import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
				import torch

				from transformers import (
				    NystromformerForMaskedLM,
				    NystromformerForMultipleChoice,
				    NystromformerForQuestionAnswering,
				    NystromformerForSequenceClassification,
				    NystromformerForTokenClassification,
				    NystromformerModel,
				)
				from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST




class 	__snake_case	:





						'''simple docstring'''






						def __init__(					self     :				Optional[int]    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				str=13    ,  _UpperCamelCase     :				List[Any]=7    ,  _UpperCamelCase     :				List[Any]=True    ,  _UpperCamelCase     :				List[Any]=True    ,  _UpperCamelCase     :				Optional[int]=True    ,  _UpperCamelCase     :				Union[str, Any]=True    ,  _UpperCamelCase     :				Any=99    ,  _UpperCamelCase     :				Union[str, Any]=32    ,  _UpperCamelCase     :				List[str]=5    ,  _UpperCamelCase     :				Any=4    ,  _UpperCamelCase     :				List[Any]=37    ,  _UpperCamelCase     :				Optional[Any]="gelu"    ,  _UpperCamelCase     :				List[Any]=0.1    ,  _UpperCamelCase     :				Dict=0.1    ,  _UpperCamelCase     :				List[Any]=512    ,  _UpperCamelCase     :				Optional[Any]=16    ,  _UpperCamelCase     :				int=2    ,  _UpperCamelCase     :				Dict=0.0_2    ,  _UpperCamelCase     :				Any=3    ,  _UpperCamelCase     :				List[Any]=4    ,  _UpperCamelCase     :				Optional[Any]=None    ,  )				->Any:


										"""simple docstring"""


										_lowerCamelCase     :		Dict 						=						parent
										_lowerCamelCase     :		str 						=						batch_size
										_lowerCamelCase     :		Optional[Any] 						=						seq_length
										_lowerCamelCase     :		List[str] 						=						is_training
										_lowerCamelCase     :		List[Any] 						=						use_input_mask
										_lowerCamelCase     :		Union[str, Any] 						=						use_token_type_ids
										_lowerCamelCase     :		Optional[Any] 						=						use_labels
										_lowerCamelCase     :		Tuple 						=						vocab_size
										_lowerCamelCase     :		Union[str, Any] 						=						hidden_size
										_lowerCamelCase     :		List[Any] 						=						num_hidden_layers
										_lowerCamelCase     :		str 						=						num_attention_heads
										_lowerCamelCase     :		int 						=						intermediate_size
										_lowerCamelCase     :		Union[str, Any] 						=						hidden_act
										_lowerCamelCase     :		Dict 						=						hidden_dropout_prob
										_lowerCamelCase     :		List[Any] 						=						attention_probs_dropout_prob
										_lowerCamelCase     :		Union[str, Any] 						=						max_position_embeddings
										_lowerCamelCase     :		Optional[int] 						=						type_vocab_size
										_lowerCamelCase     :		Dict 						=						type_sequence_label_size
										_lowerCamelCase     :		Dict 						=						initializer_range
										_lowerCamelCase     :		str 						=						num_labels
										_lowerCamelCase     :		int 						=						num_choices
										_lowerCamelCase     :		Dict 						=						scope



						def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Dict:


										"""simple docstring"""


										_lowerCamelCase     :		Tuple 						=						ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size)

										_lowerCamelCase     :		List[Any] 						=						None
										if self.use_input_mask:
														_lowerCamelCase     :		Dict 						=						random_attention_mask([self.batch_size, self.seq_length])

										_lowerCamelCase     :		int 						=						None
										if self.use_token_type_ids:
														_lowerCamelCase     :		Union[str, Any] 						=						ids_tensor([self.batch_size, self.seq_length]    ,  self.type_vocab_size)

										_lowerCamelCase     :		Dict 						=						None
										_lowerCamelCase     :		List[Any] 						=						None
										_lowerCamelCase     :		List[str] 						=						None
										if self.use_labels:
														_lowerCamelCase     :		Any 						=						ids_tensor([self.batch_size]    ,  self.type_sequence_label_size)
														_lowerCamelCase     :		str 						=						ids_tensor([self.batch_size, self.seq_length]    ,  self.num_labels)
														_lowerCamelCase     :		Tuple 						=						ids_tensor([self.batch_size]    ,  self.num_choices)

										_lowerCamelCase     :		Optional[int] 						=						self.get_config()

