

from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFRoFormerForCausalLM,
					    TFRoFormerForMaskedLM,
					    TFRoFormerForMultipleChoice,
					    TFRoFormerForQuestionAnswering,
					    TFRoFormerForSequenceClassification,
					    TFRoFormerForTokenClassification,
					    TFRoFormerModel,
					)
					from transformers.models.roformer.modeling_tf_roformer import (
					    TFRoFormerSelfAttention,
					    TFRoFormerSinusoidalPositionalEmbedding,
					)

class __UpperCamelCase     :






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__				,      lowerCamelCase__=1_3				,      lowerCamelCase__=7				,      lowerCamelCase__=True				,      lowerCamelCase__=True				,      lowerCamelCase__=True				,      lowerCamelCase__=True				,      lowerCamelCase__=9_9				,      lowerCamelCase__=3_2				,      lowerCamelCase__=2				,      lowerCamelCase__=4				,      lowerCamelCase__=3_7				,      lowerCamelCase__="gelu"				,      lowerCamelCase__=0.1				,      lowerCamelCase__=0.1				,      lowerCamelCase__=5_1_2				,      lowerCamelCase__=1_6				,      lowerCamelCase__=2				,      lowerCamelCase__=0.02				,      lowerCamelCase__=3				,      lowerCamelCase__=4				,      lowerCamelCase__=None				,      ):
						UpperCAmelCase__:	List[str]									= parent
						UpperCAmelCase__:	int									= 1_3
						UpperCAmelCase__:	Tuple									= 7
						UpperCAmelCase__:	str									= True
						UpperCAmelCase__:	Tuple									= True
						UpperCAmelCase__:	Optional[int]									= True
						UpperCAmelCase__:	Optional[Any]									= True
						UpperCAmelCase__:	List[str]									= 9_9
						UpperCAmelCase__:	List[str]									= 3_2
						UpperCAmelCase__:	Optional[int]									= 2
						UpperCAmelCase__:	Optional[Any]									= 4
						UpperCAmelCase__:	List[Any]									= 3_7
						UpperCAmelCase__:	Optional[int]									= "gelu"
						UpperCAmelCase__:	Optional[int]									= 0.1
						UpperCAmelCase__:	int									= 0.1
						UpperCAmelCase__:	Optional[Any]									= 5_1_2
						UpperCAmelCase__:	Union[str, Any]									= 1_6
						UpperCAmelCase__:	str									= 2
						UpperCAmelCase__:	Any									= 0.02
						UpperCAmelCase__:	Any									= 3
						UpperCAmelCase__:	Optional[int]									= 4
						UpperCAmelCase__:	Dict									= None





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Dict									= ids_tensor([self.batch_size, self.seq_length]				,      self.vocab_size						)

						UpperCAmelCase__:	Any									= None
						if self.use_input_mask:
							UpperCAmelCase__:	Optional[int]									= random_attention_mask([self.batch_size, self.seq_length]						)

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

						UpperCAmelCase__:	Union[str, Any]									= None
						UpperCAmelCase__:	Any									= None
						UpperCAmelCase__:	Tuple									= None
						if self.use_labels:
							UpperCAmelCase__:	Optional[int]									= ids_tensor([self.batch_size]				,      self.type_sequence_label_size						)
							UpperCAmelCase__:	Dict									= ids_tensor([self.batch_size, self.seq_length]				,      self.num_labels						)
							UpperCAmelCase__:	Tuple									= ids_tensor([self.batch_size]				,      self.num_choices						)

						UpperCAmelCase__:	Any									= RoFormerConfig(
						    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				,      initializer_range=self.initializer_range				,      return_dict=lowerCamelCase__				,      )

						return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Union[str, Any]									= TFRoFormerModel(config=lowerCamelCase__						)
						UpperCAmelCase__:	Dict									= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

						UpperCAmelCase__:	Any									= [input_ids, input_mask]
						UpperCAmelCase__:	Any									= model(lowerCamelCase__						)

