





"""simple docstring"""





import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)
def  __A   (_SCREAMING_SNAKE_CASE			)					->str:





  """simple docstring"""


  lowerCAmelCase__			:int											=							checkpoints.load_tax_checkpoint(_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:Optional[Any]											=							flatten_dict(_SCREAMING_SNAKE_CASE			)
  return flax_params
def  __A   (_SCREAMING_SNAKE_CASE			)					->Any:





  """simple docstring"""


  lowerCAmelCase__			:Any											=							{}

  lowerCAmelCase__			:List[str]											=							{
      'token_embedder': 'embeddings',
      'encoder_norm': 'layernorm',
      'kernel': 'weight',
      '.out': '.output',
      'scale': 'weight',
      'embedders_0.pos_embedding': 'row_embedder.weight',
      'embedders_1.pos_embedding': 'column_embedder.weight',
  }

  lowerCAmelCase__			:str											=							{
      'query': 'attention.query',
      'key': 'attention.key',
      'value': 'attention.value',
      'output.dense': 'output',
      'encoder_decoder_attention.o': 'encoder_decoder_attention.attention.o',
      'pre_self_attention_layer_norm': 'self_attention.layer_norm',
      'pre_cross_attention_layer_norm': 'encoder_decoder_attention.layer_norm',
      'mlp.': 'mlp.DenseReluDense.',
      'pre_mlp_layer_norm': 'mlp.layer_norm',
      'self_attention.o': 'self_attention.attention.o',
      'decoder.embeddings.embedding': 'decoder.embed_tokens.weight',
      'decoder.relpos_bias.rel_embedding': 'decoder.layer.0.self_attention.attention.relative_attention_bias.weight',
      'decoder.decoder_norm.weight': 'decoder.final_layer_norm.weight',
      'decoder.logits_dense.weight': 'decoder.lm_head.weight',
  }

  for key in flax_dict.keys():
    if "target" in key:
      # remove the first prefix from the key
      lowerCAmelCase__			:List[Any]											=							'.'.join(key[1:]			)

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        lowerCAmelCase__			:Any											=							new_key.replace(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          lowerCAmelCase__			:List[Any]											=							new_key.replace(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

      if "layers" in new_key and "decoder" not in new_key:
        # use regex to replace the layer number
        lowerCAmelCase__			:int											=							re.sub(r'layers_(\d+)'     ,   r'layer.\1'     ,   _SCREAMING_SNAKE_CASE			)
        lowerCAmelCase__			:List[Any]											=							new_key.replace('encoder'     ,   'encoder.encoder'			)

      elif "layers" in new_key and "decoder" in new_key:
        # use regex to replace the layer number
        lowerCAmelCase__			:Dict											=							re.sub(r'layers_(\d+)'     ,   r'layer.\1'     ,   _SCREAMING_SNAKE_CASE			)

      lowerCAmelCase__			:Any											=							flax_dict[key]

  lowerCAmelCase__			:List[str]											=							{}
  # convert converted_dict into torch format
  for key in converted_dict.keys():
    if ("embed_tokens" not in key) and ("embedder" not in key):
      lowerCAmelCase__			:Any											=							torch.from_numpy(converted_dict[key].T			)
    else:
      lowerCAmelCase__			:List[Any]											=							torch.from_numpy(converted_dict[key]			)

  return converted_torch_dict
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE=False     ,   _SCREAMING_SNAKE_CASE=False			)					->str:





