



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()
_snake_case		=  logging.get_logger(__name__)






def 				A				(					_lowerCamelCase				, _lowerCamelCase=False				, _lowerCamelCase=False  ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    "backbone." if is_semantic else ""

							_lowerCAmelCase     :							Optional[int]    =    []
							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				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=False				, _lowerCamelCase=False  ):





							'''simple docstring'''




							for i in range(config.num_hidden_layers  ):
														_lowerCAmelCase     :							Tuple    =    "backbone." if is_semantic else ""
														# queries, keys and values
														_lowerCAmelCase     :							int    =    state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"  )
														_lowerCAmelCase     :							Any    =    state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"  )
														_lowerCAmelCase     :							int    =    state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"  )

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

														# gamma_1 and gamma_2
														# we call them lambda because otherwise they are renamed when using .from_pretrained
														_lowerCAmelCase     :							Optional[Any]    =    state_dict.pop(F"{prefix}blocks.{i}.gamma_1"  )
														_lowerCAmelCase     :							Tuple    =    state_dict.pop(F"{prefix}blocks.{i}.gamma_2"  )

														_lowerCAmelCase     :							Dict    =    gamma_a
														_lowerCAmelCase     :							Union[str, Any]    =    gamma_a






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[int]    =    dct.pop(_lowerCamelCase  )
							_lowerCAmelCase     :							int    =    val






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							Union[str, Any]    =    "http://images.cocodataset.org/val2017/000000039769.jpg"
							_lowerCAmelCase     :							Tuple    =    Image.open(requests.get(_lowerCamelCase				, stream=_lowerCamelCase  ).raw  )
							return im






@torch.no_grad()
def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=False  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[str]    =    False if "rvlcdip" in checkpoint_url else True
							_lowerCAmelCase     :							str    =    BeitConfig(use_absolute_position_embeddings=_lowerCamelCase				, use_mask_token=_lowerCamelCase  )

							# size of the architecture
							if "large" in checkpoint_url or "dit-l" in checkpoint_url:
														_lowerCAmelCase     :							List[str]    =    1_024
														_lowerCAmelCase     :							List[Any]    =    4_096
														_lowerCAmelCase     :							Tuple    =    24
														_lowerCAmelCase     :							Any    =    16

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

							# load state_dict of original model, remove and rename some keys
							_lowerCAmelCase     :							List[str]    =    torch.hub.load_state_dict_from_url(_lowerCamelCase				, map_location="cpu"  )["model"]

							_lowerCAmelCase     :							List[str]    =    create_rename_keys(_lowerCamelCase				, has_lm_head=_lowerCamelCase  )
							for src, dest in rename_keys:
														rename_key(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )
							read_in_q_k_v(_lowerCamelCase				, _lowerCamelCase				, has_lm_head=_lowerCamelCase  )

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

							# Check outputs on an image
							_lowerCAmelCase     :							Optional[Any]    =    BeitImageProcessor(
							    size=config.image_size				, resample=PILImageResampling.BILINEAR				, do_center_crop=_lowerCamelCase  )
							_lowerCAmelCase     :							Tuple    =    prepare_img()

							_lowerCAmelCase     :							Optional[Any]    =    image_processor(images=_lowerCamelCase				, return_tensors="pt"  )
							_lowerCAmelCase     :							Optional[int]    =    encoding["pixel_values"]

							_lowerCAmelCase     :							Any    =    model(_lowerCamelCase  )
							_lowerCAmelCase     :							List[Any]    =    outputs.logits

							# verify logits
							_lowerCAmelCase     :							Optional[Any]    =    [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8_192]
							assert logits.shape == torch.Size(_lowerCamelCase  ), "Shape of logits not as expected"

							Path(_lowerCamelCase  ).mkdir(exist_ok=_lowerCamelCase  )
							print(F"Saving model to {pytorch_dump_folder_path}"  )
							model.save_pretrained(_lowerCamelCase  )
							print(F"Saving image processor to {pytorch_dump_folder_path}"  )
							image_processor.save_pretrained(_lowerCamelCase  )

							if push_to_hub:
														if has_lm_head:
																					_lowerCAmelCase     :							Optional[int]    =    "dit-base" if "base" in checkpoint_url else "dit-large"
														else:
																					_lowerCAmelCase     :							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(_lowerCamelCase				, _lowerCamelCase  )				, organization="nielsr"				, commit_message="Add image processor"				, use_temp_dir=_lowerCamelCase				, )
														model.push_to_hub(
														    repo_path_or_name=Path(_lowerCamelCase				, _lowerCamelCase  )				, organization="nielsr"				, commit_message="Add model"				, use_temp_dir=_lowerCamelCase				, )


if __name__ == "__main__":
						_snake_case		=  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",
						)
						_snake_case		=  parser.parse_args()
						convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import (
						    VersatileDiffusionDualGuidedPipeline,
						    VersatileDiffusionImageVariationPipeline,
						    VersatileDiffusionPipeline,
						    VersatileDiffusionTextToImagePipeline,
						)
else:
						from .modeling_text_unet import UNetFlatConditionModel
						from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
						from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
						from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
						from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
