


'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import LEDConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFLEDForConditionalGeneration, TFLEDModel






@require_tf
class  UpperCamelCase__:
				__magic_name__       :   Any							= LEDConfig
				__magic_name__       :   int							= {}
				__magic_name__       :   List[Any]							= "gelu"




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				int=13   ,					lowerCAmelCase    :				Any=7   ,					lowerCAmelCase    :				Dict=True   ,					lowerCAmelCase    :				List[Any]=False   ,					lowerCAmelCase    :				List[Any]=99   ,					lowerCAmelCase    :				Optional[int]=32   ,					lowerCAmelCase    :				Optional[int]=2   ,					lowerCAmelCase    :				List[str]=4   ,					lowerCAmelCase    :				Optional[int]=37   ,					lowerCAmelCase    :				Dict=0.1   ,					lowerCAmelCase    :				Dict=0.1   ,					lowerCAmelCase    :				Optional[Any]=20   ,					lowerCAmelCase    :				Dict=2   ,					lowerCAmelCase    :				Tuple=1   ,					lowerCAmelCase    :				str=0   ,					lowerCAmelCase    :				int=4   ,					)->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			parent
										UpperCAmelCase         =			batch_size
										UpperCAmelCase         =			seq_length
										UpperCAmelCase         =			is_training
										UpperCAmelCase         =			use_labels
										UpperCAmelCase         =			vocab_size
										UpperCAmelCase         =			hidden_size
										UpperCAmelCase         =			num_hidden_layers
										UpperCAmelCase         =			num_attention_heads
										UpperCAmelCase         =			intermediate_size

										UpperCAmelCase         =			hidden_dropout_prob
										UpperCAmelCase         =			attention_probs_dropout_prob
										UpperCAmelCase         =			max_position_embeddings
										UpperCAmelCase         =			eos_token_id
										UpperCAmelCase         =			pad_token_id
										UpperCAmelCase         =			bos_token_id
										UpperCAmelCase         =			attention_window

										# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
										# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
										# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
										# because its local attention only attends to `self.attention_window` and one before and one after
										UpperCAmelCase         =			self.attention_window + 2

										# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
										# the `test_attention_outputs` and `test_hidden_states_output` tests
										UpperCAmelCase         =			(
										    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
										)




				def   a__(	self    :				Dict   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			ids_tensor([self.batch_size, self.seq_length - 1]   ,					self.vocab_size   )
										UpperCAmelCase         =			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )   ,					1   )
										UpperCAmelCase         =			tf.concat([input_ids, eos_tensor]   ,					axis=1   )

										UpperCAmelCase         =			ids_tensor([self.batch_size, self.seq_length]   ,					self.vocab_size   )

										UpperCAmelCase         =			self.config_cls(
										    vocab_size=self.vocab_size   ,					d_model=self.hidden_size   ,					encoder_layers=self.num_hidden_layers   ,					decoder_layers=self.num_hidden_layers   ,					encoder_attention_heads=self.num_attention_heads   ,					decoder_attention_heads=self.num_attention_heads   ,					encoder_ffn_dim=self.intermediate_size   ,					decoder_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_ids=[2]   ,					bos_token_id=self.bos_token_id   ,					pad_token_id=self.pad_token_id   ,					decoder_start_token_id=self.pad_token_id   ,					attention_window=self.attention_window   ,					**self.config_updates   ,					)
										UpperCAmelCase         =			prepare_led_inputs_dict(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )
										UpperCAmelCase         =			tf.concat(
										    [tf.zeros_like(lowerCAmelCase   )[:, :-1], tf.ones_like(lowerCAmelCase   )[:, -1:]]   ,					axis=-1   ,					)
										UpperCAmelCase         =			global_attention_mask
										return config, inputs_dict





				def   a__(	self    :				Optional[int]   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				List[Any]   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			TFLEDModel(config=lowerCAmelCase   ).get_decoder()
										UpperCAmelCase         =			inputs_dict['''input_ids''']

