

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast



@require_vision
class 					lowercase		( unittest.TestCase      ):



 '''simple docstring'''


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






  """simple docstring"""





  UpperCAmelCase__           =       tempfile.mkdtemp()

  UpperCAmelCase__           =       CLIPImageProcessor()
  UpperCAmelCase__           =       BertTokenizer.from_pretrained(
      'hf-internal-testing/tiny-random-BertModel' , model_input_names=['input_ids', 'attention_mask']				)

  UpperCAmelCase__           =       GitProcessor(__a , __a				)

  processor.save_pretrained(self.tmpdirname				)




 def 						UpperCamelCase__							(self , **__a				)				->      str:






  """simple docstring"""





  return AutoProcessor.from_pretrained(self.tmpdirname , **__a				).tokenizer




 def 						UpperCamelCase__							(self , **__a				)				->      Optional[Any]:






  """simple docstring"""





  return AutoProcessor.from_pretrained(self.tmpdirname , **__a				).image_processor




 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  shutil.rmtree(self.tmpdirname				)




 def 						UpperCamelCase__							(self				)				->      str:






  """simple docstring"""





  UpperCAmelCase__           =       [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta				)]

  UpperCAmelCase__           =       [Image.fromarray(np.moveaxis(__a , 0 , -1				)				) for x in image_inputs]

  return image_inputs




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       GitProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor()				)
  processor.save_pretrained(self.tmpdirname				)

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

  UpperCAmelCase__           =       GitProcessor.from_pretrained(
      self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=__a , padding_value=1.0				)

  self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab()				)
  self.assertIsInstance(processor.tokenizer , __a				)

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




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       self.get_image_processor()
  UpperCAmelCase__           =       self.get_tokenizer()

  UpperCAmelCase__           =       GitProcessor(tokenizer=__a , image_processor=__a				)

  UpperCAmelCase__           =       self.prepare_image_inputs()

  UpperCAmelCase__           =       image_processor(__a , return_tensors='np'				)
  UpperCAmelCase__           =       processor(images=__a , return_tensors='np'				)

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




 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  UpperCAmelCase__           =       self.get_image_processor()
  UpperCAmelCase__           =       self.get_tokenizer()

  UpperCAmelCase__           =       GitProcessor(tokenizer=__a , image_processor=__a				)

  UpperCAmelCase__           =       'lower newer'

  UpperCAmelCase__           =       processor(text=__a				)

  UpperCAmelCase__           =       tokenizer(__a , return_token_type_ids=__a				)

  for key in encoded_tok.keys():
   self.assertListEqual(encoded_tok[key] , encoded_processor[key]				)




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






  """simple docstring"""





  UpperCAmelCase__           =       self.get_image_processor()
  UpperCAmelCase__           =       self.get_tokenizer()

  UpperCAmelCase__           =       GitProcessor(tokenizer=__a , image_processor=__a				)

  UpperCAmelCase__           =       'lower newer'
  UpperCAmelCase__           =       self.prepare_image_inputs()

  UpperCAmelCase__           =       processor(text=__a , images=__a				)

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

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




 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  UpperCAmelCase__           =       self.get_image_processor()
  UpperCAmelCase__           =       self.get_tokenizer()

  UpperCAmelCase__           =       GitProcessor(tokenizer=__a , image_processor=__a				)

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

  UpperCAmelCase__           =       processor.batch_decode(__a				)
  UpperCAmelCase__           =       tokenizer.batch_decode(__a				)

  self.assertListEqual(__a , __a				)




 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  UpperCAmelCase__           =       self.get_image_processor()
  UpperCAmelCase__           =       self.get_tokenizer()

  UpperCAmelCase__           =       GitProcessor(tokenizer=__a , image_processor=__a				)

  UpperCAmelCase__           =       'lower newer'
  UpperCAmelCase__           =       self.prepare_image_inputs()

  UpperCAmelCase__           =       processor(text=__a , images=__a				)

  # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
  self.assertListEqual(list(inputs.keys()				) , ['input_ids', 'attention_mask', 'pixel_values']				)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase     =    {
    '''configuration_fsmt''': ['''FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FSMTConfig'''],
    '''tokenization_fsmt''': ['''FSMTTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _UpperCamelCase     =    ['''FSMTForConditionalGeneration''', '''FSMTModel''', '''PretrainedFSMTModel''']


if TYPE_CHECKING:
 from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
 from .tokenization_fsmt import FSMTTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
 import sys

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



