

import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)





def      snake_case_				(__A					:						Tuple					)      ->     Any:
   __lowerCAmelCase  :      Union[str, Any]    =						model.config

   __lowerCAmelCase  :      Union[str, Any]    =						DonutSwinConfig(
       image_size=original_config.input_size ,       patch_size=4 ,       depths=original_config.encoder_layer ,       num_heads=[4, 8, 1_6, 3_2] ,       window_size=original_config.window_size ,       embed_dim=1_2_8 ,       )
   __lowerCAmelCase  :      Union[str, Any]    =						MBartConfig(
       is_decoder=__A ,       is_encoder_decoder=__A ,       add_cross_attention=__A ,       decoder_layers=original_config.decoder_layer ,       max_position_embeddings=original_config.max_position_embeddings ,       vocab_size=len(
           model.decoder.tokenizer					) ,       scale_embedding=__A ,       add_final_layer_norm=__A ,       )

   return encoder_config, decoder_config





def      snake_case_				(__A					:						List[str]					)      ->     List[Any]:
   if "encoder.model" in name:
      __lowerCAmelCase  :      Optional[int]    =						name.replace("""encoder.model""" ,       """encoder"""					)
   if "decoder.model" in name:
      __lowerCAmelCase  :      Optional[Any]    =						name.replace("""decoder.model""" ,       """decoder"""					)
   if "patch_embed.proj" in name:
      __lowerCAmelCase  :      str    =						name.replace("""patch_embed.proj""" ,       """embeddings.patch_embeddings.projection"""					)
   if "patch_embed.norm" in name:
      __lowerCAmelCase  :      Dict    =						name.replace("""patch_embed.norm""" ,       """embeddings.norm"""					)
   if name.startswith("""encoder"""					):
      if "layers" in name:
         __lowerCAmelCase  :      int    =						"""encoder.""" + name
      if "attn.proj" in name:
         __lowerCAmelCase  :      List[Any]    =						name.replace("""attn.proj""" ,       """attention.output.dense"""					)
      if "attn" in name and "mask" not in name:
         __lowerCAmelCase  :      Tuple    =						name.replace("""attn""" ,       """attention.self"""					)
      if "norm1" in name:
         __lowerCAmelCase  :      List[str]    =						name.replace("""norm1""" ,       """layernorm_before"""					)
      if "norm2" in name:
         __lowerCAmelCase  :      str    =						name.replace("""norm2""" ,       """layernorm_after"""					)
      if "mlp.fc1" in name:
         __lowerCAmelCase  :      Tuple    =						name.replace("""mlp.fc1""" ,       """intermediate.dense"""					)
      if "mlp.fc2" in name:
         __lowerCAmelCase  :      Optional[int]    =						name.replace("""mlp.fc2""" ,       """output.dense"""					)

      if name == "encoder.norm.weight":
         __lowerCAmelCase  :      Optional[Any]    =						"""encoder.layernorm.weight"""
      if name == "encoder.norm.bias":
         __lowerCAmelCase  :      List[str]    =						"""encoder.layernorm.bias"""

   return name





def      snake_case_				(__A					:						List[str] ,       __A					:						Optional[int]					)      ->     Tuple:
   for key in orig_state_dict.copy().keys():
      __lowerCAmelCase  :      Optional[Any]    =						orig_state_dict.pop(__A					)

      if "qkv" in key:
         __lowerCAmelCase  :      Optional[int]    =						key.split("""."""					)
         __lowerCAmelCase  :      Optional[Any]    =						int(key_split[3]					)
         __lowerCAmelCase  :      Tuple    =						int(key_split[5]					)
         __lowerCAmelCase  :      Optional[int]    =						model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

         if "weight" in key:
            __lowerCAmelCase  :      List[str]    =						val[:dim, :]
            __lowerCAmelCase  :      int    =						val[dim : dim * 2, :]
            __lowerCAmelCase  :      int    =						val[-dim:, :]
         else:
            __lowerCAmelCase  :      Tuple    =						val[:dim]
            __lowerCAmelCase  :      Dict    =						val[dim : dim * 2]
            __lowerCAmelCase  :      int    =						val[-dim:]
      elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
         # HuggingFace implementation doesn't use attn_mask buffer
         # and model doesn't use final LayerNorms for the encoder
         pass
      else:
         __lowerCAmelCase  :      List[Any]    =						val

   return orig_state_dict





def      snake_case_				(__A					:						Optional[int] ,       __A					:						List[str]=None ,       __A					:						Optional[Any]=False					)      ->     List[Any]:
   # load original model
   __lowerCAmelCase  :      str    =						DonutModel.from_pretrained(__A					).eval()

