





		@property
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					return [
					    "Today is a beautiful day and I want",
					    "In the city of",
					    "Paris is the capital of France and",
					    "Computers and mobile phones have taken",
					]
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	'''facebook/opt-125m'''

					_UpperCamelCase					:Optional[int]					     =	[
					    '''Today is a beautiful day and I want to''',
					    '''In the city of New York, the city''',
					    '''Paris is the capital of France and the capital''',
					    '''Computers and mobile phones have taken over the''',
					]

					_UpperCamelCase					:List[str]					     =	[]
					_UpperCamelCase					:Dict					     =	GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	TFOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__   )

					for prompt in self.prompts:
								_UpperCamelCase					:Union[str, Any]					     =	tokenizer(SCREAMING_SNAKE_CASE__					,				return_tensors='''tf'''   ).input_ids

								_UpperCamelCase					:int					     =	model.generate(SCREAMING_SNAKE_CASE__					,				max_length=10   )

								_UpperCamelCase					:Optional[int]					     =	tokenizer.batch_decode(SCREAMING_SNAKE_CASE__					,				skip_special_tokens=SCREAMING_SNAKE_CASE__   )
								predicted_outputs += generated_string

					self.assertListEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	'''facebook/opt-350m'''

					_UpperCamelCase					:Union[str, Any]					     =	GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Tuple					     =	TFOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Union[str, Any]					     =	'''left'''

					# use different length sentences to test batching
					_UpperCamelCase					:Optional[int]					     =	[
					    '''Hello, my dog is a little''',
					    '''Today, I''',
					]

					_UpperCamelCase					:Optional[Any]					     =	tokenizer(SCREAMING_SNAKE_CASE__					,				return_tensors='''tf'''					,				padding=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:int					     =	inputs['''input_ids''']

					_UpperCamelCase					:List[str]					     =	model.generate(input_ids=SCREAMING_SNAKE_CASE__					,				attention_mask=inputs['''attention_mask''']   )

					_UpperCamelCase					:Optional[int]					     =	tokenizer(sentences[0]					,				return_tensors='''tf'''   ).input_ids
					_UpperCamelCase					:Union[str, Any]					     =	model.generate(input_ids=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[str]					     =	inputs_non_padded.shape[-1] - tf.math.reduce_sum(
					    tf.cast(inputs['''attention_mask'''][-1]					,				tf.intaa   )   )
					_UpperCamelCase					:Union[str, Any]					     =	tokenizer(sentences[1]					,				return_tensors='''tf'''   ).input_ids
					_UpperCamelCase					:Union[str, Any]					     =	model.generate(input_ids=SCREAMING_SNAKE_CASE__					,				max_length=model.config.max_length - num_paddings   )

					_UpperCamelCase					:Union[str, Any]					     =	tokenizer.batch_decode(SCREAMING_SNAKE_CASE__					,				skip_special_tokens=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Dict					     =	tokenizer.decode(output_non_padded[0]					,				skip_special_tokens=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[Any]					     =	tokenizer.decode(output_padded[0]					,				skip_special_tokens=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Tuple					     =	[
					    '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
					    '''Today, I was in the middle of a conversation with a friend about the''',
					]
					self.assertListEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					self.assertListEqual(SCREAMING_SNAKE_CASE__					,				[non_padded_sentence, padded_sentence]   )





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

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	'''facebook/opt-350m'''

					_UpperCamelCase					:List[str]					     =	[
					    '''Today is a beautiful day and I want to''',
					    '''In the city of San Francisco, the city''',
					    '''Paris is the capital of France and the capital''',
					    '''Computers and mobile phones have taken over the''',
					]

					_UpperCamelCase					:Tuple					     =	[]
					_UpperCamelCase					:Optional[Any]					     =	GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:str					     =	TFOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__   )

					for prompt in self.prompts:
								_UpperCamelCase					:Any					     =	tokenizer(SCREAMING_SNAKE_CASE__					,				return_tensors='''tf'''   ).input_ids

								_UpperCamelCase					:Dict					     =	model.generate(SCREAMING_SNAKE_CASE__					,				max_length=10   )

								_UpperCamelCase					:Union[str, Any]					     =	tokenizer.batch_decode(SCREAMING_SNAKE_CASE__					,				skip_special_tokens=SCREAMING_SNAKE_CASE__   )
								predicted_outputs += generated_string

					self.assertListEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )





"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


UpperCamelCase__				:List[Any]			   =	logging.get_logger(__name__)


UpperCamelCase__				:Tuple			   =	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
UpperCamelCase__				:List[str]			   =	{
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

