

import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax
					import jax.numpy as jnp

					from transformers.modeling_flax_pytorch_utils import (
					    convert_pytorch_state_dict_to_flax,
					    load_flax_weights_in_pytorch_model,
					)
					from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
					import torch





class       snake_case_  :







			'''simple docstring'''

			def __init__(	self					:							Optional[int]				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							Union[str, Any]=1_4				,  _UpperCamelCase					:							int=7				,  _UpperCamelCase					:							Any=True				,  _UpperCamelCase					:							List[Any]=True				,  _UpperCamelCase					:							Any=False				,  _UpperCamelCase					:							int=True				,  _UpperCamelCase					:							List[str]=9_9				,  _UpperCamelCase					:							Dict=3_2				,  _UpperCamelCase					:							Dict=4				,  _UpperCamelCase					:							Optional[int]=4				,  _UpperCamelCase					:							List[Any]=4				,  _UpperCamelCase					:							Dict=3_7				,  _UpperCamelCase					:							int="gelu"				,  _UpperCamelCase					:							str=0.1				,  _UpperCamelCase					:							Any=0.1				,  _UpperCamelCase					:							Union[str, Any]=5_1_2				,  _UpperCamelCase					:							Optional[int]=0.02				,  ) ->List[Any]:
									snake_case_  =      parent
									snake_case_  =      batch_size
									snake_case_  =      seq_length
									snake_case_  =      is_training
									snake_case_  =      use_input_mask
									snake_case_  =      use_token_type_ids
									snake_case_  =      use_labels
									snake_case_  =      vocab_size
									snake_case_  =      hidden_size
									snake_case_  =      rotary_dim
									snake_case_  =      num_hidden_layers
									snake_case_  =      num_attention_heads
									snake_case_  =      intermediate_size
									snake_case_  =      hidden_act
									snake_case_  =      hidden_dropout_prob
									snake_case_  =      attention_probs_dropout_prob
									snake_case_  =      max_position_embeddings
									snake_case_  =      initializer_range
									snake_case_  =      None
									snake_case_  =      vocab_size - 1
									snake_case_  =      vocab_size - 1
									snake_case_  =      vocab_size - 1







			def 							snake_case__(	self					:							Optional[int]				) ->List[Any]:
									snake_case_  =      ids_tensor([self.batch_size, self.seq_length]				,  self.vocab_size				)

									snake_case_  =      None
									if self.use_input_mask:
															snake_case_  =      random_attention_mask([self.batch_size, self.seq_length]				)

									snake_case_  =      GPTJConfig(
									    vocab_size=self.vocab_size				,  n_embd=self.hidden_size				,  n_layer=self.num_hidden_layers				,  n_head=self.num_attention_heads				,  n_positions=self.max_position_embeddings				,  use_cache=_UpperCamelCase				,  bos_token_id=self.bos_token_id				,  eos_token_id=self.eos_token_id				,  pad_token_id=self.pad_token_id				,  rotary_dim=self.rotary_dim				,  )

									return (config, input_ids, input_mask)







			def 							snake_case__(	self					:							Dict				) ->Union[str, Any]:
									snake_case_  =      self.prepare_config_and_inputs()
									snake_case_,       snake_case_,       snake_case_  =      config_and_inputs
									snake_case_  =      {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
									return config, inputs_dict







			def 							snake_case__(	self					:							Optional[int]				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							Tuple				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							Union[str, Any]				) ->Union[str, Any]:
									snake_case_  =      2_0
									snake_case_  =      model_class_name(_UpperCamelCase				)

									snake_case_  =      model.init_cache(input_ids.shape[0]				,  _UpperCamelCase				)
									snake_case_  =      jnp.ones((input_ids.shape[0], max_decoder_length)				,  dtype='''i4'''				)

									snake_case_  =      jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1				)[None, :]				,  (input_ids.shape[0], input_ids.shape[-1] - 1)				)
									snake_case_  =      model(
									    input_ids[:, :-1]				,  attention_mask=_UpperCamelCase				,  past_key_values=_UpperCamelCase				,  position_ids=_UpperCamelCase				,  )

									snake_case_  =      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,  dtype='''i4'''				)
									snake_case_  =      model(
									    input_ids[:, -1:]				,  attention_mask=_UpperCamelCase				,  past_key_values=outputs_cache.past_key_values				,  position_ids=_UpperCamelCase				,  )

									snake_case_  =      model(_UpperCamelCase				)

									snake_case_  =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
									self.parent.assertTrue(diff < 1e-3				,  msg=f'''Max diff is {diff}'''				)