   # load HuggingFace model
   __lowerCAmelCase  :      Union[str, Any]    =						get_configs(__A					)
   __lowerCAmelCase  :      int    =						DonutSwinModel(__A					)
   __lowerCAmelCase  :      List[Any]    =						MBartForCausalLM(__A					)
   __lowerCAmelCase  :      List[Any]    =						VisionEncoderDecoderModel(encoder=__A ,       decoder=__A					)
   model.eval()

   __lowerCAmelCase  :      Any    =						original_model.state_dict()
   __lowerCAmelCase  :      Union[str, Any]    =						convert_state_dict(__A ,       __A					)
   model.load_state_dict(__A					)

   # verify results on scanned document
   __lowerCAmelCase  :      Dict    =						load_dataset("""hf-internal-testing/example-documents"""					)
   __lowerCAmelCase  :      Dict    =						dataset["""test"""][0]["""image"""].convert("""RGB"""					)

   __lowerCAmelCase  :      Union[str, Any]    =						XLMRobertaTokenizerFast.from_pretrained(__A ,       from_slow=__A					)
   __lowerCAmelCase  :      Any    =						DonutImageProcessor(
       do_align_long_axis=original_model.config.align_long_axis ,       size=original_model.config.input_size[::-1]					)
   __lowerCAmelCase  :      Tuple    =						DonutProcessor(__A ,       __A					)
   __lowerCAmelCase  :      List[str]    =						processor(__A ,       return_tensors="""pt"""					).pixel_values

   if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
      __lowerCAmelCase  :      Tuple    =						"""<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
      __lowerCAmelCase  :      List[Any]    =						"""When is the coffee break?"""
      __lowerCAmelCase  :      str    =						task_prompt.replace("""{user_input}""" ,       __A					)
   elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
      __lowerCAmelCase  :      int    =						"""<s_rvlcdip>"""
   elif model_name in [
       "naver-clova-ix/donut-base-finetuned-cord-v1",
       "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
   ]:
      __lowerCAmelCase  :      Dict    =						"""<s_cord>"""
   elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
      __lowerCAmelCase  :      Union[str, Any]    =						"""s_cord-v2>"""
   elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
      __lowerCAmelCase  :      Union[str, Any]    =						"""<s_zhtrainticket>"""
   elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
      # use a random prompt
      __lowerCAmelCase  :      List[str]    =						"""hello world"""
   else:
      raise ValueError("""Model name not supported"""					)
   __lowerCAmelCase  :      str    =						original_model.decoder.tokenizer(__A ,       add_special_tokens=__A ,       return_tensors="""pt"""					)[
       """input_ids"""
   ]

   __lowerCAmelCase  :      int    =						original_model.encoder.model.patch_embed(__A					)
   __lowerCAmelCase  :      Any    =						model.encoder.embeddings(__A					)
   assert torch.allclose(__A ,       __A ,       atol=1e-3					)

   # verify encoder hidden states
   __lowerCAmelCase  :      List[str]    =						original_model.encoder(__A					)
   __lowerCAmelCase  :      Optional[Any]    =						model.encoder(__A					).last_hidden_state
   assert torch.allclose(__A ,       __A ,       atol=1e-2					)

   # verify decoder hidden states
   __lowerCAmelCase  :      List[str]    =						original_model(__A ,       __A ,       __A					).logits
   __lowerCAmelCase  :      List[str]    =						model(__A ,       decoder_input_ids=__A					).logits
   assert torch.allclose(__A ,       __A ,       atol=1e-3					)
   print("""Looks ok!"""					)

   if pytorch_dump_folder_path is not None:
      print(f'''Saving model and processor to {pytorch_dump_folder_path}'''					)
      model.save_pretrained(__A					)
      processor.save_pretrained(__A					)

