

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
A_		:      Optional[Any]					    = logging.get_logger(__name__)


def    __a							( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE=False							, SCREAMING_SNAKE_CASE=False   )   ->  int:






    '''simple docstring'''
    __UpperCAmelCase					       =      '''backbone.''' if is_semantic else ''''''

    __UpperCAmelCase					       =      []
    for i in range(config.num_hidden_layers   ):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((f'''{prefix}blocks.{i}.norm1.weight''', f'''beit.encoder.layer.{i}.layernorm_before.weight''')   )
        rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias''')   )
        rename_keys.append(
            (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight''')   )
        rename_keys.append(
            (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias''')   )
        rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight''')   )
        rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias''')   )
        rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight''')   )
        rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias''')   )
        rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight''')   )
        rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias''')   )

    # projection layer + position embeddings
    rename_keys.extend(
        [
            (f'''{prefix}cls_token''', '''beit.embeddings.cls_token'''),
            (f'''{prefix}patch_embed.proj.weight''', '''beit.embeddings.patch_embeddings.projection.weight'''),
            (f'''{prefix}patch_embed.proj.bias''', '''beit.embeddings.patch_embeddings.projection.bias'''),
            (f'''{prefix}pos_embed''', '''beit.embeddings.position_embeddings'''),
        ]   )

    if has_lm_head:
        # mask token + layernorm
        rename_keys.extend(
            [
                ('''mask_token''', '''beit.embeddings.mask_token'''),
                ('''norm.weight''', '''layernorm.weight'''),
                ('''norm.bias''', '''layernorm.bias'''),
            ]   )
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
                ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
                ('''head.weight''', '''classifier.weight'''),
                ('''head.bias''', '''classifier.bias'''),
            ]   )

    return rename_keys


def    __a							( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE=False							, SCREAMING_SNAKE_CASE=False   )   ->  str:






    '''simple docstring'''
    for i in range(config.num_hidden_layers   ):
        __UpperCAmelCase					       =      '''backbone.''' if is_semantic else ''''''
        # queries, keys and values
        __UpperCAmelCase					       =      state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''   )
        __UpperCAmelCase					       =      state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''   )
        __UpperCAmelCase					       =      state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''   )

        __UpperCAmelCase					       =      in_proj_weight[
            : config.hidden_size, :
        ]
        __UpperCAmelCase					       =      q_bias
        __UpperCAmelCase					       =      in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        __UpperCAmelCase					       =      in_proj_weight[
            -config.hidden_size :, :
        ]
        __UpperCAmelCase					       =      v_bias

        # gamma_1 and gamma_2
        # we call them lambda because otherwise they are renamed when using .from_pretrained
        __UpperCAmelCase					       =      state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''   )
        __UpperCAmelCase					       =      state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''   )

        __UpperCAmelCase					       =      gamma_a
        __UpperCAmelCase					       =      gamma_a


def    __a							( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE   )   ->  Tuple:






    '''simple docstring'''
    __UpperCAmelCase					       =      dct.pop(lowercase__   )
    __UpperCAmelCase					       =      val


def    __a							( )   ->  int:






    '''simple docstring'''
    __UpperCAmelCase					       =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __UpperCAmelCase					       =      Image.open(requests.get(lowercase__							, stream=lowercase__   ).raw   )
    return im


@torch.no_grad()
def    __a							( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE=False   )   ->  Union[str, Any]:






    '''simple docstring'''
    __UpperCAmelCase					       =      False if '''rvlcdip''' in checkpoint_url else True
    __UpperCAmelCase					       =      BeitConfig(use_absolute_position_embeddings=lowercase__							, use_mask_token=lowercase__   )

    # size of the architecture
    if "large" in checkpoint_url or "dit-l" in checkpoint_url:
        __UpperCAmelCase					       =      1_0_2_4
        __UpperCAmelCase					       =      4_0_9_6
        __UpperCAmelCase					       =      2_4
        __UpperCAmelCase					       =      1_6

    # labels
    if "rvlcdip" in checkpoint_url:
        __UpperCAmelCase					       =      1_6
        __UpperCAmelCase					       =      '''huggingface/label-files'''
        __UpperCAmelCase					       =      '''rvlcdip-id2label.json'''
        __UpperCAmelCase					       =      json.load(open(hf_hub_download(lowercase__							, lowercase__							, repo_type='''dataset'''   )							, '''r'''   )   )
        __UpperCAmelCase					       =      {int(lowercase__   ): v for k, v in idalabel.items()}
        __UpperCAmelCase					       =      idalabel
        __UpperCAmelCase					       =      {v: k for k, v in idalabel.items()}

    # load state_dict of original model, remove and rename some keys
    __UpperCAmelCase					       =      torch.hub.load_state_dict_from_url(lowercase__							, map_location='''cpu'''   )['''model''']

