





"""simple docstring"""


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

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


if is_flax_available():
  import os

  # The slow tests are often failing with OOM error on GPU
  # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
  # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
  lowerCamelCase__      =      "platform"

  import jax
  import jax.numpy as jnp

  from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_=None						,lowercase_=None	)    ->						Optional[Any]:




      """simple docstring"""

      if attention_mask is None:
            _UpperCamelCase					:		str  =				np.where(input_ids != config.pad_token_id						,1						,0	)
      return {
          "input_ids": input_ids,
          "attention_mask": attention_mask,
      }



@require_flax
class    __SCREAMING_SNAKE_CASE			:







  '''simple docstring'''


  def __init__(						self      :				Optional[Any]    ,      __a      :				Optional[int]    ,      __a      :				str=13    ,      __a      :				Dict=7    ,      __a      :				List[str]=True    ,      __a      :				List[str]=False    ,      __a      :				Any=99    ,      __a      :				Any=16    ,      __a      :				Dict=2    ,      __a      :				Any=4    ,      __a      :				Any=4    ,      __a      :				int="gelu"    ,      __a      :				List[str]=0.1    ,      __a      :				Union[str, Any]=0.1    ,      __a      :				Tuple=20    ,      __a      :				str=2    ,      __a      :				Tuple=1    ,      __a      :				Union[str, Any]=0    ,      __a      :				Optional[int]=16    ,      __a      :				Tuple=16    ,      __a      :				Optional[Any]=0.02    ,      )						-> str:
        _UpperCamelCase					:		Any  =				parent
        _UpperCamelCase					:		Tuple  =				batch_size
        _UpperCamelCase					:		Dict  =				seq_length
        _UpperCamelCase					:		List[Any]  =				is_training
        _UpperCamelCase					:		Tuple  =				use_labels
        _UpperCamelCase					:		Dict  =				vocab_size
        _UpperCamelCase					:		str  =				hidden_size
        _UpperCamelCase					:		Optional[int]  =				num_hidden_layers
        _UpperCamelCase					:		List[Any]  =				num_attention_heads
        _UpperCamelCase					:		Tuple  =				intermediate_size
        _UpperCamelCase					:		List[str]  =				hidden_act
        _UpperCamelCase					:		Optional[Any]  =				hidden_dropout_prob
        _UpperCamelCase					:		str  =				attention_probs_dropout_prob
        _UpperCamelCase					:		int  =				max_position_embeddings
        _UpperCamelCase					:		List[Any]  =				eos_token_id
        _UpperCamelCase					:		Union[str, Any]  =				pad_token_id
        _UpperCamelCase					:		List[str]  =				bos_token_id
        _UpperCamelCase					:		Optional[Any]  =				embed_dim
        _UpperCamelCase					:		Dict  =				word_embed_proj_dim
        _UpperCamelCase					:		Optional[int]  =				initializer_range
        _UpperCamelCase					:		str  =				False

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]		)						-> Optional[int]:
        _UpperCamelCase					:		int  =				np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,      self.vocab_size		)    ,      3    ,      self.vocab_size		)
        _UpperCamelCase					:		Union[str, Any]  =				np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,      dtype=np.intaa		))    ,      -1		)

        _UpperCamelCase					:		List[str]  =				OPTConfig(
            vocab_size=self.vocab_size    ,      hidden_size=self.hidden_size    ,      num_hidden_layers=self.num_hidden_layers    ,      num_attention_heads=self.num_attention_heads    ,      ffn_dim=self.intermediate_size    ,      dropout=self.hidden_dropout_prob    ,      attention_dropout=self.attention_probs_dropout_prob    ,      max_position_embeddings=self.max_position_embeddings    ,      eos_token_id=self.eos_token_id    ,      bos_token_id=self.bos_token_id    ,      pad_token_id=self.pad_token_id    ,      embed_dim=self.embed_dim    ,      is_encoder_decoder=__a    ,      word_embed_proj_dim=self.word_embed_proj_dim    ,      initializer_range=self.initializer_range    ,      use_cache=__a    ,      )
        _UpperCamelCase					:		Tuple  =				prepare_opt_inputs_dict(__a    ,      __a		)
        return config, inputs_dict

