


"""simple docstring"""




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     __a   (	a       ):





       """simple docstring"""







       _a							 =					model.config

       _a							 =					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,		)
       _a							 =					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     __a   (	a       ):





       """simple docstring"""







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

              if name == "encoder.norm.weight":
                     _a							 =					"encoder.layernorm.weight"
              if name == "encoder.norm.bias":
                     _a							 =					"encoder.layernorm.bias"

       return name



def     __a   (	a,		a       ):





       """simple docstring"""







       for key in orig_state_dict.copy().keys():
              _a							 =					orig_state_dict.pop(a       )

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

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

       return orig_state_dict



def     __a   (	a,		a=None,		a=False       ):





       """simple docstring"""







       _a							 =					DonutModel.from_pretrained(a       ).eval()

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

       _a							 =					original_model.state_dict()
       _a							 =					convert_state_dict(a,		a       )
       model.load_state_dict(a       )

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

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

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

       _a							 =					original_model.encoder.model.patch_embed(a       )
       _a							 =					model.encoder.embeddings(a       )
       assert torch.allclose(a,		a,		atol=1e-3       )

       # verify encoder hidden states
       _a							 =					original_model.encoder(a       )
       _a							 =					model.encoder(a       ).last_hidden_state
       assert torch.allclose(a,		a,		atol=1e-2       )

       # verify decoder hidden states
       _a							 =					original_model(a,		a,		a       ).logits
       _a							 =					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__":
   __SCREAMING_SNAKE_CASE      		=    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.""",
   )

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



_lowerCAmelCase  :      int			 ="""
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

_lowerCAmelCase  :      List[str]			 =[{"""type""": """code""", """content""": INSTALL_CONTENT}]
_lowerCAmelCase  :      int			 ={
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}