


"""simple docstring"""







import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_=False       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    []
					for i in range(config.num_hidden_layers       ):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append((f"""module.blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")       )
										rename_keys.append((f"""module.blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")       )
										rename_keys.append(
										    (f"""module.blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")       )
										rename_keys.append((f"""module.blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")       )
										rename_keys.append((f"""module.blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")       )
										rename_keys.append((f"""module.blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")       )
										rename_keys.append((f"""module.blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")       )
										rename_keys.append((f"""module.blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")       )
										rename_keys.append((f"""module.blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")       )
										rename_keys.append((f"""module.blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")       )

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        ("module.cls_token", "vit.embeddings.cls_token"),
					        ("module.patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
					        ("module.patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
					        ("module.pos_embed", "vit.embeddings.position_embeddings"),
					    ]       )

					if base_model:
										# layernorm + pooler
										rename_keys.extend(
										    [
										        ("module.norm.weight", "layernorm.weight"),
										        ("module.norm.bias", "layernorm.bias"),
										    ]       )

										# if just the base model, we should remove "vit" from all keys that start with "vit"
										__SCREAMING_SNAKE_CASE						       =    [(pair[0], pair[1][4:]) if pair[1].startswith("vit"       ) else pair for pair in rename_keys]
					else:
										# layernorm + classification head
										rename_keys.extend(
										    [
										        ("norm.weight", "vit.layernorm.weight"),
										        ("norm.bias", "vit.layernorm.bias"),
										        ("head.weight", "classifier.weight"),
										        ("head.bias", "classifier.bias"),
										    ]       )

					return rename_keys




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=False       ):




					'''simple docstring'''


					for i in range(config.num_hidden_layers       ):
										if base_model:
															__SCREAMING_SNAKE_CASE						       =    ""
										else:
															__SCREAMING_SNAKE_CASE						       =    "vit."
										# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
										__SCREAMING_SNAKE_CASE						       =    state_dict.pop(f"""module.blocks.{i}.attn.qkv.weight"""       )
										__SCREAMING_SNAKE_CASE						       =    state_dict.pop(f"""module.blocks.{i}.attn.qkv.bias"""       )
										# next, add query, keys and values (in that order) to the state dict
										__SCREAMING_SNAKE_CASE						       =    in_proj_weight[
										    : config.hidden_size, :
										]
										__SCREAMING_SNAKE_CASE						       =    in_proj_bias[: config.hidden_size]
										__SCREAMING_SNAKE_CASE						       =    in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										__SCREAMING_SNAKE_CASE						       =    in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										__SCREAMING_SNAKE_CASE						       =    in_proj_weight[
										    -config.hidden_size :, :
										]
										__SCREAMING_SNAKE_CASE						       =    in_proj_bias[-config.hidden_size :]




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    ["head.weight", "head.bias"]
					for k in ignore_keys:
										state_dict.pop(lowerCAmelCase_  ,						lowerCAmelCase_       )




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    [
					    "module.fc.fc1.weight",
					    "module.fc.fc1.bias",
					    "module.fc.bn1.weight",
					    "module.fc.bn1.bias",
					    "module.fc.bn1.running_mean",
					    "module.fc.bn1.running_var",
					    "module.fc.bn1.num_batches_tracked",
					    "module.fc.fc2.weight",
					    "module.fc.fc2.bias",
					    "module.fc.bn2.weight",
					    "module.fc.bn2.bias",
					    "module.fc.bn2.running_mean",
					    "module.fc.bn2.running_var",
					    "module.fc.bn2.num_batches_tracked",
					    "module.fc.fc3.weight",
					    "module.fc.fc3.bias",
					]
					for k in ignore_keys:
										state_dict.pop(lowerCAmelCase_  ,						lowerCAmelCase_       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    dct.pop(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    val




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    ViTMSNConfig()
					__SCREAMING_SNAKE_CASE						       =    1000

