






"""simple docstring"""






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()
UpperCAmelCase_   : Any   			= logging.get_logger(__name__)



def 					_A			(__a    ,				__a=False    ,				__a=False							) ->       Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  int    						=		'''backbone.''' if is_semantic else ''''''

    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[]
    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			(__a    ,				__a    ,				__a=False    ,				__a=False							) ->       Union[str, Any]:
    """simple docstring"""






    for i in range(config.num_hidden_layers							):
        SCREAMING_SNAKE_CASE_      :  Dict    						=		'''backbone.''' if is_semantic else ''''''
        # queries, keys and values
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'							)
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'							)
        SCREAMING_SNAKE_CASE_      :  Any    						=		state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'							)

        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		in_proj_weight[
            : config.hidden_size, :
        ]
        SCREAMING_SNAKE_CASE_      :  Dict    						=		q_bias
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		in_proj_weight[
            -config.hidden_size :, :
        ]
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		v_bias

        # gamma_1 and gamma_2
        # we call them lambda because otherwise they are renamed when using .from_pretrained
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		state_dict.pop(f'{prefix}blocks.{i}.gamma_1'							)
        SCREAMING_SNAKE_CASE_      :  Dict    						=		state_dict.pop(f'{prefix}blocks.{i}.gamma_2'							)

        SCREAMING_SNAKE_CASE_      :  int    						=		gamma_a
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		gamma_a



def 					_A			(__a    ,				__a    ,				__a							) ->       List[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		dct.pop(__a							)
    SCREAMING_SNAKE_CASE_      :  int    						=		val



def 					_A			() ->       List[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  str    						=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE_      :  Any    						=		Image.open(requests.get(__a    ,				stream=__a							).raw							)
    return im



@torch.no_grad()
def 					_A			(__a    ,				__a    ,				__a=False							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		False if '''rvlcdip''' in checkpoint_url else True
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		BeitConfig(use_absolute_position_embeddings=__a    ,				use_mask_token=__a							)

    # size of the architecture
    if "large" in checkpoint_url or "dit-l" in checkpoint_url:
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		10_24
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		40_96
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		24
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		16

    # labels
    if "rvlcdip" in checkpoint_url:
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		16
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		'''huggingface/label-files'''
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		'''rvlcdip-id2label.json'''
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		json.load(open(hf_hub_download(__a    ,				__a    ,				repo_type='''dataset'''							)    ,				'''r'''							)							)
        SCREAMING_SNAKE_CASE_      :  Any    						=		{int(__a							): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE_      :  str    						=		idalabel
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		{v: k for k, v in idalabel.items()}

    # load state_dict of original model, remove and rename some keys
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		torch.hub.load_state_dict_from_url(__a    ,				map_location='''cpu'''							)['''model''']

    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		create_rename_keys(__a    ,				has_lm_head=__a							)
    for src, dest in rename_keys:
        rename_key(__a    ,				__a    ,				__a							)
    read_in_q_k_v(__a    ,				__a    ,				has_lm_head=__a							)

    # load HuggingFace model
    SCREAMING_SNAKE_CASE_      :  Dict    						=		BeitForMaskedImageModeling(__a							) if has_lm_head else BeitForImageClassification(__a							)
    model.eval()
    model.load_state_dict(__a							)

    # Check outputs on an image
    SCREAMING_SNAKE_CASE_      :  Dict    						=		BeitImageProcessor(
        size=config.image_size    ,				resample=PILImageResampling.BILINEAR    ,				do_center_crop=__a							)
    SCREAMING_SNAKE_CASE_      :  int    						=		prepare_img()

    SCREAMING_SNAKE_CASE_      :  List[str]    						=		image_processor(images=__a    ,				return_tensors='''pt'''							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		encoding['''pixel_values''']

    SCREAMING_SNAKE_CASE_      :  Any    						=		model(__a							)
    SCREAMING_SNAKE_CASE_      :  Any    						=		outputs.logits

    # verify logits
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		[1, 16] if '''rvlcdip''' in checkpoint_url else [1, 1_96, 81_92]
    assert logits.shape == torch.Size(__a							), "Shape of logits not as expected"

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

    if push_to_hub:
        if has_lm_head:
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		'''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
        else:
            SCREAMING_SNAKE_CASE_      :  Dict    						=		'''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(__a    ,				__a							)    ,				organization='''nielsr'''    ,				commit_message='''Add image processor'''    ,				use_temp_dir=__a    ,				)
        model.push_to_hub(
            repo_path_or_name=Path(__a    ,				__a							)    ,				organization='''nielsr'''    ,				commit_message='''Add model'''    ,				use_temp_dir=__a    ,				)


if __name__ == "__main__":
  UpperCAmelCase_   : str   			= 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""",
  )
  UpperCAmelCase_   : List[Any]   			= parser.parse_args()
  convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCamelCase  :      Optional[Any]							      =		{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Tuple							      =		[
							    '''RagModel''',
							    '''RagPreTrainedModel''',
							    '''RagSequenceForGeneration''',
							    '''RagTokenForGeneration''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Union[str, Any]							      =		[
							    '''TFRagModel''',
							    '''TFRagPreTrainedModel''',
							    '''TFRagSequenceForGeneration''',
							    '''TFRagTokenForGeneration''',
							]


if TYPE_CHECKING:
							from .configuration_rag import RagConfig
							from .retrieval_rag import RagRetriever
							from .tokenization_rag import RagTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_rag import (
														    TFRagModel,
														    TFRagPreTrainedModel,
														    TFRagSequenceForGeneration,
														    TFRagTokenForGeneration,
														)

else:
							import sys

							__UpperCamelCase  :      Dict							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




