
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_						     =    logging.get_logger(__name__)

def  lowerCamelCase__ (					_a							,			_a=False							,			_a=False):
	SCREAMING_SNAKE_CASE   :      str				     =       '''backbone.''' if is_semantic else ''''''

	SCREAMING_SNAKE_CASE   :      Optional[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  lowerCamelCase__ (					_a							,			_a							,			_a=False							,			_a=False):







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

		SCREAMING_SNAKE_CASE   :      Dict				     =       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   :      Optional[Any]				     =       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   :      str				     =       state_dict.pop(f"{prefix}blocks.{i}.gamma_1")
		SCREAMING_SNAKE_CASE   :      Any				     =       state_dict.pop(f"{prefix}blocks.{i}.gamma_2")

		SCREAMING_SNAKE_CASE   :      Dict				     =       gamma_a
		SCREAMING_SNAKE_CASE   :      str				     =       gamma_a

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      int				     =       dct.pop(__UpperCamelCase)
	SCREAMING_SNAKE_CASE   :      Any				     =       val

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      Dict				     =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
	SCREAMING_SNAKE_CASE   :      int				     =       Image.open(requests.get(__UpperCamelCase							,			stream=__UpperCamelCase).raw)
	return im

@torch.no_grad()
def  lowerCamelCase__ (					_a							,			_a							,			_a=False):
	SCREAMING_SNAKE_CASE   :      List[str]				     =       False if '''rvlcdip''' in checkpoint_url else True
	SCREAMING_SNAKE_CASE   :      int				     =       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   :      Union[str, Any]				     =       1024
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       4096
		SCREAMING_SNAKE_CASE   :      Any				     =       24
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       16

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

	SCREAMING_SNAKE_CASE   :      List[str]				     =       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   :      Optional[Any]				     =       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   :      Optional[Any]				     =       prepare_img()

	SCREAMING_SNAKE_CASE   :      Any				     =       image_processor(images=__UpperCamelCase							,			return_tensors="pt")
	SCREAMING_SNAKE_CASE   :      List[str]				     =       encoding['''pixel_values''']

	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       model(__UpperCamelCase)
	SCREAMING_SNAKE_CASE   :      Dict				     =       outputs.logits

	# verify logits
	SCREAMING_SNAKE_CASE   :      str				     =       [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8192]
	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   :      int				     =       '''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(__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__":
			a_						     =    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_						     =    parser.parse_args()
			convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)

os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)

def  lowerCamelCase__ (					_a):
	if hor == 128:
		SCREAMING_SNAKE_CASE   :      str				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      List[str]				     =       (32, 128, 256)
		SCREAMING_SNAKE_CASE   :      List[str]				     =       ("UpResnetBlock1D", "UpResnetBlock1D")

	elif hor == 32:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (32, 64, 128, 256)
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
	SCREAMING_SNAKE_CASE   :      Any				     =       torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch")
	SCREAMING_SNAKE_CASE   :      int				     =       model.state_dict()
	SCREAMING_SNAKE_CASE   :      Dict				     =       {
	    "down_block_types": down_block_types,
	    "block_out_channels": block_out_channels,
	    "up_block_types": up_block_types,
	    "layers_per_block": 1,
	    "use_timestep_embedding": True,
	    "out_block_type": "OutConv1DBlock",
	    "norm_num_groups": 8,
	    "downsample_each_block": False,
	    "in_channels": 14,
	    "out_channels": 14,
	    "extra_in_channels": 0,
	    "time_embedding_type": "positional",
	    "flip_sin_to_cos": False,
	    "freq_shift": 1,
	    "sample_size": 65536,
	    "mid_block_type": "MidResTemporalBlock1D",
	    "act_fn": "mish",
	}
	SCREAMING_SNAKE_CASE   :      str				     =       UNetaDModel(**_a)
	print(f"length of state dict: {len(state_dict.keys())}")
	print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")
	SCREAMING_SNAKE_CASE   :      List[str]				     =       dict(zip(model.state_dict().keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      int				     =       state_dict.pop(_a)
	hf_value_function.load_state_dict(_a)

	torch.save(hf_value_function.state_dict()							,			f"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin")
	with open(f"hub/hopper-medium-v2/unet/hor{hor}/config.json"							,			"w") as f:
		json.dump(_a							,			_a)

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      int				     =       {
	    "in_channels": 14,
	    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
	    "up_block_types": (),
	    "out_block_type": "ValueFunction",
	    "mid_block_type": "ValueFunctionMidBlock1D",
	    "block_out_channels": (32, 64, 128, 256),
	    "layers_per_block": 1,
	    "downsample_each_block": True,
	    "sample_size": 65536,
	    "out_channels": 14,
	    "extra_in_channels": 0,
	    "time_embedding_type": "positional",
	    "use_timestep_embedding": True,
	    "flip_sin_to_cos": False,
	    "freq_shift": 1,
	    "norm_num_groups": 8,
	    "act_fn": "mish",
	}

	SCREAMING_SNAKE_CASE   :      Dict				     =       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch")
	SCREAMING_SNAKE_CASE   :      Dict				     =       model
	SCREAMING_SNAKE_CASE   :      List[str]				     =       UNetaDModel(**_a)
	print(f"length of state dict: {len(state_dict.keys())}")
	print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")

	SCREAMING_SNAKE_CASE   :      int				     =       dict(zip(state_dict.keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       state_dict.pop(_a)

	hf_value_function.load_state_dict(_a)

	torch.save(hf_value_function.state_dict()							,			"hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin")
	with open("hub/hopper-medium-v2/value_function/config.json"							,			"w") as f:
		json.dump(_a							,			_a)


if __name__ == "__main__":
			unet(32)
			# unet(128)
			value_function()