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



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


										"""simple docstring"""


										return NystromformerConfig(
										    vocab_size=self.vocab_size    ,  hidden_size=self.hidden_size    ,  num_hidden_layers=self.num_hidden_layers    ,  num_attention_heads=self.num_attention_heads    ,  intermediate_size=self.intermediate_size    ,  hidden_act=self.hidden_act    ,  hidden_dropout_prob=self.hidden_dropout_prob    ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,  max_position_embeddings=self.max_position_embeddings    ,  type_vocab_size=self.type_vocab_size    ,  is_decoder=UpperCAmelCase__    ,  initializer_range=self.initializer_range    ,  )



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


										"""simple docstring"""


										_lowerCamelCase     :		Optional[int] 						=						NystromformerModel(config=UpperCAmelCase__)
										model.to(UpperCAmelCase__)
										model.eval()
										_lowerCamelCase     :		int 						=						model(UpperCAmelCase__    ,  attention_mask=UpperCAmelCase__    ,  token_type_ids=UpperCAmelCase__)
										_lowerCamelCase     :		Any 						=						model(UpperCAmelCase__    ,  token_type_ids=UpperCAmelCase__)
										_lowerCamelCase     :		Any 						=						model(UpperCAmelCase__)
										self.parent.assertEqual(result.last_hidden_state.shape    ,  (self.batch_size, self.seq_length, self.hidden_size))



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


										"""simple docstring"""


										_lowerCamelCase     :		Tuple 						=						NystromformerForMaskedLM(config=UpperCAmelCase__)
										model.to(UpperCAmelCase__)
										model.eval()
										_lowerCamelCase     :		Tuple 						=						model(UpperCAmelCase__    ,  attention_mask=UpperCAmelCase__    ,  token_type_ids=UpperCAmelCase__    ,  labels=UpperCAmelCase__)
										self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.vocab_size))



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


										"""simple docstring"""


										_lowerCamelCase     :		Optional[Any] 						=						NystromformerForQuestionAnswering(config=UpperCAmelCase__)
										model.to(UpperCAmelCase__)
										model.eval()
										_lowerCamelCase     :		int 						=						model(
										    UpperCAmelCase__    ,  attention_mask=UpperCAmelCase__    ,  token_type_ids=UpperCAmelCase__    ,  start_positions=UpperCAmelCase__    ,  end_positions=UpperCAmelCase__    ,  )
										self.parent.assertEqual(result.start_logits.shape    ,  (self.batch_size, self.seq_length))
										self.parent.assertEqual(result.end_logits.shape    ,  (self.batch_size, self.seq_length))



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


										"""simple docstring"""


										_lowerCamelCase     :		Optional[Any] 						=						self.num_labels
										_lowerCamelCase     :		Any 						=						NystromformerForSequenceClassification(UpperCAmelCase__)
										model.to(UpperCAmelCase__)
										model.eval()
										_lowerCamelCase     :		Optional[int] 						=						model(UpperCAmelCase__    ,  attention_mask=UpperCAmelCase__    ,  token_type_ids=UpperCAmelCase__    ,  labels=UpperCAmelCase__)
										self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels))



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


										"""simple docstring"""


										_lowerCamelCase     :		List[str] 						=						self.num_labels
										_lowerCamelCase     :		Optional[Any] 						=						NystromformerForTokenClassification(config=UpperCAmelCase__)
										model.to(UpperCAmelCase__)
										model.eval()
										_lowerCamelCase     :		int 						=						model(UpperCAmelCase__    ,  attention_mask=UpperCAmelCase__    ,  token_type_ids=UpperCAmelCase__    ,  labels=UpperCAmelCase__)
										self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.num_labels))



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


										"""simple docstring"""


										_lowerCamelCase     :		Any 						=						self.num_choices
										_lowerCamelCase     :		List[str] 						=						NystromformerForMultipleChoice(config=UpperCAmelCase__)
										model.to(UpperCAmelCase__)
										model.eval()
										_lowerCamelCase     :		List[Any] 						=						input_ids.unsqueeze(1).expand(-1    ,  self.num_choices    ,  -1).contiguous()
										_lowerCamelCase     :		int 						=						token_type_ids.unsqueeze(1).expand(-1    ,  self.num_choices    ,  -1).contiguous()
										_lowerCamelCase     :		Optional[int] 						=						input_mask.unsqueeze(1).expand(-1    ,  self.num_choices    ,  -1).contiguous()
										_lowerCamelCase     :		Union[str, Any] 						=						model(
										    UpperCAmelCase__    ,  attention_mask=UpperCAmelCase__    ,  token_type_ids=UpperCAmelCase__    ,  labels=UpperCAmelCase__    ,  )
										self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_choices))




						def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


										"""simple docstring"""


