

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 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						model.config

    SCREAMING_SNAKE_CASE_									=						DonutSwinConfig(
        image_size=original_config.input_size ,   patch_size=4 ,   depths=original_config.encoder_layer ,   num_heads=[4, 8, 16, 32] ,   window_size=original_config.window_size ,   embed_dim=128 ,   )
    SCREAMING_SNAKE_CASE_									=						MBartConfig(
        is_decoder=_SCREAMING_SNAKE_CASE ,   is_encoder_decoder=_SCREAMING_SNAKE_CASE ,   add_cross_attention=_SCREAMING_SNAKE_CASE ,   decoder_layers=original_config.decoder_layer ,   max_position_embeddings=original_config.max_position_embeddings ,   vocab_size=len(
            model.decoder.tokenizer       ) ,   scale_embedding=_SCREAMING_SNAKE_CASE ,   add_final_layer_norm=_SCREAMING_SNAKE_CASE ,   )

    return encoder_config, decoder_config


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    if "encoder.model" in name:
        SCREAMING_SNAKE_CASE_									=						name.replace('encoder.model' ,   'encoder'       )
    if "decoder.model" in name:
        SCREAMING_SNAKE_CASE_									=						name.replace('decoder.model' ,   'decoder'       )
    if "patch_embed.proj" in name:
        SCREAMING_SNAKE_CASE_									=						name.replace('patch_embed.proj' ,   'embeddings.patch_embeddings.projection'       )
    if "patch_embed.norm" in name:
        SCREAMING_SNAKE_CASE_									=						name.replace('patch_embed.norm' ,   'embeddings.norm'       )
    if name.startswith('encoder'       ):
        if "layers" in name:
            SCREAMING_SNAKE_CASE_									=						'encoder.' + name
        if "attn.proj" in name:
            SCREAMING_SNAKE_CASE_									=						name.replace('attn.proj' ,   'attention.output.dense'       )
        if "attn" in name and "mask" not in name:
            SCREAMING_SNAKE_CASE_									=						name.replace('attn' ,   'attention.self'       )
        if "norm1" in name:
            SCREAMING_SNAKE_CASE_									=						name.replace('norm1' ,   'layernorm_before'       )
        if "norm2" in name:
            SCREAMING_SNAKE_CASE_									=						name.replace('norm2' ,   'layernorm_after'       )
        if "mlp.fc1" in name:
            SCREAMING_SNAKE_CASE_									=						name.replace('mlp.fc1' ,   'intermediate.dense'       )
        if "mlp.fc2" in name:
            SCREAMING_SNAKE_CASE_									=						name.replace('mlp.fc2' ,   'output.dense'       )

        if name == "encoder.norm.weight":
            SCREAMING_SNAKE_CASE_									=						'encoder.layernorm.weight'
        if name == "encoder.norm.bias":
            SCREAMING_SNAKE_CASE_									=						'encoder.layernorm.bias'

    return name


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		Tuple       ):





    """simple docstring"""


    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE_									=						orig_state_dict.pop(_SCREAMING_SNAKE_CASE       )

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

            if "weight" in key:
                SCREAMING_SNAKE_CASE_									=						val[:dim, :]
                SCREAMING_SNAKE_CASE_									=						val[dim : dim * 2, :]
                SCREAMING_SNAKE_CASE_									=						val[-dim:, :]
            else:
                SCREAMING_SNAKE_CASE_									=						val[:dim]
                SCREAMING_SNAKE_CASE_									=						val[dim : dim * 2]
                SCREAMING_SNAKE_CASE_									=						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:
            SCREAMING_SNAKE_CASE_									=						val

    return orig_state_dict


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		List[str]=None ,   _SCREAMING_SNAKE_CASE					:		str=False       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						DonutModel.from_pretrained(_SCREAMING_SNAKE_CASE       ).eval()

    # load HuggingFace model
    SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						get_configs(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						DonutSwinModel(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						MBartForCausalLM(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						VisionEncoderDecoderModel(encoder=_SCREAMING_SNAKE_CASE ,   decoder=_SCREAMING_SNAKE_CASE       )
    model.eval()

