
import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
 import numpy as np
 import torch

 from . import (
     AlbertForPreTraining,
     BartForConditionalGeneration,
     BertForPreTraining,
     BertForQuestionAnswering,
     BertForSequenceClassification,
     CamembertForMaskedLM,
     CTRLLMHeadModel,
     DistilBertForMaskedLM,
     DistilBertForQuestionAnswering,
     DPRContextEncoder,
     DPRQuestionEncoder,
     DPRReader,
     ElectraForPreTraining,
     FlaubertWithLMHeadModel,
     GPTaLMHeadModel,
     LayoutLMForMaskedLM,
     LxmertForPreTraining,
     LxmertVisualFeatureEncoder,
     OpenAIGPTLMHeadModel,
     RobertaForMaskedLM,
     RobertaForSequenceClassification,
     TaForConditionalGeneration,
     TransfoXLLMHeadModel,
     XLMRobertaForMaskedLM,
     XLMWithLMHeadModel,
     XLNetLMHeadModel,
 )


logging.set_verbosity_info()

__lowerCAmelCase					:			Optional[int]			      =		{
    "bart": (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    "bert": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "bert-large-uncased-whole-word-masking-finetuned-squad": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "bert-large-cased-whole-word-masking-finetuned-squad": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "bert-base-cased-finetuned-mrpc": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "dpr": (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    "gpt2": (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "xlnet": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "xlm": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "xlm-roberta": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "transfo-xl": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "openai-gpt": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "roberta": (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "layoutlm": (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    "roberta-large-mnli": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "camembert": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "flaubert": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "distilbert": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "distilbert-base-distilled-squad": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "lxmert": (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "lxmert-visual-feature-encoder": (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "ctrl": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "albert": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "t5": (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "electra": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "wav2vec2": (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase=False	,					__lowerCAmelCase=True	) ->						Any:
    if model_type not in MODEL_CLASSES:
        raise ValueError(F'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()	)}.'	)

    __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase		:			int	    =       MODEL_CLASSES[model_type]

    # Initialise TF model
    if config_file in aws_config_map:
        __lowercase		:			Tuple	    =       cached_file(__lowerCAmelCase	,					__lowerCAmelCase	,					force_download=not use_cached_models	)
    __lowercase		:			Any	    =       config_class.from_json_file(__lowerCAmelCase	)
    __lowercase		:			str	    =       True
    __lowercase		:			str	    =       True
    print(F'Building TensorFlow model from configuration: {config}'	)
    __lowercase		:			int	    =       model_class(__lowerCAmelCase	)

    # Load weights from tf checkpoint
    if pytorch_checkpoint_path in aws_config_map.keys():
        __lowercase		:			List[str]	    =       cached_file(
            __lowerCAmelCase	,					__lowerCAmelCase	,					force_download=not use_cached_models	)
    # Load PyTorch checkpoint in tf2 model:
    __lowercase		:			Any	    =       load_pytorch_checkpoint_in_tfa_model(__lowerCAmelCase	,					__lowerCAmelCase	)

    if compare_with_pt_model:
        __lowercase		:			Dict	    =       tf_model(tf_model.dummy_inputs	,					training=__lowerCAmelCase	)  # build the network

        __lowercase		:			Union[str, Any]	    =       torch.load(__lowerCAmelCase	,					map_location='''cpu'''	)
        __lowercase		:			Dict	    =       pt_model_class.from_pretrained(
            pretrained_model_name_or_path=__lowerCAmelCase	,					config=__lowerCAmelCase	,					state_dict=__lowerCAmelCase	)

        with torch.no_grad():
            __lowercase		:			List[Any]	    =       pt_model(**pt_model.dummy_inputs	)

        __lowercase		:			int	    =       pto[0].numpy()
        __lowercase		:			Dict	    =       tfo[0].numpy()
        __lowercase		:			Optional[Any]	    =       np.amax(np.abs(np_pt - np_tf	)	)
        print(F'Max absolute difference between models outputs {diff}'	)
        assert diff <= 2E-2, F'Error, model absolute difference is >2e-2: {diff}'

    # Save pytorch-model
    print(F'Save TensorFlow model to {tf_dump_path}'	)
    tf_model.save_weights(__lowerCAmelCase	,					save_format='''h5'''	)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase=None	,					__lowerCAmelCase=None	,					__lowerCAmelCase=False	,					__lowerCAmelCase=False	,					__lowerCAmelCase=False	,					__lowerCAmelCase=False	,					) ->						List[str]:
    if args_model_type is None:
        __lowercase		:			Dict	    =       list(MODEL_CLASSES.keys()	)
    else:
        __lowercase		:			Optional[Any]	    =       [args_model_type]

    for j, model_type in enumerate(__lowerCAmelCase	,					start=1	):
        print('''=''' * 100	)
        print(F' Converting model type {j}/{len(__lowerCAmelCase	)}: {model_type}'	)
        print('''=''' * 100	)
        if model_type not in MODEL_CLASSES:
            raise ValueError(F'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()	)}.'	)

