




'''simple docstring'''


import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import SwiftFormerForImageClassification, SwiftFormerModel
  from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor





class    __lowercase						:







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase=13      ,							UpperCamelCase=3      ,							UpperCamelCase=True      ,							UpperCamelCase=True      ,							UpperCamelCase=0.1      ,							UpperCamelCase=0.1      ,							UpperCamelCase=224      ,							UpperCamelCase=1000      ,							UpperCamelCase=[3, 3, 6, 4]      ,							UpperCamelCase=[48, 56, 112, 220]      ,							)				->    List[Any]:
     __a								=					parent
     __a								=					batch_size
     __a								=					num_channels
     __a								=					is_training
     __a								=					use_labels
     __a								=					hidden_dropout_prob
     __a								=					attention_probs_dropout_prob
     __a								=					num_labels
     __a								=					image_size
     __a								=					layer_depths
     __a								=					embed_dims







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     __a								=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

     __a								=					None
     if self.use_labels:
       __a								=					ids_tensor([self.batch_size]      ,							self.num_labels      )

     __a								=					self.get_config()

     return config, pixel_values, labels







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     return SwiftFormerConfig(
         depths=self.layer_depths      ,							embed_dims=self.embed_dims      ,							mlp_ratio=4      ,							downsamples=[True, True, True, True]      ,							hidden_act='gelu'      ,							num_labels=self.num_labels      ,							down_patch_size=3      ,							down_stride=2      ,							down_pad=1      ,							drop_rate=0.0      ,							drop_path_rate=0.0      ,							use_layer_scale=_A      ,							layer_scale_init_value=1e-5      ,							)







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    List[str]:
     __a								=					SwiftFormerModel(config=_A      )
     model.to(_A      )
     model.eval()
     __a								=					model(_A      )
     self.parent.assertEqual(result.last_hidden_state.shape      ,							(self.batch_size, self.embed_dims[-1], 7, 7)      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Tuple:
     __a								=					self.num_labels
     __a								=					SwiftFormerForImageClassification(_A      )
     model.to(_A      )
     model.eval()
     __a								=					model(_A      ,							labels=_A      )
     self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_labels)      )

     __a								=					SwiftFormerForImageClassification(_A      )
     model.to(_A      )
     model.eval()

     __a								=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )
     __a								=					model(_A      )
     self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_labels)      )







   def 		UpperCamelCase__			(	self      )				->    Dict:
     (__a)								=					self.prepare_config_and_inputs()
     __a								=					{'pixel_values': pixel_values}
     return config, inputs_dict





@require_torch
class    __lowercase						(  a__       ,							a__       ,							unittest.TestCase ):
   _a							=      (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
   _a							=      (
       {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
       if is_torch_available()
       else {}
   )

   _a							=      False
   _a							=      False
   _a							=      False
   _a							=      False
   _a							=      False







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     __a								=					SwiftFormerModelTester(self      )
     __a								=					ConfigTester(
         self      ,							config_class=_A      ,							has_text_modality=_A      ,							hidden_size=37      ,							num_attention_heads=12      ,							num_hidden_layers=12      ,							)







   def 		UpperCamelCase__			(	self      )				->    str:
     self.config_tester.run_common_tests()







   @unittest.skip(reason='SwiftFormer does not use inputs_embeds'      )
   def 		UpperCamelCase__			(	self      )				->    List[str]:
     pass







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     __a								=					self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       __a								=					model_class(_A      )
       __a								=					model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(_A      ,							nn.Linear      )      )







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __a								=					self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       __a								=					model_class(_A      )
       __a								=					inspect.signature(model.forward      )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __a								=					[*signature.parameters.keys()]

       __a								=					['pixel_values']
       self.assertListEqual(arg_names[:1]      ,							_A      )







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a								=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_A      )







   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a								=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_A      )







   @slow
   def 		UpperCamelCase__			(	self      )				->    int:

     for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __a								=					SwiftFormerModel.from_pretrained(_A      )
       self.assertIsNotNone(_A      )







   @unittest.skip(reason='SwiftFormer does not output attentions'      )
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     pass







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

     def check_hidden_states_output(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ):
       __a								=					model_class(_A      )
       model.to(_A      )
       model.eval()

       with torch.no_grad():
         __a								=					model(**self._prepare_for_class(_A      ,							_A      )      )