    __UpperCAmelCase					       =      create_rename_keys(lowercase__							, has_lm_head=lowercase__   )
    for src, dest in rename_keys:
        rename_key(lowercase__							, lowercase__							, lowercase__   )
    read_in_q_k_v(lowercase__							, lowercase__							, has_lm_head=lowercase__   )

    # load HuggingFace model
    __UpperCAmelCase					       =      BeitForMaskedImageModeling(lowercase__   ) if has_lm_head else BeitForImageClassification(lowercase__   )
    model.eval()
    model.load_state_dict(lowercase__   )

    # Check outputs on an image
    __UpperCAmelCase					       =      BeitImageProcessor(
        size=config.image_size							, resample=PILImageResampling.BILINEAR							, do_center_crop=lowercase__   )
    __UpperCAmelCase					       =      prepare_img()

    __UpperCAmelCase					       =      image_processor(images=lowercase__							, return_tensors='''pt'''   )
    __UpperCAmelCase					       =      encoding['''pixel_values''']

    __UpperCAmelCase					       =      model(lowercase__   )
    __UpperCAmelCase					       =      outputs.logits

    # verify logits
    __UpperCAmelCase					       =      [1, 1_6] if '''rvlcdip''' in checkpoint_url else [1, 1_9_6, 8_1_9_2]
    assert logits.shape == torch.Size(lowercase__   ), "Shape of logits not as expected"

    Path(lowercase__   ).mkdir(exist_ok=lowercase__   )
    print(f'''Saving model to {pytorch_dump_folder_path}'''   )
    model.save_pretrained(lowercase__   )
    print(f'''Saving image processor to {pytorch_dump_folder_path}'''   )
    image_processor.save_pretrained(lowercase__   )

    if push_to_hub:
        if has_lm_head:
            __UpperCAmelCase					       =      '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
        else:
            __UpperCAmelCase					       =      '''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
        image_processor.push_to_hub(
            repo_path_or_name=Path(lowercase__							, lowercase__   )							, organization='''nielsr'''							, commit_message='''Add image processor'''							, use_temp_dir=lowercase__							, )
        model.push_to_hub(
            repo_path_or_name=Path(lowercase__							, lowercase__   )							, organization='''nielsr'''							, commit_message='''Add model'''							, use_temp_dir=lowercase__							, )


if __name__ == "__main__":
       A_		:      Optional[int]					    = argparse.ArgumentParser()

       parser.add_argument(
           '--checkpoint_url',
           default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
           type=str,
           help='URL to the original PyTorch checkpoint (.pth file).',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
       )
       parser.add_argument(
           '--push_to_hub',
           action='store_true',
       )
       A_		:      str					    = parser.parse_args()
       convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""




import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


__magic_name__						: Optional[Any]    =				'http://www.mocksite.com/file1.txt'
__magic_name__						: Tuple    =				'"text": ["foo", "foo"]'
__magic_name__						: str    =				'6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'




class 			__snake_case   :
							__a					 =       200
							__a					 =       {'''Content-Length''': '''100'''}
							__a					 =       {}




							def   __a   (     self:    List[str]     ,							**A_:    List[Any]   ):
										return [bytes(A_     ,							"""utf-8"""   )]





def       a_ (	*lowercase__ :List[Any],			**lowercase__ :Any     ):
			return MockResponse()





@pytest.mark.parametrize("""urls_type""",			[str, list, dict]     )
def       a_ (	lowercase__ :Optional[int],			lowercase__ :Any,			lowercase__ :Optional[int]     ):
			import requests

			monkeypatch.setattr(lowercase__,			"""request""",			lowercase__     )

			__lowerCamelCase          =      URL
			if issubclass(lowercase__,			lowercase__     ):
						__lowerCamelCase          =      url
			elif issubclass(lowercase__,			lowercase__     ):
						__lowerCamelCase          =      [url]
			elif issubclass(lowercase__,			lowercase__     ):
						__lowerCamelCase          =      {"""train""": url}
			__lowerCamelCase          =      """dummy"""
			__lowerCamelCase          =      """downloads"""
			__lowerCamelCase          =      tmp_path
			__lowerCamelCase          =      DownloadConfig(
			    cache_dir=os.path.join(lowercase__,			lowercase__     ),			use_etag=lowercase__,			)
			__lowerCamelCase          =      DownloadManager(dataset_name=lowercase__,			download_config=lowercase__     )
			__lowerCamelCase          =      dl_manager.download(lowercase__     )
			__lowerCamelCase          =      urls
			for downloaded_paths in [downloaded_paths]:
						if isinstance(lowercase__,			lowercase__     ):
									__lowerCamelCase          =      [downloaded_paths]
									__lowerCamelCase          =      [urls]
						elif isinstance(lowercase__,			lowercase__     ):
									assert "train" in downloaded_paths.keys()
									__lowerCamelCase          =      downloaded_paths.values()
									__lowerCamelCase          =      urls.values()
						assert downloaded_paths
						for downloaded_path, input_url in zip(lowercase__,			lowercase__     ):
									assert downloaded_path == dl_manager.downloaded_paths[input_url]
									__lowerCamelCase          =      Path(lowercase__     )
									__lowerCamelCase          =      downloaded_path.parts
									assert parts[-1] == HASH
									assert parts[-2] == cache_subdir
									assert downloaded_path.exists()
									__lowerCamelCase          =      downloaded_path.read_text()
									assert content == CONTENT
									__lowerCamelCase          =      downloaded_path.with_suffix(""".json"""     )
									assert metadata_downloaded_path.exists()
									__lowerCamelCase          =      json.loads(metadata_downloaded_path.read_text()     )
									assert metadata_content == {"url": URL, "etag": None}





