





import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Union[str, Any]     	=	["image_processor", "tokenizer"]
   a		:							Optional[int]     	=	"OwlViTImageProcessor"
   a		:							Optional[int]     	=	("CLIPTokenizer", "CLIPTokenizerFast")




   def __init__(					self,	__magic_name__=None,	__magic_name__=None,	**__magic_name__							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   None
       if "feature_extractor" in kwargs:
           warnings.warn(
               '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
               ''' instead.''',	__magic_name__,	)
           UpperCamelCase__							:       List[str]      =   kwargs.pop('''feature_extractor'''							)

       UpperCamelCase__							:       Optional[Any]      =   image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
           raise ValueError('''You need to specify an `image_processor`.'''							)
       if tokenizer is None:
           raise ValueError('''You need to specify a `tokenizer`.'''							)

       super().__init__(__magic_name__,	__magic_name__							)




   def __call__(					self,	__magic_name__=None,	__magic_name__=None,	__magic_name__=None,	__magic_name__="max_length",	__magic_name__="np",	**__magic_name__							)				->   List[str]:






       """simple docstring"""

       if text is None and query_images is None and images is None:
           raise ValueError(
               '''You have to specify at least one text or query image or image. All three cannot be none.'''							)

       if text is not None:
           if isinstance(__magic_name__,	__magic_name__							) or (isinstance(__magic_name__,	__magic_name__							) and not isinstance(text[0],	__magic_name__							)):
               UpperCamelCase__							:       Tuple      =   [self.tokenizer(__magic_name__,	padding=__magic_name__,	return_tensors=__magic_name__,	**__magic_name__							)]

           elif isinstance(__magic_name__,	__magic_name__							) and isinstance(text[0],	__magic_name__							):
               UpperCamelCase__							:       int      =   []

               # Maximum number of queries across batch
               UpperCamelCase__							:       Any      =   max([len(__magic_name__							) for t in text]							)

               # Pad all batch samples to max number of text queries
               for t in text:
                   if len(__magic_name__							) != max_num_queries:
                       UpperCamelCase__							:       Tuple      =   t + [''' '''] * (max_num_queries - len(__magic_name__							))

                   UpperCamelCase__							:       Optional[int]      =   self.tokenizer(__magic_name__,	padding=__magic_name__,	return_tensors=__magic_name__,	**__magic_name__							)
                   encodings.append(__magic_name__							)
           else:
               raise TypeError('''Input text should be a string, a list of strings or a nested list of strings'''							)

           if return_tensors == "np":
               UpperCamelCase__							:       Tuple      =   np.concatenate([encoding['''input_ids'''] for encoding in encodings],	axis=0							)
               UpperCamelCase__							:       Dict      =   np.concatenate([encoding['''attention_mask'''] for encoding in encodings],	axis=0							)

           elif return_tensors == "jax" and is_flax_available():
               import jax.numpy as jnp

               UpperCamelCase__							:       List[str]      =   jnp.concatenate([encoding['''input_ids'''] for encoding in encodings],	axis=0							)
               UpperCamelCase__							:       List[str]      =   jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings],	axis=0							)

           elif return_tensors == "pt" and is_torch_available():
               import torch

               UpperCamelCase__							:       Dict      =   torch.cat([encoding['''input_ids'''] for encoding in encodings],	dim=0							)
               UpperCamelCase__							:       Any      =   torch.cat([encoding['''attention_mask'''] for encoding in encodings],	dim=0							)

           elif return_tensors == "tf" and is_tf_available():
               import tensorflow as tf

               UpperCamelCase__							:       Any      =   tf.stack([encoding['''input_ids'''] for encoding in encodings],	axis=0							)
               UpperCamelCase__							:       Any      =   tf.stack([encoding['''attention_mask'''] for encoding in encodings],	axis=0							)

           else:
               raise ValueError('''Target return tensor type could not be returned'''							)

           UpperCamelCase__							:       Optional[Any]      =   BatchEncoding()
           UpperCamelCase__							:       Tuple      =   input_ids
           UpperCamelCase__							:       int      =   attention_mask

       if query_images is not None:
           UpperCamelCase__							:       List[Any]      =   BatchEncoding()
           UpperCamelCase__							:       str      =   self.image_processor(
               __magic_name__,	return_tensors=__magic_name__,	**__magic_name__							).pixel_values
           UpperCamelCase__							:       Any      =   query_pixel_values

       if images is not None:
           UpperCamelCase__							:       Optional[int]      =   self.image_processor(__magic_name__,	return_tensors=__magic_name__,	**__magic_name__							)

       if text is not None and images is not None:
           UpperCamelCase__							:       Union[str, Any]      =   image_features.pixel_values
           return encoding
       elif query_images is not None and images is not None:
           UpperCamelCase__							:       Dict      =   image_features.pixel_values
           return encoding
       elif text is not None or query_images is not None:
           return encoding
       else:
           return BatchEncoding(data=dict(**__magic_name__							),	tensor_type=__magic_name__							)




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   Dict:






       """simple docstring"""
       return self.image_processor.post_process(*__magic_name__,	**__magic_name__							)




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   Union[str, Any]:






       """simple docstring"""
       return self.image_processor.post_process_object_detection(*__magic_name__,	**__magic_name__							)