										UpperCAmelCase         =			input_ids[:1, :]
										UpperCAmelCase         =			inputs_dict['''attention_mask'''][:1, :]
										UpperCAmelCase         =			1

										# first forward pass
										UpperCAmelCase         =			model(lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					use_cache=lowerCAmelCase   )

										UpperCAmelCase       ,       UpperCAmelCase         =			outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										UpperCAmelCase         =			ids_tensor((self.batch_size, 3)   ,					config.vocab_size   )
										UpperCAmelCase         =			tf.cast(ids_tensor((self.batch_size, 3)   ,					2   )   ,					tf.inta   )

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

										UpperCAmelCase         =			model(lowerCAmelCase   ,					attention_mask=lowerCAmelCase   )[0]
										UpperCAmelCase         =			model(lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					past_key_values=lowerCAmelCase   )[0]

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

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

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







def    lowerCamelCase__   ( A     :    int   ,     A     :    Tuple   ,     A     :    Any   ,     A     :    Optional[int]=None   ,     A     :    List[str]=None   ,     A     :    int=None   ,     A     :    Union[str, Any]=None   ,     ):




						'''simple docstring'''






						if attention_mask is None:
												UpperCAmelCase         =			tf.cast(tf.math.not_equal(A   ,     config.pad_token_id  )   ,     tf.inta  )
						if decoder_attention_mask is None:
												UpperCAmelCase         =			tf.concat(
												    [
												        tf.ones(decoder_input_ids[:, :1].shape   ,     dtype=tf.inta  ),
												        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]   ,     config.pad_token_id  )   ,     tf.inta  ),
												    ]   ,     axis=-1   ,     )
						if head_mask is None:
												UpperCAmelCase         =			tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
						if decoder_head_mask is None:
												UpperCAmelCase         =			tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
						return {
						    "input_ids": input_ids,
						    "attention_mask": attention_mask,
						    "decoder_input_ids": decoder_input_ids,
						    "decoder_attention_mask": decoder_attention_mask,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						}






@require_tf
class  UpperCamelCase__(     lowerCAmelCase		,       lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   List[str]							= (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
				__magic_name__       :   Tuple							= (TFLEDForConditionalGeneration,) if is_tf_available() else ()
				__magic_name__       :   Optional[Any]							= (
				    {
				        "conversational": TFLEDForConditionalGeneration,
				        "feature-extraction": TFLEDModel,
				        "summarization": TFLEDForConditionalGeneration,
				        "text2text-generation": TFLEDForConditionalGeneration,
				        "translation": TFLEDForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				__magic_name__       :   Any							= True
				__magic_name__       :   Union[str, Any]							= False
				__magic_name__       :   List[str]							= False
				__magic_name__       :   str							= False




				def   a__(	self    :				List[str]   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			TFLEDModelTester(self   )
										UpperCAmelCase         =			ConfigTester(self   ,					config_class=lowerCAmelCase   )




				def   a__(	self    :				Optional[int]   )->  Optional[int]:

										"""simple docstring"""




										self.config_tester.run_common_tests()




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

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase   )




				def   a__(	self    :				Dict   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()
										UpperCAmelCase         =			tf.zeros_like(inputs_dict['''attention_mask''']   )
										UpperCAmelCase         =			2
										UpperCAmelCase         =			tf.where(
										    tf.range(self.model_tester.seq_length   )[None, :] < num_global_attn_indices   ,					1   ,					inputs_dict['''global_attention_mask''']   ,					)

										UpperCAmelCase         =			True
										UpperCAmelCase         =			self.model_tester.seq_length
										UpperCAmelCase         =			self.model_tester.encoder_seq_length

										def check_decoder_attentions_output(lowerCAmelCase    :				Tuple   ):
																UpperCAmelCase         =			outputs.decoder_attentions
																self.assertEqual(len(lowerCAmelCase   )   ,					self.model_tester.num_hidden_layers   )
																self.assertListEqual(
																    list(decoder_attentions[0].shape[-3:]   )   ,					[self.model_tester.num_attention_heads, seq_length, seq_length]   ,					)

										def check_encoder_attentions_output(lowerCAmelCase    :				List[str]   ):
																UpperCAmelCase         =			[t.numpy() for t in outputs.encoder_attentions]
																UpperCAmelCase         =			[t.numpy() for t in outputs.encoder_global_attentions]
																self.assertEqual(len(lowerCAmelCase   )   ,					self.model_tester.num_hidden_layers   )
																self.assertEqual(len(lowerCAmelCase   )   ,					self.model_tester.num_hidden_layers   )
																self.assertListEqual(
																    list(attentions[0].shape[-3:]   )   ,					[self.model_tester.num_attention_heads, seq_length, seq_length]   ,					)
																self.assertListEqual(
																    list(global_attentions[0].shape[-3:]   )   ,					[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]   ,					)

										for model_class in self.all_model_classes:
																UpperCAmelCase         =			True
																UpperCAmelCase         =			False
																UpperCAmelCase         =			False
																UpperCAmelCase         =			model_class(lowerCAmelCase   )
																UpperCAmelCase         =			model(self._prepare_for_class(lowerCAmelCase   ,					lowerCAmelCase   )   )
																UpperCAmelCase         =			len(lowerCAmelCase   )
																self.assertEqual(config.output_hidden_states   ,					lowerCAmelCase   )
																check_encoder_attentions_output(lowerCAmelCase   )

																if self.is_encoder_decoder:
																						UpperCAmelCase         =			model_class(lowerCAmelCase   )
																						UpperCAmelCase         =			model(self._prepare_for_class(lowerCAmelCase   ,					lowerCAmelCase   )   )
																						self.assertEqual(config.output_hidden_states   ,					lowerCAmelCase   )
																						check_decoder_attentions_output(lowerCAmelCase   )

																# Check that output attentions can also be changed via the config
																del inputs_dict["output_attentions"]
																UpperCAmelCase         =			True
																UpperCAmelCase         =			model_class(lowerCAmelCase   )
																UpperCAmelCase         =			model(self._prepare_for_class(lowerCAmelCase   ,					lowerCAmelCase   )   )
																self.assertEqual(config.output_hidden_states   ,					lowerCAmelCase   )
																check_encoder_attentions_output(lowerCAmelCase   )

																# Check attention is always last and order is fine
																UpperCAmelCase         =			True
																UpperCAmelCase         =			True
																UpperCAmelCase         =			model_class(lowerCAmelCase   )
																UpperCAmelCase         =			model(self._prepare_for_class(lowerCAmelCase   ,					lowerCAmelCase   )   )

																self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)   ,					len(lowerCAmelCase   )   )
																self.assertEqual(model.config.output_hidden_states   ,					lowerCAmelCase   )
																check_encoder_attentions_output(lowerCAmelCase   )




				@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'''   )
				def   a__(	self    :				Optional[int]   )->  List[str]:

										"""simple docstring"""




										pass





				def   a__(	self    :				Optional[int]   )->  List[Any]:

										"""simple docstring"""




										pass







def    lowerCamelCase__   ( A     :    str  ):




						'''simple docstring'''






						return tf.constant(A   ,     dtype=tf.intaa  )


_lowercase    :    Dict			     =  1E-4






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




				def   a__(	self    :				Optional[int]   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''   ).led

										# change to intended input here
										UpperCAmelCase         =			_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]   )
										UpperCAmelCase         =			_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]   )
										UpperCAmelCase         =			prepare_led_inputs_dict(model.config   ,					lowerCAmelCase   ,					lowerCAmelCase   )
										UpperCAmelCase         =			model(**lowerCAmelCase   )[0]
										UpperCAmelCase         =			(1, 1024, 768)
										self.assertEqual(output.shape   ,					lowerCAmelCase   )
										# change to expected output here
										UpperCAmelCase         =			tf.convert_to_tensor(
										    [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]   ,					)
										tf.debugging.assert_near(output[:, :3, :3]   ,					lowerCAmelCase   ,					atol=1E-3   )