@pytest.mark.parametrize("""paths_type""",			[str, list, dict]     )
def       a_ (	lowercase__ :Dict,			lowercase__ :Optional[Any],			lowercase__ :Dict     ):
			__lowerCamelCase          =      str(lowercase__     )
			if issubclass(lowercase__,			lowercase__     ):
						__lowerCamelCase          =      filename
			elif issubclass(lowercase__,			lowercase__     ):
						__lowerCamelCase          =      [filename]
			elif issubclass(lowercase__,			lowercase__     ):
						__lowerCamelCase          =      {"""train""": filename}
			__lowerCamelCase          =      """dummy"""
			__lowerCamelCase          =      xz_file.parent
			__lowerCamelCase          =      """extracted"""
			__lowerCamelCase          =      DownloadConfig(
			    cache_dir=lowercase__,			use_etag=lowercase__,			)
			__lowerCamelCase          =      DownloadManager(dataset_name=lowercase__,			download_config=lowercase__     )
			__lowerCamelCase          =      dl_manager.extract(lowercase__     )
			__lowerCamelCase          =      paths
			for extracted_paths in [extracted_paths]:
						if isinstance(lowercase__,			lowercase__     ):
									__lowerCamelCase          =      [extracted_paths]
									__lowerCamelCase          =      [paths]
						elif isinstance(lowercase__,			lowercase__     ):
									assert "train" in extracted_paths.keys()
									__lowerCamelCase          =      extracted_paths.values()
									__lowerCamelCase          =      paths.values()
						assert extracted_paths
						for extracted_path, input_path in zip(lowercase__,			lowercase__     ):
									assert extracted_path == dl_manager.extracted_paths[input_path]
									__lowerCamelCase          =      Path(lowercase__     )
									__lowerCamelCase          =      extracted_path.parts
									assert parts[-1] == hash_url_to_filename(lowercase__,			etag=lowercase__     )
									assert parts[-2] == extracted_subdir
									assert extracted_path.exists()
									__lowerCamelCase          =      extracted_path.read_text()
									__lowerCamelCase          =      text_file.read_text()
									assert extracted_file_content == expected_file_content





def       a_ (	lowercase__ :List[str],			lowercase__ :int     ):
			assert path.endswith(""".jsonl"""     )
			for num_items, line in enumerate(lowercase__,			start=1     ):
						__lowerCamelCase          =      json.loads(line.decode("""utf-8"""     )     )
						assert item.keys() == {"col_1", "col_2", "col_3"}
			assert num_items == 4





@pytest.mark.parametrize("""archive_jsonl""",			["""tar_jsonl_path""", """zip_jsonl_path"""]     )
def       a_ (	lowercase__ :Optional[int],			lowercase__ :Union[str, Any]     ):
			__lowerCamelCase          =      request.getfixturevalue(lowercase__     )
			__lowerCamelCase          =      DownloadManager()
			for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(lowercase__     ),			start=1     ):
						_test_jsonl(lowercase__,			lowercase__     )
			assert num_jsonl == 2





@pytest.mark.parametrize("""archive_nested_jsonl""",			["""tar_nested_jsonl_path""", """zip_nested_jsonl_path"""]     )
def       a_ (	lowercase__ :Optional[int],			lowercase__ :List[Any]     ):
			__lowerCamelCase          =      request.getfixturevalue(lowercase__     )
			__lowerCamelCase          =      DownloadManager()
			for num_tar, (path, file) in enumerate(dl_manager.iter_archive(lowercase__     ),			start=1     ):
						for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(lowercase__     ),			start=1     ):
									_test_jsonl(lowercase__,			lowercase__     )
			assert num_tar == 1
			assert num_jsonl == 2





def       a_ (	lowercase__ :Tuple     ):
			__lowerCamelCase          =      DownloadManager()
			for num_file, file in enumerate(dl_manager.iter_files(lowercase__     ),			start=1     ):
						assert os.path.basename(lowercase__     ) == ("test.txt" if num_file == 1 else "train.txt")
			assert num_file == 2