   if push_to_hub:
      model.push_to_hub("""nielsr/""" + model_name.split("""/"""					)[-1] ,       commit_message="""Update model"""					)
      processor.push_to_hub("""nielsr/""" + model_name.split("""/"""					)[-1] ,       commit_message="""Update model"""					)


if __name__ == "__main__":
   __UpperCAmelCase								=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model_name""",
       default="""naver-clova-ix/donut-base-finetuned-docvqa""",
       required=False,
       type=str,
       help="""Name of the original model you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default=None,
       required=False,
       type=str,
       help="""Path to the output PyTorch model directory.""",
   )
   parser.add_argument(
       """--push_to_hub""",
       action="""store_true""",
       help="""Whether or not to push the converted model and processor to the 🤗 hub.""",
   )

   __UpperCAmelCase								=			parser.parse_args()
   convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   import jax

   from transformers.models.big_bird.modeling_flax_big_bird import (
       FlaxBigBirdForCausalLM,
       FlaxBigBirdForMaskedLM,
       FlaxBigBirdForMultipleChoice,
       FlaxBigBirdForPreTraining,
       FlaxBigBirdForQuestionAnswering,
       FlaxBigBirdForSequenceClassification,
       FlaxBigBirdForTokenClassification,
       FlaxBigBirdModel,
   )



class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def __init__(      self			:   Dict	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   str=2	,							lowerCAmelCase			:   Optional[Any]=56	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   Optional[int]=True	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   Union[str, Any]=True	,							lowerCAmelCase			:   Tuple=99	,							lowerCAmelCase			:   Optional[Any]=32	,							lowerCAmelCase			:   List[str]=2	,							lowerCAmelCase			:   int=2	,							lowerCAmelCase			:   str=7	,							lowerCAmelCase			:   List[Any]="gelu_new"	,							lowerCAmelCase			:   Optional[int]=0.1	,							lowerCAmelCase			:   Optional[Any]=0.1	,							lowerCAmelCase			:   Optional[Any]=5_12	,							lowerCAmelCase			:   Dict=16	,							lowerCAmelCase			:   int=2	,							lowerCAmelCase			:   Optional[Any]=0.02	,							lowerCAmelCase			:   Tuple=4	,							lowerCAmelCase			:   Union[str, Any]="block_sparse"	,							lowerCAmelCase			:   List[str]=True	,							lowerCAmelCase			:   Optional[int]=False	,							lowerCAmelCase			:   Optional[Any]=2	,							lowerCAmelCase			:   List[Any]=3	,							)							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						parent
     __lowerCAmelCase  :      Union[str, Any]    =						batch_size
     __lowerCAmelCase  :      List[Any]    =						seq_length
     __lowerCAmelCase  :      int    =						is_training
     __lowerCAmelCase  :      Union[str, Any]    =						use_attention_mask
     __lowerCAmelCase  :      Tuple    =						use_token_type_ids
     __lowerCAmelCase  :      Union[str, Any]    =						use_labels
     __lowerCAmelCase  :      List[str]    =						vocab_size
     __lowerCAmelCase  :      int    =						hidden_size
     __lowerCAmelCase  :      Tuple    =						num_hidden_layers
     __lowerCAmelCase  :      List[str]    =						num_attention_heads
     __lowerCAmelCase  :      Optional[Any]    =						intermediate_size
     __lowerCAmelCase  :      List[Any]    =						hidden_act
     __lowerCAmelCase  :      Optional[Any]    =						hidden_dropout_prob
     __lowerCAmelCase  :      List[str]    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      Optional[int]    =						max_position_embeddings
     __lowerCAmelCase  :      str    =						type_vocab_size
     __lowerCAmelCase  :      List[Any]    =						type_sequence_label_size
     __lowerCAmelCase  :      Tuple    =						initializer_range
     __lowerCAmelCase  :      str    =						num_choices

     __lowerCAmelCase  :      Any    =						rescale_embeddings
     __lowerCAmelCase  :      str    =						attention_type
     __lowerCAmelCase  :      List[Any]    =						use_bias
     __lowerCAmelCase  :      List[str]    =						block_size
     __lowerCAmelCase  :      Union[str, Any]    =						num_random_blocks







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size   )

     __lowerCAmelCase  :      Optional[Any]    =						None
     if self.use_attention_mask:
        __lowerCAmelCase  :      Optional[Any]    =						random_attention_mask([self.batch_size, self.seq_length]   )