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

										"""simple docstring"""




										UpperCAmelCase         =			TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''   )

										# change to intended input here
										UpperCAmelCase         =			_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]   )
										UpperCAmelCase         =			_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]   )
										UpperCAmelCase         =			prepare_led_inputs_dict(model.config   ,					lowerCAmelCase   ,					lowerCAmelCase   )
										UpperCAmelCase         =			model(**lowerCAmelCase   )[0]
										UpperCAmelCase         =			(1, 1024, model.config.vocab_size)
										self.assertEqual(output.shape   ,					lowerCAmelCase   )
										# change to expected output here
										UpperCAmelCase         =			tf.convert_to_tensor(
										    [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]   ,					)
										tf.debugging.assert_near(output[:, :3, :3]   ,					lowerCAmelCase   ,					atol=1E-3   ,					rtol=1E-3   )









'''simple docstring'''



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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_lowercase    :    str			     =  logging.get_logger(__name__)

_lowercase    :    List[Any]			     =  """▁"""

_lowercase    :    Optional[Any]			     =  {"""vocab_file""": """sentencepiece.bpe.model"""}

_lowercase    :    Any			     =  {
    """vocab_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

_lowercase    :    int			     =  {
    """facebook/mbart-large-50-one-to-many-mmt""": 1024,
}

# fmt: off
_lowercase    :    int			     =  ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN""", """af_ZA""", """az_AZ""", """bn_IN""", """fa_IR""", """he_IL""", """hr_HR""", """id_ID""", """ka_GE""", """km_KH""", """mk_MK""", """ml_IN""", """mn_MN""", """mr_IN""", """pl_PL""", """ps_AF""", """pt_XX""", """sv_SE""", """sw_KE""", """ta_IN""", """te_IN""", """th_TH""", """tl_XX""", """uk_UA""", """ur_PK""", """xh_ZA""", """gl_ES""", """sl_SI"""]






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Optional[int]							= VOCAB_FILES_NAMES
				__magic_name__       :   Union[str, Any]							= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__       :   Optional[int]							= PRETRAINED_VOCAB_FILES_MAP
				__magic_name__       :   Union[str, Any]							= ["input_ids", "attention_mask"]

				__magic_name__       :   List[int]							= []
				__magic_name__       :   List[int]							= []




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Optional[int]=None   ,					lowerCAmelCase    :				str=None   ,					lowerCAmelCase    :				List[Any]="</s>"   ,					lowerCAmelCase    :				Union[str, Any]="</s>"   ,					lowerCAmelCase    :				Union[str, Any]="<s>"   ,					lowerCAmelCase    :				int="<unk>"   ,					lowerCAmelCase    :				str="<pad>"   ,					lowerCAmelCase    :				Optional[int]="<mask>"   ,					lowerCAmelCase    :				Optional[Dict[str, Any]] = None   ,					**lowerCAmelCase    :				List[Any]   ,					)->  None:

										"""simple docstring"""




										UpperCAmelCase         =			AddedToken(lowerCAmelCase   ,					lstrip=lowerCAmelCase   ,					rstrip=lowerCAmelCase   ) if isinstance(lowerCAmelCase   ,					lowerCAmelCase   ) else mask_token

										UpperCAmelCase         =			{} if sp_model_kwargs is None else sp_model_kwargs

										UpperCAmelCase         =			kwargs.get('''additional_special_tokens'''   ,					[]   )
										kwargs["additional_special_tokens"] += [
										    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
										]

										super().__init__(
										    src_lang=lowerCAmelCase   ,					tgt_lang=lowerCAmelCase   ,					eos_token=lowerCAmelCase   ,					unk_token=lowerCAmelCase   ,					sep_token=lowerCAmelCase   ,					cls_token=lowerCAmelCase   ,					pad_token=lowerCAmelCase   ,					mask_token=lowerCAmelCase   ,					sp_model_kwargs=self.sp_model_kwargs   ,					**lowerCAmelCase   ,					)