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   Any:






       """simple docstring"""
       return self.image_processor.post_process_image_guided_detection(*__magic_name__,	**__magic_name__							)




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   Any:






       """simple docstring"""
       return self.tokenizer.batch_decode(*__magic_name__,	**__magic_name__							)




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   Any:






       """simple docstring"""
       return self.tokenizer.decode(*__magic_name__,	**__magic_name__							)




   @property
   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''',	__magic_name__,	)
       return self.image_processor_class




   @property
   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''',	__magic_name__,	)
       return self.image_processor








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 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]   )   ->			Union[str, Any]:
    UpperCamelCase__							:       List[str]      =   model.config

    UpperCamelCase__							:       Optional[Any]      =   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	,     )
    UpperCamelCase__							:       Union[str, Any]      =   MBartConfig(
        is_decoder=__UpperCAmelCase	,     is_encoder_decoder=__UpperCAmelCase	,     add_cross_attention=__UpperCAmelCase	,     decoder_layers=original_config.decoder_layer	,     max_position_embeddings=original_config.max_position_embeddings	,     vocab_size=len(
            model.decoder.tokenizer   )	,     scale_embedding=__UpperCAmelCase	,     add_final_layer_norm=__UpperCAmelCase	,     )

    return encoder_config, decoder_config




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

        if name == "encoder.norm.weight":
            UpperCamelCase__							:       Optional[Any]      =   '''encoder.layernorm.weight'''
        if name == "encoder.norm.bias":
            UpperCamelCase__							:       Any      =   '''encoder.layernorm.bias'''

    return name




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str	,     __UpperCAmelCase:    str   )   ->			Dict:
    for key in orig_state_dict.copy().keys():
        UpperCamelCase__							:       Dict      =   orig_state_dict.pop(__UpperCAmelCase   )

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

            if "weight" in key:
                UpperCamelCase__							:       Tuple      =   val[:dim, :]
                UpperCamelCase__							:       Dict      =   val[dim : dim * 2, :]
                UpperCamelCase__							:       Dict      =   val[-dim:, :]
            else:
                UpperCamelCase__							:       Optional[int]      =   val[:dim]
                UpperCamelCase__							:       str      =   val[dim : dim * 2]
                UpperCamelCase__							:       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:
            UpperCamelCase__							:       Any      =   val

    return orig_state_dict




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[Any]	,     __UpperCAmelCase:    List[Any]=None	,     __UpperCAmelCase:    Tuple=False   )   ->			Optional[int]:
    # load original model
    UpperCamelCase__							:       List[Any]      =   DonutModel.from_pretrained(__UpperCAmelCase   ).eval()

    # load HuggingFace model
    UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   get_configs(__UpperCAmelCase   )
    UpperCamelCase__							:       int      =   DonutSwinModel(__UpperCAmelCase   )
    UpperCamelCase__							:       Optional[Any]      =   MBartForCausalLM(__UpperCAmelCase   )
    UpperCamelCase__							:       Optional[int]      =   VisionEncoderDecoderModel(encoder=__UpperCAmelCase	,     decoder=__UpperCAmelCase   )
    model.eval()

    UpperCamelCase__							:       List[Any]      =   original_model.state_dict()
    UpperCamelCase__							:       List[Any]      =   convert_state_dict(__UpperCAmelCase	,     __UpperCAmelCase   )
    model.load_state_dict(__UpperCAmelCase   )

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

    UpperCamelCase__							:       Optional[Any]      =   XLMRobertaTokenizerFast.from_pretrained(__UpperCAmelCase	,     from_slow=__UpperCAmelCase   )
    UpperCamelCase__							:       Dict      =   DonutImageProcessor(
        do_align_long_axis=original_model.config.align_long_axis	,     size=original_model.config.input_size[::-1]   )
    UpperCamelCase__							:       Any      =   DonutProcessor(__UpperCAmelCase	,     __UpperCAmelCase   )
    UpperCamelCase__							:       str      =   processor(__UpperCAmelCase	,     return_tensors='''pt'''   ).pixel_values

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

    UpperCamelCase__							:       List[str]      =   original_model.encoder.model.patch_embed(__UpperCAmelCase   )
    UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   model.encoder.embeddings(__UpperCAmelCase   )
    assert torch.allclose(__UpperCAmelCase	,     __UpperCAmelCase	,     atol=1e-3   )

    # verify encoder hidden states
    UpperCamelCase__							:       Dict      =   original_model.encoder(__UpperCAmelCase   )
    UpperCamelCase__							:       Optional[Any]      =   model.encoder(__UpperCAmelCase   ).last_hidden_state
    assert torch.allclose(__UpperCAmelCase	,     __UpperCAmelCase	,     atol=1e-2   )

    # verify decoder hidden states
    UpperCamelCase__							:       Optional[Any]      =   original_model(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   ).logits
    UpperCamelCase__							:       str      =   model(__UpperCAmelCase	,     decoder_input_ids=__UpperCAmelCase   ).logits
    assert torch.allclose(__UpperCAmelCase	,     __UpperCAmelCase	,     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(__UpperCAmelCase   )
        processor.save_pretrained(__UpperCAmelCase   )

    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)