						UpperCAmelCase__:	Tuple									= model(lowerCamelCase__						)

						self.parent.assertEqual(result.last_hidden_state.shape				,      (self.batch_size, self.seq_length, self.hidden_size)						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Tuple									= True
						UpperCAmelCase__:	int									= TFRoFormerForCausalLM(config=lowerCamelCase__						)
						UpperCAmelCase__:	Dict									= {
						    "input_ids": input_ids,
						    "attention_mask": input_mask,
						    "token_type_ids": token_type_ids,
						}
						UpperCAmelCase__:	Union[str, Any]									= model(lowerCamelCase__						)["logits"]
						self.parent.assertListEqual(
						    list(prediction_scores.numpy().shape						)				,      [self.batch_size, self.seq_length, self.vocab_size]						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Dict									= TFRoFormerForMaskedLM(config=lowerCamelCase__						)
						UpperCAmelCase__:	int									= {
						    "input_ids": input_ids,
						    "attention_mask": input_mask,
						    "token_type_ids": token_type_ids,
						}
						UpperCAmelCase__:	Dict									= model(lowerCamelCase__						)
						self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.seq_length, self.vocab_size)						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	int									= self.num_labels
						UpperCAmelCase__:	Tuple									= TFRoFormerForSequenceClassification(config=lowerCamelCase__						)
						UpperCAmelCase__:	str									= {
						    "input_ids": input_ids,
						    "attention_mask": input_mask,
						    "token_type_ids": token_type_ids,
						}

						UpperCAmelCase__:	Optional[int]									= model(lowerCamelCase__						)
						self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.num_labels)						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	str									= self.num_choices
						UpperCAmelCase__:	Any									= TFRoFormerForMultipleChoice(config=lowerCamelCase__						)
						UpperCAmelCase__:	Union[str, Any]									= tf.tile(tf.expand_dims(lowerCamelCase__				,      1						)				,      (1, self.num_choices, 1)						)
						UpperCAmelCase__:	int									= tf.tile(tf.expand_dims(lowerCamelCase__				,      1						)				,      (1, self.num_choices, 1)						)
						UpperCAmelCase__:	Dict									= tf.tile(tf.expand_dims(lowerCamelCase__				,      1						)				,      (1, self.num_choices, 1)						)
						UpperCAmelCase__:	Dict									= {
						    "input_ids": multiple_choice_inputs_ids,
						    "attention_mask": multiple_choice_input_mask,
						    "token_type_ids": multiple_choice_token_type_ids,
						}
						UpperCAmelCase__:	Optional[int]									= model(lowerCamelCase__						)
						self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.num_choices)						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	List[Any]									= self.num_labels
						UpperCAmelCase__:	Union[str, Any]									= TFRoFormerForTokenClassification(config=lowerCamelCase__						)
						UpperCAmelCase__:	Optional[Any]									= {
						    "input_ids": input_ids,
						    "attention_mask": input_mask,
						    "token_type_ids": token_type_ids,
						}
						UpperCAmelCase__:	Union[str, Any]									= model(lowerCamelCase__						)
						self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.seq_length, self.num_labels)						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	List[str]									= TFRoFormerForQuestionAnswering(config=lowerCamelCase__						)
						UpperCAmelCase__:	Union[str, Any]									= {
						    "input_ids": input_ids,
						    "attention_mask": input_mask,
						    "token_type_ids": token_type_ids,
						}

						UpperCAmelCase__:	Dict									= model(lowerCamelCase__						)
						self.parent.assertEqual(result.start_logits.shape				,      (self.batch_size, self.seq_length)						)
						self.parent.assertEqual(result.end_logits.shape				,      (self.batch_size, self.seq_length)						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Tuple									= self.prepare_config_and_inputs()
						(
						    (
						    UpperCAmelCase__
						)							,						(
						    UpperCAmelCase__
						)							,						(
						    UpperCAmelCase__
						)							,						(
						    UpperCAmelCase__
						)							,						(
						    UpperCAmelCase__
						)							,						(
						    UpperCAmelCase__
						)							,						(
						    UpperCAmelCase__
						)							,						
						):	Union[str, Any]									= config_and_inputs
						UpperCAmelCase__:	str									= {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
						return config, inputs_dict

@require_tf
class __UpperCamelCase     (				_a					,_a					,unittest.TestCase		):






					'''simple docstring'''