        __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase		:			Optional[Any]	    =       MODEL_CLASSES[model_type]

        if model_shortcut_names_or_path is None:
            __lowercase		:			str	    =       list(aws_model_maps.keys()	)
        if config_shortcut_names_or_path is None:
            __lowercase		:			int	    =       model_shortcut_names_or_path

        for i, (model_shortcut_name, config_shortcut_name) in enumerate(
            zip(__lowerCAmelCase	,					__lowerCAmelCase	)	,					start=1	):
            print('''-''' * 100	)
            if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                if not only_convert_finetuned_models:
                    print(F'    Skipping finetuned checkpoint {model_shortcut_name}'	)
                    continue
                __lowercase		:			str	    =       model_shortcut_name
            elif only_convert_finetuned_models:
                print(F'    Skipping not finetuned checkpoint {model_shortcut_name}'	)
                continue
            print(
                F'    Converting checkpoint {i}/{len(__lowerCAmelCase	)}: {model_shortcut_name} - model_type {model_type}'	)
            print('''-''' * 100	)

            if config_shortcut_name in aws_config_map:
                __lowercase		:			int	    =       cached_file(__lowerCAmelCase	,					__lowerCAmelCase	,					force_download=not use_cached_models	)
            else:
                __lowercase		:			Union[str, Any]	    =       config_shortcut_name

            if model_shortcut_name in aws_model_maps:
                __lowercase		:			str	    =       cached_file(__lowerCAmelCase	,					__lowerCAmelCase	,					force_download=not use_cached_models	)
            else:
                __lowercase		:			Optional[int]	    =       model_shortcut_name

            if os.path.isfile(__lowerCAmelCase	):
                __lowercase		:			Union[str, Any]	    =       '''converted_model'''

            convert_pt_checkpoint_to_tf(
                model_type=__lowerCAmelCase	,					pytorch_checkpoint_path=__lowerCAmelCase	,					config_file=__lowerCAmelCase	,					tf_dump_path=os.path.join(__lowerCAmelCase	,					model_shortcut_name + '''-tf_model.h5'''	)	,					compare_with_pt_model=__lowerCAmelCase	,					)
            if remove_cached_files:
                os.remove(__lowerCAmelCase	)
                os.remove(__lowerCAmelCase	)


if __name__ == "__main__":
 __lowerCAmelCase					:			List[str]			      =		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_dump_path", default=None, type=str, required=True, help="Path to the output Tensorflow dump file."
 )
 parser.add_argument(
     "--model_type",
     default=None,
     type=str,
     help=(
         F'Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '
         "convert all the models from AWS."
     ),
 )
 parser.add_argument(
     "--pytorch_checkpoint_path",
     default=None,
     type=str,
     help=(
         "Path to the PyTorch checkpoint path or shortcut name to download from AWS. "
         "If not given, will download and convert all the checkpoints from AWS."
     ),
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     help=(
         "The config json file corresponding to the pre-trained model. \n"
         "This specifies the model architecture. If not given and "
         "--pytorch_checkpoint_path is not given or is a shortcut name "
         "use the configuration associated to the shortcut name on the AWS"
     ),
 )
 parser.add_argument(
     "--compare_with_pt_model", action="store_true", help="Compare Tensorflow and PyTorch model predictions."
 )
 parser.add_argument(
     "--use_cached_models",
     action="store_true",
     help="Use cached models if possible instead of updating to latest checkpoint versions.",
 )
 parser.add_argument(
     "--remove_cached_files",
     action="store_true",
     help="Remove pytorch models after conversion (save memory when converting in batches).",
 )
 parser.add_argument("--only_convert_finetuned_models", action="store_true", help="Only convert finetuned models.")
 __lowerCAmelCase					:			Optional[int]			      =		parser.parse_args()

 # if args.pytorch_checkpoint_path is not None:
 #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
 #                                 args.pytorch_checkpoint_path,
 #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
 #                                 args.tf_dump_path,
 #                                 compare_with_pt_model=args.compare_with_pt_model,
 #                                 use_cached_models=args.use_cached_models)
 # else:
 convert_all_pt_checkpoints_to_tf(
     args.model_type.lower() if args.model_type is not None else None,
     args.tf_dump_path,
     model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
     if args.pytorch_checkpoint_path is not None
     else None,
     config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
     compare_with_pt_model=args.compare_with_pt_model,
     use_cached_models=args.use_cached_models,
     remove_cached_files=args.remove_cached_files,
     only_convert_finetuned_models=args.only_convert_finetuned_models,
 )




