


"""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()
__magic_name__						: Optional[Any]    =				logging.get_logger(__name__)





def       a_ (	lowercase__ :List[Any],			lowercase__ :Tuple=False,			lowercase__ :Tuple=False     ):
			__lowerCamelCase          =      """backbone.""" if is_semantic else """"""

			__lowerCamelCase          =      []
			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_ (	lowercase__ :Tuple,			lowercase__ :List[Any],			lowercase__ :int=False,			lowercase__ :int=False     ):
			for i in range(config.num_hidden_layers     ):
						__lowerCamelCase          =      """backbone.""" if is_semantic else """"""
						# queries, keys and values
						__lowerCamelCase          =      state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'     )
						__lowerCamelCase          =      state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'     )
						__lowerCamelCase          =      state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'     )

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

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

						__lowerCamelCase          =      gamma_a
						__lowerCamelCase          =      gamma_a





def       a_ (	lowercase__ :int,			lowercase__ :Union[str, Any],			lowercase__ :Any     ):
			__lowerCamelCase          =      dct.pop(lowercase__     )
			__lowerCamelCase          =      val





def       a_ (	):
			__lowerCamelCase          =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
			__lowerCamelCase          =      Image.open(requests.get(lowercase__,			stream=lowercase__     ).raw     )
			return im





@torch.no_grad()
def       a_ (	lowercase__ :Any,			lowercase__ :str,			lowercase__ :List[str]=False     ):
			__lowerCamelCase          =      False if """rvlcdip""" in checkpoint_url else True
			__lowerCamelCase          =      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:
						__lowerCamelCase          =      1024
						__lowerCamelCase          =      4096
						__lowerCamelCase          =      24
						__lowerCamelCase          =      16

			# labels
			if "rvlcdip" in checkpoint_url:
						__lowerCamelCase          =      16
						__lowerCamelCase          =      """huggingface/label-files"""
						__lowerCamelCase          =      """rvlcdip-id2label.json"""
						__lowerCamelCase          =      json.load(open(hf_hub_download(lowercase__,			lowercase__,			repo_type="""dataset"""     ),			"""r"""     )     )
						__lowerCamelCase          =      {int(lowercase__     ): v for k, v in idalabel.items()}
						__lowerCamelCase          =      idalabel
						__lowerCamelCase          =      {v: k for k, v in idalabel.items()}

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

			__lowerCamelCase          =      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
			__lowerCamelCase          =      BeitForMaskedImageModeling(lowercase__     ) if has_lm_head else BeitForImageClassification(lowercase__     )
			model.eval()
			model.load_state_dict(lowercase__     )

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

			__lowerCamelCase          =      image_processor(images=lowercase__,			return_tensors="""pt"""     )
			__lowerCamelCase          =      encoding["""pixel_values"""]

			__lowerCamelCase          =      model(lowercase__     )
			__lowerCamelCase          =      outputs.logits

			# verify logits
			__lowerCamelCase          =      [1, 16] if """rvlcdip""" in checkpoint_url else [1, 196, 8192]
			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:
									__lowerCamelCase          =      """dit-base""" if """base""" in checkpoint_url else """dit-large"""
						else:
									__lowerCamelCase          =      """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__":
					__magic_name__						: 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',
					)
					__magic_name__						: str    =				parser.parse_args()
					convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available




@dataclass
class 			__snake_case   (lowerCamelCase  ):
							__a					 =       42


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .pipeline_text_to_video_synth import TextToVideoSDPipeline
					from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
					from .pipeline_text_to_video_zero import TextToVideoZeroPipeline




