
"""simple docstring"""







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      __a      (       unittest.TestCase      ):







       def  _snake_case      (       self		):



          '''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(lowerCamelCase_				,lowerCamelCase_		)

          processor.save_pretrained(self.tmpdirname		)







       def  _snake_case      (       self				,**lowerCamelCase_		):



          '''simple docstring'''

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







       def  _snake_case      (       self				,**lowerCamelCase_		):



          '''simple docstring'''

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







       def  _snake_case      (       self		):



          '''simple docstring'''

          shutil.rmtree(self.tmpdirname		)







       def  _snake_case      (       self		):



          '''simple docstring'''

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

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

          return image_inputs







       def  _snake_case      (       self		):



          '''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=lowerCamelCase_				,padding_value=1.0		)

          _UpperCAmelCase 						=       GitProcessor.from_pretrained(
              self.tmpdirname				,bos_token="(BOS)"				,eos_token="(EOS)"				,do_normalize=lowerCamelCase_				,padding_value=1.0		)

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

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







       def  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase 						=       self.get_image_processor()
          _UpperCAmelCase 						=       self.get_tokenizer()

          _UpperCAmelCase 						=       GitProcessor(tokenizer=lowerCamelCase_				,image_processor=lowerCamelCase_		)

          _UpperCAmelCase 						=       self.prepare_image_inputs()

          _UpperCAmelCase 						=       image_processor(lowerCamelCase_				,return_tensors="np"		)
          _UpperCAmelCase 						=       processor(images=lowerCamelCase_				,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  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase 						=       self.get_image_processor()
          _UpperCAmelCase 						=       self.get_tokenizer()

          _UpperCAmelCase 						=       GitProcessor(tokenizer=lowerCamelCase_				,image_processor=lowerCamelCase_		)

          _UpperCAmelCase 						=       "lower newer"

          _UpperCAmelCase 						=       processor(text=lowerCamelCase_		)

          _UpperCAmelCase 						=       tokenizer(lowerCamelCase_				,return_token_type_ids=lowerCamelCase_		)

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







       def  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase 						=       self.get_image_processor()
          _UpperCAmelCase 						=       self.get_tokenizer()

          _UpperCAmelCase 						=       GitProcessor(tokenizer=lowerCamelCase_				,image_processor=lowerCamelCase_		)

          _UpperCAmelCase 						=       "lower newer"
          _UpperCAmelCase 						=       self.prepare_image_inputs()

          _UpperCAmelCase 						=       processor(text=lowerCamelCase_				,images=lowerCamelCase_		)

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

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







       def  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase 						=       self.get_image_processor()
          _UpperCAmelCase 						=       self.get_tokenizer()

          _UpperCAmelCase 						=       GitProcessor(tokenizer=lowerCamelCase_				,image_processor=lowerCamelCase_		)

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

          _UpperCAmelCase 						=       processor.batch_decode(lowerCamelCase_		)
          _UpperCAmelCase 						=       tokenizer.batch_decode(lowerCamelCase_		)

          self.assertListEqual(lowerCamelCase_				,lowerCamelCase_		)







       def  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase 						=       self.get_image_processor()
          _UpperCAmelCase 						=       self.get_tokenizer()

          _UpperCAmelCase 						=       GitProcessor(tokenizer=lowerCamelCase_				,image_processor=lowerCamelCase_		)

          _UpperCAmelCase 						=       "lower newer"
          _UpperCAmelCase 						=       self.prepare_image_inputs()

          _UpperCAmelCase 						=       processor(text=lowerCamelCase_				,images=lowerCamelCase_		)

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



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE_  					= {
    'configuration_deformable_detr': ['DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeformableDetrConfig'],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_  					= ['DeformableDetrFeatureExtractor']
  SCREAMING_SNAKE_CASE_  					= ['DeformableDetrImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_  					= [
      'DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DeformableDetrForObjectDetection',
      'DeformableDetrModel',
      'DeformableDetrPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
    from .image_processing_deformable_detr import DeformableDetrImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deformable_detr import (
        DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        DeformableDetrForObjectDetection,
        DeformableDetrModel,
        DeformableDetrPreTrainedModel,
    )

else:
  import sys

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