    SCREAMING_SNAKE_CASE_									=						original_model.state_dict()
    SCREAMING_SNAKE_CASE_									=						convert_state_dict(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
    model.load_state_dict(_SCREAMING_SNAKE_CASE       )

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

    SCREAMING_SNAKE_CASE_									=						XLMRobertaTokenizerFast.from_pretrained(_SCREAMING_SNAKE_CASE ,   from_slow=_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						DonutImageProcessor(
        do_align_long_axis=original_model.config.align_long_axis ,   size=original_model.config.input_size[::-1]       )
    SCREAMING_SNAKE_CASE_									=						DonutProcessor(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						processor(_SCREAMING_SNAKE_CASE ,   return_tensors='pt'       ).pixel_values

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

    SCREAMING_SNAKE_CASE_									=						original_model.encoder.model.patch_embed(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						model.encoder.embeddings(_SCREAMING_SNAKE_CASE       )
    assert torch.allclose(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   atol=1E-3       )

    # verify encoder hidden states
    SCREAMING_SNAKE_CASE_									=						original_model.encoder(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						model.encoder(_SCREAMING_SNAKE_CASE       ).last_hidden_state
    assert torch.allclose(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   atol=1E-2       )

    # verify decoder hidden states
    SCREAMING_SNAKE_CASE_									=						original_model(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       ).logits
    SCREAMING_SNAKE_CASE_									=						model(_SCREAMING_SNAKE_CASE ,   decoder_input_ids=_SCREAMING_SNAKE_CASE       ).logits
    assert torch.allclose(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   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(_SCREAMING_SNAKE_CASE       )
        processor.save_pretrained(_SCREAMING_SNAKE_CASE       )

    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__  :      Optional[int]			=    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__  :      Any			=    parser.parse_args()
       convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM



@require_torch
class       __snake_case  :

  def __init__(					self						,		_A						,		_A=99						,		_A=13						,		_A=16						,		_A=7						,		_A=True						,		_A=True						,		_A=True						,		_A=False						,		_A=True						,		_A=2						,		_A=32						,		_A=4						,		_A=4						,		_A=30						,		_A=0						,		_A=1						,		_A=2						,		_A=None						,		):
      SCREAMING_SNAKE_CASE_									=						parent
      SCREAMING_SNAKE_CASE_									=						batch_size
      SCREAMING_SNAKE_CASE_									=						decoder_seq_length
      # For common tests
      SCREAMING_SNAKE_CASE_									=						self.decoder_seq_length
      SCREAMING_SNAKE_CASE_									=						is_training
      SCREAMING_SNAKE_CASE_									=						use_attention_mask
      SCREAMING_SNAKE_CASE_									=						use_labels

      SCREAMING_SNAKE_CASE_									=						vocab_size
      SCREAMING_SNAKE_CASE_									=						d_model
      SCREAMING_SNAKE_CASE_									=						d_model
      SCREAMING_SNAKE_CASE_									=						decoder_layers
      SCREAMING_SNAKE_CASE_									=						decoder_layers
      SCREAMING_SNAKE_CASE_									=						decoder_ffn_dim
      SCREAMING_SNAKE_CASE_									=						decoder_attention_heads
      SCREAMING_SNAKE_CASE_									=						decoder_attention_heads
      SCREAMING_SNAKE_CASE_									=						eos_token_id
      SCREAMING_SNAKE_CASE_									=						bos_token_id
      SCREAMING_SNAKE_CASE_									=						pad_token_id
      SCREAMING_SNAKE_CASE_									=						decoder_start_token_id
      SCREAMING_SNAKE_CASE_									=						use_cache
      SCREAMING_SNAKE_CASE_									=						max_position_embeddings

      SCREAMING_SNAKE_CASE_									=						None
      SCREAMING_SNAKE_CASE_									=						decoder_seq_length
      SCREAMING_SNAKE_CASE_									=						2
      SCREAMING_SNAKE_CASE_									=						1

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						ids_tensor([self.batch_size, self.decoder_seq_length]						,		self.vocab_size)

      SCREAMING_SNAKE_CASE_									=						None
      if self.use_attention_mask:
          SCREAMING_SNAKE_CASE_									=						ids_tensor([self.batch_size, self.decoder_seq_length]						,		vocab_size=2)

