




'''simple docstring'''





from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
  from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import MobileViTImageProcessor




class     UpperCamelCase					(  __UpperCAmelCase					):



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       Dict       					=			self.config_class(**self.inputs_dict   )
      self.parent.assertTrue(hasattr(lowerCamelCase__		,    """hidden_sizes"""   )   )
      self.parent.assertTrue(hasattr(lowerCamelCase__		,    """neck_hidden_sizes"""   )   )
      self.parent.assertTrue(hasattr(lowerCamelCase__		,    """num_attention_heads"""   )   )






class     UpperCamelCase					:



  def __init__( self		,    __lowerCAmelCase		,    __lowerCAmelCase=13		,    __lowerCAmelCase=32		,    __lowerCAmelCase=2		,    __lowerCAmelCase=3		,    __lowerCAmelCase=640		,    __lowerCAmelCase=4		,    __lowerCAmelCase="silu"		,    __lowerCAmelCase=3		,    __lowerCAmelCase=32		,    __lowerCAmelCase=0.1		,    __lowerCAmelCase=0.1		,    __lowerCAmelCase=0.1		,    __lowerCAmelCase=0.02		,    __lowerCAmelCase=True		,    __lowerCAmelCase=True		,    __lowerCAmelCase=10		,    __lowerCAmelCase=None		,    ):







      '''simple docstring'''


      _a							:       List[str]       					=			parent
      _a							:       Tuple       					=			batch_size
      _a							:       Tuple       					=			image_size
      _a							:       Any       					=			patch_size
      _a							:       List[str]       					=			num_channels
      _a							:       str       					=			last_hidden_size
      _a							:       int       					=			num_attention_heads
      _a							:       Optional[int]       					=			hidden_act
      _a							:       List[str]       					=			conv_kernel_size
      _a							:       Optional[Any]       					=			output_stride
      _a							:       List[Any]       					=			hidden_dropout_prob
      _a							:       Optional[int]       					=			attention_probs_dropout_prob
      _a							:       str       					=			classifier_dropout_prob
      _a							:       Optional[Any]       					=			use_labels
      _a							:       Optional[Any]       					=			is_training
      _a							:       Optional[Any]       					=			num_labels
      _a							:       str       					=			initializer_range
      _a							:       Tuple       					=			scope



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       List[str]       					=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

      _a							:       Union[str, Any]       					=			None
      _a							:       Any       					=			None
      if self.use_labels:
          _a							:       str       					=			ids_tensor([self.batch_size]		,    self.num_labels   )
          _a							:       Any       					=			ids_tensor([self.batch_size, self.image_size, self.image_size]		,    self.num_labels   )

      _a							:       Tuple       					=			self.get_config()

      return config, pixel_values, labels, pixel_labels



  def 						a_       ( self   ):







      '''simple docstring'''


      return MobileViTConfig(
          image_size=self.image_size		,    patch_size=self.patch_size		,    num_channels=self.num_channels		,    num_attention_heads=self.num_attention_heads		,    hidden_act=self.hidden_act		,    conv_kernel_size=self.conv_kernel_size		,    output_stride=self.output_stride		,    hidden_dropout_prob=self.hidden_dropout_prob		,    attention_probs_dropout_prob=self.attention_probs_dropout_prob		,    classifier_dropout_prob=self.classifier_dropout_prob		,    initializer_range=self.initializer_range		,    )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       Optional[int]       					=			TFMobileViTModel(config=lowerCamelCase__   )
      _a							:       List[str]       					=			model(lowerCamelCase__		,    training=lowerCamelCase__   )
      _a							:       Tuple       					=			self.image_size // self.output_stride
      self.parent.assertEqual(
          result.last_hidden_state.shape		,    (self.batch_size, self.last_hidden_size, expected_height, expected_width)   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       Optional[Any]       					=			self.num_labels
      _a							:       str       					=			TFMobileViTForImageClassification(lowerCamelCase__   )
      _a							:       Optional[Any]       					=			model(lowerCamelCase__		,    labels=lowerCamelCase__		,    training=lowerCamelCase__   )
      self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_labels)   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       Any       					=			self.num_labels
      _a							:       Any       					=			TFMobileViTForSemanticSegmentation(lowerCamelCase__   )
      _a							:       Dict       					=			self.image_size // self.output_stride

