import inspect
import unittest

from transformers import MobileNetVaConfig
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 transformers import MobileNetVaForImageClassification, MobileNetVaModel
				from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import MobileNetVaImageProcessor




class 	__snake_case	( __lowerCAmelCase		):





						'''simple docstring'''






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


										"""simple docstring"""


										_lowerCamelCase     :		List[Any] 						=						self.config_class(**self.inputs_dict)
										self.parent.assertTrue(hasattr(_UpperCamelCase    ,  """tf_padding"""))
										self.parent.assertTrue(hasattr(_UpperCamelCase    ,  """depth_multiplier"""))





class 	__snake_case	:





						'''simple docstring'''






						def __init__(					self     :				int    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Dict=13    ,  _UpperCamelCase     :				Optional[Any]=3    ,  _UpperCamelCase     :				int=32    ,  _UpperCamelCase     :				Any=0.2_5    ,  _UpperCamelCase     :				str=8    ,  _UpperCamelCase     :				int=True    ,  _UpperCamelCase     :				List[Any]=1024    ,  _UpperCamelCase     :				Optional[Any]=32    ,  _UpperCamelCase     :				Tuple="relu6"    ,  _UpperCamelCase     :				List[Any]=0.1    ,  _UpperCamelCase     :				Optional[int]=0.0_2    ,  _UpperCamelCase     :				str=True    ,  _UpperCamelCase     :				List[Any]=True    ,  _UpperCamelCase     :				List[str]=10    ,  _UpperCamelCase     :				Optional[int]=None    ,  )				->List[Any]:


										"""simple docstring"""


										_lowerCamelCase     :		Optional[int] 						=						parent
										_lowerCamelCase     :		Tuple 						=						batch_size
										_lowerCamelCase     :		str 						=						num_channels
										_lowerCamelCase     :		Tuple 						=						image_size
										_lowerCamelCase     :		Union[str, Any] 						=						depth_multiplier
										_lowerCamelCase     :		Tuple 						=						min_depth
										_lowerCamelCase     :		List[str] 						=						tf_padding
										_lowerCamelCase     :		Optional[int] 						=						int(last_hidden_size * depth_multiplier)
										_lowerCamelCase     :		Optional[int] 						=						output_stride
										_lowerCamelCase     :		Optional[int] 						=						hidden_act
										_lowerCamelCase     :		Optional[int] 						=						classifier_dropout_prob
										_lowerCamelCase     :		List[str] 						=						use_labels
										_lowerCamelCase     :		Dict 						=						is_training
										_lowerCamelCase     :		Tuple 						=						num_labels
										_lowerCamelCase     :		Optional[int] 						=						initializer_range
										_lowerCamelCase     :		List[Any] 						=						scope



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


										"""simple docstring"""


										_lowerCamelCase     :		List[Any] 						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

										_lowerCamelCase     :		List[Any] 						=						None
										_lowerCamelCase     :		Optional[Any] 						=						None
										if self.use_labels:
														_lowerCamelCase     :		List[Any] 						=						ids_tensor([self.batch_size]    ,  self.num_labels)
														_lowerCamelCase     :		str 						=						ids_tensor([self.batch_size, self.image_size, self.image_size]    ,  self.num_labels)

										_lowerCamelCase     :		List[str] 						=						self.get_config()

										return config, pixel_values, labels, pixel_labels



						def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Dict:


										"""simple docstring"""


										return MobileNetVaConfig(
										    num_channels=self.num_channels    ,  image_size=self.image_size    ,  depth_multiplier=self.depth_multiplier    ,  min_depth=self.min_depth    ,  tf_padding=self.tf_padding    ,  hidden_act=self.hidden_act    ,  classifier_dropout_prob=self.classifier_dropout_prob    ,  initializer_range=self.initializer_range    ,  )



						def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				Optional[Any])				->Optional[int]:


										"""simple docstring"""


										_lowerCamelCase     :		List[str] 						=						MobileNetVaModel(config=_UpperCamelCase)
										model.to(_UpperCamelCase)
										model.eval()
										_lowerCamelCase     :		List[str] 						=						model(_UpperCamelCase)
										self.parent.assertEqual(
										    result.last_hidden_state.shape    ,  (
										        self.batch_size,
										        self.last_hidden_size,
										        self.image_size // self.output_stride,
										        self.image_size // self.output_stride,
										    )    ,  )



						def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				int)				->Optional[int]:


										"""simple docstring"""


										_lowerCamelCase     :		str 						=						self.num_labels
										_lowerCamelCase     :		int 						=						MobileNetVaForImageClassification(_UpperCamelCase)
										model.to(_UpperCamelCase)
										model.eval()
										_lowerCamelCase     :		Optional[int] 						=						model(_UpperCamelCase    ,  labels=_UpperCamelCase)
										self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels))




						def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Tuple:


										"""simple docstring"""


										_lowerCamelCase     :		Any 						=						self.prepare_config_and_inputs()
										_lowerCamelCase     :		Dict 						=						config_and_inputs
										_lowerCamelCase     :		Any 						=						{"""pixel_values""": pixel_values}
										return config, inputs_dict





@require_torch
class 	__snake_case	( __lowerCAmelCase ,  __lowerCAmelCase ,  unittest.TestCase		):





						'''simple docstring'''






						_snake_case    		=       (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
						_snake_case    		=       (
						    {'feature-extraction': MobileNetVaModel, 'image-classification': MobileNetVaForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_snake_case    		=       False
						_snake_case    		=       False
						_snake_case    		=       False
						_snake_case    		=       False



						def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->int:


										"""simple docstring"""


										_lowerCamelCase     :		Dict 						=						MobileNetVaModelTester(self)
										_lowerCamelCase     :		List[Any] 						=						MobileNetVaConfigTester(self    ,  config_class=_UpperCamelCase    ,  has_text_modality=_UpperCamelCase)



						def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Any:


										"""simple docstring"""


										self.config_tester.run_common_tests()



						@unittest.skip(reason="""MobileNetV1 does not use inputs_embeds""")
						def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->int:


										"""simple docstring"""


										pass



						@unittest.skip(reason="""MobileNetV1 does not support input and output embeddings""")
						def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Dict:


										"""simple docstring"""


										pass



						@unittest.skip(reason="""MobileNetV1 does not output attentions""")
						def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Any:


										"""simple docstring"""


										pass



						def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->List[str]:


										"""simple docstring"""


										_lowerCamelCase     :		List[str] 						=						self.model_tester.prepare_config_and_inputs_for_common()

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

														_lowerCamelCase     :		Dict 						=						["""pixel_values"""]
														self.assertListEqual(arg_names[:1]    ,  _UpperCamelCase)



						def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->List[str]:


										"""simple docstring"""


										_lowerCamelCase     :		Union[str, Any] 						=						self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_UpperCamelCase)



						def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->str:


										"""simple docstring"""


										def check_hidden_states_output(_UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				List[Any]):
														_lowerCamelCase     :		Optional[int] 						=						model_class(_UpperCamelCase)
														model.to(_UpperCamelCase)
														model.eval()

														with torch.no_grad():
																		_lowerCamelCase     :		Optional[Any] 						=						model(**self._prepare_for_class(_UpperCamelCase    ,  _UpperCamelCase))

														_lowerCamelCase     :		Dict 						=						outputs.hidden_states

														_lowerCamelCase     :		Any 						=						26
														self.assertEqual(len(_UpperCamelCase)    ,  _UpperCamelCase)

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

										for model_class in self.all_model_classes:
														_lowerCamelCase     :		int 						=						True
														check_hidden_states_output(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														_lowerCamelCase     :		List[str] 						=						True

														check_hidden_states_output(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)



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


										"""simple docstring"""


										_lowerCamelCase     :		Tuple 						=						self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase)




						@slow
						def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[Any]:


										"""simple docstring"""


										for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_lowerCamelCase     :		Optional[int] 						=						MobileNetVaModel.from_pretrained(_UpperCamelCase)
														self.assertIsNotNone(_UpperCamelCase)







def 						A__  (					):







				'''simple docstring'''
				_lowerCamelCase     :		str 						=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
				return image




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





						'''simple docstring'''






						@cached_property
						def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Optional[int]:


										"""simple docstring"""


										return (
										    MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224""") if is_vision_available() else None
										)




						@slow
						def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[Any]:


										"""simple docstring"""


										_lowerCamelCase     :		Optional[int] 						=						MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224""").to(_UpperCamelCase)

										_lowerCamelCase     :		Optional[Any] 						=						self.default_image_processor
										_lowerCamelCase     :		str 						=						prepare_img()
										_lowerCamelCase     :		Optional[int] 						=						image_processor(images=_UpperCamelCase    ,  return_tensors="""pt""").to(_UpperCamelCase)

										# forward pass
										with torch.no_grad():
														_lowerCamelCase     :		Optional[Any] 						=						model(**_UpperCamelCase)

										# verify the logits
										_lowerCamelCase     :		Optional[Any] 						=						torch.Size((1, 1001))
										self.assertEqual(outputs.logits.shape    ,  _UpperCamelCase)

										_lowerCamelCase     :		List[str] 						=						torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]).to(_UpperCamelCase)

										self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,  _UpperCamelCase    ,  atol=1E-4))





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
    import torch


if is_vision_available():
    from PIL import Image

    from transformers import MgpstrProcessor, ViTImageProcessor




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





      '''simple docstring'''






      _snake_case    		=       ViTImageProcessor if is_vision_available() else None



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Dict:


          """simple docstring"""


          return self.image_processor_tester.prepare_image_processor_dict()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						(3, 32, 128)
          _lowerCamelCase     :		str 						=						tempfile.mkdtemp()

          # fmt: off
          _lowerCamelCase     :		Dict 						=						["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
          # fmt: on
          _lowerCamelCase     :		str 						=						dict(zip(_UpperCamelCase    ,  range(len(_UpperCamelCase))))

          _lowerCamelCase     :		int 						=						os.path.join(self.tmpdirname    ,  VOCAB_FILES_NAMES["""vocab_file"""])
          with open(self.vocab_file    ,  """w"""    ,  encoding="""utf-8""") as fp:
              fp.write(json.dumps(_UpperCamelCase) + """\n""")

          _lowerCamelCase     :		Any 						=						{
              """do_normalize""": False,
              """do_resize""": True,
              """image_processor_type""": """ViTImageProcessor""",
              """resample""": 3,
              """size""": {"""height""": 32, """width""": 128},
          }
          _lowerCamelCase     :		Union[str, Any] 						=						os.path.join(self.tmpdirname    ,  _UpperCamelCase)
          with open(self.image_processor_file    ,  """w"""    ,  encoding="""utf-8""") as fp:
              json.dump(_UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  **_UpperCamelCase     :				Any)				->Tuple:


          """simple docstring"""


          return MgpstrTokenizer.from_pretrained(self.tmpdirname    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  **_UpperCamelCase     :				Optional[Any])				->List[Any]:


          """simple docstring"""


          return ViTImageProcessor.from_pretrained(self.tmpdirname    ,  **_UpperCamelCase)



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


          """simple docstring"""


          shutil.rmtree(self.tmpdirname)



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


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						np.random.randint(255    ,  size=(3, 30, 400)    ,  dtype=np.uinta)

          _lowerCamelCase     :		int 						=						Image.fromarray(np.moveaxis(_UpperCamelCase    ,  0    ,  -1))

          return image_input



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.get_tokenizer()
          _lowerCamelCase     :		Tuple 						=						self.get_image_processor()

          _lowerCamelCase     :		Union[str, Any] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)
          processor.save_pretrained(self.tmpdirname)
          _lowerCamelCase     :		int 						=						MgpstrProcessor.from_pretrained(self.tmpdirname    ,  use_fast=_UpperCamelCase)

          self.assertEqual(processor.char_tokenizer.get_vocab()    ,  tokenizer.get_vocab())
          self.assertIsInstance(processor.char_tokenizer    ,  _UpperCamelCase)

          self.assertEqual(processor.image_processor.to_json_string()    ,  image_processor.to_json_string())
          self.assertIsInstance(processor.image_processor    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.get_tokenizer()
          _lowerCamelCase     :		Optional[Any] 						=						self.get_image_processor()

          _lowerCamelCase     :		Any 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)
          processor.save_pretrained(self.tmpdirname)