      SCREAMING_SNAKE_CASE_									=						None
      if self.use_labels:
          SCREAMING_SNAKE_CASE_									=						ids_tensor([self.batch_size, self.decoder_seq_length]						,		self.vocab_size)

      SCREAMING_SNAKE_CASE_									=						TrOCRConfig(
          vocab_size=self.vocab_size						,		d_model=self.d_model						,		decoder_layers=self.decoder_layers						,		decoder_ffn_dim=self.decoder_ffn_dim						,		decoder_attention_heads=self.decoder_attention_heads						,		eos_token_id=self.eos_token_id						,		bos_token_id=self.bos_token_id						,		use_cache=self.use_cache						,		pad_token_id=self.pad_token_id						,		decoder_start_token_id=self.decoder_start_token_id						,		max_position_embeddings=self.max_position_embeddings						,		)

      return (config, input_ids, attention_mask, lm_labels)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		):
      SCREAMING_SNAKE_CASE_									=						True
      SCREAMING_SNAKE_CASE_									=						TrOCRDecoder(config=_A).to(_A).eval()
      SCREAMING_SNAKE_CASE_									=						input_ids[:2]

      input_ids[input_ids == 0] += 1
      # first forward pass
      SCREAMING_SNAKE_CASE_									=						model(_A						,		use_cache=_A)
      SCREAMING_SNAKE_CASE_									=						model(_A)
      SCREAMING_SNAKE_CASE_									=						model(_A						,		use_cache=_A)

      self.parent.assertTrue(len(_A) == len(_A))
      self.parent.assertTrue(len(_A) == len(_A) + 1)

      SCREAMING_SNAKE_CASE_									=						outputs['past_key_values']

      # create hypothetical next token and extent to next_input_ids
      SCREAMING_SNAKE_CASE_									=						ids_tensor((2, 1)						,		config.vocab_size - 1) + 1

      # append to next input_ids and
      SCREAMING_SNAKE_CASE_									=						torch.cat([input_ids, next_tokens]						,		dim=-1)

      SCREAMING_SNAKE_CASE_									=						model(_A)['last_hidden_state']
      SCREAMING_SNAKE_CASE_									=						model(_A						,		past_key_values=_A)['last_hidden_state']

      # select random slice
      SCREAMING_SNAKE_CASE_									=						ids_tensor((1,)						,		output_from_past.shape[-1]).item()
      SCREAMING_SNAKE_CASE_									=						output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
      SCREAMING_SNAKE_CASE_									=						output_from_past[:, 0, random_slice_idx].detach()

      # test that outputs are equal for slice
      assert torch.allclose(_A						,		_A						,		atol=1E-3)


  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.prepare_config_and_inputs()
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						config_and_inputs

      SCREAMING_SNAKE_CASE_									=						{'input_ids': input_ids, 'attention_mask': attention_mask}
      return config, inputs_dict






@require_torch
class       __snake_case  (    lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__     , unittest.TestCase       ):
  __lowerCAmelCase     :	Tuple						 =  (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
  __lowerCAmelCase     :	Union[str, Any]						 =  (TrOCRForCausalLM,) if is_torch_available() else ()
  __lowerCAmelCase     :	str						 =  {'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
  __lowerCAmelCase     :	Any						 =  True
  __lowerCAmelCase     :	str						 =  False

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						TrOCRStandaloneDecoderModelTester(self						,		is_training=_A)
      SCREAMING_SNAKE_CASE_									=						ConfigTester(self						,		config_class=_A)

  def 						lowerCAmelCase__     (					self):
      pass

  def 						lowerCAmelCase__     (					self):
      pass

  def 						lowerCAmelCase__     (					self):
      pass

  def 						lowerCAmelCase__     (					self):
      self.config_tester.run_common_tests()

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_decoder_model_past(*_A)

  def 						lowerCAmelCase__     (					self):
      return


  @unittest.skip('The model doesn\'t support left padding')  # and it's not used enough to be worth fixing :)
  def 						lowerCAmelCase__     (					self):
      pass