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

      _a							:       Optional[int]       					=			model(lowerCamelCase__		,    labels=lowerCamelCase__		,    training=lowerCamelCase__   )
      self.parent.assertEqual(
          result.logits.shape		,    (self.batch_size, self.num_labels, expected_height, expected_width)   )



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       str       					=			self.prepare_config_and_inputs()
      _a					,   _a					,   _a					,   _a							:       str       					=			config_and_inputs
      _a							:       Union[str, Any]       					=			{"""pixel_values""": pixel_values}
      return config, inputs_dict






@require_tf
class     UpperCamelCase					(  __UpperCAmelCase			,		__UpperCAmelCase			,		unittest.TestCase					):
  a    :Dict      					=				(
      (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
      if is_tf_available()
      else ()
  )
  a    :Optional[int]      					=				(
      {"""feature-extraction""": TFMobileViTModel, """image-classification""": TFMobileViTForImageClassification}
      if is_tf_available()
      else {}
  )

  a    :List[str]      					=				False
  a    :List[Any]      					=				False
  a    :Union[str, Any]      					=				False
  a    :Optional[int]      					=				False
  a    :Union[str, Any]      					=				False



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       Dict       					=			TFMobileViTModelTester(self   )
      _a							:       Any       					=			TFMobileViTConfigTester(self		,    config_class=lowerCamelCase__		,    has_text_modality=lowerCamelCase__   )



  def 						a_       ( self   ):







      '''simple docstring'''


      self.config_tester.run_common_tests()



  @unittest.skip(reason="""MobileViT does not use inputs_embeds"""   )
  def 						a_       ( self   ):







      '''simple docstring'''


      pass



  @unittest.skip(reason="""MobileViT does not support input and output embeddings"""   )
  def 						a_       ( self   ):







      '''simple docstring'''


      pass



  @unittest.skip(reason="""MobileViT does not output attentions"""   )
  def 						a_       ( self   ):







      '''simple docstring'''


      pass



  def 						a_       ( self   ):







      '''simple docstring'''


      _a					,   _a							:       List[Any]       					=			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          _a							:       List[str]       					=			model_class(lowerCamelCase__   )
          _a							:       Optional[Any]       					=			inspect.signature(model.call   )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          _a							:       List[Any]       					=			[*signature.parameters.keys()]

          _a							:       Optional[int]       					=			["""pixel_values"""]
          self.assertListEqual(arg_names[:1]		,    lowerCamelCase__   )



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       Dict       					=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase__   )



  def 						a_       ( self   ):







      '''simple docstring'''


      def check_hidden_states_output(__lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   ):
          _a							:       Tuple       					=			model_class(lowerCamelCase__   )

          _a							:       Optional[Any]       					=			model(**self._prepare_for_class(lowerCamelCase__		,    lowerCamelCase__   )   )

          _a							:       Any       					=			outputs.hidden_states

          _a							:       Optional[int]       					=			5
          self.assertEqual(len(lowerCamelCase__   )		,    lowerCamelCase__   )

          # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
          # with the width and height being successively divided by 2.
          _a							:       Tuple       					=			2
          for i in range(len(lowerCamelCase__   )   ):
              self.assertListEqual(
                  list(hidden_states[i].shape[-2:]   )		,    [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]		,    )
              divisor *= 2

          self.assertEqual(self.model_tester.output_stride		,    divisor // 2   )

      _a					,   _a							:       Dict       					=			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          _a							:       List[Any]       					=			True
          check_hidden_states_output(lowerCamelCase__		,    lowerCamelCase__		,    lowerCamelCase__   )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          _a							:       Optional[int]       					=			True

          check_hidden_states_output(lowerCamelCase__		,    lowerCamelCase__		,    lowerCamelCase__   )



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       List[str]       					=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__   )



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       int       					=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase__   )



  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""   )   ) == 0		,    reason="""TF does not support backprop for grouped convolutions on CPU."""		,    )
  def 						a_       ( self   ):







