


'''simple docstring'''





import unittest

from transformers import MPNetConfig, 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 (
        MPNetForMaskedLM,
        MPNetForMultipleChoice,
        MPNetForQuestionAnswering,
        MPNetForSequenceClassification,
        MPNetForTokenClassification,
        MPNetModel,
    )




class 				UpperCAmelCase			:


 def __init__(      self							:	Optional[int]			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	Dict=1_3			,       __lowerCamelCase							:	Tuple=7			,       __lowerCamelCase							:	Any=True			,       __lowerCamelCase							:	Tuple=True			,       __lowerCamelCase							:	Union[str, Any]=False			,       __lowerCamelCase							:	Tuple=True			,       __lowerCamelCase							:	str=9_9			,       __lowerCamelCase							:	str=6_4			,       __lowerCamelCase							:	Union[str, Any]=5			,       __lowerCamelCase							:	List[str]=4			,       __lowerCamelCase							:	Union[str, Any]=6_4			,       __lowerCamelCase							:	Dict="gelu"			,       __lowerCamelCase							:	str=0.1			,       __lowerCamelCase							:	Dict=0.1			,       __lowerCamelCase							:	List[str]=5_1_2			,       __lowerCamelCase							:	Optional[int]=1_6			,       __lowerCamelCase							:	Optional[Any]=2			,       __lowerCamelCase							:	Optional[Any]=0.02			,       __lowerCamelCase							:	Optional[Any]=3			,       __lowerCamelCase							:	Dict=4			,       __lowerCamelCase							:	Optional[int]=None			,       ):
    UpperCAmelCase__	:Optional[Any]      =      parent
    UpperCAmelCase__	:Any      =      batch_size
    UpperCAmelCase__	:Optional[int]      =      seq_length
    UpperCAmelCase__	:List[str]      =      is_training
    UpperCAmelCase__	:Optional[int]      =      use_input_mask
    UpperCAmelCase__	:List[Any]      =      use_token_type_ids
    UpperCAmelCase__	:str      =      use_labels
    UpperCAmelCase__	:int      =      vocab_size
    UpperCAmelCase__	:Optional[int]      =      hidden_size
    UpperCAmelCase__	:str      =      num_hidden_layers
    UpperCAmelCase__	:List[Any]      =      num_attention_heads
    UpperCAmelCase__	:int      =      intermediate_size
    UpperCAmelCase__	:Tuple      =      hidden_act
    UpperCAmelCase__	:Tuple      =      hidden_dropout_prob
    UpperCAmelCase__	:List[str]      =      attention_probs_dropout_prob
    UpperCAmelCase__	:Tuple      =      max_position_embeddings
    UpperCAmelCase__	:Optional[int]      =      type_vocab_size
    UpperCAmelCase__	:Union[str, Any]      =      type_sequence_label_size
    UpperCAmelCase__	:Tuple      =      initializer_range
    UpperCAmelCase__	:str      =      num_labels
    UpperCAmelCase__	:Union[str, Any]      =      num_choices
    UpperCAmelCase__	:Optional[Any]      =      scope


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    return MPNetConfig.from_pretrained('''microsoft/mpnet-base''' )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:List[Any]      =      ids_tensor([self.batch_size, self.seq_length]			,       self.vocab_size )

    UpperCAmelCase__	:str      =      None
    if self.use_input_mask:
       UpperCAmelCase__	:List[str]      =      random_attention_mask([self.batch_size, self.seq_length] )

    UpperCAmelCase__	:Optional[Any]      =      None
    UpperCAmelCase__	:Any      =      None
    UpperCAmelCase__	:str      =      None
    if self.use_labels:
       UpperCAmelCase__	:Union[str, Any]      =      ids_tensor([self.batch_size]			,       self.type_sequence_label_size )
       UpperCAmelCase__	:str      =      ids_tensor([self.batch_size, self.seq_length]			,       self.num_labels )
       UpperCAmelCase__	:Dict      =      ids_tensor([self.batch_size]			,       self.num_choices )