UpperCamelCase__				:List[Any]			   =	{
    """RUCAIBox/mvp""": 1_024,
}






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							VOCAB_FILES_NAMES
		A    =							PRETRAINED_VOCAB_FILES_MAP
		A    =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A    =							["input_ids", "attention_mask"]
		A    =							MvpTokenizer
		def __init__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__="replace"					,				SCREAMING_SNAKE_CASE__="<s>"					,				SCREAMING_SNAKE_CASE__="</s>"					,				SCREAMING_SNAKE_CASE__="</s>"					,				SCREAMING_SNAKE_CASE__="<s>"					,				SCREAMING_SNAKE_CASE__="<unk>"					,				SCREAMING_SNAKE_CASE__="<pad>"					,				SCREAMING_SNAKE_CASE__="<mask>"					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=True					,				**SCREAMING_SNAKE_CASE__					,				)      ->	List[str]:

					"""simple docstring"""



					super().__init__(
					    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				tokenizer_file=SCREAMING_SNAKE_CASE__					,				errors=SCREAMING_SNAKE_CASE__					,				bos_token=SCREAMING_SNAKE_CASE__					,				eos_token=SCREAMING_SNAKE_CASE__					,				sep_token=SCREAMING_SNAKE_CASE__					,				cls_token=SCREAMING_SNAKE_CASE__					,				unk_token=SCREAMING_SNAKE_CASE__					,				pad_token=SCREAMING_SNAKE_CASE__					,				mask_token=SCREAMING_SNAKE_CASE__					,				add_prefix_space=SCREAMING_SNAKE_CASE__					,				trim_offsets=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__					,				)

					_UpperCamelCase					:int					     =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
					if pre_tok_state.get('''add_prefix_space'''					,				SCREAMING_SNAKE_CASE__   ) != add_prefix_space:
								_UpperCamelCase					:int					     =	getattr(SCREAMING_SNAKE_CASE__					,				pre_tok_state.pop('''type'''   )   )
								_UpperCamelCase					:Optional[int]					     =	add_prefix_space
								_UpperCamelCase					:List[Any]					     =	pre_tok_class(**SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[int]					     =	add_prefix_space

					# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
					_UpperCamelCase					:Any					     =	'''post_processor'''
					_UpperCamelCase					:Optional[Any]					     =	getattr(self.backend_tokenizer					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					if tokenizer_component_instance:
								_UpperCamelCase					:Optional[Any]					     =	json.loads(tokenizer_component_instance.__getstate__()   )

								# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
								if "sep" in state:
											_UpperCamelCase					:Optional[Any]					     =	tuple(state['''sep''']   )
								if "cls" in state:
											_UpperCamelCase					:Optional[Any]					     =	tuple(state['''cls''']   )

								_UpperCamelCase					:str					     =	False

								if state.get('''add_prefix_space'''					,				SCREAMING_SNAKE_CASE__   ) != add_prefix_space:
											_UpperCamelCase					:Optional[int]					     =	add_prefix_space
											_UpperCamelCase					:Union[str, Any]					     =	True

								if state.get('''trim_offsets'''					,				SCREAMING_SNAKE_CASE__   ) != trim_offsets:
											_UpperCamelCase					:Union[str, Any]					     =	trim_offsets
											_UpperCamelCase					:Optional[Any]					     =	True

								if changes_to_apply:
											_UpperCamelCase					:Union[str, Any]					     =	getattr(SCREAMING_SNAKE_CASE__					,				state.pop('''type'''   )   )
											_UpperCamelCase					:Optional[Any]					     =	component_class(**SCREAMING_SNAKE_CASE__   )
											setattr(self.backend_tokenizer					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		@property
		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					if self._mask_token is None:
								if self.verbose:
											logger.error('''Using mask_token, but it is not set yet.'''   )
								return None
					return str(self._mask_token   )
		@mask_token.setter
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	AddedToken(SCREAMING_SNAKE_CASE__					,				lstrip=SCREAMING_SNAKE_CASE__					,				rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ) else value
					_UpperCamelCase					:int					     =	value
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	BatchEncoding:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	kwargs.get('''is_split_into_words'''					,				SCREAMING_SNAKE_CASE__   )

					if is_split_into_words and not self.add_prefix_space:
								raise ValueError(
								    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
								    '''to use it with pretokenized inputs.'''   )

					return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	BatchEncoding:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	kwargs.get('''is_split_into_words'''					,				SCREAMING_SNAKE_CASE__   )

					if is_split_into_words and not self.add_prefix_space:
								raise ValueError(
								    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
								    '''to use it with pretokenized inputs.'''   )

					return super()._encode_plus(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None   )      ->	Tuple[str]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	self._tokenizer.model.save(SCREAMING_SNAKE_CASE__					,				name=SCREAMING_SNAKE_CASE__   )
					return tuple(SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=None   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	[self.bos_token_id] + token_ids_a + [self.eos_token_id]
					if token_ids_a is None:
								return output

					return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None   )      ->	List[int]:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	[self.sep_token_id]
					_UpperCamelCase					:List[str]					     =	[self.cls_token_id]

					if token_ids_a is None:
								return len(cls + token_ids_a + sep   ) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]