					__SCREAMING_SNAKE_CASE						       =    "datasets/huggingface/label-files"
					__SCREAMING_SNAKE_CASE						       =    "imagenet-1k-id2label.json"
					__SCREAMING_SNAKE_CASE						       =    json.load(open(hf_hub_download(lowerCAmelCase_  ,						lowerCAmelCase_       )  ,						"r"       )       )
					__SCREAMING_SNAKE_CASE						       =    {int(lowerCAmelCase_       ): v for k, v in idalabel.items()}
					__SCREAMING_SNAKE_CASE						       =    idalabel
					__SCREAMING_SNAKE_CASE						       =    {v: k for k, v in idalabel.items()}

					if "s16" in checkpoint_url:
										__SCREAMING_SNAKE_CASE						       =    384
										__SCREAMING_SNAKE_CASE						       =    1536
										__SCREAMING_SNAKE_CASE						       =    6
					elif "l16" in checkpoint_url:
										__SCREAMING_SNAKE_CASE						       =    1024
										__SCREAMING_SNAKE_CASE						       =    4096
										__SCREAMING_SNAKE_CASE						       =    24
										__SCREAMING_SNAKE_CASE						       =    16
										__SCREAMING_SNAKE_CASE						       =    0.1
					elif "b4" in checkpoint_url:
										__SCREAMING_SNAKE_CASE						       =    4
					elif "l7" in checkpoint_url:
										__SCREAMING_SNAKE_CASE						       =    7
										__SCREAMING_SNAKE_CASE						       =    1024
										__SCREAMING_SNAKE_CASE						       =    4096
										__SCREAMING_SNAKE_CASE						       =    24
										__SCREAMING_SNAKE_CASE						       =    16
										__SCREAMING_SNAKE_CASE						       =    0.1

					__SCREAMING_SNAKE_CASE						       =    ViTMSNModel(lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    torch.hub.load_state_dict_from_url(lowerCAmelCase_  ,						map_location="cpu"       )["target_encoder"]

					__SCREAMING_SNAKE_CASE						       =    ViTImageProcessor(size=config.image_size       )

					remove_projection_head(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    create_rename_keys(lowerCAmelCase_  ,						base_model=lowerCAmelCase_       )

					for src, dest in rename_keys:
										rename_key(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )
					read_in_q_k_v(lowerCAmelCase_  ,						lowerCAmelCase_  ,						base_model=lowerCAmelCase_       )

					model.load_state_dict(lowerCAmelCase_       )
					model.eval()

					__SCREAMING_SNAKE_CASE						       =    "http://images.cocodataset.org/val2017/000000039769.jpg"

					__SCREAMING_SNAKE_CASE						       =    Image.open(requests.get(lowerCAmelCase_  ,						stream=lowerCAmelCase_       ).raw       )
					__SCREAMING_SNAKE_CASE						       =    ViTImageProcessor(
					    size=config.image_size  ,						image_mean=lowerCAmelCase_  ,						image_std=lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    image_processor(images=lowerCAmelCase_  ,						return_tensors="pt"       )

					# forward pass
					torch.manual_seed(2       )
					__SCREAMING_SNAKE_CASE						       =    model(**lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    outputs.last_hidden_state

					# The following Colab Notebook was used to generate these outputs:
					# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
					if "s16" in checkpoint_url:
										__SCREAMING_SNAKE_CASE						       =    torch.tensor([[-1.0915, -1.4876, -1.1809]]       )
					elif "b16" in checkpoint_url:
										__SCREAMING_SNAKE_CASE						       =    torch.tensor([[14.2889, -18.9045, 11.7281]]       )
					elif "l16" in checkpoint_url:
										__SCREAMING_SNAKE_CASE						       =    torch.tensor([[41.5028, -22.8681, 45.6475]]       )
					elif "b4" in checkpoint_url:
										__SCREAMING_SNAKE_CASE						       =    torch.tensor([[-4.3868, 5.2932, -0.4137]]       )
					else:
										__SCREAMING_SNAKE_CASE						       =    torch.tensor([[-0.1792, -0.6465, 2.4263]]       )

					# verify logits
					assert torch.allclose(last_hidden_state[:, 0, :3]  ,						lowerCAmelCase_  ,						atol=1E-4       )

					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 __name__ == "__main__":
		a__    :							int            =					argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint_url''',
		    default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
		    type=str,
		    help='''URL of the checkpoint you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)

		a__    :							Any            =					parser.parse_args()
		convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




"""simple docstring"""







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


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
		from .pipeline_kandinsky import KandinskyPipeline
		from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
		from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
		from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
		from .text_encoder import MultilingualCLIP