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 						__lowerCAmelCase	:



       """simple docstring"""







       @staticmethod
       def 		snake_case_			(      *_snake_case		:       Union[str, Any]				,       **_snake_case		:       str				):
           pass






 def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Union[str, Any]:
     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.
__lowerCAmelCase					:			Dict			      =		(
    "https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"
)


@is_pipeline_test
@require_torch
@require_vision
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      A__	:			int        =    MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING




      @require_pytesseract
      @require_vision
      def 		snake_case_			(      self		:       List[Any]				,       _snake_case		:       Any				,       _snake_case		:       Dict				,       _snake_case		:       Any				):
          __lowercase		:			List[str]	    =       pipeline(
              '''document-question-answering'''				,       model=_snake_case				,       tokenizer=_snake_case				,       image_processor=_snake_case				)

          __lowercase		:			int	    =       INVOICE_URL
          __lowercase		:			Optional[int]	    =       list(zip(*apply_tesseract(load_image(_snake_case				)				,       _snake_case				,       ''''''				)				)				)
          __lowercase		:			List[str]	    =       '''What is the placebo?'''
          __lowercase		:			Tuple	    =       [
              {
                  '''image''': load_image(_snake_case				),
                  '''question''': question,
              },
              {
                  '''image''': image,
                  '''question''': question,
              },
              {
                  '''image''': image,
                  '''question''': question,
                  '''word_boxes''': word_boxes,
              },
          ]
          return dqa_pipeline, examples




      def 		snake_case_			(      self		:       List[str]				,       _snake_case		:       int				,       _snake_case		:       List[Any]				):
          __lowercase		:			Optional[Any]	    =       dqa_pipeline(_snake_case				,       top_k=2				)
          self.assertEqual(
              _snake_case				,       [
                  [
                      {'''score''': ANY(_snake_case				), '''answer''': ANY(_snake_case				), '''start''': ANY(_snake_case				), '''end''': ANY(_snake_case				)},
                      {'''score''': ANY(_snake_case				), '''answer''': ANY(_snake_case				), '''start''': ANY(_snake_case				), '''end''': ANY(_snake_case				)},
                  ]
              ]
              * 3				,       )




      @require_torch
      @require_detectrona
      @require_pytesseract
      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			List[str]	    =       pipeline('''document-question-answering'''				,       model='''hf-internal-testing/tiny-random-layoutlmv2'''				)
          __lowercase		:			Any	    =       INVOICE_URL
          __lowercase		:			str	    =       '''How many cats are there?'''

          __lowercase		:			Optional[int]	    =       [
              {'''score''': 0.00_01, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
              {'''score''': 0.00_01, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
          ]
          __lowercase		:			Optional[Any]	    =       dqa_pipeline(image=_snake_case				,       question=_snake_case				,       top_k=2				)
          self.assertEqual(nested_simplify(_snake_case				,       decimals=4				)				,       _snake_case				)

          __lowercase		:			Optional[Any]	    =       dqa_pipeline({'''image''': image, '''question''': question}				,       top_k=2				)
          self.assertEqual(nested_simplify(_snake_case				,       decimals=4				)				,       _snake_case				)

          # This image does not detect ANY text in it, meaning layoutlmv2 should fail.
          # Empty answer probably
          __lowercase		:			Optional[int]	    =       '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
          __lowercase		:			str	    =       dqa_pipeline(image=_snake_case				,       question=_snake_case				,       top_k=2				)
          self.assertEqual(_snake_case				,       []				)

          # We can optionnally pass directly the words and bounding boxes
          __lowercase		:			List[Any]	    =       '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
          __lowercase		:			Dict	    =       []
          __lowercase		:			List[str]	    =       []
          __lowercase		:			Tuple	    =       dqa_pipeline(image=_snake_case				,       question=_snake_case				,       words=_snake_case				,       boxes=_snake_case				,       top_k=2				)
          self.assertEqual(_snake_case				,       []				)




      @slow
      @require_torch
      @require_detectrona
      @require_pytesseract
      def 		snake_case_			(      self		:       int				):
          __lowercase		:			List[Any]	    =       pipeline(
              '''document-question-answering'''				,       model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''				,       revision='''9977165'''				,       )
          __lowercase		:			List[str]	    =       INVOICE_URL
          __lowercase		:			Optional[Any]	    =       '''What is the invoice number?'''