										UpperCAmelCase         =			spm.SentencePieceProcessor(**self.sp_model_kwargs   )
										self.sp_model.Load(str(lowerCAmelCase   )   )
										UpperCAmelCase         =			vocab_file

										# Original fairseq vocab and spm vocab must be "aligned":
										# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
										# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
										# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
										# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

										# Mimic fairseq token-to-id alignment for the first 4 token
										UpperCAmelCase         =			{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

										# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
										UpperCAmelCase         =			1

										UpperCAmelCase         =			len(self.sp_model   )
										UpperCAmelCase         =			{
										    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowerCAmelCase   )
										}
										UpperCAmelCase         =			{v: k for k, v in self.lang_code_to_id.items()}
										UpperCAmelCase         =			len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset

										self.fairseq_tokens_to_ids.update(self.lang_code_to_id   )
										UpperCAmelCase         =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}

										UpperCAmelCase         =			src_lang if src_lang is not None else '''en_XX'''
										UpperCAmelCase         =			self.lang_code_to_id[self._src_lang]
										UpperCAmelCase         =			tgt_lang
										self.set_src_lang_special_tokens(self._src_lang   )




				@property
				def   a__(	self    :				Union[str, Any]   )->  int:

										"""simple docstring"""




										return len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset + 1  # Plus 1 for the mask token




				@property
				def   a__(	self    :				str   )->  str:

										"""simple docstring"""




										return self._src_lang




				@src_lang.setter
				def   a__(	self    :				Any   ,					lowerCAmelCase    :				str   )->  None:

										"""simple docstring"""




										UpperCAmelCase         =			new_src_lang
										self.set_src_lang_special_tokens(self._src_lang   )




				def __getstate__(	self    :				Tuple   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			self.__dict__.copy()
										UpperCAmelCase         =			None
										return state




				def __setstate__(	self    :				Dict   ,					lowerCAmelCase    :				Dict   )->  None:

										"""simple docstring"""




										UpperCAmelCase         =			d

										# for backward compatibility
										if not hasattr(self   ,					'''sp_model_kwargs'''   ):
																UpperCAmelCase         =			{}

										UpperCAmelCase         =			spm.SentencePieceProcessor(**self.sp_model_kwargs   )
										self.sp_model.Load(self.vocab_file   )




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

										"""simple docstring"""




										UpperCAmelCase         =			{self.convert_ids_to_tokens(lowerCAmelCase   ): i for i in range(self.vocab_size   )}
										vocab.update(self.added_tokens_encoder   )
										return vocab




				def   a__(	self    :				str   ,					lowerCAmelCase    :				str   )->  List[str]:

										"""simple docstring"""




										return self.sp_model.encode(lowerCAmelCase   ,					out_type=lowerCAmelCase   )




				def   a__(	self    :				Optional[int]   ,					lowerCAmelCase    :				str   )->  int:

										"""simple docstring"""




										if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
										UpperCAmelCase         =			self.sp_model.PieceToId(lowerCAmelCase   )

										# Need to return unknown token if the SP model returned 0
										return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				int   )->  str:

										"""simple docstring"""




										if index in self.fairseq_ids_to_tokens:
																return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(index - self.fairseq_offset   )




				def   a__(	self    :				int   ,					lowerCAmelCase    :				List[Any]   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			[]
										UpperCAmelCase         =			''''''
										UpperCAmelCase         =			False
										for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																						if not prev_is_special:
																												out_string += " "
																						out_string += self.sp_model.decode(lowerCAmelCase   ) + token
																						UpperCAmelCase         =			True
																						UpperCAmelCase         =			[]
																else:
																						current_sub_tokens.append(lowerCAmelCase   )
																						UpperCAmelCase         =			False
										out_string += self.sp_model.decode(lowerCAmelCase   )
										return out_string.strip()




				def   a__(	self    :				Union[str, Any]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Optional[str] = None   )->  Tuple[str]:

