




from __future__ import annotations

import bisect




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_ = 0					,     lowercase_ = -1      )       ->	int:

    """simple docstring"""


    if hi < 0:
        __UpperCamelCase  		=						len(_SCREAMING_SNAKE_CASE      )

    while lo < hi:
        __UpperCamelCase  		=						lo + (hi - lo) // 2
        if sorted_collection[mid] < item:
            __UpperCamelCase  		=						mid + 1
        else:
            __UpperCamelCase  		=						mid

    return lo




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_ = 0					,     lowercase_ = -1      )       ->	int:

    """simple docstring"""


    if hi < 0:
        __UpperCamelCase  		=						len(_SCREAMING_SNAKE_CASE      )

    while lo < hi:
        __UpperCamelCase  		=						lo + (hi - lo) // 2
        if sorted_collection[mid] <= item:
            __UpperCamelCase  		=						mid + 1
        else:
            __UpperCamelCase  		=						mid

    return lo




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_ = 0					,     lowercase_ = -1      )       ->	None:

    """simple docstring"""


    sorted_collection.insert(bisect_left(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE      )					,     _SCREAMING_SNAKE_CASE      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_ = 0					,     lowercase_ = -1      )       ->	None:

    """simple docstring"""


    sorted_collection.insert(bisect_right(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE      )					,     _SCREAMING_SNAKE_CASE      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	int | None:

    """simple docstring"""


    __UpperCamelCase  		=						0
    __UpperCamelCase  		=						len(_SCREAMING_SNAKE_CASE      ) - 1

    while left <= right:
        __UpperCamelCase  		=						left + (right - left) // 2
        __UpperCamelCase  		=						sorted_collection[midpoint]
        if current_item == item:
            return midpoint
        elif item < current_item:
            __UpperCamelCase  		=						midpoint - 1
        else:
            __UpperCamelCase  		=						midpoint + 1
    return None




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	int | None:

    """simple docstring"""


    __UpperCamelCase  		=						bisect.bisect_left(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE      )
    if index != len(_SCREAMING_SNAKE_CASE      ) and sorted_collection[index] == item:
        return index
    return None




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	int | None:

    """simple docstring"""


    if right < left:
        return None

    __UpperCamelCase  		=						left + (right - left) // 2

    if sorted_collection[midpoint] == item:
        return midpoint
    elif sorted_collection[midpoint] > item:
        return binary_search_by_recursion(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     midpoint - 1      )
    else:
        return binary_search_by_recursion(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     midpoint + 1					,     _SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
 a_   		=     input("Enter numbers separated by comma:\n").strip()
 a_   		=     sorted(int(item) for item in user_input.split(","))
 a_   		=     int(input("Enter a single number to be found in the list:\n"))
 a_   		=     binary_search(collection, target)
 if result is None:
  print(f"""{target} was not found in {collection}.""")
 else:
  print(f"""{target} was found at position {result} in {collection}.""")







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 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Optional[int]:

    """simple docstring"""


    __UpperCamelCase  		=						model.config

    __UpperCamelCase  		=						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=1_28					,     )
    __UpperCamelCase  		=						MBartConfig(
        is_decoder=lowercase_					,     is_encoder_decoder=lowercase_					,     add_cross_attention=lowercase_					,     decoder_layers=original_config.decoder_layer					,     max_position_embeddings=original_config.max_position_embeddings					,     vocab_size=len(
            model.decoder.tokenizer      )					,     scale_embedding=lowercase_					,     add_final_layer_norm=lowercase_					,     )

    return encoder_config, decoder_config




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Any:

    """simple docstring"""


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

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

    return name




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	int:

    """simple docstring"""


    for key in orig_state_dict.copy().keys():
        __UpperCamelCase  		=						orig_state_dict.pop(lowercase_      )

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

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

    return orig_state_dict




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_=None					,     lowercase_=False      )       ->	Dict:

    """simple docstring"""


    __UpperCamelCase  		=						DonutModel.from_pretrained(lowercase_      ).eval()

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

    __UpperCamelCase  		=						original_model.state_dict()
    __UpperCamelCase  		=						convert_state_dict(lowercase_					,     lowercase_      )
    model.load_state_dict(lowercase_      )

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

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

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

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

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

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

    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__":
 a_   		=     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.",
 )

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






