






'''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				):




      """simple docstring"""




      UpperCamelCase_								=			ViTImageProcessor if is_vision_available() else None






      @property
      def 				A__      ( self			:		Optional[int]		)  ->  int:


       '''simple docstring'''

       return self.image_processor_tester.prepare_image_processor_dict()






      def 				A__      ( self			:		int		)  ->  str:


       '''simple docstring'''

       lowercase					:   Tuple				  =(3, 32, 128)
       lowercase					:   str				  =tempfile.mkdtemp()

       # fmt: off
       lowercase					:   Tuple				  =['''[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
       lowercase					:   Union[str, Any]				  =dict(zip(UpperCAmelCase     ,      range(len(UpperCAmelCase		)		)		)		)

       lowercase					:   Tuple				  =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'''		)

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






      def 				A__      ( self			:		Dict     ,      **UpperCAmelCase			:		Tuple		)  ->  List[Any]:


       '''simple docstring'''

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






      def 				A__      ( self			:		str     ,      **UpperCAmelCase			:		Optional[Any]		)  ->  List[str]:


       '''simple docstring'''

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






      def 				A__      ( self			:		List[str]		)  ->  Dict:


       '''simple docstring'''

       shutil.rmtree(self.tmpdirname		)






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


       '''simple docstring'''

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

       lowercase					:   List[Any]				  =Image.fromarray(np.moveaxis(UpperCAmelCase     ,      0     ,      -1		)		)

       return image_input






      def 				A__      ( self			:		Any		)  ->  int:


       '''simple docstring'''

       lowercase					:   Tuple				  =self.get_tokenizer()
       lowercase					:   Dict				  =self.get_image_processor()

       lowercase					:   Tuple				  =MgpstrProcessor(tokenizer=UpperCAmelCase     ,      image_processor=UpperCAmelCase		)
       processor.save_pretrained(self.tmpdirname		)
       lowercase					:   str				  =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 				A__      ( self			:		str		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   str				  =self.get_tokenizer()
       lowercase					:   List[Any]				  =self.get_image_processor()

       lowercase					:   Dict				  =MgpstrProcessor(tokenizer=UpperCAmelCase     ,      image_processor=UpperCAmelCase		)
       processor.save_pretrained(self.tmpdirname		)

       lowercase					:   str				  =self.get_tokenizer(bos_token='''(BOS)'''     ,      eos_token='''(EOS)'''		)
       lowercase					:   str				  =self.get_image_processor(do_normalize=UpperCAmelCase     ,      padding_value=1.0		)

       lowercase					:   Any				  =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 				A__      ( self			:		List[Any]		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =self.get_image_processor()
       lowercase					:   Dict				  =self.get_tokenizer()

       lowercase					:   List[Any]				  =MgpstrProcessor(tokenizer=UpperCAmelCase     ,      image_processor=UpperCAmelCase		)

       lowercase					:   Any				  =self.prepare_image_inputs()

       lowercase					:   List[Any]				  =image_processor(UpperCAmelCase     ,      return_tensors='''np'''		)
       lowercase					:   str				  =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 				A__      ( self			:		Union[str, Any]		)  ->  Optional[Any]:


       '''simple docstring'''

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

       lowercase					:   str				  =MgpstrProcessor(tokenizer=UpperCAmelCase     ,      image_processor=UpperCAmelCase		)

       lowercase					:   List[str]				  ='''test'''

       lowercase					:   Optional[int]				  =processor(text=UpperCAmelCase		)

       lowercase					:   Union[str, Any]				  =tokenizer(UpperCAmelCase		)
       for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]     ,      encoded_processor[key]		)






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


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =self.get_image_processor()
       lowercase					:   Optional[int]				  =self.get_tokenizer()

       lowercase					:   Optional[Any]				  =MgpstrProcessor(tokenizer=UpperCAmelCase     ,      image_processor=UpperCAmelCase		)

       lowercase					:   Any				  ='''test'''
       lowercase					:   Any				  =self.prepare_image_inputs()

       lowercase					:   str				  =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 				A__      ( self			:		List[str]		)  ->  str:


       '''simple docstring'''

       lowercase					:   str				  =self.get_image_processor()
       lowercase					:   Optional[Any]				  =self.get_tokenizer()

       lowercase					:   Dict				  =MgpstrProcessor(tokenizer=UpperCAmelCase     ,      image_processor=UpperCAmelCase		)

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

       lowercase					:   Optional[int]				  =processor.char_decode(UpperCAmelCase		)
       lowercase					:   Union[str, Any]				  =tokenizer.batch_decode(UpperCAmelCase		)
       lowercase					:   Tuple				  =[seq.replace(''' '''     ,      ''''''		) for seq in decoded_tok]

       self.assertListEqual(UpperCAmelCase     ,      UpperCAmelCase		)






      def 				A__      ( self			:		Tuple		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   str				  =self.get_image_processor()
       lowercase					:   List[Any]				  =self.get_tokenizer()

       lowercase					:   Any				  =MgpstrProcessor(tokenizer=UpperCAmelCase     ,      image_processor=UpperCAmelCase		)

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

       lowercase					:   Optional[int]				  =processor(text=UpperCAmelCase     ,      images=UpperCAmelCase		)

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






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


       '''simple docstring'''

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

       lowercase					:   int				  =MgpstrProcessor(tokenizer=UpperCAmelCase     ,      image_processor=UpperCAmelCase		)

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

       lowercase					:   Union[str, Any]				  =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_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE						=	{
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
          'M2M100ForConditionalGeneration',
          'M2M100Model',
          'M2M100PreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
      from .tokenization_mam_aaa import MaMaaaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mam_aaa import (
                M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaMaaaForConditionalGeneration,
                MaMaaaModel,
                MaMaaaPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