  """simple docstring"""


  lowerCAmelCase__			:Any											=							get_flax_param(_SCREAMING_SNAKE_CASE			)

  if not use_large:
    lowerCAmelCase__			:List[str]											=							PixaStructVisionConfig()
    lowerCAmelCase__			:Tuple											=							PixaStructTextConfig()
  else:
    lowerCAmelCase__			:Union[str, Any]											=							PixaStructVisionConfig(
        hidden_size=1536     ,   d_ff=3968     ,   num_attention_heads=24     ,   num_hidden_layers=18			)
    lowerCAmelCase__			:List[str]											=							PixaStructTextConfig(hidden_size=1536     ,   d_ff=3968     ,   num_heads=24     ,   num_layers=18			)
  lowerCAmelCase__			:Union[str, Any]											=							PixaStructConfig(
      vision_config=encoder_config.to_dict()     ,   text_config=decoder_config.to_dict()     ,   is_vqa=_SCREAMING_SNAKE_CASE			)

  lowerCAmelCase__			:List[Any]											=							PixaStructForConditionalGeneration(_SCREAMING_SNAKE_CASE			)

  lowerCAmelCase__			:Union[str, Any]											=							rename_and_convert_flax_params(_SCREAMING_SNAKE_CASE			)
  model.load_state_dict(_SCREAMING_SNAKE_CASE			)

  lowerCAmelCase__			:Tuple											=							AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'			)
  lowerCAmelCase__			:Union[str, Any]											=							PixaStructImageProcessor()
  lowerCAmelCase__			:Union[str, Any]											=							PixaStructProcessor(image_processor=_SCREAMING_SNAKE_CASE     ,   tokenizer=_SCREAMING_SNAKE_CASE			)

  if use_large:
    lowerCAmelCase__			:Tuple											=							4096

  lowerCAmelCase__			:Dict											=							True

  # mkdir if needed
  os.makedirs(_SCREAMING_SNAKE_CASE     ,   exist_ok=_SCREAMING_SNAKE_CASE			)

  model.save_pretrained(_SCREAMING_SNAKE_CASE			)
  processor.save_pretrained(_SCREAMING_SNAKE_CASE			)

  print('Model saved in {}'.format(_SCREAMING_SNAKE_CASE			)			)


if __name__ == "__main__":
  __A											=		argparse.ArgumentParser()
  parser.add_argument("""--t5x_checkpoint_path""", default=None, type=str, help="""Path to the original T5x checkpoint.""")
  parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  parser.add_argument("""--use_large""", action="""store_true""", help="""Use large model.""")
  parser.add_argument("""--is_vqa""", action="""store_true""", help="""Use large model.""")
  __A											=		parser.parse_args()

  convert_pixastruct_original_pytorch_checkpoint_to_hf(
      args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
  )







"""simple docstring"""





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						(    *__UpperCAmelCase    , **__UpperCAmelCase      ):



           '''simple docstring'''

           pass






  def  __A   (_SCREAMING_SNAKE_CASE			)					->int:





    """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.
__A											=		(
    """https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"""
)






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




       __magic_name__     :str		  =					MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING






       @require_pytesseract
       @require_vision
       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							pipeline(
             'document-question-answering'    , model=__UpperCAmelCase    , tokenizer=__UpperCAmelCase    , image_processor=__UpperCAmelCase      )

         lowerCAmelCase__			:Dict											=							INVOICE_URL
         lowerCAmelCase__			:Dict											=							list(zip(*apply_tesseract(load_image(__UpperCAmelCase      )    , __UpperCAmelCase    , ''      )      )      )
         lowerCAmelCase__			:List[Any]											=							'What is the placebo?'
         lowerCAmelCase__			:Dict											=							[
             {
                 'image': load_image(__UpperCAmelCase      ),
                 'question': question,
             },
             {
                 'image': image,
                 'question': question,
             },
             {
                 'image': image,
                 'question': question,
                 'word_boxes': word_boxes,
             },
         ]
         return dqa_pipeline, examples






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							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      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							pipeline('document-question-answering'    , model='hf-internal-testing/tiny-random-layoutlmv2'      )
         lowerCAmelCase__			:Union[str, Any]											=							INVOICE_URL
         lowerCAmelCase__			:Tuple											=							'How many cats are there?'

