

import unittest

from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_detectrona,
    require_pytesseract,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
    from PIL import Image

    from transformers.image_utils import load_image
else:

    class     __snake_case    :
           """simple docstring"""







           @staticmethod
           def 			a		(		*_UpperCamelCase      , **_UpperCamelCase   )      ->							List[Any]:






                  """simple docstring"""


                  pass







    def 						lowerCamelCase__      ( __A    :Any		):


           """simple docstring"""




           return None


# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
UpperCamelCase__		 =     (
    '''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)

@is_pipeline_test
@require_torch
@require_vision
class     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING


       @require_pytesseract
       @require_vision
       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							pipeline(
                  """document-question-answering"""      , model=_UpperCamelCase      , tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )

              __snake_case       	=							INVOICE_URL
              __snake_case       	=							list(zip(*apply_tesseract(load_image(_UpperCamelCase   )      , _UpperCamelCase      , """"""   )   )   )
              __snake_case       	=							"""What is the placebo?"""
              __snake_case       	=							[
                  {
                      """image""": load_image(_UpperCamelCase   ),
                      """question""": question,
                  },
                  {
                      """image""": image,
                      """question""": question,
                  },
                  {
                      """image""": image,
                      """question""": question,
                      """word_boxes""": word_boxes,
                  },
              ]
              return dqa_pipeline, examples


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase   )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							dqa_pipeline(_UpperCamelCase      , top_k=2   )
              self.assertEqual(
                  _UpperCamelCase      , [
                      [
                          {"""score""": ANY(_UpperCamelCase   ), """answer""": ANY(_UpperCamelCase   ), """start""": ANY(_UpperCamelCase   ), """end""": ANY(_UpperCamelCase   )},
                          {"""score""": ANY(_UpperCamelCase   ), """answer""": ANY(_UpperCamelCase   ), """start""": ANY(_UpperCamelCase   ), """end""": ANY(_UpperCamelCase   )},
                      ]
                  ]
                  * 3      , )


       @require_torch
       @require_detectrona
       @require_pytesseract
       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							pipeline("""document-question-answering"""      , model="""hf-internal-testing/tiny-random-layoutlmv2"""   )
              __snake_case       	=							INVOICE_URL
              __snake_case       	=							"""How many cats are there?"""

              __snake_case       	=							[
                  {"""score""": 0.0001, """answer""": """oy 2312/2019""", """start""": 38, """end""": 39},
                  {"""score""": 0.0001, """answer""": """oy 2312/2019 DUE""", """start""": 38, """end""": 40},
              ]
              __snake_case       	=							dqa_pipeline(image=_UpperCamelCase      , question=_UpperCamelCase      , top_k=2   )
              self.assertEqual(nested_simplify(_UpperCamelCase      , decimals=4   )      , _UpperCamelCase   )

              __snake_case       	=							dqa_pipeline({"""image""": image, """question""": question}      , top_k=2   )
              self.assertEqual(nested_simplify(_UpperCamelCase      , decimals=4   )      , _UpperCamelCase   )

              # This image does not detect ANY text in it, meaning layoutlmv2 should fail.
              # Empty answer probably
              __snake_case       	=							"""./tests/fixtures/tests_samples/COCO/000000039769.png"""
              __snake_case       	=							dqa_pipeline(image=_UpperCamelCase      , question=_UpperCamelCase      , top_k=2   )
              self.assertEqual(_UpperCamelCase      , []   )

              # We can optionnally pass directly the words and bounding boxes
              __snake_case       	=							"""./tests/fixtures/tests_samples/COCO/000000039769.png"""
              __snake_case       	=							[]
              __snake_case       	=							[]
              __snake_case       	=							dqa_pipeline(image=_UpperCamelCase      , question=_UpperCamelCase      , words=_UpperCamelCase      , boxes=_UpperCamelCase      , top_k=2   )
              self.assertEqual(_UpperCamelCase      , []   )


       @slow
       @require_torch
       @require_detectrona
       @require_pytesseract
       def 			a		(		self   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							pipeline(
                  """document-question-answering"""      , model="""tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"""      , revision="""9977165"""      , )
              __snake_case       	=							INVOICE_URL
              __snake_case       	=							"""What is the invoice number?"""

              __snake_case       	=							dqa_pipeline(image=_UpperCamelCase      , question=_UpperCamelCase      , top_k=2   )
              self.assertEqual(
                  nested_simplify(_UpperCamelCase      , decimals=4   )      , [
                      {"""score""": 0.9944, """answer""": """us-001""", """start""": 16, """end""": 16},
                      {"""score""": 0.0009, """answer""": """us-001""", """start""": 16, """end""": 16},
                  ]      , )

              __snake_case       	=							dqa_pipeline({"""image""": image, """question""": question}      , top_k=2   )
              self.assertEqual(
                  nested_simplify(_UpperCamelCase      , decimals=4   )      , [
                      {"""score""": 0.9944, """answer""": """us-001""", """start""": 16, """end""": 16},
                      {"""score""": 0.0009, """answer""": """us-001""", """start""": 16, """end""": 16},
                  ]      , )

