






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 		lowercase_					(	unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  ViTImageProcessor if is_vision_available() else None







      @property
      def       __lowerCAmelCase					(      self      :						Dict  )    ->Tuple:




       """simple docstring"""


       return self.image_processor_tester.prepare_image_processor_dict()







      def       __lowerCAmelCase					(      self      :						Dict  )    ->int:




       """simple docstring"""


       a		  =      (3, 32, 128)
       a		  =      tempfile.mkdtemp()

       # fmt: off
       a		  =      ['''[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
       a		  =      dict(zip(__UpperCAmelCase ,					range(len(__UpperCAmelCase  )  )  )  )

       a		  =      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'''  )

       a		  =      {
           '''do_normalize''': False,
           '''do_resize''': True,
           '''image_processor_type''': '''ViTImageProcessor''',
           '''resample''': 3,
           '''size''': {'''height''': 32, '''width''': 128},
       }
       a		  =      os.path.join(self.tmpdirname ,					__UpperCAmelCase  )
       with open(self.image_processor_file ,					'''w''' ,					encoding='''utf-8'''  ) as fp:
        json.dump(__UpperCAmelCase ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Union[str, Any] ,					**__UpperCAmelCase      :						List[Any]  )    ->List[Any]:




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						List[Any] ,					**__UpperCAmelCase      :						Tuple  )    ->List[str]:




       """simple docstring"""


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







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




       """simple docstring"""


       shutil.rmtree(self.tmpdirname  )







      def       __lowerCAmelCase					(      self      :						int  )    ->Dict:




       """simple docstring"""


       a		  =      np.random.randint(255 ,					size=(3, 30, 400) ,					dtype=np.uinta  )

       a		  =      Image.fromarray(np.moveaxis(__UpperCAmelCase ,					0 ,					-1  )  )

       return image_input







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




       """simple docstring"""


       a		  =      self.get_tokenizer()
       a		  =      self.get_image_processor()

       a		  =      MgpstrProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )
       processor.save_pretrained(self.tmpdirname  )
       a		  =      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       __lowerCAmelCase					(      self      :						Tuple  )    ->str:




       """simple docstring"""


       a		  =      self.get_tokenizer()
       a		  =      self.get_image_processor()

       a		  =      MgpstrProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )
       processor.save_pretrained(self.tmpdirname  )

       a		  =      self.get_tokenizer(bos_token='''(BOS)''' ,					eos_token='''(EOS)'''  )
       a		  =      self.get_image_processor(do_normalize=__UpperCAmelCase ,					padding_value=1.0  )

       a		  =      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       __lowerCAmelCase					(      self      :						Tuple  )    ->str:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      MgpstrProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      self.prepare_image_inputs()

       a		  =      image_processor(__UpperCAmelCase ,					return_tensors='''np'''  )
       a		  =      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       __lowerCAmelCase					(      self      :						List[str]  )    ->Any:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      MgpstrProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      '''test'''

       a		  =      processor(text=__UpperCAmelCase  )

       a		  =      tokenizer(__UpperCAmelCase  )
       for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key] ,					encoded_processor[key]  )







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




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      MgpstrProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      '''test'''
       a		  =      self.prepare_image_inputs()

       a		  =      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       __lowerCAmelCase					(      self      :						str  )    ->Dict:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      MgpstrProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

       a		  =      processor.char_decode(__UpperCAmelCase  )
       a		  =      tokenizer.batch_decode(__UpperCAmelCase  )
       a		  =      [seq.replace(''' ''' ,					''''''  ) for seq in decoded_tok]

       self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						int  )    ->int:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      MgpstrProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      None
       a		  =      self.prepare_image_inputs()

       a		  =      processor(text=__UpperCAmelCase ,					images=__UpperCAmelCase  )

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







      def       __lowerCAmelCase					(      self      :						Any  )    ->Any:




       """simple docstring"""


       a		  =      self.get_image_processor()
       a		  =      self.get_tokenizer()

       a		  =      MgpstrProcessor(tokenizer=__UpperCAmelCase ,					image_processor=__UpperCAmelCase  )

       a		  =      torch.randn(1 ,					27 ,					38  )
       a		  =      torch.randn(1 ,					27 ,					50_257  )
       a		  =      torch.randn(1 ,					27 ,					30_522  )

       a		  =      processor.batch_decode([char_input, bpe_input, wp_input]  )

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









"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_     :						List[str]						=					{'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						str						=					['XLNetTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						List[str]						=					['XLNetTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						int						=					[
       'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'XLNetForMultipleChoice',
       'XLNetForQuestionAnswering',
       'XLNetForQuestionAnsweringSimple',
       'XLNetForSequenceClassification',
       'XLNetForTokenClassification',
       'XLNetLMHeadModel',
       'XLNetModel',
       'XLNetPreTrainedModel',
       'load_tf_weights_in_xlnet',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						Union[str, Any]						=					[
       'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFXLNetForMultipleChoice',
       'TFXLNetForQuestionAnsweringSimple',
       'TFXLNetForSequenceClassification',
       'TFXLNetForTokenClassification',
       'TFXLNetLMHeadModel',
       'TFXLNetMainLayer',
       'TFXLNetModel',
       'TFXLNetPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet import XLNetTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet_fast import XLNetTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlnet import (
          XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLNetForMultipleChoice,
          XLNetForQuestionAnswering,
          XLNetForQuestionAnsweringSimple,
          XLNetForSequenceClassification,
          XLNetForTokenClassification,
          XLNetLMHeadModel,
          XLNetModel,
          XLNetPreTrainedModel,
          load_tf_weights_in_xlnet,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlnet import (
          TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLNetForMultipleChoice,
          TFXLNetForQuestionAnsweringSimple,
          TFXLNetForSequenceClassification,
          TFXLNetForTokenClassification,
          TFXLNetLMHeadModel,
          TFXLNetMainLayer,
          TFXLNetModel,
          TFXLNetPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase_     :						List[Any]						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)