  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> Union[str, Any]:
        _UpperCamelCase,     _UpperCamelCase					:		List[str]  =				self.prepare_config_and_inputs()
        return config, inputs_dict

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]    ,      __a      :				Any    ,      __a      :				Union[str, Any]    ,      __a      :				Optional[int]		)						-> int:
        _UpperCamelCase					:		int  =				20
        _UpperCamelCase					:		Tuple  =				model_class_name(__a		)

        _UpperCamelCase					:		Union[str, Any]  =				inputs_dict["input_ids"]
        _UpperCamelCase					:		List[Any]  =				inputs_dict["attention_mask"]

        _UpperCamelCase					:		Dict  =				model.init_cache(input_ids.shape[0]    ,      __a		)
        _UpperCamelCase					:		List[Any]  =				jnp.ones((input_ids.shape[0], max_length)    ,      dtype="i4"		)

        _UpperCamelCase					:		Optional[Any]  =				jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1		)[None, :]    ,      (input_ids.shape[0], input_ids.shape[-1] - 1)    ,      )
        _UpperCamelCase					:		Union[str, Any]  =				model(
            input_ids[:, :-1]    ,      attention_mask=__a    ,      past_key_values=__a    ,      position_ids=__a    ,      )

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

        _UpperCamelCase					:		List[str]  =				model(__a		)

        _UpperCamelCase					:		Optional[Any]  =				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       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]    ,      __a      :				Any    ,      __a      :				Optional[int]    ,      __a      :				List[str]		)						-> List[str]:
        _UpperCamelCase					:		Any  =				20
        _UpperCamelCase					:		Union[str, Any]  =				model_class_name(__a		)

        _UpperCamelCase,     _UpperCamelCase					:		Union[str, Any]  =				(
            inputs_dict["input_ids"],
            inputs_dict["attention_mask"],
        )

        _UpperCamelCase					:		Dict  =				jnp.concatenate(
            [
                attention_mask,
                jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])		),
            ]    ,      axis=-1    ,      )

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

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

        _UpperCamelCase					:		Tuple  =				model(__a    ,      attention_mask=__a		)

        _UpperCamelCase					:		Tuple  =				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    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase						,     unittest.TestCase						,     _UpperCamelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Any		   =    (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
  SCREAMING_SNAKE_CASE__							:Dict		   =    () if is_flax_available() else ()

  def       __SCREAMING_SNAKE_CASE (						self      :				Any		)						-> List[str]:
        _UpperCamelCase					:		Optional[Any]  =				FlaxOPTModelTester(self		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]		)						-> Optional[Any]:
        _UpperCamelCase,     _UpperCamelCase					:		str  =				self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(__a    ,      __a    ,      __a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				str		)						-> Optional[int]:
        _UpperCamelCase,     _UpperCamelCase					:		Optional[int]  =				self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward_with_attn_mask(__a    ,      __a    ,      __a		)


  @slow
  def       __SCREAMING_SNAKE_CASE (						self      :				Dict		)						-> Dict:
        for model_class_name in self.all_model_classes:
              _UpperCamelCase					:		Tuple  =				model_class_name.from_pretrained("facebook/opt-125m"		)
              _UpperCamelCase					:		Optional[Any]  =				np.ones((1, 1)		) * model.config.eos_token_id
              _UpperCamelCase					:		Any  =				model(__a		)
              self.assertIsNotNone(__a		)





@require_sentencepiece
@require_flax
class    __SCREAMING_SNAKE_CASE			(  unittest.TestCase      ):







  '''simple docstring'''


  @slow
  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> int:
        _UpperCamelCase					:		Optional[int]  =				FlaxOPTModel.from_pretrained("facebook/opt-350m"		)
        _UpperCamelCase					:		List[Any]  =				jnp.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]		)
        _UpperCamelCase					:		List[str]  =				model(input_ids=__a		).last_hidden_state
        _UpperCamelCase					:		List[str]  =				(1, 11, 512)
        self.assertEqual(output.shape    ,      __a		)
        _UpperCamelCase					:		Tuple  =				jnp.array(
            [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]]		)
        self.assertTrue(jnp.allclose(output[:, :3, :3]    ,      __a    ,      atol=4e-2		)		)