              __snake_case       	=							dqa_pipeline(
                  [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]      , top_k=2   )
              self.assertEqual(
                  nested_simplify(_UpperCamelCase      , decimals=4   )      , [
                      [
                          {"""score""": 0.9944, """answer""": """us-001""", """start""": 16, """end""": 16},
                          {"""score""": 0.0009, """answer""": """us-001""", """start""": 16, """end""": 16},
                      ],
                  ]
                  * 2      , )


       @slow
       @require_torch
       @require_detectrona
       @require_pytesseract
       def 			a		(		self   )      ->							Optional[Any]:






              """simple docstring"""


              __snake_case       	=							pipeline(
                  """document-question-answering"""      , model="""tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"""      , revision="""9977165"""      , max_seq_len=50      , )
              __snake_case       	=							INVOICE_URL
              __snake_case       	=							"""What is the invoice number?"""

              __snake_case       	=							dqa_pipeline(image=_UpperCamelCase      , question=_UpperCamelCase      , top_k=2   )
              self.assertEqual(
                  nested_simplify(_UpperCamelCase      , decimals=4   )      , [
                      {"""score""": 0.9974, """answer""": """1110212019""", """start""": 23, """end""": 23},
                      {"""score""": 0.9948, """answer""": """us-001""", """start""": 16, """end""": 16},
                  ]      , )

              __snake_case       	=							dqa_pipeline({"""image""": image, """question""": question}      , top_k=2   )
              self.assertEqual(
                  nested_simplify(_UpperCamelCase      , decimals=4   )      , [
                      {"""score""": 0.9974, """answer""": """1110212019""", """start""": 23, """end""": 23},
                      {"""score""": 0.9948, """answer""": """us-001""", """start""": 16, """end""": 16},
                  ]      , )

              __snake_case       	=							dqa_pipeline(
                  [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]      , top_k=2   )
              self.assertEqual(
                  nested_simplify(_UpperCamelCase      , decimals=4   )      , [
                      [
                          {"""score""": 0.9974, """answer""": """1110212019""", """start""": 23, """end""": 23},
                          {"""score""": 0.9948, """answer""": """us-001""", """start""": 16, """end""": 16},
                      ]
                  ]
                  * 2      , )


       @slow
       @require_torch
       @require_pytesseract
       @require_vision
       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              __snake_case       	=							AutoTokenizer.from_pretrained(
                  """impira/layoutlm-document-qa"""      , revision="""3dc6de3"""      , add_prefix_space=_UpperCamelCase   )
              __snake_case       	=							pipeline(
                  """document-question-answering"""      , model="""impira/layoutlm-document-qa"""      , tokenizer=_UpperCamelCase      , revision="""3dc6de3"""      , )
              __snake_case       	=							INVOICE_URL
              __snake_case       	=							"""What is the invoice number?"""

              __snake_case       	=							dqa_pipeline(image=_UpperCamelCase      , question=_UpperCamelCase      , top_k=2   )
              self.assertEqual(
                  nested_simplify(_UpperCamelCase      , decimals=4   )      , [
                      {"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
                      {"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
                  ]      , )

              __snake_case       	=							dqa_pipeline({"""image""": image, """question""": question}      , top_k=2   )
              self.assertEqual(
                  nested_simplify(_UpperCamelCase      , decimals=4   )      , [
                      {"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
                      {"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
                  ]      , )

              __snake_case       	=							dqa_pipeline(
                  [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]      , top_k=2   )
              self.assertEqual(
                  nested_simplify(_UpperCamelCase      , decimals=4   )      , [
                      [
                          {"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
                          {"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
                      ]
                  ]
                  * 2      , )

              __snake_case       	=							list(zip(*apply_tesseract(load_image(_UpperCamelCase   )      , _UpperCamelCase      , """"""   )   )   )

              # This model should also work if `image` is set to None
              __snake_case       	=							dqa_pipeline({"""image""": None, """word_boxes""": word_boxes, """question""": question}      , top_k=2   )
              self.assertEqual(
                  nested_simplify(_UpperCamelCase      , decimals=4   )      , [
                      {"""score""": 0.4251, """answer""": """us-001""", """start""": 16, """end""": 16},
                      {"""score""": 0.0819, """answer""": """1110212019""", """start""": 23, """end""": 23},
                  ]      , )


       @slow
       @require_torch
       @require_pytesseract
       @require_vision
       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							AutoTokenizer.from_pretrained(
                  """impira/layoutlm-document-qa"""      , revision="""3dc6de3"""      , add_prefix_space=_UpperCamelCase   )
              __snake_case       	=							pipeline(
                  """document-question-answering"""      , model="""impira/layoutlm-document-qa"""      , tokenizer=_UpperCamelCase      , revision="""3dc6de3"""      , max_seq_len=50      , )
              __snake_case       	=							INVOICE_URL
              __snake_case       	=							"""What is the invoice number?"""