			def 							snake_case__(	self					:							str				,  _UpperCamelCase					:							Tuple				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							List[str]				) ->List[Any]:
									snake_case_  =      2_0
									snake_case_  =      model_class_name(_UpperCamelCase				)

									snake_case_  =      jnp.concatenate(
									    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])				)]				,  axis=-1				,  )

									snake_case_  =      model.init_cache(input_ids.shape[0]				,  _UpperCamelCase				)
									snake_case_  =      jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1				)[None, :]				,  (input_ids.shape[0], input_ids.shape[-1] - 1)				)

									snake_case_  =      model(
									    input_ids[:, :-1]				,  attention_mask=_UpperCamelCase				,  past_key_values=_UpperCamelCase				,  position_ids=_UpperCamelCase				,  )
									snake_case_  =      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,  dtype='''i4'''				)
									snake_case_  =      model(
									    input_ids[:, -1:]				,  past_key_values=outputs_cache.past_key_values				,  attention_mask=_UpperCamelCase				,  position_ids=_UpperCamelCase				,  )

									snake_case_  =      model(_UpperCamelCase				,  attention_mask=_UpperCamelCase				)

									snake_case_  =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
									self.parent.assertTrue(diff < 1e-3				,  msg=f'''Max diff is {diff}'''				)






@require_flax
class       snake_case_  (						__A      ,			__A      ,			unittest.TestCase   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Optional[int]		     =							(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
			SCREAMING_SNAKE_CASE	:  Optional[Any]		     =							(FlaxGPTJForCausalLM,) if is_flax_available() else ()







			def 							snake_case__(	self					:							List[Any]				) ->Union[str, Any]:
									snake_case_  =      FlaxGPTJModelTester(self				)







			def 							snake_case__(	self					:							int				) ->str:
									for model_class_name in self.all_model_classes:
															snake_case_,       snake_case_,       snake_case_  =      self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward(_UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				)







			def 							snake_case__(	self					:							List[Any]				) ->str:
									for model_class_name in self.all_model_classes:
															snake_case_,       snake_case_,       snake_case_  =      self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward_with_attn_mask(
															    _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				)







			@tooslow
			def 							snake_case__(	self					:							str				) ->Dict:
									snake_case_  =      GPTaTokenizer.from_pretrained('''gpt2'''				,  pad_token='''<|endoftext|>'''				,  padding_side='''left'''				)
									snake_case_  =      tokenizer(['''Hello this is a long string''', '''Hey''']				,  return_tensors='''np'''				,  padding=_UpperCamelCase				,  truncation=_UpperCamelCase				)

									snake_case_  =      FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''				)
									snake_case_  =      False
									snake_case_  =      model.config.eos_token_id

									snake_case_  =      jax.jit(model.generate				)

									snake_case_  =      jit_generate(
									    inputs['''input_ids''']				,  attention_mask=inputs['''attention_mask''']				,  pad_token_id=tokenizer.pad_token_id				).sequences

									snake_case_  =      tokenizer.batch_decode(_UpperCamelCase				,  skip_special_tokens=_UpperCamelCase				)

									snake_case_  =      [
									    '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
									    '''Hey, I\'m a little late to the party. I\'m going to''',
									]

									self.assertListEqual(_UpperCamelCase				,  _UpperCamelCase				)







			@is_pt_flax_cross_test
			def 							snake_case__(	self					:							List[str]				) ->List[str]:
									snake_case_,       snake_case_  =      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__				):
																					# prepare inputs
																					snake_case_  =      self._prepare_for_class(_UpperCamelCase				,  _UpperCamelCase				)
																					snake_case_  =      {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

																					# load corresponding PyTorch class
																					snake_case_  =      model_class.__name__[4:]  # Skip the "Flax" at the beginning
																					snake_case_  =      getattr(_UpperCamelCase				,  _UpperCamelCase				)

																					snake_case_,       snake_case_  =      pt_inputs['''input_ids'''].shape
																					snake_case_  =      np.random.randint(0				,  seq_length - 1				,  size=(batch_size,)				)
																					for batch_idx, start_index in enumerate(_UpperCamelCase				):
																											snake_case_  =      0
																											snake_case_  =      1
																											snake_case_  =      0
																											snake_case_  =      1
																					snake_case_  =      pt_model_class(_UpperCamelCase				).eval()
																					snake_case_  =      model_class(_UpperCamelCase				,  dtype=jnp.floataa				)