    UpperCAmelCase__	:Any      =      self.get_config()
    return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    return MPNetConfig(
        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			,       initializer_range=self.initializer_range			,       )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any			,       __lowerCamelCase							:	Optional[Any]			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	Optional[Any]			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	str ):
    UpperCAmelCase__	:Union[str, Any]      =      MPNetModel(config=__lowerCamelCase )
    model.to(__lowerCamelCase )
    model.eval()
    UpperCAmelCase__	:List[Any]      =      model(__lowerCamelCase			,       __lowerCamelCase )
    UpperCAmelCase__	:List[Any]      =      model(__lowerCamelCase )
    self.parent.assertEqual(result.last_hidden_state.shape			,       (self.batch_size, self.seq_length, self.hidden_size) )
    self.parent.assertEqual(result.pooler_output.shape			,       (self.batch_size, self.hidden_size) )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	List[str]			,       __lowerCamelCase							:	Union[str, Any]			,       __lowerCamelCase							:	Union[str, Any]			,       __lowerCamelCase							:	List[str]			,       __lowerCamelCase							:	Dict			,       __lowerCamelCase							:	List[Any] ):
    UpperCAmelCase__	:Any      =      MPNetForQuestionAnswering(config=__lowerCamelCase )
    model.to(__lowerCamelCase )
    model.eval()
    UpperCAmelCase__	:Optional[int]      =      model(
        __lowerCamelCase			,       attention_mask=__lowerCamelCase			,       start_positions=__lowerCamelCase			,       end_positions=__lowerCamelCase			,       )
    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							:	List[str]			,       __lowerCamelCase							:	Union[str, Any]			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	List[Any]			,       __lowerCamelCase							:	List[str]			,       __lowerCamelCase							:	str ):
    UpperCAmelCase__	:Dict      =      self.num_labels
    UpperCAmelCase__	:List[Any]      =      MPNetForSequenceClassification(__lowerCamelCase )
    model.to(__lowerCamelCase )
    model.eval()
    UpperCAmelCase__	:List[Any]      =      model(__lowerCamelCase			,       attention_mask=__lowerCamelCase			,       labels=__lowerCamelCase )
    self.parent.assertEqual(result.logits.shape			,       (self.batch_size, self.num_labels) )


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


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	Optional[Any]			,       __lowerCamelCase							:	Tuple			,       __lowerCamelCase							:	Optional[Any]			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	Optional[Any] ):
    UpperCAmelCase__	:List[str]      =      self.num_labels
    UpperCAmelCase__	:Any      =      MPNetForTokenClassification(config=__lowerCamelCase )
    model.to(__lowerCamelCase )
    model.eval()
    UpperCAmelCase__	:Any      =      model(__lowerCamelCase			,       attention_mask=__lowerCamelCase			,       labels=__lowerCamelCase )
    self.parent.assertEqual(result.logits.shape			,       (self.batch_size, self.seq_length, self.num_labels) )





 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple ):
    UpperCAmelCase__	:Any      =      self.prepare_config_and_inputs()
    ((UpperCAmelCase__)   ,						(UpperCAmelCase__)   ,						(UpperCAmelCase__)   ,						(UpperCAmelCase__)   ,						(UpperCAmelCase__)   ,						(UpperCAmelCase__))	:List[Any]      =      config_and_inputs
    UpperCAmelCase__	:Union[str, Any]      =      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
    return config, inputs_dict