              __snake_case       	=							dqa_pipeline(image=_UpperCamelCase      , question=_UpperCamelCase      , top_k=2   )
              self.assertEqual(
                  nested_simplify(_UpperCamelCase      , decimals=4   )      , [
                      {"""score""": 0.9999, """answer""": """us-001""", """start""": 16, """end""": 16},
                      {"""score""": 0.9998, """answer""": """us-001""", """start""": 16, """end""": 16},
                  ]      , )

              __snake_case       	=							dqa_pipeline(
                  [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]      , top_k=2   )
              self.assertEqual(
                  nested_simplify(_UpperCamelCase      , decimals=4   )      , [
                      [
                          {"""score""": 0.9999, """answer""": """us-001""", """start""": 16, """end""": 16},
                          {"""score""": 0.9998, """answer""": """us-001""", """start""": 16, """end""": 16},
                      ]
                  ]
                  * 2      , )

              __snake_case       	=							list(zip(*apply_tesseract(load_image(_UpperCamelCase   )      , _UpperCamelCase      , """"""   )   )   )

              # This model should also work if `image` is set to None
              __snake_case       	=							dqa_pipeline({"""image""": None, """word_boxes""": word_boxes, """question""": question}      , top_k=2   )
              self.assertEqual(
                  nested_simplify(_UpperCamelCase      , decimals=4   )      , [
                      {"""score""": 0.9999, """answer""": """us-001""", """start""": 16, """end""": 16},
                      {"""score""": 0.9998, """answer""": """us-001""", """start""": 16, """end""": 16},
                  ]      , )


       @slow
       @require_torch
       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case       	=							pipeline(
                  """document-question-answering"""      , model="""naver-clova-ix/donut-base-finetuned-docvqa"""      , tokenizer=AutoTokenizer.from_pretrained("""naver-clova-ix/donut-base-finetuned-docvqa"""   )      , feature_extractor="""naver-clova-ix/donut-base-finetuned-docvqa"""      , )

              __snake_case       	=							INVOICE_URL
              __snake_case       	=							"""What is the invoice number?"""
              __snake_case       	=							dqa_pipeline(image=_UpperCamelCase      , question=_UpperCamelCase      , top_k=2   )
              self.assertEqual(nested_simplify(_UpperCamelCase      , decimals=4   )      , [{"""answer""": """us-001"""}]   )






       @require_tf
       @unittest.skip("""Document question answering not implemented in TF"""   )
       def 			a		(		self   )      ->							int:






              """simple docstring"""


              pass




import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


UpperCamelCase__		 =     version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')







def 						lowerCamelCase__      ( __A    :Any	,__A    :tuple	,__A    :Path	,__A    :int	,__A    :Union[str, Any]	,__A    :Optional[Any]	,__A    :Optional[Any]	,__A    :List[Any]=False	,):


       """simple docstring"""




       output_path.parent.mkdir(parents=__A	,exist_ok=__A		)
       # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
       # so we check the torch version for backwards compatibility
       if is_torch_less_than_1_11:
              export(
                  __A	,__A	,f=output_path.as_posix()	,input_names=__A	,output_names=__A	,dynamic_axes=__A	,do_constant_folding=__A	,use_external_data_format=__A	,enable_onnx_checker=__A	,opset_version=__A	,)
       else:
              export(
                  __A	,__A	,f=output_path.as_posix()	,input_names=__A	,output_names=__A	,dynamic_axes=__A	,do_constant_folding=__A	,opset_version=__A	,)







@torch.no_grad()
def 						lowerCamelCase__      ( __A    :str	,__A    :str	,__A    :int	,__A    :bool = False		):


       """simple docstring"""




       __snake_case       	=							torch.floataa if fpaa else torch.floataa
       if fpaa and torch.cuda.is_available():
              __snake_case       	=							"""cuda"""
       elif fpaa and not torch.cuda.is_available():
              raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
       else:
              __snake_case       	=							"""cpu"""
       __snake_case       	=							Path(__A		)

       # VAE DECODER
       __snake_case       	=							AutoencoderKL.from_pretrained(model_path + """/vae"""		)
       __snake_case       	=							vae_decoder.config.latent_channels
       # forward only through the decoder part
       __snake_case       	=							vae_decoder.decode
       onnx_export(
           __A	,model_args=(
               torch.randn(1	,__A	,2_5	,2_5		).to(device=__A	,dtype=__A		),
               False,
           )	,output_path=output_path / """vae_decoder""" / """model.onnx"""	,ordered_input_names=["""latent_sample""", """return_dict"""]	,output_names=["""sample"""]	,dynamic_axes={
               """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
           }	,opset=__A	,)
       del vae_decoder


if __name__ == "__main__":
    UpperCamelCase__		 =     argparse.ArgumentParser()

    parser.add_argument(
        '''--model_path''',
        type=str,
        required=True,
        help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
    )

    parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
    parser.add_argument(
        '''--opset''',
        default=14,
        type=int,
        help='''The version of the ONNX operator set to use.''',
    )
    parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')

    UpperCamelCase__		 =     parser.parse_args()
    print(args.output_path)
    convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
    print('''SD: Done: ONNX''')