      '''simple docstring'''


      super().test_dataset_conversion()



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase=2e-1		,    __lowerCAmelCase=2e-1   ):







      '''simple docstring'''


      self.assertTrue(np.allclose(lowerCamelCase__		,    lowerCamelCase__		,    atol=lowerCamelCase__		,    rtol=lowerCamelCase__   )   )



  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""   )   ) == 0		,    reason="""TF does not support backprop for grouped convolutions on CPU."""		,    )
  @slow
  def 						a_       ( self   ):







      '''simple docstring'''


      _a					,   _a							:       Dict       					=			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
          if model_class.__name__ != "TFMobileViTModel":
              _a							:       Union[str, Any]       					=			model_class(lowerCamelCase__   )
              if getattr(lowerCamelCase__		,    """hf_compute_loss"""		,    lowerCamelCase__   ):
                  super().test_keras_fit()



  def 						a_       ( self   ):







      '''simple docstring'''


      _a					,   _a							:       Tuple       					=			self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
          # set an ignore index to correctly test the masked loss used in
          # `TFMobileViTForSemanticSegmentation`.
          if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
              _a							:       Union[str, Any]       					=			5

          _a							:       str       					=			model_class(lowerCamelCase__   )
          if getattr(lowerCamelCase__		,    """hf_compute_loss"""		,    lowerCamelCase__   ):
              # The number of elements in the loss should be the same as the number of elements in the label
              _a							:       Union[str, Any]       					=			self._prepare_for_class(inputs_dict.copy()		,    lowerCamelCase__		,    return_labels=lowerCamelCase__   )
              _a							:       List[Any]       					=			prepared_for_class[
                  sorted(prepared_for_class.keys() - inputs_dict.keys()		,    reverse=lowerCamelCase__   )[0]
              ]
              _a							:       int       					=			added_label.shape.as_list()[:1]

              # Test that model correctly compute the loss with kwargs
              _a							:       str       					=			self._prepare_for_class(inputs_dict.copy()		,    lowerCamelCase__		,    return_labels=lowerCamelCase__   )
              _a							:       Union[str, Any]       					=			{"""input_ids""", """pixel_values""", """input_features"""}
              _a							:       Dict       					=			possible_input_names.intersection(set(lowerCamelCase__   )   ).pop()
              _a							:       Tuple       					=			prepared_for_class.pop(lowerCamelCase__   )

              _a							:       int       					=			model(lowerCamelCase__		,    **lowerCamelCase__   )[0]
              self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]   )

              # Test that model correctly compute the loss when we mask some positions
              _a							:       List[Any]       					=			self._prepare_for_class(inputs_dict.copy()		,    lowerCamelCase__		,    return_labels=lowerCamelCase__   )
              _a							:       List[Any]       					=			{"""input_ids""", """pixel_values""", """input_features"""}
              _a							:       int       					=			possible_input_names.intersection(set(lowerCamelCase__   )   ).pop()
              _a							:       Union[str, Any]       					=			prepared_for_class.pop(lowerCamelCase__   )
              if "labels" in prepared_for_class:
                  _a							:       int       					=			prepared_for_class["""labels"""].numpy()
                  if len(labels.shape   ) > 1 and labels.shape[1] != 1:
                      # labels[0] = -100
                      _a							:       Any       					=			tf.convert_to_tensor(lowerCamelCase__   )
                      _a							:       int       					=			model(lowerCamelCase__		,    **lowerCamelCase__   )[0]
                      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]   )
                      self.assertTrue(not np.any(np.isnan(loss.numpy()   )   )   )

                # Test that model correctly compute the loss with a dict
              _a							:       Tuple       					=			self._prepare_for_class(inputs_dict.copy()		,    lowerCamelCase__		,    return_labels=lowerCamelCase__   )
              _a							:       Union[str, Any]       					=			model(lowerCamelCase__   )[0]
              self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]   )

              # Test that model correctly compute the loss with a tuple
              _a							:       List[Any]       					=			self._prepare_for_class(inputs_dict.copy()		,    lowerCamelCase__		,    return_labels=lowerCamelCase__   )