       __a								=					outputs.hidden_states

       __a								=					8
       self.assertEqual(len(_A      )      ,							_A      )  # TODO

       # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
       # with the width and height being successively divided by 2, after every 2 blocks
       for i in range(len(_A      )      ):
         self.assertEqual(
             hidden_states[i].shape      ,							torch.Size(
                 [
                     self.model_tester.batch_size,
                     self.model_tester.embed_dims[i // 2],
                     (self.model_tester.image_size // 4) // 2 ** (i // 2),
                     (self.model_tester.image_size // 4) // 2 ** (i // 2),
                 ]      )      ,							)

     __a								=					self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       __a								=					True
       check_hidden_states_output(_A      ,							_A      ,							_A      )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       __a								=					True

       check_hidden_states_output(_A      ,							_A      ,							_A      )







   def 		UpperCamelCase__			(	self      )				->    List[Any]:

     def _config_zero_init(UpperCamelCase      ):
       __a								=					copy.deepcopy(_A      )
       for key in configs_no_init.__dict__.keys():
         if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
           setattr(_A      ,							_A      ,							1e-10      )
         if isinstance(getattr(_A      ,							_A      ,							_A      )      ,							_A      ):
           __a								=					_config_zero_init(getattr(_A      ,							_A      )      )
           setattr(_A      ,							_A      ,							_A      )
       return configs_no_init

     __a								=					self.model_tester.prepare_config_and_inputs_for_common()

     __a								=					_config_zero_init(_A      )
     for model_class in self.all_model_classes:
       __a								=					model_class(config=_A      )
       for name, param in model.named_parameters():
         if param.requires_grad:
           self.assertIn(
               ((param.data.mean() * 1e9) / 1e9).round().item()      ,							[0.0, 1.0]      ,							msg=f"Parameter {name} of model {model_class} seems not properly initialized"      ,							)







   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'      )
   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     pass


def 						SCREAMING_SNAKE_CASE       (      ):
  __a								=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
  return image





@require_torch
@require_vision
class    __lowercase						(  unittest.TestCase ):







   @cached_property
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'      ) if is_vision_available() else None







   @slow
   def 		UpperCamelCase__			(	self      )				->    Any:
     __a								=					SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'      ).to(_A      )

     __a								=					self.default_image_processor
     __a								=					prepare_img()
     __a								=					image_processor(images=_A      ,							return_tensors='pt'      ).to(_A      )

     # forward pass
     with torch.no_grad():
       __a								=					model(**_A      )

     # verify the logits
     __a								=					torch.Size((1, 1000)      )
     self.assertEqual(outputs.logits.shape      ,							_A      )

     __a								=					torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]      ).to(_A      )
     self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,							_A      ,							atol=1e-4      )      )









'''simple docstring'''


import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_ 		=      get_tests_dir("fixtures/test_sentencepiece.model")

if is_torch_available():
  from transformers.models.mbart.modeling_mbart import shift_tokens_right

UpperCAmelCase_ 		=      25_00_04
UpperCAmelCase_ 		=      25_00_20





@require_sentencepiece
@require_tokenizers
class    __lowercase						(  __magic_name__       ,							unittest.TestCase ):
   _a							=      MBartaaTokenizer
   _a							=      MBartaaTokenizerFast
   _a							=      True
   _a							=      True







   def 		UpperCamelCase__			(	self      )				->    Dict:
     super().setUp()

     # We have a SentencePiece fixture for testing
     __a			=					MBartaaTokenizer(UpperCamelCase      ,							src_lang='en_XX'      ,							tgt_lang='ro_RO'      ,							keep_accents=UpperCamelCase      )
     tokenizer.save_pretrained(self.tmpdirname      )







   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					'<s>'
     __a			=					0

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase      )      ,							UpperCamelCase      )
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase      )      ,							UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     __a			=					list(self.get_tokenizer().get_vocab().keys()      )

     self.assertEqual(vocab_keys[0]      ,							'<s>'      )
     self.assertEqual(vocab_keys[1]      ,							'<pad>'      )
     self.assertEqual(vocab_keys[-1]      ,							'<mask>'      )
     self.assertEqual(len(UpperCamelCase      )      ,							1054      )







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     self.assertEqual(self.get_tokenizer().vocab_size      ,							1054      )







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					MBartaaTokenizer(UpperCamelCase      ,							src_lang='en_XX'      ,							tgt_lang='ro_RO'      ,							keep_accents=UpperCamelCase      )

     __a			=					tokenizer.tokenize('This is a test'      )
     self.assertListEqual(UpperCamelCase      ,							['▁This', '▁is', '▁a', '▁t', 'est']      )

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(UpperCamelCase      )      ,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]      ,							)