@require_flax
@slow
class    __SCREAMING_SNAKE_CASE			(  unittest.TestCase      ):







  '''simple docstring'''


  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> str:
        super().setUp()
        _UpperCamelCase					:		Union[str, Any]  =				"facebook/opt-350m"


  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]		)						-> Dict:
        _UpperCamelCase					:		Tuple  =				FlaxOPTForCausalLM.from_pretrained(self.path_model		)
        _UpperCamelCase					:		Optional[int]  =				GPTaTokenizer.from_pretrained(self.path_model		)

        _UpperCamelCase					:		Tuple  =				[
            "Today is a beautiful day and I want to",
            "In the city of",
            "Paris is the capital of France and",
            "Computers and mobile phones have taken",
        ]
        # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
        _UpperCamelCase					:		List[Any]  =				tokenizer(__a    ,      return_tensors="jax"    ,      padding=__a    ,      add_special_tokens=__a		)
        _UpperCamelCase					:		Optional[int]  =				model(inputs.input_ids    ,      attention_mask=inputs.attention_mask		)[0].mean(axis=-1		)
        _UpperCamelCase					:		Union[str, Any]  =				jnp.array(
            [
                [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
                [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
                [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
                [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
            ]		)
        self.assertTrue(jnp.allclose(__a    ,      __a    ,      atol=4e-2		)		)

        _UpperCamelCase					:		int  =				jax.jit(__a		)
        _UpperCamelCase					:		str  =				model(inputs.input_ids    ,      attention_mask=inputs.attention_mask		)[0].mean(axis=-1		)
        self.assertTrue(jnp.allclose(__a    ,      __a    ,      atol=4e-2		)		)





@require_flax
@slow
class    __SCREAMING_SNAKE_CASE			(  unittest.TestCase      ):







  '''simple docstring'''


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

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]		)						-> Optional[Any]:
        _UpperCamelCase					:		Optional[int]  =				"facebook/opt-125m"

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

        _UpperCamelCase					:		List[str]  =				[]

        _UpperCamelCase					:		List[Any]  =				FlaxOPTForCausalLM.from_pretrained(__a		)
        _UpperCamelCase					:		Optional[int]  =				GPTaTokenizer.from_pretrained(__a		)

        for prompt in self.prompts:
              _UpperCamelCase					:		Optional[int]  =				tokenizer(__a    ,      return_tensors="jax"		).input_ids

              _UpperCamelCase					:		List[str]  =				model.generate(__a    ,      max_length=10		)
              _UpperCamelCase					:		int  =				generated_ids[0]

              _UpperCamelCase					:		Tuple  =				tokenizer.batch_decode(__a    ,      skip_special_tokens=__a		)
              predicted_outputs += generated_string

        self.assertListEqual(__a    ,      __a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Any		)						-> List[str]:
        _UpperCamelCase					:		Optional[int]  =				"facebook/opt-350m"

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

        _UpperCamelCase					:		List[Any]  =				[]
        _UpperCamelCase					:		Dict  =				FlaxOPTForCausalLM.from_pretrained(__a		)
        _UpperCamelCase					:		str  =				GPTaTokenizer.from_pretrained(__a		)

        for prompt in self.prompts:
              _UpperCamelCase					:		int  =				tokenizer(__a    ,      return_tensors="jax"		).input_ids

              _UpperCamelCase					:		Union[str, Any]  =				model.generate(__a    ,      max_length=10		)
              _UpperCamelCase					:		int  =				generated_ids[0]