              # Get keys that were added with the _prepare_for_class function
              _a							:       Tuple       					=			prepared_for_class.keys() - inputs_dict.keys()
              _a							:       Tuple       					=			inspect.signature(model.call   ).parameters
              _a							:       int       					=			list(signature.keys()   )

              # Create a dictionary holding the location of the tensors in the tuple
              _a							:       Dict       					=			{0: input_name}
              for label_key in label_keys:
                  _a							:       Dict       					=			signature_names.index(lowerCamelCase__   )
                  _a							:       str       					=			label_key
              _a							:       Dict       					=			sorted(tuple_index_mapping.items()   )
              # Initialize a list with their default values, update the values and convert to a tuple
              _a							:       List[str]       					=			[]

              for name in signature_names:
                  if name != "kwargs":
                      list_input.append(signature[name].default   )

              for index, value in sorted_tuple_index_mapping:
                  _a							:       Dict       					=			prepared_for_class[value]

              _a							:       List[str]       					=			tuple(lowerCamelCase__   )

              # Send to model
              _a							:       Tuple       					=			model(tuple_input[:-1]   )[0]

              self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]   )



  @slow
  def 						a_       ( self   ):







      '''simple docstring'''


      for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _a							:       Any       					=			TFMobileViTModel.from_pretrained(lowerCamelCase__   )
          self.assertIsNotNone(lowerCamelCase__   )






def UpperCAmelCase						(					):
    _a							:       Dict       					=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
    return image





@require_tf
class     UpperCamelCase					(  unittest.TestCase					):



  @slow
  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       Optional[Any]       					=			TFMobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""   )

      _a							:       Optional[int]       					=			MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""   )
      _a							:       Optional[Any]       					=			prepare_img()
      _a							:       Optional[Any]       					=			image_processor(images=lowerCamelCase__		,    return_tensors="""tf"""   )

      # forward pass
      _a							:       Optional[Any]       					=			model(**lowerCamelCase__		,    training=lowerCamelCase__   )

      # verify the logits
      _a							:       List[Any]       					=			tf.TensorShape((1, 1000)   )
      self.assertEqual(outputs.logits.shape		,    lowerCamelCase__   )

      _a							:       str       					=			tf.constant([-1.9364, -1.2327, -0.4653]   )

      tf.debugging.assert_near(outputs.logits[0, :3]		,    lowerCamelCase__		,    atol=1e-4		,    rtol=1e-04   )



  @slow
  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       int       					=			TFMobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""   )

      _a							:       Any       					=			MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""   )

      _a							:       List[Any]       					=			prepare_img()
      _a							:       Any       					=			image_processor(images=lowerCamelCase__		,    return_tensors="""tf"""   )

      # forward pass
      _a							:       int       					=			model(inputs.pixel_values		,    training=lowerCamelCase__   )
      _a							:       Any       					=			outputs.logits

      # verify the logits
      _a							:       Dict       					=			tf.TensorShape((1, 21, 32, 32)   )
      self.assertEqual(logits.shape		,    lowerCamelCase__   )

      _a							:       Union[str, Any]       					=			tf.constant(
          [
              [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
              [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
              [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
          ]   )

      tf.debugging.assert_near(logits[0, :3, :3, :3]		,    lowerCamelCase__		,    rtol=1e-4		,    atol=1e-4   )








'''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:
  a		:  str   			=			None


a		:  List[str]   			=			logging.get_logger(__name__)

a		:  Dict   			=			{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

a		:  Optional[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'''
        ),
    },
    '''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'''
        ),
    },
}

a		:  Any   			=			{
    '''facebook/mbart-large-50-one-to-many-mmt''': 1_0_2_4,
}

# fmt: off
a		:  Dict   			=			['''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					(  snake_case__					):
  a    :str      					=				VOCAB_FILES_NAMES
  a    :int      					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a    :Dict      					=				PRETRAINED_VOCAB_FILES_MAP
  a    :List[str]      					=				['input_ids', 'attention_mask']
  a    :Optional[Any]      					=				MBartaaTokenizer

  a    :List[int]      					=				[]
  a    :List[int]      					=				[]



  def __init__( self		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase="</s>"		,    __lowerCAmelCase="</s>"		,    __lowerCAmelCase="<s>"		,    __lowerCAmelCase="<unk>"		,    __lowerCAmelCase="<pad>"		,    __lowerCAmelCase="<mask>"		,    **__lowerCAmelCase		,    ):







