






"""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()
a			:    Union[str, Any]				 =		logging.get_logger(__name__)





def 						lowercase__(A ,		A=False ,		A=False						)    ->Optional[int]:




						"""simple docstring"""



						lowercase__    :				List[Any]=	"backbone." if is_semantic else ""

						lowercase__    :				Tuple=	[]
						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 						lowercase__(A ,		A ,		A=False ,		A=False						)    ->Union[str, Any]:




						"""simple docstring"""



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

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

												# gamma_1 and gamma_2
												# we call them lambda because otherwise they are renamed when using .from_pretrained
												lowercase__    :				str=	state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''						)
												lowercase__    :				Union[str, Any]=	state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''						)

												lowercase__    :				List[str]=	gamma_a
												lowercase__    :				Any=	gamma_a





def 						lowercase__(A ,		A ,		A						)    ->Tuple:




						"""simple docstring"""



						lowercase__    :				int=	dct.pop(A						)
						lowercase__    :				Optional[Any]=	val





def 						lowercase__()    ->Tuple:




						"""simple docstring"""



						lowercase__    :				Dict=	"http://images.cocodataset.org/val2017/000000039769.jpg"
						lowercase__    :				List[Any]=	Image.open(requests.get(A ,		stream=A						).raw						)
						return im





@torch.no_grad()
def 						lowercase__(A ,		A ,		A=False						)    ->int:




						"""simple docstring"""



						lowercase__    :				List[str]=	False if "rvlcdip" in checkpoint_url else True
						lowercase__    :				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:
												lowercase__    :				int=	1_024
												lowercase__    :				List[str]=	4_096
												lowercase__    :				List[str]=	24
												lowercase__    :				Optional[int]=	16

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

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

						lowercase__    :				str=	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
						lowercase__    :				List[str]=	BeitForMaskedImageModeling(A						) if has_lm_head else BeitForImageClassification(A						)
						model.eval()
						model.load_state_dict(A						)

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

						lowercase__    :				Tuple=	image_processor(images=A ,		return_tensors="pt"						)
						lowercase__    :				Dict=	encoding["pixel_values"]

						lowercase__    :				Union[str, Any]=	model(A						)
						lowercase__    :				Dict=	outputs.logits

						# verify logits
						lowercase__    :				Union[str, Any]=	[1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8_192]
						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:
																		lowercase__    :				List[str]=	"dit-base" if "base" in checkpoint_url else "dit-large"
												else:
																		lowercase__    :				Optional[Any]=	"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__":
			a			:    Union[str, Any]				 =		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			:    Optional[int]				 =		parser.parse_args()
			convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)














"""simple docstring"""







import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
a			:    Any				 =		"""src/diffusers"""

# Matches is_xxx_available()
a			:    Optional[Any]				 =		re.compile(r"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
a			:    Dict				 =		re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


a			:    Dict				 =		"""
{0} = None
"""

a			:    List[Any]				 =		"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


a			:    Any				 =		"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""





def 						lowercase__(A						)    ->List[str]:




						"""simple docstring"""



						lowercase__    :				Optional[int]=	_re_backend.findall(A						)
						if len(A						) == 0:
												return None

						return "_and_".join(A						)





def 						lowercase__()    ->int:




						"""simple docstring"""



						with open(os.path.join(A ,		"__init__.py"						) ,		"r" ,		encoding="utf-8" ,		newline="\n"						) as f:
												lowercase__    :				Union[str, Any]=	f.readlines()

						# Get to the point we do the actual imports for type checking
						lowercase__    :				List[Any]=	0
						lowercase__    :				List[Any]=	{}
						# Go through the end of the file
						while line_index < len(A						):
												# If the line contains is_backend_available, we grab all objects associated with the `else` block
												lowercase__    :				Optional[Any]=	find_backend(lines[line_index]						)
												if backend is not None:
																		while not lines[line_index].startswith("else:"						):
																								line_index += 1
																		line_index += 1
																		lowercase__    :				str=	[]
																		# Until we unindent, add backend objects to the list
																		while line_index < len(A						) and len(lines[line_index]						) > 1:
																								lowercase__    :				List[str]=	lines[line_index]
																								lowercase__    :				Any=	_re_single_line_import.search(A						)
																								if single_line_import_search is not None:
																														objects.extend(single_line_import_search.groups()[0].split(", "						)						)
																								elif line.startswith(" " * 8						):
																														objects.append(line[8:-2]						)
																								line_index += 1

																		if len(A						) > 0:
																								lowercase__    :				str=	objects
												else:
																		line_index += 1

						return backend_specific_objects





def 						lowercase__(A ,		A						)    ->List[Any]:




						"""simple docstring"""



						if name.isupper():
												return DUMMY_CONSTANT.format(A						)
						elif name.islower():
												return DUMMY_FUNCTION.format(A ,		A						)
						else:
												return DUMMY_CLASS.format(A ,		A						)





def 						lowercase__(A=None						)    ->Optional[Any]:




						"""simple docstring"""



						if backend_specific_objects is None:
												lowercase__    :				int=	read_init()
						# For special correspondence backend to module name as used in the function requires_modulename
						lowercase__    :				Dict=	{}

						for backend, objects in backend_specific_objects.items():
												lowercase__    :				str=	"[" + ", ".join(f'''"{b}"''' for b in backend.split("_and_"						)						) + "]"
												lowercase__    :				List[str]=	"# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
												dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
												dummy_file += "\n".join([create_dummy_object(A ,		A						) for o in objects]						)
												lowercase__    :				Optional[Any]=	dummy_file

						return dummy_files





def 						lowercase__(A=False						)    ->List[Any]:




						"""simple docstring"""



						lowercase__    :				Tuple=	create_dummy_files()
						# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
						lowercase__    :				int=	{"torch": "pt"}

						# Locate actual dummy modules and read their content.
						lowercase__    :				Dict=	os.path.join(A ,		"utils"						)
						lowercase__    :				str=	{
						    backend: os.path.join(A ,		f'''dummy_{short_names.get(A ,		A						)}_objects.py'''						)
						    for backend in dummy_files.keys()
						}

						lowercase__    :				Union[str, Any]=	{}
						for backend, file_path in dummy_file_paths.items():
												if os.path.isfile(A						):
																		with open(A ,		"r" ,		encoding="utf-8" ,		newline="\n"						) as f:
																								lowercase__    :				List[Any]=	f.read()
												else:
																		lowercase__    :				Tuple=	""

						for backend in dummy_files.keys():
												if dummy_files[backend] != actual_dummies[backend]:
																		if overwrite:
																								print(
																								    f'''Updating diffusers.utils.dummy_{short_names.get(A ,		A						)}_objects.py as the main '''
																								    "__init__ has new objects."						)
																								with open(dummy_file_paths[backend] ,		"w" ,		encoding="utf-8" ,		newline="\n"						) as f:
																														f.write(dummy_files[backend]						)
																		else:
																								raise ValueError(
																								    "The main __init__ has objects that are not present in "
																								    f'''diffusers.utils.dummy_{short_names.get(A ,		A						)}_objects.py. Run `make fix-copies` '''
																								    "to fix this."						)


if __name__ == "__main__":
			a			:    Optional[Any]				 =		argparse.ArgumentParser()
			parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
			a			:    Tuple				 =		parser.parse_args()

			check_dummies(args.fix_and_overwrite)