					__magic_name__        =   (
					    (
					        TFRoFormerModel,
					        TFRoFormerForCausalLM,
					        TFRoFormerForMaskedLM,
					        TFRoFormerForQuestionAnswering,
					        TFRoFormerForSequenceClassification,
					        TFRoFormerForTokenClassification,
					        TFRoFormerForMultipleChoice,
					    )
					    if is_tf_available()
					    else ()
					)
					__magic_name__        =   (
					    {
					        "feature-extraction": TFRoFormerModel,
					        "fill-mask": TFRoFormerForMaskedLM,
					        "question-answering": TFRoFormerForQuestionAnswering,
					        "text-classification": TFRoFormerForSequenceClassification,
					        "text-generation": TFRoFormerForCausalLM,
					        "token-classification": TFRoFormerForTokenClassification,
					        "zero-shot": TFRoFormerForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)

					__magic_name__        =   False
					__magic_name__        =   False





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						if pipeline_test_casse_name == "TextGenerationPipelineTests":
							return True

						return False





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[Any]									= TFRoFormerModelTester(self						)
						UpperCAmelCase__:	List[Any]									= ConfigTester(self				,      config_class=lowerCamelCase__				,      hidden_size=3_7						)





					def    _UpperCAmelCase    (				self						):
						self.config_tester.run_common_tests()





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[Any]									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	str									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[int]									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_lm_head(*lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	int									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[str]									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[int]									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[Any]									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__						)





					@slow
					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[Any]									= TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base"						)
						self.assertIsNotNone(lowerCamelCase__						)

@require_tf
class __UpperCamelCase     (				unittest.TestCase		):






					'''simple docstring'''



					@slow
					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[str]									= TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"						)
						UpperCAmelCase__:	Dict									= tf.constant([[0, 1, 2, 3, 4, 5]]						)
						UpperCAmelCase__:	Union[str, Any]									= model(lowerCamelCase__						)[0]

						# TODO Replace vocab size
						UpperCAmelCase__:	Optional[Any]									= 5_0_0_0_0

						UpperCAmelCase__:	Union[str, Any]									= [1, 6, vocab_size]
						self.assertEqual(output.shape				,      lowerCamelCase__						)

						print(output[:, :3, :3]						)

						# TODO Replace values below with what was printed above.
						UpperCAmelCase__:	Optional[Any]									= tf.constant(
						    [
						        [
						            [-0.12_053_341, -1.0_264_901, 0.29_221_946],
						            [-1.5_133_783, 0.197_433, 0.15_190_607],
						            [-5.0_135_403, -3.900_256, -0.84_038_764],
						        ]
						    ]						)
						tf.debugging.assert_near(output[:, :3, :3]				,      lowerCamelCase__				,      atol=1e-4						)

@require_tf
class __UpperCamelCase     (				unittest.TestCase		):






					'''simple docstring'''



					__magic_name__        =   1E-4





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	str									= tf.constant([[4, 1_0]]						)
						UpperCAmelCase__:	List[Any]									= TFRoFormerSinusoidalPositionalEmbedding(num_positions=6				,      embedding_dim=6						)

						UpperCAmelCase__:	Union[str, Any]									= emba(input_ids.shape						)
						UpperCAmelCase__:	Union[str, Any]									= tf.constant(
						    [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]]						)

						tf.debugging.assert_near(lowerCamelCase__				,      lowerCamelCase__				,      atol=self.tolerance						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Dict									= tf.constant(
						    [
						        [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
						        [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
						        [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
						    ]						)
						UpperCAmelCase__:	Optional[Any]									= TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2				,      embedding_dim=5_1_2						)
						emba([2, 1_6, 5_1_2]						)
						UpperCAmelCase__:	Optional[int]									= emba.weight[:3, :5]

						tf.debugging.assert_near(lowerCamelCase__				,      lowerCamelCase__				,      atol=self.tolerance						)

@require_tf
class __UpperCamelCase     (				unittest.TestCase		):






					'''simple docstring'''



					__magic_name__        =   1E-4





					def    _UpperCAmelCase    (				self						):
						# 2,12,16,64
						UpperCAmelCase__:	Union[str, Any]									= tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4				,      dtype=tf.floataa						)				,      shape=(2, 1_2, 1_6, 6_4)						) / 1_0_0