              _UpperCamelCase					:		Dict  =				tokenizer.batch_decode(__a    ,      skip_special_tokens=__a		)
              predicted_outputs += generated_string

        self.assertListEqual(__a    ,      __a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> List[str]:
        _UpperCamelCase					:		str  =				"facebook/opt-125m"
        _UpperCamelCase					:		int  =				[
            "Today is a beautiful day and I want to thank",
            "In the city of Rome Canaver Canaver Canaver Canaver",
        ]
        _UpperCamelCase					:		int  =				FlaxOPTForCausalLM.from_pretrained(__a		)
        _UpperCamelCase					:		int  =				GPTaTokenizer.from_pretrained(__a		)
        _UpperCamelCase					:		Union[str, Any]  =				tokenizer(
            [
                "Today is a beautiful day and I want to",
                "In the city of",
            ]    ,      return_tensors="jax"    ,      padding=__a    ,      )

        _UpperCamelCase					:		str  =				jax.jit(model.generate		)

        _UpperCamelCase					:		List[Any]  =				jit_generate(inputs["input_ids"]    ,      attention_mask=inputs["attention_mask"]		).sequences

        _UpperCamelCase					:		List[str]  =				tokenizer.batch_decode(__a    ,      skip_special_tokens=__a		)

        self.assertIsNotNone(__a    ,      __a		)


  def       __SCREAMING_SNAKE_CASE (						self      :				Dict		)						-> Union[str, Any]:
        _UpperCamelCase					:		List[Any]  =				"facebook/opt-350m"

        _UpperCamelCase					:		Dict  =				GPTaTokenizer.from_pretrained(__a		)
        _UpperCamelCase					:		Tuple  =				FlaxOPTForCausalLM.from_pretrained(__a		)

        _UpperCamelCase					:		Optional[Any]  =				"left"

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

        _UpperCamelCase					:		Dict  =				tokenizer(__a    ,      return_tensors="jax"    ,      padding=__a		)
        _UpperCamelCase					:		Union[str, Any]  =				inputs["input_ids"]

        _UpperCamelCase					:		Optional[int]  =				model.generate(input_ids=__a    ,      attention_mask=inputs["attention_mask"]    ,      trace=__a		)

        _UpperCamelCase					:		str  =				tokenizer(sentences[0]    ,      return_tensors="jax"		).input_ids
        _UpperCamelCase					:		str  =				model.generate(input_ids=__a		)

        _UpperCamelCase					:		List[str]  =				inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
        _UpperCamelCase					:		Any  =				tokenizer(sentences[1]    ,      return_tensors="jax"		).input_ids
        _UpperCamelCase					:		Dict  =				model.generate(input_ids=__a    ,      max_length=model.config.max_length - num_paddings		)

        _UpperCamelCase					:		str  =				tokenizer.batch_decode(outputs[0]    ,      skip_special_tokens=__a		)
        _UpperCamelCase					:		Optional[int]  =				tokenizer.decode(output_non_padded[0][0]    ,      skip_special_tokens=__a		)
        _UpperCamelCase					:		Union[str, Any]  =				tokenizer.decode(output_padded[0][0]    ,      skip_special_tokens=__a		)

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







"""simple docstring"""


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_mbartaa import MBartaaTokenizer
else:
  lowerCamelCase__      =      None


lowerCamelCase__      =      logging.get_logger(__name__)

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

lowerCamelCase__      =      {
    "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"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"
        ),
    },
}

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

# fmt: off
lowerCamelCase__      =      ["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    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Tuple		   =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__							:List[Any]		   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__							:Union[str, Any]		   =    PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__							:int		   =    ["input_ids", "attention_mask"]
  SCREAMING_SNAKE_CASE__							:Dict		   =    MBartaaTokenizer

  SCREAMING_SNAKE_CASE__							:List[int]		   =    []
  SCREAMING_SNAKE_CASE__							:List[int]		   =    []

  def __init__(						self      :				str    ,      __a      :				int=None    ,      __a      :				List[str]=None    ,      __a      :				Optional[Any]=None    ,      __a      :				List[str]=None    ,      __a      :				Tuple="</s>"    ,      __a      :				Union[str, Any]="</s>"    ,      __a      :				str="<s>"    ,      __a      :				Any="<unk>"    ,      __a      :				List[str]="<pad>"    ,      __a      :				Optional[Any]="<mask>"    ,      **__a      :				Any    ,      )						-> Union[str, Any]:
        # Mask token behave like a normal word, i.e. include the space before it
        _UpperCamelCase					:		Dict  =				AddedToken(__a    ,      lstrip=__a    ,      rstrip=__a		) if isinstance(__a    ,      __a		) else mask_token