          _lowerCamelCase     :		Tuple 						=						self.get_tokenizer(bos_token="""(BOS)"""    ,  eos_token="""(EOS)""")
          _lowerCamelCase     :		Union[str, Any] 						=						self.get_image_processor(do_normalize=_UpperCamelCase    ,  padding_value=1.0)

          _lowerCamelCase     :		Tuple 						=						MgpstrProcessor.from_pretrained(
              self.tmpdirname    ,  bos_token="""(BOS)"""    ,  eos_token="""(EOS)"""    ,  do_normalize=_UpperCamelCase    ,  padding_value=1.0)

          self.assertEqual(processor.char_tokenizer.get_vocab()    ,  tokenizer_add_kwargs.get_vocab())
          self.assertIsInstance(processor.char_tokenizer    ,  _UpperCamelCase)

          self.assertEqual(processor.image_processor.to_json_string()    ,  image_processor_add_kwargs.to_json_string())
          self.assertIsInstance(processor.image_processor    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->int:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						self.get_image_processor()
          _lowerCamelCase     :		int 						=						self.get_tokenizer()

          _lowerCamelCase     :		List[str] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		List[str] 						=						self.prepare_image_inputs()

          _lowerCamelCase     :		Optional[int] 						=						image_processor(_UpperCamelCase    ,  return_tensors="""np""")
          _lowerCamelCase     :		int 						=						processor(images=_UpperCamelCase    ,  return_tensors="""np""")

          for key in input_image_proc.keys():
              self.assertAlmostEqual(input_image_proc[key].sum()    ,  input_processor[key].sum()    ,  delta=1E-2)



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


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						self.get_image_processor()
          _lowerCamelCase     :		int 						=						self.get_tokenizer()

          _lowerCamelCase     :		Any 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		Optional[int] 						=						"""test"""

          _lowerCamelCase     :		Union[str, Any] 						=						processor(text=_UpperCamelCase)

          _lowerCamelCase     :		Dict 						=						tokenizer(_UpperCamelCase)
          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]    ,  encoded_processor[key])



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


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.get_image_processor()
          _lowerCamelCase     :		List[Any] 						=						self.get_tokenizer()

          _lowerCamelCase     :		Any 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		Any 						=						"""test"""
          _lowerCamelCase     :		List[str] 						=						self.prepare_image_inputs()

          _lowerCamelCase     :		int 						=						processor(text=_UpperCamelCase    ,  images=_UpperCamelCase)

          self.assertListEqual(list(inputs.keys())    ,  ["""pixel_values""", """labels"""])

          # test if it raises when no input is passed
          with pytest.raises(_UpperCamelCase):
              processor()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.get_image_processor()
          _lowerCamelCase     :		List[str] 						=						self.get_tokenizer()

          _lowerCamelCase     :		Dict 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		int 						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

          _lowerCamelCase     :		Any 						=						processor.char_decode(_UpperCamelCase)
          _lowerCamelCase     :		Tuple 						=						tokenizer.batch_decode(_UpperCamelCase)
          _lowerCamelCase     :		List[str] 						=						[seq.replace(""" """    ,  """""") for seq in decoded_tok]

          self.assertListEqual(_UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->str:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.get_image_processor()
          _lowerCamelCase     :		str 						=						self.get_tokenizer()

          _lowerCamelCase     :		List[Any] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		int 						=						None
          _lowerCamelCase     :		Union[str, Any] 						=						self.prepare_image_inputs()

          _lowerCamelCase     :		Union[str, Any] 						=						processor(text=_UpperCamelCase    ,  images=_UpperCamelCase)

          self.assertListEqual(list(inputs.keys())    ,  processor.model_input_names)




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


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.get_image_processor()
          _lowerCamelCase     :		int 						=						self.get_tokenizer()

          _lowerCamelCase     :		Union[str, Any] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		Any 						=						torch.randn(1    ,  27    ,  38)
          _lowerCamelCase     :		List[Any] 						=						torch.randn(1    ,  27    ,  5_0257)
          _lowerCamelCase     :		List[str] 						=						torch.randn(1    ,  27    ,  3_0522)

          _lowerCamelCase     :		int 						=						processor.batch_decode([char_input, bpe_input, wp_input])

          self.assertListEqual(list(results.keys())    ,  ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""])