@require_torch
class 				UpperCAmelCase			(			_snake_case						, _snake_case						, unittest.TestCase						):
 UpperCAmelCase				=					(
     (
         MPNetForMaskedLM,
         MPNetForMultipleChoice,
         MPNetForQuestionAnswering,
         MPNetForSequenceClassification,
         MPNetForTokenClassification,
         MPNetModel,
     )
     if is_torch_available()
     else ()
 )
 UpperCAmelCase				=					(
     {
         "feature-extraction": MPNetModel,
         "fill-mask": MPNetForMaskedLM,
         "question-answering": MPNetForQuestionAnswering,
         "text-classification": MPNetForSequenceClassification,
         "token-classification": MPNetForTokenClassification,
         "zero-shot": MPNetForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 UpperCAmelCase				=					False
 UpperCAmelCase				=					True


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    UpperCAmelCase__	:Optional[Any]      =      MPNetModelTester(self )
    UpperCAmelCase__	:Any      =      ConfigTester(self			,       config_class=__lowerCamelCase			,       hidden_size=3_7 )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    self.config_tester.run_common_tests()


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    UpperCAmelCase__	:Union[str, Any]      =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_model(*__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    UpperCAmelCase__	:int      =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_sequence_classification(*__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple ):
    UpperCAmelCase__	:List[Any]      =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_multiple_choice(*__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    UpperCAmelCase__	:Optional[Any]      =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_token_classification(*__lowerCamelCase )





 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    UpperCAmelCase__	:Union[str, Any]      =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_question_answering(*__lowerCamelCase )




@require_torch
class 				UpperCAmelCase			(			unittest.TestCase						):


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:List[str]      =      MPNetModel.from_pretrained('''microsoft/mpnet-base''' )
    UpperCAmelCase__	:int      =      torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
    UpperCAmelCase__	:List[str]      =      model(__lowerCamelCase )[0]
    UpperCAmelCase__	:Optional[int]      =      torch.Size((1, 1_1, 7_6_8) )
    self.assertEqual(output.shape			,       __lowerCamelCase )
    UpperCAmelCase__	:Dict      =      torch.tensor(
        [[[-0.05_50, 0.19_43, -0.07_40], [-0.05_62, 0.22_11, -0.05_79], [-0.04_37, 0.33_37, -0.06_41]]] )
    # compare the actual values for a slice.
    self.assertTrue(torch.allclose(output[:, :3, :3]			,       __lowerCamelCase			,       atol=1e-4 ) )







'''simple docstring'''





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 				UpperCAmelCase			(			unittest.TestCase						):
 UpperCAmelCase				=					ViTImageProcessor if is_vision_available() else None


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    return self.image_processor_tester.prepare_image_processor_dict()


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:Any      =      (3, 3_2, 1_2_8)
    UpperCAmelCase__	:Optional[int]      =      tempfile.mkdtemp()

    # fmt: off
    UpperCAmelCase__	:Union[str, Any]      =      ['''[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
    UpperCAmelCase__	:str      =      dict(zip(__lowerCamelCase			,       range(len(__lowerCamelCase ) ) ) )

    UpperCAmelCase__	:str      =      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(__lowerCamelCase ) + '''\n''' )

    UpperCAmelCase__	:Dict      =      {
        '''do_normalize''': False,
        '''do_resize''': True,
        '''image_processor_type''': '''ViTImageProcessor''',
        '''resample''': 3,
        '''size''': {'''height''': 3_2, '''width''': 1_2_8},
    }
    UpperCAmelCase__	:Optional[Any]      =      os.path.join(self.tmpdirname			,       __lowerCamelCase )
    with open(self.image_processor_file			,       '''w'''			,       encoding='''utf-8''' ) as fp:
       json.dump(__lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple			,       **__lowerCamelCase							:	Any ):
    return MgpstrTokenizer.from_pretrained(self.tmpdirname			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       **__lowerCamelCase							:	str ):
    return ViTImageProcessor.from_pretrained(self.tmpdirname			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    shutil.rmtree(self.tmpdirname )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:Optional[int]      =      np.random.randint(2_5_5			,       size=(3, 3_0, 4_0_0)			,       dtype=np.uinta )