     __a			=					tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
     self.assertListEqual(
         UpperCamelCase      ,							[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '9', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', 'é', '.']      ,							)
     __a			=					tokenizer.convert_tokens_to_ids(UpperCamelCase      )
     self.assertListEqual(
         UpperCamelCase      ,							[
             value + tokenizer.fairseq_offset
             for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
         ]      ,							)

     __a			=					tokenizer.convert_ids_to_tokens(UpperCamelCase      )
     self.assertListEqual(
         UpperCamelCase      ,							[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '<unk>', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', '<unk>', '.']      ,							)







   @slow
   def 		UpperCamelCase__			(	self      )				->    Dict:
     # fmt: off
     __a			=					{'input_ids': [[25_0004, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [25_0004, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_0004, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=UpperCamelCase      ,							model_name='facebook/mbart-large-50'      ,							revision='d3913889c59cd5c9e456b269c376325eabad57e2'      ,							)







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     if not self.test_slow_tokenizer:
       # as we don't have a slow version, we can't compare the outputs between slow and fast versions
       return

     __a			=					(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
         __a			=					self.rust_tokenizer_class.from_pretrained(UpperCamelCase      ,							**UpperCamelCase      )
         __a			=					self.tokenizer_class.from_pretrained(UpperCamelCase      ,							**UpperCamelCase      )

         __a			=					tempfile.mkdtemp()

         __a			=					tokenizer_r.save_pretrained(UpperCamelCase      )
         __a			=					tokenizer_p.save_pretrained(UpperCamelCase      )

         # Checks it save with the same files + the tokenizer.json file for the fast one
         self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files      )      )
         __a			=					tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f      )
         self.assertSequenceEqual(UpperCamelCase      ,							UpperCamelCase      )

         # Checks everything loads correctly in the same way
         __a			=					tokenizer_r.from_pretrained(UpperCamelCase      )
         __a			=					tokenizer_p.from_pretrained(UpperCamelCase      )

         # Check special tokens are set accordingly on Rust and Python
         for key in tokenizer_pp.special_tokens_map:
           self.assertTrue(hasattr(UpperCamelCase      ,							UpperCamelCase      )      )
           # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
           # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

         shutil.rmtree(UpperCamelCase      )

         # Save tokenizer rust, legacy_format=True
         __a			=					tempfile.mkdtemp()

         __a			=					tokenizer_r.save_pretrained(UpperCamelCase      ,							legacy_format=UpperCamelCase      )
         __a			=					tokenizer_p.save_pretrained(UpperCamelCase      )

         # Checks it save with the same files
         self.assertSequenceEqual(UpperCamelCase      ,							UpperCamelCase      )

         # Checks everything loads correctly in the same way
         __a			=					tokenizer_r.from_pretrained(UpperCamelCase      )
         __a			=					tokenizer_p.from_pretrained(UpperCamelCase      )

         # Check special tokens are set accordingly on Rust and Python
         for key in tokenizer_pp.special_tokens_map:
           self.assertTrue(hasattr(UpperCamelCase      ,							UpperCamelCase      )      )

         shutil.rmtree(UpperCamelCase      )

         # Save tokenizer rust, legacy_format=False
         __a			=					tempfile.mkdtemp()

         __a			=					tokenizer_r.save_pretrained(UpperCamelCase      ,							legacy_format=UpperCamelCase      )
         __a			=					tokenizer_p.save_pretrained(UpperCamelCase      )

         # Checks it saved the tokenizer.json file
         self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files      )      )

         # Checks everything loads correctly in the same way
         __a			=					tokenizer_r.from_pretrained(UpperCamelCase      )
         __a			=					tokenizer_p.from_pretrained(UpperCamelCase      )

         # Check special tokens are set accordingly on Rust and Python
         for key in tokenizer_pp.special_tokens_map:
           self.assertTrue(hasattr(UpperCamelCase      ,							UpperCamelCase      )      )

         shutil.rmtree(UpperCamelCase      )





@require_torch
@require_sentencepiece
@require_tokenizers
class    __lowercase						(  unittest.TestCase ):
   _a							=      """facebook/mbart-large-50-one-to-many-mmt"""
   _a							=      [
       """ UN Chief Says There Is No Military Solution in Syria""",
       """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
   ]
   _a							=      [
       """Şeful ONU declară că nu există o soluţie militară în Siria""",
       """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
       """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
       """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
   ]
   _a							=      [EN_CODE, 8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2]







   @classmethod
   def 		UpperCamelCase__			(	cls      )				->    Optional[int]:
     __a			=					MBartaaTokenizer.from_pretrained(
         cls.checkpoint_name      ,							src_lang='en_XX'      ,							tgt_lang='ro_RO'      )
     __a			=					1
     return cls







   def 		UpperCamelCase__			(	self      )				->    int:
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']      ,							25_0001      )
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']      ,							25_0004      )
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']      ,							25_0020      )
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']      ,							25_0038      )







