




import argparse
import os
import re

import packaging.version


a_   		=     "examples/"
a_   		=     {
    "examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
    "doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
a_   		=     {
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
a_   		=     "README.md"




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	List[Any]:

    """simple docstring"""


    with open(lowercase_					,     '''r'''					,     encoding='''utf-8'''					,     newline='''\n'''      ) as f:
        __UpperCamelCase  		=						f.read()
    __UpperCamelCase		,      __UpperCamelCase  		=						REPLACE_PATTERNS[pattern]
    __UpperCamelCase  		=						replace.replace('''VERSION'''					,     lowercase_      )
    __UpperCamelCase  		=						re_pattern.sub(lowercase_					,     lowercase_      )
    with open(lowercase_					,     '''w'''					,     encoding='''utf-8'''					,     newline='''\n'''      ) as f:
        f.write(lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Optional[Any]:

    """simple docstring"""


    for folder, directories, fnames in os.walk(lowercase_      ):
        # Removing some of the folders with non-actively maintained examples from the walk
        if "research_projects" in directories:
            directories.remove('''research_projects'''      )
        if "legacy" in directories:
            directories.remove('''legacy'''      )
        for fname in fnames:
            if fname.endswith('''.py'''      ):
                update_version_in_file(os.path.join(lowercase_					,     lowercase_      )					,     lowercase_					,     pattern='''examples'''      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_=False      )       ->	str:

    """simple docstring"""


    for pattern, fname in REPLACE_FILES.items():
        update_version_in_file(lowercase_					,     lowercase_					,     lowercase_      )
    if not patch:
        update_version_in_examples(lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  )       ->	Any:

    """simple docstring"""


    __UpperCamelCase  		=						'''🤗 Transformers currently provides the following architectures'''
    __UpperCamelCase  		=						'''1. Want to contribute a new model?'''
    with open(lowercase_					,     '''r'''					,     encoding='''utf-8'''					,     newline='''\n'''      ) as f:
        __UpperCamelCase  		=						f.readlines()

    # Find the start of the list.
    __UpperCamelCase  		=						0
    while not lines[start_index].startswith(_start_prompt      ):
        start_index += 1
    start_index += 1

    __UpperCamelCase  		=						start_index
    # Update the lines in the model list.
    while not lines[index].startswith(_end_prompt      ):
        if lines[index].startswith('''1.'''      ):
            __UpperCamelCase  		=						lines[index].replace(
                '''https://huggingface.co/docs/transformers/main/model_doc'''					,     '''https://huggingface.co/docs/transformers/model_doc'''					,     )
        index += 1

    with open(lowercase_					,     '''w'''					,     encoding='''utf-8'''					,     newline='''\n'''      ) as f:
        f.writelines(lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  )       ->	Dict:

    """simple docstring"""


    with open(REPLACE_FILES['''init''']					,     '''r'''      ) as f:
        __UpperCamelCase  		=						f.read()
    __UpperCamelCase  		=						REPLACE_PATTERNS['''init'''][0].search(lowercase_      ).groups()[0]
    return packaging.version.parse(lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_=False      )       ->	List[Any]:

    """simple docstring"""


    __UpperCamelCase  		=						get_version()
    if patch and default_version.is_devrelease:
        raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''      )
    if default_version.is_devrelease:
        __UpperCamelCase  		=						default_version.base_version
    elif patch:
        __UpperCamelCase  		=						F"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
    else:
        __UpperCamelCase  		=						F"{default_version.major}.{default_version.minor + 1}.0"

    # Now let's ask nicely if that's the right one.
    __UpperCamelCase  		=						input(F"Which version are you releasing? [{default_version}]"      )
    if len(lowercase_      ) == 0:
        __UpperCamelCase  		=						default_version

    print(F"Updating version to {version}."      )
    global_version_update(lowercase_					,     patch=lowercase_      )
    if not patch:
        print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''      )
        clean_main_ref_in_model_list()




def 					__SCREAMING_SNAKE_CASE   (  )       ->	Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase  		=						get_version()
    __UpperCamelCase  		=						F"{current_version.major}.{current_version.minor + 1}.0.dev0"
    __UpperCamelCase  		=						current_version.base_version

    # Check with the user we got that right.
    __UpperCamelCase  		=						input(F"Which version are we developing now? [{dev_version}]"      )
    if len(lowercase_      ) == 0:
        __UpperCamelCase  		=						dev_version

    print(F"Updating version to {version}."      )
    global_version_update(lowercase_      )
    print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''      )
    clean_main_ref_in_model_list()


if __name__ == "__main__":
 a_   		=     argparse.ArgumentParser()
 parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
 parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
 a_   		=     parser.parse_args()
 if not args.post_release:
  pre_release_work(patch=args.patch)
 elif args.patch:
  print("Nothing to do after a patch :-)")
 else:
  post_release_work()












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)