						UpperCAmelCase__:	List[str]									= -tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4				,      dtype=tf.floataa						)				,      shape=(2, 1_2, 1_6, 6_4)						) / 1_0_0

						UpperCAmelCase__:	Optional[Any]									= TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2				,      embedding_dim=6_4						)
						UpperCAmelCase__:	str									= embed_positions([2, 1_6, 7_6_8]						)[None, None, :, :]

						UpperCAmelCase__							,						UpperCAmelCase__:	List[str]									= TFRoFormerSelfAttention.apply_rotary_position_embeddings(
						    lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						)

						UpperCAmelCase__:	List[Any]									= tf.constant(
						    [
						        [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
						        [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
						        [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
						        [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
						        [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
						        [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
						    ]						)
						UpperCAmelCase__:	List[str]									= tf.constant(
						    [
						        [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
						        [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
						        [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
						        [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
						        [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
						        [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
						    ]						)

						tf.debugging.assert_near(query_layer[0, 0, :6, :8]				,      lowerCamelCase__				,      atol=self.tolerance						)
						tf.debugging.assert_near(key_layer[0, 0, :6, :8]				,      lowerCamelCase__				,      atol=self.tolerance						)


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_nllb import NllbTokenizer
else:
					_lowerCAmelCase  :      Any			 =None


_lowerCAmelCase  :      Union[str, Any]			 =logging.get_logger(__name__)


_lowerCAmelCase  :      Dict			 ={"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase  :      Optional[int]			 ={
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"""
        ),
    },
}

_lowerCAmelCase  :      List[Any]			 ={
    """facebook/nllb-large-en-ro""": 10_24,
    """facebook/nllb-200-distilled-600M""": 10_24,
}

# fmt: off
_lowerCAmelCase  :      Union[str, Any]			 =["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   VOCAB_FILES_NAMES
					__magic_name__        =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__magic_name__        =   PRETRAINED_VOCAB_FILES_MAP
					__magic_name__        =   ["input_ids", "attention_mask"]
					__magic_name__        =   NllbTokenizer

					__magic_name__        =   []
					__magic_name__        =   []





					def __init__(				self				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__="<s>"				,      lowerCamelCase__="</s>"				,      lowerCamelCase__="</s>"				,      lowerCamelCase__="<s>"				,      lowerCamelCase__="<unk>"				,      lowerCamelCase__="<pad>"				,      lowerCamelCase__="<mask>"				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=False				,      **lowerCamelCase__				,      ):
						# Mask token behave like a normal word, i.e. include the space before it
						UpperCAmelCase__:	int									= AddedToken(lowerCamelCase__				,      lstrip=lowerCamelCase__				,      rstrip=lowerCamelCase__						) if isinstance(lowerCamelCase__				,      lowerCamelCase__						) else mask_token
						UpperCAmelCase__:	Tuple									= legacy_behaviour
						super().__init__(
						    vocab_file=lowerCamelCase__				,      tokenizer_file=lowerCamelCase__				,      bos_token=lowerCamelCase__				,      eos_token=lowerCamelCase__				,      sep_token=lowerCamelCase__				,      cls_token=lowerCamelCase__				,      unk_token=lowerCamelCase__				,      pad_token=lowerCamelCase__				,      mask_token=lowerCamelCase__				,      src_lang=lowerCamelCase__				,      tgt_lang=lowerCamelCase__				,      additional_special_tokens=lowerCamelCase__				,      legacy_behaviour=lowerCamelCase__				,      **lowerCamelCase__				,      )

						UpperCAmelCase__:	Optional[int]									= vocab_file
						UpperCAmelCase__:	Tuple									= False if not self.vocab_file else True

						UpperCAmelCase__:	str									= FAIRSEQ_LANGUAGE_CODES.copy()

						if additional_special_tokens is not None:
							# Only add those special tokens if they are not already there.
							_additional_special_tokens.extend(
							    [t for t in additional_special_tokens if t not in _additional_special_tokens]						)

						self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}						)
						UpperCAmelCase__:	Optional[int]									= {
						    lang_code: self.convert_tokens_to_ids(lowerCamelCase__						) for lang_code in FAIRSEQ_LANGUAGE_CODES
						}