    UpperCAmelCase__	:Union[str, Any]      =      Image.fromarray(np.moveaxis(__lowerCamelCase			,       0			,       -1 ) )

    return image_input


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    UpperCAmelCase__	:Optional[Any]      =      self.get_tokenizer()
    UpperCAmelCase__	:str      =      self.get_image_processor()

    UpperCAmelCase__	:List[str]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )
    processor.save_pretrained(self.tmpdirname )
    UpperCAmelCase__	:Optional[int]      =      MgpstrProcessor.from_pretrained(self.tmpdirname			,       use_fast=__lowerCamelCase )

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

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:Any      =      self.get_tokenizer()
    UpperCAmelCase__	:int      =      self.get_image_processor()

    UpperCAmelCase__	:Union[str, Any]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )
    processor.save_pretrained(self.tmpdirname )

    UpperCAmelCase__	:List[Any]      =      self.get_tokenizer(bos_token='''(BOS)'''			,       eos_token='''(EOS)''' )
    UpperCAmelCase__	:List[Any]      =      self.get_image_processor(do_normalize=__lowerCamelCase			,       padding_value=1.0 )

    UpperCAmelCase__	:Any      =      MgpstrProcessor.from_pretrained(
        self.tmpdirname			,       bos_token='''(BOS)'''			,       eos_token='''(EOS)'''			,       do_normalize=__lowerCamelCase			,       padding_value=1.0 )

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

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:Tuple      =      self.get_image_processor()
    UpperCAmelCase__	:Any      =      self.get_tokenizer()

    UpperCAmelCase__	:Any      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Optional[Any]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:str      =      image_processor(__lowerCamelCase			,       return_tensors='''np''' )
    UpperCAmelCase__	:Tuple      =      processor(images=__lowerCamelCase			,       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[int] ):
    UpperCAmelCase__	:Optional[Any]      =      self.get_image_processor()
    UpperCAmelCase__	:List[Any]      =      self.get_tokenizer()

    UpperCAmelCase__	:List[str]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      '''test'''

    UpperCAmelCase__	:Tuple      =      processor(text=__lowerCamelCase )

    UpperCAmelCase__	:Tuple      =      tokenizer(__lowerCamelCase )
    for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]			,       encoded_processor[key] )


 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Dict      =      self.get_tokenizer()

    UpperCAmelCase__	:int      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:List[Any]      =      '''test'''
    UpperCAmelCase__	:List[Any]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:Any      =      processor(text=__lowerCamelCase			,       images=__lowerCamelCase )

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

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Optional[int]      =      self.get_tokenizer()

    UpperCAmelCase__	:Union[str, Any]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Any      =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

    UpperCAmelCase__	:Optional[Any]      =      processor.char_decode(__lowerCamelCase )
    UpperCAmelCase__	:int      =      tokenizer.batch_decode(__lowerCamelCase )
    UpperCAmelCase__	:int      =      [seq.replace(''' '''			,       '''''' ) for seq in decoded_tok]

    self.assertListEqual(__lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Optional[int]      =      self.get_tokenizer()

    UpperCAmelCase__	:Dict      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:List[str]      =      None
    UpperCAmelCase__	:Optional[int]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:Tuple      =      processor(text=__lowerCamelCase			,       images=__lowerCamelCase )

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





 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple ):
    UpperCAmelCase__	:Dict      =      self.get_image_processor()
    UpperCAmelCase__	:int      =      self.get_tokenizer()

    UpperCAmelCase__	:Any      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      torch.randn(1			,       2_7			,       3_8 )
    UpperCAmelCase__	:Any      =      torch.randn(1			,       2_7			,       5_0_2_5_7 )
    UpperCAmelCase__	:Union[str, Any]      =      torch.randn(1			,       2_7			,       3_0_5_2_2 )

    UpperCAmelCase__	:Optional[Any]      =      processor.batch_decode([char_input, bpe_input, wp_input] )

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