         lowerCAmelCase__			:List[str]											=							[
             {'score': 0.00_01, 'answer': 'oy 2312/2019', 'start': 3_8, 'end': 3_9},
             {'score': 0.00_01, 'answer': 'oy 2312/2019 DUE', 'start': 3_8, 'end': 4_0},
         ]
         lowerCAmelCase__			:Any											=							dqa_pipeline(image=__UpperCAmelCase    , question=__UpperCAmelCase    , top_k=2      )
         self.assertEqual(nested_simplify(__UpperCAmelCase    , decimals=4      )    , __UpperCAmelCase      )

         lowerCAmelCase__			:Any											=							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
         lowerCAmelCase__			:List[Any]											=							'./tests/fixtures/tests_samples/COCO/000000039769.png'
         lowerCAmelCase__			:List[Any]											=							dqa_pipeline(image=__UpperCAmelCase    , question=__UpperCAmelCase    , top_k=2      )
         self.assertEqual(__UpperCAmelCase    , []      )

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






       @slow
       @require_torch
       @require_detectrona
       @require_pytesseract
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							pipeline(
             'document-question-answering'    , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'    , revision='9977165'    , )
         lowerCAmelCase__			:str											=							INVOICE_URL
         lowerCAmelCase__			:List[Any]											=							'What is the invoice number?'

         lowerCAmelCase__			:Tuple											=							dqa_pipeline(image=__UpperCAmelCase    , question=__UpperCAmelCase    , top_k=2      )
         self.assertEqual(
             nested_simplify(__UpperCAmelCase    , decimals=4      )    , [
                 {'score': 0.99_44, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 {'score': 0.00_09, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
             ]    , )

         lowerCAmelCase__			:Union[str, Any]											=							dqa_pipeline({'image': image, 'question': question}    , top_k=2      )
         self.assertEqual(
             nested_simplify(__UpperCAmelCase    , decimals=4      )    , [
                 {'score': 0.99_44, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 {'score': 0.00_09, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
             ]    , )

         lowerCAmelCase__			:Dict											=							dqa_pipeline(
             [{'image': image, 'question': question}, {'image': image, 'question': question}]    , top_k=2      )
         self.assertEqual(
             nested_simplify(__UpperCAmelCase    , decimals=4      )    , [
                 [
                     {'score': 0.99_44, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                     {'score': 0.00_09, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 ],
             ]
             * 2    , )






       @slow
       @require_torch
       @require_detectrona
       @require_pytesseract
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							pipeline(
             'document-question-answering'    , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'    , revision='9977165'    , max_seq_len=5_0    , )
         lowerCAmelCase__			:List[Any]											=							INVOICE_URL
         lowerCAmelCase__			:List[Any]											=							'What is the invoice number?'

         lowerCAmelCase__			:Optional[Any]											=							dqa_pipeline(image=__UpperCAmelCase    , question=__UpperCAmelCase    , top_k=2      )
         self.assertEqual(
             nested_simplify(__UpperCAmelCase    , decimals=4      )    , [
                 {'score': 0.99_74, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
                 {'score': 0.99_48, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
             ]    , )

         lowerCAmelCase__			:List[str]											=							dqa_pipeline({'image': image, 'question': question}    , top_k=2      )
         self.assertEqual(
             nested_simplify(__UpperCAmelCase    , decimals=4      )    , [
                 {'score': 0.99_74, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
                 {'score': 0.99_48, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
             ]    , )

         lowerCAmelCase__			:int											=							dqa_pipeline(
             [{'image': image, 'question': question}, {'image': image, 'question': question}]    , top_k=2      )
         self.assertEqual(
             nested_simplify(__UpperCAmelCase    , decimals=4      )    , [
                 [
                     {'score': 0.99_74, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
                     {'score': 0.99_48, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 ]
             ]
             * 2    , )






       @slow
       @require_torch
       @require_pytesseract
       @require_vision
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							AutoTokenizer.from_pretrained(
             'impira/layoutlm-document-qa'    , revision='3dc6de3'    , add_prefix_space=__UpperCAmelCase      )
         lowerCAmelCase__			:Optional[Any]											=							pipeline(
             'document-question-answering'    , model='impira/layoutlm-document-qa'    , tokenizer=__UpperCAmelCase    , revision='3dc6de3'    , )
         lowerCAmelCase__			:List[str]											=							INVOICE_URL
         lowerCAmelCase__			:Any											=							'What is the invoice number?'