						UpperCAmelCase__:	List[Any]									= src_lang if src_lang is not None else "eng_Latn"
						UpperCAmelCase__:	Union[str, Any]									= self.convert_tokens_to_ids(self._src_lang						)
						UpperCAmelCase__:	List[str]									= tgt_lang
						self.set_src_lang_special_tokens(self._src_lang						)





					@property
					def    _UpperCAmelCase    (				self						):
						return self._src_lang





					@src_lang.setter
					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Any									= new_src_lang
						self.set_src_lang_special_tokens(self._src_lang						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None						):
						if token_ids_a is None:
							return self.prefix_tokens + token_ids_a + self.suffix_tokens
						# We don't expect to process pairs, but leave the pair logic for API consistency
						return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None						):
						UpperCAmelCase__:	List[Any]									= [self.sep_token_id]
						UpperCAmelCase__:	Tuple									= [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]





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						):
						if src_lang is None or tgt_lang is None:
							raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"						)
						UpperCAmelCase__:	str									= src_lang
						UpperCAmelCase__:	Any									= self(lowerCamelCase__				,      add_special_tokens=lowerCamelCase__				,      return_tensors=lowerCamelCase__				,      **lowerCamelCase__						)
						UpperCAmelCase__:	List[Any]									= self.convert_tokens_to_ids(lowerCamelCase__						)
						UpperCAmelCase__:	int									= tgt_lang_id
						return inputs





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = "eng_Latn"				,      lowerCamelCase__ = None				,      lowerCamelCase__ = "fra_Latn"				,      **lowerCamelCase__				,      ):
						UpperCAmelCase__:	Union[str, Any]									= src_lang
						UpperCAmelCase__:	Optional[int]									= tgt_lang
						return super().prepare_seqaseq_batch(lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						return self.set_src_lang_special_tokens(self.src_lang						)





					def    _UpperCAmelCase    (				self						):
						return self.set_tgt_lang_special_tokens(self.tgt_lang						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	int									= self.convert_tokens_to_ids(lowerCamelCase__						)

						if self.legacy_behaviour:
							UpperCAmelCase__:	List[str]									= []
							UpperCAmelCase__:	List[Any]									= [self.eos_token_id, self.cur_lang_code]
						else:
							UpperCAmelCase__:	Optional[Any]									= [self.cur_lang_code]
							UpperCAmelCase__:	List[Any]									= [self.eos_token_id]

						UpperCAmelCase__:	Optional[Any]									= self.convert_ids_to_tokens(self.prefix_tokens						)
						UpperCAmelCase__:	Optional[Any]									= self.convert_ids_to_tokens(self.suffix_tokens						)

						UpperCAmelCase__:	Any									= processors.TemplateProcessing(
						    single=prefix_tokens_str + ["$A"] + suffix_tokens_str				,      pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str				,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,      self.prefix_tokens + self.suffix_tokens						)						)				,      )





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[int]									= self.convert_tokens_to_ids(lowerCamelCase__						)
						if self.legacy_behaviour:
							UpperCAmelCase__:	int									= []
							UpperCAmelCase__:	str									= [self.eos_token_id, self.cur_lang_code]
						else:
							UpperCAmelCase__:	Tuple									= [self.cur_lang_code]
							UpperCAmelCase__:	Tuple									= [self.eos_token_id]

						UpperCAmelCase__:	Union[str, Any]									= self.convert_ids_to_tokens(self.prefix_tokens						)
						UpperCAmelCase__:	Optional[int]									= self.convert_ids_to_tokens(self.suffix_tokens						)

						UpperCAmelCase__:	Optional[Any]									= processors.TemplateProcessing(
						    single=prefix_tokens_str + ["$A"] + suffix_tokens_str				,      pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str				,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,      self.prefix_tokens + self.suffix_tokens						)						)				,      )





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None						):
						if not self.can_save_slow_tokenizer:
							raise ValueError(
							    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
							    "tokenizer."						)

						if not os.path.isdir(lowerCamelCase__						):
							logger.error(F"Vocabulary path ({save_directory}) should be a directory."						)
							return
						UpperCAmelCase__:	str									= os.path.join(
						    lowerCamelCase__				,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(lowerCamelCase__						):
							copyfile(self.vocab_file				,      lowerCamelCase__						)

						return (out_vocab_file,)