																					snake_case_  =      convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,  _UpperCamelCase				)
																					snake_case_  =      fx_state

																					with torch.no_grad():
																											snake_case_  =      pt_model(**_UpperCamelCase				).to_tuple()

																					snake_case_  =      fx_model(**_UpperCamelCase				).to_tuple()
																					self.assertEqual(len(_UpperCamelCase				)				,  len(_UpperCamelCase				)				,  '''Output lengths differ between Flax and PyTorch'''				)
																					for fx_output, pt_output in zip(_UpperCamelCase				,  _UpperCamelCase				):
																											self.assert_almost_equals(fx_output[:, -1]				,  pt_output[:, -1].numpy()				,  4e-2				)

																					with tempfile.TemporaryDirectory() as tmpdirname:
																											pt_model.save_pretrained(_UpperCamelCase				)
																											snake_case_  =      model_class.from_pretrained(_UpperCamelCase				,  from_pt=_UpperCamelCase				)

																					snake_case_  =      fx_model_loaded(**_UpperCamelCase				).to_tuple()
																					self.assertEqual(
																					    len(_UpperCamelCase				)				,  len(_UpperCamelCase				)				,  '''Output lengths differ between Flax and PyTorch'''				)
																					for fx_output_loaded, pt_output in zip(_UpperCamelCase				,  _UpperCamelCase				):
																											self.assert_almost_equals(fx_output_loaded[:, -1]				,  pt_output[:, -1].numpy()				,  4e-2				)







			@is_pt_flax_cross_test
			def 							snake_case__(	self					:							Optional[int]				) ->Dict:
									snake_case_,       snake_case_  =      self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__				):
																					# prepare inputs
																					snake_case_  =      self._prepare_for_class(_UpperCamelCase				,  _UpperCamelCase				)
																					snake_case_  =      {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

																					# load corresponding PyTorch class
																					snake_case_  =      model_class.__name__[4:]  # Skip the "Flax" at the beginning
																					snake_case_  =      getattr(_UpperCamelCase				,  _UpperCamelCase				)

																					snake_case_  =      pt_model_class(_UpperCamelCase				).eval()
																					snake_case_  =      model_class(_UpperCamelCase				,  dtype=jnp.floataa				)

																					snake_case_  =      load_flax_weights_in_pytorch_model(_UpperCamelCase				,  fx_model.params				)
																					snake_case_,       snake_case_  =      pt_inputs['''input_ids'''].shape
																					snake_case_  =      np.random.randint(0				,  seq_length - 1				,  size=(batch_size,)				)
																					for batch_idx, start_index in enumerate(_UpperCamelCase				):
																											snake_case_  =      0
																											snake_case_  =      1
																											snake_case_  =      0
																											snake_case_  =      1

																					# make sure weights are tied in PyTorch
																					pt_model.tie_weights()

																					with torch.no_grad():
																											snake_case_  =      pt_model(**_UpperCamelCase				).to_tuple()

																					snake_case_  =      fx_model(**_UpperCamelCase				).to_tuple()
																					self.assertEqual(len(_UpperCamelCase				)				,  len(_UpperCamelCase				)				,  '''Output lengths differ between Flax and PyTorch'''				)
																					for fx_output, pt_output in zip(_UpperCamelCase				,  _UpperCamelCase				):
																											self.assert_almost_equals(fx_output[:, -1]				,  pt_output[:, -1].numpy()				,  4e-2				)

																					with tempfile.TemporaryDirectory() as tmpdirname:
																											fx_model.save_pretrained(_UpperCamelCase				)
																											snake_case_  =      pt_model_class.from_pretrained(_UpperCamelCase				,  from_flax=_UpperCamelCase				)

																					with torch.no_grad():
																											snake_case_  =      pt_model_loaded(**_UpperCamelCase				).to_tuple()

																					self.assertEqual(
																					    len(_UpperCamelCase				)				,  len(_UpperCamelCase				)				,  '''Output lengths differ between Flax and PyTorch'''				)
																					for fx_output, pt_output in zip(_UpperCamelCase				,  _UpperCamelCase				):
																											self.assert_almost_equals(fx_output[:, -1]				,  pt_output[:, -1].numpy()				,  4e-2				)







			@tooslow
			def 							snake_case__(	self					:							Optional[int]				) ->int:
									for model_class_name in self.all_model_classes:
															snake_case_  =      model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''				)
															snake_case_  =      model(np.ones((1, 1)				)				)
															self.assertIsNotNone(_UpperCamelCase				)


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,)