         lowerCAmelCase__			:List[Any]											=							dqa_pipeline(image=__UpperCAmelCase    , question=__UpperCAmelCase    , top_k=2      )
         self.assertEqual(
             nested_simplify(__UpperCAmelCase    , decimals=4      )    , [
                 {'score': 0.42_51, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 {'score': 0.08_19, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
             ]    , )

         lowerCAmelCase__			:Optional[int]											=							dqa_pipeline({'image': image, 'question': question}    , top_k=2      )
         self.assertEqual(
             nested_simplify(__UpperCAmelCase    , decimals=4      )    , [
                 {'score': 0.42_51, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 {'score': 0.08_19, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
             ]    , )

         lowerCAmelCase__			:List[str]											=							dqa_pipeline(
             [{'image': image, 'question': question}, {'image': image, 'question': question}]    , top_k=2      )
         self.assertEqual(
             nested_simplify(__UpperCAmelCase    , decimals=4      )    , [
                 [
                     {'score': 0.42_51, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                     {'score': 0.08_19, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
                 ]
             ]
             * 2    , )

         lowerCAmelCase__			:Dict											=							list(zip(*apply_tesseract(load_image(__UpperCAmelCase      )    , __UpperCAmelCase    , ''      )      )      )

         # This model should also work if `image` is set to None
         lowerCAmelCase__			:Tuple											=							dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question}    , top_k=2      )
         self.assertEqual(
             nested_simplify(__UpperCAmelCase    , decimals=4      )    , [
                 {'score': 0.42_51, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 {'score': 0.08_19, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
             ]    , )






       @slow
       @require_torch
       @require_pytesseract
       @require_vision
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							AutoTokenizer.from_pretrained(
             'impira/layoutlm-document-qa'    , revision='3dc6de3'    , add_prefix_space=__UpperCAmelCase      )
         lowerCAmelCase__			:Tuple											=							pipeline(
             'document-question-answering'    , model='impira/layoutlm-document-qa'    , tokenizer=__UpperCAmelCase    , revision='3dc6de3'    , max_seq_len=5_0    , )
         lowerCAmelCase__			:Dict											=							INVOICE_URL
         lowerCAmelCase__			:List[Any]											=							'What is the invoice number?'

         lowerCAmelCase__			:List[str]											=							dqa_pipeline(image=__UpperCAmelCase    , question=__UpperCAmelCase    , top_k=2      )
         self.assertEqual(
             nested_simplify(__UpperCAmelCase    , decimals=4      )    , [
                 {'score': 0.99_99, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 {'score': 0.99_98, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
             ]    , )

         lowerCAmelCase__			:List[str]											=							dqa_pipeline(
             [{'image': image, 'question': question}, {'image': image, 'question': question}]    , top_k=2      )
         self.assertEqual(
             nested_simplify(__UpperCAmelCase    , decimals=4      )    , [
                 [
                     {'score': 0.99_99, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                     {'score': 0.99_98, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 ]
             ]
             * 2    , )

         lowerCAmelCase__			:Optional[Any]											=							list(zip(*apply_tesseract(load_image(__UpperCAmelCase      )    , __UpperCAmelCase    , ''      )      )      )

         # This model should also work if `image` is set to None
         lowerCAmelCase__			:List[str]											=							dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question}    , top_k=2      )
         self.assertEqual(
             nested_simplify(__UpperCAmelCase    , decimals=4      )    , [
                 {'score': 0.99_99, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
                 {'score': 0.99_98, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
             ]    , )






       @slow
       @require_torch
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							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'    , )

         lowerCAmelCase__			:Dict											=							INVOICE_URL
         lowerCAmelCase__			:str											=							'What is the invoice number?'
         lowerCAmelCase__			:Tuple											=							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      snake_case						(    self      ):



         '''simple docstring'''

         pass