     __lowerCAmelCase  :      Any    =						None
     if self.use_token_type_ids:
        __lowerCAmelCase  :      Optional[int]    =						ids_tensor([self.batch_size, self.seq_length]	,							self.type_vocab_size   )

     __lowerCAmelCase  :      List[Any]    =						BigBirdConfig(
         vocab_size=self.vocab_size	,							hidden_size=self.hidden_size	,							num_hidden_layers=self.num_hidden_layers	,							num_attention_heads=self.num_attention_heads	,							intermediate_size=self.intermediate_size	,							hidden_act=self.hidden_act	,							hidden_dropout_prob=self.hidden_dropout_prob	,							attention_probs_dropout_prob=self.attention_probs_dropout_prob	,							max_position_embeddings=self.max_position_embeddings	,							type_vocab_size=self.type_vocab_size	,							is_decoder=lowerCAmelCase	,							initializer_range=self.initializer_range	,							attention_type=self.attention_type	,							block_size=self.block_size	,							num_random_blocks=self.num_random_blocks	,							use_bias=self.use_bias	,							rescale_embeddings=self.rescale_embeddings	,							)

     return config, input_ids, token_type_ids, attention_mask







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.prepare_config_and_inputs()
     __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      Any    =						config_and_inputs
     __lowerCAmelCase  :      Union[str, Any]    =						{
         """input_ids""": input_ids,
         """token_type_ids""": token_type_ids,
         """attention_mask""": attention_mask,
     }
     return config, inputs_dict






@require_flax
class SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      List[Any]										=(
      (
          FlaxBigBirdForCausalLM,
          FlaxBigBirdModel,
          FlaxBigBirdForPreTraining,
          FlaxBigBirdForMaskedLM,
          FlaxBigBirdForMultipleChoice,
          FlaxBigBirdForQuestionAnswering,
          FlaxBigBirdForSequenceClassification,
          FlaxBigBirdForTokenClassification,
      )
      if is_flax_available()
      else ()
  )

  lowerCamelCase   :      List[str]										=False
  lowerCamelCase   :      Union[str, Any]										=False







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						FlaxBigBirdModelTester(self   )







  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Optional[Any]:






     """simple docstring"""







     super().test_from_pretrained_save_pretrained()







  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[str]:






     """simple docstring"""







     super().test_from_pretrained_with_no_automatic_init()







  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Optional[int]:






     """simple docstring"""







     super().test_no_automatic_init()







  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     super().test_hidden_states_output()







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     for model_class_name in self.all_model_classes:
        __lowerCAmelCase  :      List[str]    =						model_class_name.from_pretrained("""google/bigbird-roberta-base"""   )
        self.assertIsNotNone(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     List[str]:






     """simple docstring"""







     if self.test_attn_probs:
        super().test_attention_outputs()







  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      Tuple    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__   ):
           __lowerCAmelCase  :      List[str]    =						self._prepare_for_class(lowerCAmelCase	,							lowerCAmelCase   )
           __lowerCAmelCase  :      Dict    =						model_class(lowerCAmelCase   )

           @jax.jit
           def model_jitted(lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   Optional[Any]=None	,							**lowerCAmelCase			:   Union[str, Any]   ):
              return model(input_ids=lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							**lowerCAmelCase   )

           with self.subTest("""JIT Enabled"""   ):
              __lowerCAmelCase  :      str    =						model_jitted(**lowerCAmelCase   ).to_tuple()

           with self.subTest("""JIT Disabled"""   ):
              with jax.disable_jit():
                 __lowerCAmelCase  :      List[Any]    =						model_jitted(**lowerCAmelCase   ).to_tuple()

           self.assertEqual(len(lowerCAmelCase   )	,							len(lowerCAmelCase   )   )
           for jitted_output, output in zip(lowerCAmelCase	,							lowerCAmelCase   ):
              self.assertEqual(jitted_output.shape	,							output.shape   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]=1e-5	,							lowerCAmelCase			:   Union[str, Any]="outputs"	,							lowerCAmelCase			:   Union[str, Any]=None   )							->     Optional[int]:






     """simple docstring"""







     if name.startswith("""outputs.attentions"""   ):
        return
     else:
        super().check_pt_flax_outputs(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )


