





from typing import TYPE_CHECKING

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


lowercase     :			List[str]     						=	{
    """configuration_donut_swin""": ["""DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DonutSwinConfig"""],
    """processing_donut""": ["""DonutProcessor"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase     :			Dict     						=	[
           """DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """DonutSwinModel""",
           """DonutSwinPreTrainedModel""",
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase     :			Dict     						=	["""DonutFeatureExtractor"""]
       lowercase     :			Dict     						=	["""DonutImageProcessor"""]


if TYPE_CHECKING:
       from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
       from .processing_donut import DonutProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_donut_swin import (
                  DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DonutSwinModel,
                  DonutSwinPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_donut import DonutFeatureExtractor
              from .image_processing_donut import DonutImageProcessor

else:
       import sys

       lowercase     :			Union[str, Any]     						=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













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 						_UpperCamelCase  (   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(A,							A   )

            processor.save_pretrained(self.tmpdirname   )

     def 						_UpperCamelCase  (   self,							**A   ):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self,							**A   ):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            shutil.rmtree(self.tmpdirname   )

     def 						_UpperCamelCase  (   self   ):





            """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   ):





            """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   ):





            """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   ):





            """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   ):





            """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   ):





            """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   ):





            """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''']   )






