

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()
lowerCAmelCase							:  Dict            =						logging.get_logger(__name__)







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase=False    ,     _UpperCAmelCase=False   ):
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	"backbone." if is_semantic else ""

						SCREAMING_SNAKE_CASE_:			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       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase=False    ,     _UpperCAmelCase=False   ):
						for i in range(config.num_hidden_layers   ):
												SCREAMING_SNAKE_CASE_:			str  		=	"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_:			str  		=	state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias"   )

												SCREAMING_SNAKE_CASE_:			int  		=	in_proj_weight[
												    : config.hidden_size, :
												]
												SCREAMING_SNAKE_CASE_:			int  		=	q_bias
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												SCREAMING_SNAKE_CASE_:			str  		=	in_proj_weight[
												    -config.hidden_size :, :
												]
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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_:			Optional[int]  		=	state_dict.pop(f"{prefix}blocks.{i}.gamma_2"   )

												SCREAMING_SNAKE_CASE_:			Dict  		=	gamma_a
												SCREAMING_SNAKE_CASE_:			Dict  		=	gamma_a







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	dct.pop(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			int  		=	val







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			str  		=	"http://images.cocodataset.org/val2017/000000039769.jpg"
						SCREAMING_SNAKE_CASE_:			Tuple  		=	Image.open(requests.get(_UpperCAmelCase    ,     stream=_UpperCAmelCase   ).raw   )
						return im







@torch.no_grad()
def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase=False   ):
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	False if "rvlcdip" in checkpoint_url else True
						SCREAMING_SNAKE_CASE_:			str  		=	BeitConfig(use_absolute_position_embeddings=_UpperCAmelCase    ,     use_mask_token=_UpperCAmelCase   )

						# size of the architecture
						if "large" in checkpoint_url or "dit-l" in checkpoint_url:
												SCREAMING_SNAKE_CASE_:			Dict  		=	10_24
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	40_96
												SCREAMING_SNAKE_CASE_:			Dict  		=	24
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	16

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

						SCREAMING_SNAKE_CASE_:			Any  		=	create_rename_keys(_UpperCAmelCase    ,     has_lm_head=_UpperCAmelCase   )
						for src, dest in rename_keys:
												rename_key(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )
						read_in_q_k_v(_UpperCAmelCase    ,     _UpperCAmelCase    ,     has_lm_head=_UpperCAmelCase   )

						# load HuggingFace model
						SCREAMING_SNAKE_CASE_:			List[str]  		=	BeitForMaskedImageModeling(_UpperCAmelCase   ) if has_lm_head else BeitForImageClassification(_UpperCAmelCase   )
						model.eval()
						model.load_state_dict(_UpperCAmelCase   )

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

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

						SCREAMING_SNAKE_CASE_:			Any  		=	model(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	outputs.logits

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

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

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


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









# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCAmelCase							:  List[str]            =						"""Run commands across TPU VMs for initial setup before running `accelerate launch`."""







def       A_				(   _UpperCAmelCase=None   ):
						if subparsers is not None:
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	subparsers.add_parser("tpu-config"    ,     description=_description   )
						else:
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	argparse.ArgumentParser("Accelerate tpu-config command"    ,     description=_description   )
						# Core arguments
						SCREAMING_SNAKE_CASE_:			Any  		=	parser.add_argument_group(
						    "Config Arguments"    ,     "Arguments that can be configured through `accelerate config`."   )
						config_args.add_argument(
						    "--config_file"    ,     type=_UpperCAmelCase    ,     default=_UpperCAmelCase    ,     help="Path to the config file to use for accelerate."    ,     )
						config_args.add_argument(
						    "--tpu_name"    ,     default=_UpperCAmelCase    ,     help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."    ,     )
						config_args.add_argument(
						    "--tpu_zone"    ,     default=_UpperCAmelCase    ,     help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."    ,     )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	parser.add_argument_group("TPU Arguments"    ,     "Arguments for options ran inside the TPU."   )
						pod_args.add_argument(
						    "--use_alpha"    ,     action="store_true"    ,     help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."    ,     )
						pod_args.add_argument(
						    "--command_file"    ,     default=_UpperCAmelCase    ,     help="The path to the file containing the commands to run on the pod on startup."    ,     )
						pod_args.add_argument(
						    "--command"    ,     action="append"    ,     nargs="+"    ,     help="A command to run on the pod. Can be passed multiple times."    ,     )
						pod_args.add_argument(
						    "--install_accelerate"    ,     action="store_true"    ,     help="Whether to install accelerate on the pod. Defaults to False."    ,     )
						pod_args.add_argument(
						    "--accelerate_version"    ,     default="latest"    ,     help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."    ,     )
						pod_args.add_argument(
						    "--debug"    ,     action="store_true"    ,     help="If set, will print the command that would be run instead of running it."   )

						if subparsers is not None:
												parser.set_defaults(func=_UpperCAmelCase   )
						return parser







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Tuple  		=	None

						# Get the default from the config file if it exists.
						if args.config_file is not None or os.path.isfile(_UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			List[Any]  		=	load_config_from_file(args.config_file   )
												if not args.command_file and defaults.command_file is not None and not args.command:
																		SCREAMING_SNAKE_CASE_:			Any  		=	defaults.command_file
												if not args.command and defaults.commands is not None:
																		SCREAMING_SNAKE_CASE_:			int  		=	defaults.commands
												if not args.tpu_name:
																		SCREAMING_SNAKE_CASE_:			List[str]  		=	defaults.tpu_name
												if not args.tpu_zone:
																		SCREAMING_SNAKE_CASE_:			Dict  		=	defaults.tpu_zone
						if args.accelerate_version == "dev":
												SCREAMING_SNAKE_CASE_:			Tuple  		=	"git+https://github.com/huggingface/accelerate.git"
						elif args.accelerate_version == "latest":
												SCREAMING_SNAKE_CASE_:			str  		=	"accelerate -U"
						elif isinstance(parse(args.accelerate_version   )    ,     _UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			List[str]  		=	f"accelerate=={args.accelerate_version}"

						if not args.command_file and not args.command:
												raise ValueError("You must specify either a command file or a command to run on the pod."   )

						if args.command_file:
												with open(args.command_file    ,     "r"   ) as f:
																		SCREAMING_SNAKE_CASE_:			Dict  		=	[f.read().splitlines()]

    # To turn list of lists into list of strings
						if isinstance(args.command[0]    ,     _UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			List[str]  		=	[line for cmd in args.command for line in cmd]
						# Default to the shared folder and install accelerate
						SCREAMING_SNAKE_CASE_:			Dict  		=	["cd /usr/share"]
						if args.install_accelerate:
												new_cmd += [f"pip install {args.accelerate_version}"]
						new_cmd += args.command
						SCREAMING_SNAKE_CASE_:			List[str]  		=	"; ".join(_UpperCAmelCase   )

						# Then send it to gcloud
						# Eventually try to use google-api-core to do this instead of subprocess
						SCREAMING_SNAKE_CASE_:			int  		=	["gcloud"]
						if args.use_alpha:
												cmd += ["alpha"]
						cmd += [
						    "compute",
						    "tpus",
						    "tpu-vm",
						    "ssh",
						    args.tpu_name,
						    "--zone",
						    args.tpu_zone,
						    "--command",
						    args.command,
						    "--worker",
						    "all",
						]
						if args.debug:
												print(f"Running {' '.join(_UpperCAmelCase   )}"   )
												return
						subprocess.run(_UpperCAmelCase   )
						print("Successfully setup pod."   )







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	tpu_command_parser()
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	parser.parse_args()

						tpu_command_launcher(_UpperCAmelCase   )