          __lowercase		:			int	    =       dqa_pipeline(image=_snake_case				,       question=_snake_case				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  {'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  {'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
              ]				,       )

          __lowercase		:			Union[str, Any]	    =       dqa_pipeline({'''image''': image, '''question''': question}				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  {'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  {'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
              ]				,       )

          __lowercase		:			List[str]	    =       dqa_pipeline(
              [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  [
                      {'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                      {'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  ],
              ]
              * 2				,       )




      @slow
      @require_torch
      @require_detectrona
      @require_pytesseract
      def 		snake_case_			(      self		:       int				):
          __lowercase		:			Optional[Any]	    =       pipeline(
              '''document-question-answering'''				,       model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''				,       revision='''9977165'''				,       max_seq_len=50				,       )
          __lowercase		:			Union[str, Any]	    =       INVOICE_URL
          __lowercase		:			Any	    =       '''What is the invoice number?'''

          __lowercase		:			Any	    =       dqa_pipeline(image=_snake_case				,       question=_snake_case				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  {'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
                  {'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
              ]				,       )

          __lowercase		:			Dict	    =       dqa_pipeline({'''image''': image, '''question''': question}				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  {'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
                  {'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
              ]				,       )

          __lowercase		:			List[Any]	    =       dqa_pipeline(
              [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  [
                      {'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
                      {'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  ]
              ]
              * 2				,       )




      @slow
      @require_torch
      @require_pytesseract
      @require_vision
      def 		snake_case_			(      self		:       str				):
          __lowercase		:			List[Any]	    =       AutoTokenizer.from_pretrained(
              '''impira/layoutlm-document-qa'''				,       revision='''3dc6de3'''				,       add_prefix_space=_snake_case				)
          __lowercase		:			Union[str, Any]	    =       pipeline(
              '''document-question-answering'''				,       model='''impira/layoutlm-document-qa'''				,       tokenizer=_snake_case				,       revision='''3dc6de3'''				,       )
          __lowercase		:			Tuple	    =       INVOICE_URL
          __lowercase		:			Any	    =       '''What is the invoice number?'''

          __lowercase		:			Optional[Any]	    =       dqa_pipeline(image=_snake_case				,       question=_snake_case				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
              ]				,       )

          __lowercase		:			List[Any]	    =       dqa_pipeline({'''image''': image, '''question''': question}				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
              ]				,       )

          __lowercase		:			int	    =       dqa_pipeline(
              [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  [
                      {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                      {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
                  ]
              ]
              * 2				,       )

          __lowercase		:			Union[str, Any]	    =       list(zip(*apply_tesseract(load_image(_snake_case				)				,       _snake_case				,       ''''''				)				)				)

          # This model should also work if `image` is set to None
          __lowercase		:			Union[str, Any]	    =       dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
              ]				,       )




      @slow
      @require_torch
      @require_pytesseract
      @require_vision
      def 		snake_case_			(      self		:       List[str]				):
          __lowercase		:			Optional[int]	    =       AutoTokenizer.from_pretrained(
              '''impira/layoutlm-document-qa'''				,       revision='''3dc6de3'''				,       add_prefix_space=_snake_case				)
          __lowercase		:			List[str]	    =       pipeline(
              '''document-question-answering'''				,       model='''impira/layoutlm-document-qa'''				,       tokenizer=_snake_case				,       revision='''3dc6de3'''				,       max_seq_len=50				,       )
          __lowercase		:			Tuple	    =       INVOICE_URL
          __lowercase		:			Optional[int]	    =       '''What is the invoice number?'''

          __lowercase		:			Any	    =       dqa_pipeline(image=_snake_case				,       question=_snake_case				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  {'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  {'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
              ]				,       )

          __lowercase		:			Any	    =       dqa_pipeline(
              [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  [
                      {'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                      {'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  ]
              ]
              * 2				,       )

          __lowercase		:			int	    =       list(zip(*apply_tesseract(load_image(_snake_case				)				,       _snake_case				,       ''''''				)				)				)

          # This model should also work if `image` is set to None
          __lowercase		:			Tuple	    =       dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  {'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  {'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
              ]				,       )




      @slow
      @require_torch
      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			int	    =       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'''				,       )

          __lowercase		:			List[Any]	    =       INVOICE_URL
          __lowercase		:			List[Any]	    =       '''What is the invoice number?'''
          __lowercase		:			int	    =       dqa_pipeline(image=_snake_case				,       question=_snake_case				,       top_k=2				)
          self.assertEqual(nested_simplify(_snake_case				,       decimals=4				)				,       [{'''answer''': '''us-001'''}]				)




      @require_tf
      @unittest.skip('''Document question answering not implemented in TF'''				)
      def 		snake_case_			(      self		:       int				):
          pass