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
     self.assertListEqual(self.expected_src_tokens      ,							UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    int:
     self.assertIn(UpperCamelCase      ,							self.tokenizer.all_special_ids      )
     __a			=					[RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
     __a			=					self.tokenizer.decode(UpperCamelCase      ,							skip_special_tokens=UpperCamelCase      )
     __a			=					self.tokenizer.decode(generated_ids[1:]      ,							skip_special_tokens=UpperCamelCase      )
     self.assertEqual(UpperCamelCase      ,							UpperCamelCase      )
     self.assertNotIn(self.tokenizer.eos_token      ,							UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					['this is gunna be a long sentence ' * 20]
     assert isinstance(src_text[0]      ,							UpperCamelCase      )
     __a			=					10
     __a			=					self.tokenizer(UpperCamelCase      ,							max_length=UpperCamelCase      ,							truncation=UpperCamelCase      ).input_ids[0]
     self.assertEqual(ids[0]      ,							UpperCamelCase      )
     self.assertEqual(ids[-1]      ,							2      )
     self.assertEqual(len(UpperCamelCase      )      ,							UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    str:
     self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']      )      ,							[25_0053, 25_0001]      )







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __a			=					tempfile.mkdtemp()
     __a			=					self.tokenizer.fairseq_tokens_to_ids
     self.tokenizer.save_pretrained(UpperCamelCase      )
     __a			=					MBartaaTokenizer.from_pretrained(UpperCamelCase      )
     self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,							UpperCamelCase      )







   @require_torch
   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     __a			=					self.tokenizer(self.src_text      ,							text_target=self.tgt_text      ,							padding=UpperCamelCase      ,							return_tensors='pt'      )
     __a			=					shift_tokens_right(batch['labels']      ,							self.tokenizer.pad_token_id      )

     # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
     assert batch.input_ids[1][0] == EN_CODE
     assert batch.input_ids[1][-1] == 2
     assert batch.labels[1][0] == RO_CODE
     assert batch.labels[1][-1] == 2
     assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]







   @require_torch
   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					self.tokenizer(
         self.src_text      ,							text_target=self.tgt_text      ,							padding=UpperCamelCase      ,							truncation=UpperCamelCase      ,							max_length=len(self.expected_src_tokens      )      ,							return_tensors='pt'      ,							)
     __a			=					shift_tokens_right(batch['labels']      ,							self.tokenizer.pad_token_id      )

     self.assertIsInstance(UpperCamelCase      ,							UpperCamelCase      )

     self.assertEqual((2, 14)      ,							batch.input_ids.shape      )
     self.assertEqual((2, 14)      ,							batch.attention_mask.shape      )
     __a			=					batch.input_ids.tolist()[0]
     self.assertListEqual(self.expected_src_tokens      ,							UpperCamelCase      )
     self.assertEqual(2      ,							batch.decoder_input_ids[0, 0]      )  # decoder_start_token_id
     # Test that special tokens are reset
     self.assertEqual(self.tokenizer.prefix_tokens      ,							[EN_CODE]      )
     self.assertEqual(self.tokenizer.suffix_tokens      ,							[self.tokenizer.eos_token_id]      )







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __a			=					self.tokenizer(self.src_text      ,							padding=UpperCamelCase      ,							truncation=UpperCamelCase      ,							max_length=3      ,							return_tensors='pt'      )
     __a			=					self.tokenizer(
         text_target=self.tgt_text      ,							padding=UpperCamelCase      ,							truncation=UpperCamelCase      ,							max_length=10      ,							return_tensors='pt'      )
     __a			=					targets['input_ids']
     __a			=					shift_tokens_right(UpperCamelCase      ,							self.tokenizer.pad_token_id      )

     self.assertEqual(batch.input_ids.shape[1]      ,							3      )
     self.assertEqual(batch.decoder_input_ids.shape[1]      ,							10      )







   @require_torch
   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					self.tokenizer._build_translation_inputs(
         'A test'      ,							return_tensors='pt'      ,							src_lang='en_XX'      ,							tgt_lang='ar_AR'      )

     self.assertEqual(
         nested_simplify(UpperCamelCase      )      ,							{
             # en_XX, A, test, EOS
             'input_ids': [[25_0004, 62, 3034, 2]],
             'attention_mask': [[1, 1, 1, 1]],
             # ar_AR
             'forced_bos_token_id': 25_0001,
         }      ,							)