										"""simple docstring"""




										if not os.path.isdir(lowerCAmelCase   ):
																logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
																return
										UpperCAmelCase         =			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   ) and os.path.isfile(self.vocab_file   ):
																copyfile(self.vocab_file   ,					lowerCAmelCase   )
										elif not os.path.isfile(self.vocab_file   ):
																with open(lowerCAmelCase   ,					'''wb'''   ) as fi:
																						UpperCAmelCase         =			self.sp_model.serialized_model_proto()
																						fi.write(lowerCAmelCase   )

										return (out_vocab_file,)




				def   a__(	self    :				List[str]   ,					lowerCAmelCase    :				List[int]   ,					lowerCAmelCase    :				Optional[List[int]] = None   ,					lowerCAmelCase    :				bool = False   )->  List[int]:

										"""simple docstring"""




										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=lowerCAmelCase   ,					token_ids_a=lowerCAmelCase   ,					already_has_special_tokens=lowerCAmelCase   )

										UpperCAmelCase         =			[1] * len(self.prefix_tokens   )
										UpperCAmelCase         =			[1] * len(self.suffix_tokens   )
										if token_ids_a is None:
																return prefix_ones + ([0] * len(lowerCAmelCase   )) + suffix_ones
										return prefix_ones + ([0] * len(lowerCAmelCase   )) + ([0] * len(lowerCAmelCase   )) + suffix_ones




				def   a__(	self    :				Tuple   ,					lowerCAmelCase    :				List[int]   ,					lowerCAmelCase    :				Optional[List[int]] = None   )->  List[int]:

										"""simple docstring"""




										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   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Optional[str]   ,					lowerCAmelCase    :				Optional[str]   ,					**lowerCAmelCase    :				Optional[int]   )->  Optional[Any]:

										"""simple docstring"""




										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         =			src_lang
										UpperCAmelCase         =			self(lowerCAmelCase   ,					add_special_tokens=lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )
										UpperCAmelCase         =			self.convert_tokens_to_ids(lowerCAmelCase   )
										UpperCAmelCase         =			tgt_lang_id
										return inputs




				def   a__(	self    :				str   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				str = "en_XX"   ,					lowerCAmelCase    :				Optional[List[str]] = None   ,					lowerCAmelCase    :				str = "ro_RO"   ,					**lowerCAmelCase    :				List[str]   ,					)->  BatchEncoding:

										"""simple docstring"""




										UpperCAmelCase         =			src_lang
										UpperCAmelCase         =			tgt_lang
										return super().prepare_seqaseq_batch(lowerCAmelCase   ,					lowerCAmelCase   ,					**lowerCAmelCase   )




				def   a__(	self    :				Optional[int]   )->  Union[str, Any]:

										"""simple docstring"""




										return self.set_src_lang_special_tokens(self.src_lang   )




				def   a__(	self    :				List[Any]   )->  int:

										"""simple docstring"""




										return self.set_tgt_lang_special_tokens(self.tgt_lang   )




				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				str   )->  None:

										"""simple docstring"""




										UpperCAmelCase         =			self.lang_code_to_id[src_lang]
										UpperCAmelCase         =			[self.cur_lang_code_id]
										UpperCAmelCase         =			[self.eos_token_id]





				def   a__(	self    :				int   ,					lowerCAmelCase    :				str   )->  None:

										"""simple docstring"""




										UpperCAmelCase         =			self.lang_code_to_id[tgt_lang]
										UpperCAmelCase         =			[self.cur_lang_code_id]
										UpperCAmelCase         =			[self.eos_token_id]