        _UpperCamelCase					:		str  =				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__(
            __a    ,      src_lang=__a    ,      tgt_lang=__a    ,      tokenizer_file=__a    ,      eos_token=__a    ,      sep_token=__a    ,      cls_token=__a    ,      unk_token=__a    ,      pad_token=__a    ,      mask_token=__a    ,      **__a    ,      )

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

        _UpperCamelCase					:		int  =				{
            lang_code: self.convert_tokens_to_ids(__a		) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        _UpperCamelCase					:		List[str]  =				src_lang if src_lang is not None else "en_XX"
        _UpperCamelCase					:		Dict  =				tgt_lang
        _UpperCamelCase					:		int  =				self.lang_code_to_id[self._src_lang]
        self.set_src_lang_special_tokens(self._src_lang		)

  @property
  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]		)						-> str:
        return self._src_lang

  @src_lang.setter
  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]    ,      __a      :				str		)						-> None:
        _UpperCamelCase					:		int  =				new_src_lang
        self.set_src_lang_special_tokens(self._src_lang		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]    ,      __a      :				List[int]    ,      __a      :				Optional[List[int]] = None		)						-> List[int]:
        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       __SCREAMING_SNAKE_CASE (						self      :				Any    ,      __a      :				List[str]    ,      __a      :				str = "en_XX"    ,      __a      :				Optional[List[str]] = None    ,      __a      :				str = "ro_RO"    ,      **__a      :				Tuple    ,      )						-> BatchEncoding:
        _UpperCamelCase					:		List[Any]  =				src_lang
        _UpperCamelCase					:		Union[str, Any]  =				tgt_lang
        return super().prepare_seqaseq_batch(__a    ,      __a    ,      **__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> int:
        return self.set_src_lang_special_tokens(self.src_lang		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple		)						-> Tuple:
        return self.set_tgt_lang_special_tokens(self.tgt_lang		)

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]    ,      __a      :				str		)						-> None:
        _UpperCamelCase					:		List[Any]  =				self.convert_tokens_to_ids(__a		)
        _UpperCamelCase					:		Union[str, Any]  =				[self.cur_lang_code_id]
        _UpperCamelCase					:		List[Any]  =				[self.eos_token_id]

        _UpperCamelCase					:		Union[str, Any]  =				self.convert_ids_to_tokens(self.prefix_tokens		)
        _UpperCamelCase					:		Union[str, Any]  =				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       __SCREAMING_SNAKE_CASE (						self      :				Dict    ,      __a      :				str		)						-> None:
        _UpperCamelCase					:		Tuple  =				self.convert_tokens_to_ids(__a		)
        _UpperCamelCase					:		Union[str, Any]  =				[self.cur_lang_code_id]
        _UpperCamelCase					:		Optional[Any]  =				[self.eos_token_id]

        _UpperCamelCase					:		int  =				self.convert_ids_to_tokens(self.prefix_tokens		)
        _UpperCamelCase					:		Any  =				self.convert_ids_to_tokens(self.suffix_tokens		)

        _UpperCamelCase					:		Tuple  =				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       __SCREAMING_SNAKE_CASE (						self      :				Dict    ,      __a      :				Optional[int]    ,      __a      :				str    ,      __a      :				Optional[str]    ,      __a      :				Optional[str]    ,      **__a      :				int		)						-> List[str]:
        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					:		List[str]  =				src_lang
        _UpperCamelCase					:		List[str]  =				self(__a    ,      add_special_tokens=__a    ,      return_tensors=__a    ,      **__a		)
        _UpperCamelCase					:		str  =				self.convert_tokens_to_ids(__a		)
        _UpperCamelCase					:		str  =				tgt_lang_id
        return inputs


  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]    ,      __a      :				str    ,      __a      :				Optional[str] = None		)						-> Tuple[str]:
        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(__a		):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
              return
        _UpperCamelCase					:		int  =				os.path.join(
            __a    ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

        return (out_vocab_file,)