										_lowerCamelCase     :		Dict 						=						self.prepare_config_and_inputs()
										(
										    _lowerCamelCase  
										)     :		Union[str, Any] 						=						config_and_inputs
										_lowerCamelCase     :		Dict 						=						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
										return config, inputs_dict





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





						'''simple docstring'''






						_snake_case    		=       (
						    (
						        NystromformerModel,
						        NystromformerForMaskedLM,
						        NystromformerForMultipleChoice,
						        NystromformerForQuestionAnswering,
						        NystromformerForSequenceClassification,
						        NystromformerForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						_snake_case    		=       (
						    {
						        'feature-extraction': NystromformerModel,
						        'fill-mask': NystromformerForMaskedLM,
						        'question-answering': NystromformerForQuestionAnswering,
						        'text-classification': NystromformerForSequenceClassification,
						        'token-classification': NystromformerForTokenClassification,
						        'zero-shot': NystromformerForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_snake_case    		=       False
						_snake_case    		=       False



						def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->str:


										"""simple docstring"""


										_lowerCamelCase     :		List[Any] 						=						NystromformerModelTester(self)
										_lowerCamelCase     :		Tuple 						=						ConfigTester(self    ,  config_class=UpperCAmelCase__    ,  hidden_size=37)



						def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->int:


										"""simple docstring"""


										self.config_tester.run_common_tests()



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


										"""simple docstring"""


										_lowerCamelCase     :		Tuple 						=						self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCAmelCase__)



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


										"""simple docstring"""


										_lowerCamelCase     :		Dict 						=						self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
														_lowerCamelCase     :		Dict 						=						type
														self.model_tester.create_and_check_model(*UpperCAmelCase__)



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


										"""simple docstring"""


										_lowerCamelCase     :		Optional[int] 						=						self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__)



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


										"""simple docstring"""


										_lowerCamelCase     :		Any 						=						self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase__)



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


										"""simple docstring"""


										_lowerCamelCase     :		Tuple 						=						self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__)



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


										"""simple docstring"""


										_lowerCamelCase     :		int 						=						self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__)



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


										"""simple docstring"""


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




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


										"""simple docstring"""


										for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_lowerCamelCase     :		List[Any] 						=						NystromformerModel.from_pretrained(UpperCAmelCase__)
														self.assertIsNotNone(UpperCAmelCase__)





@require_torch
class 	__snake_case	( unittest.TestCase		):





						'''simple docstring'''






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


										"""simple docstring"""


										_lowerCamelCase     :		Dict 						=						NystromformerModel.from_pretrained("""uw-madison/nystromformer-512""")
										_lowerCamelCase     :		Any 						=						torch.tensor([[0, 1, 2, 3, 4, 5]])

										with torch.no_grad():
														_lowerCamelCase     :		Tuple 						=						model(UpperCAmelCase__)[0]

										_lowerCamelCase     :		int 						=						torch.Size((1, 6, 768))
										self.assertEqual(output.shape    ,  UpperCAmelCase__)

										_lowerCamelCase     :		Dict 						=						torch.tensor(
										    [[[-0.4_5_3_2, -0.0_9_3_6, 0.5_1_3_7], [-0.2_6_7_6, 0.0_6_2_8, 0.6_1_8_6], [-0.3_6_2_9, -0.1_7_2_6, 0.4_7_1_6]]])

										self.assertTrue(torch.allclose(output[:, :3, :3]    ,  UpperCAmelCase__    ,  atol=1E-4))




						@slow
						def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Tuple:


										"""simple docstring"""


										_lowerCamelCase     :		Optional[int] 						=						'''the [MASK] of Belgium is Brussels'''

										_lowerCamelCase     :		Dict 						=						AutoTokenizer.from_pretrained("""uw-madison/nystromformer-512""")
										_lowerCamelCase     :		Optional[int] 						=						NystromformerForMaskedLM.from_pretrained("""uw-madison/nystromformer-512""")

										_lowerCamelCase     :		List[Any] 						=						tokenizer(UpperCAmelCase__    ,  return_tensors="""pt""")

										with torch.no_grad():
														_lowerCamelCase     :		Optional[int] 						=						model(encoding.input_ids).logits

										_lowerCamelCase     :		Tuple 						=						token_logits[:, 2, :].argmax(-1)[0]

										self.assertEqual(tokenizer.decode(UpperCAmelCase__)    ,  """capital""")





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"""])