      '''simple docstring'''


      _a							:       List[str]       					=			AddedToken(__lowerCAmelCase		,    lstrip=__lowerCAmelCase		,    rstrip=__lowerCAmelCase   ) if isinstance(__lowerCAmelCase		,    __lowerCAmelCase   ) else mask_token

      _a							:       List[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__(
          __lowerCAmelCase		,    src_lang=__lowerCAmelCase		,    tgt_lang=__lowerCAmelCase		,    tokenizer_file=__lowerCAmelCase		,    eos_token=__lowerCAmelCase		,    sep_token=__lowerCAmelCase		,    cls_token=__lowerCAmelCase		,    unk_token=__lowerCAmelCase		,    pad_token=__lowerCAmelCase		,    mask_token=__lowerCAmelCase		,    **__lowerCAmelCase		,    )

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

      _a							:       str       					=			{
          lang_code: self.convert_tokens_to_ids(__lowerCAmelCase   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
      }

      _a							:       Dict       					=			src_lang if src_lang is not None else """en_XX"""
      _a							:       Tuple       					=			tgt_lang
      _a							:       Tuple       					=			self.lang_code_to_id[self._src_lang]
      self.set_src_lang_special_tokens(self._src_lang   )



  @property
  def 						a_       ( self   ):







      '''simple docstring'''


      return self._src_lang



  @src_lang.setter
  def 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       int       					=			new_src_lang
      self.set_src_lang_special_tokens(self._src_lang   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = None   ):







      '''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		,    __lowerCAmelCase		,    __lowerCAmelCase = "en_XX"		,    __lowerCAmelCase = None		,    __lowerCAmelCase = "ro_RO"		,    **__lowerCAmelCase		,    ):







      '''simple docstring'''


      _a							:       List[Any]       					=			src_lang
      _a							:       Any       					=			tgt_lang
      return super().prepare_seqaseq_batch(__lowerCAmelCase		,    __lowerCAmelCase		,    **__lowerCAmelCase   )



  def 						a_       ( self   ):







      '''simple docstring'''


      return self.set_src_lang_special_tokens(self.src_lang   )



  def 						a_       ( self   ):







      '''simple docstring'''


      return self.set_tgt_lang_special_tokens(self.tgt_lang   )



  def 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       Union[str, Any]       					=			self.convert_tokens_to_ids(__lowerCAmelCase   )
      _a							:       Optional[Any]       					=			[self.cur_lang_code_id]
      _a							:       int       					=			[self.eos_token_id]

      _a							:       Any       					=			self.convert_ids_to_tokens(self.prefix_tokens   )
      _a							:       Dict       					=			self.convert_ids_to_tokens(self.suffix_tokens   )

      _a							:       Union[str, 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 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       Optional[int]       					=			self.convert_tokens_to_ids(__lowerCAmelCase   )
      _a							:       List[str]       					=			[self.cur_lang_code_id]
      _a							:       Tuple       					=			[self.eos_token_id]

      _a							:       List[Any]       					=			self.convert_ids_to_tokens(self.prefix_tokens   )
      _a							:       int       					=			self.convert_ids_to_tokens(self.suffix_tokens   )

      _a							:       str       					=			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 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    **__lowerCAmelCase   ):







      '''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"""   )
      _a							:       List[Any]       					=			src_lang
      _a							:       List[str]       					=			self(__lowerCAmelCase		,    add_special_tokens=__lowerCAmelCase		,    return_tensors=__lowerCAmelCase		,    **__lowerCAmelCase   )
      _a							:       Optional[Any]       					=			self.convert_tokens_to_ids(__lowerCAmelCase   )
      _a							:       Tuple       					=			tgt_lang_id
      return inputs



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = None   ):







      '''simple docstring'''


      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
      _a							:       Union[str, Any]       					=			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,)


