



from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class   _A   ( _lowercase	):


						'''simple docstring'''





						def __init__(	self	:							str							,      lowerCamelCase	:							TransformeraDModel							,      lowerCamelCase	:							AutoencoderKL							,      lowerCamelCase	:							KarrasDiffusionSchedulers							,      lowerCamelCase	:							Optional[Dict[int, str]] = None							,      ):






							'''simple docstring'''



							super().__init__()
							self.register_modules(transformer=lowerCamelCase							,      vae=lowerCamelCase							,      scheduler=lowerCamelCase	)

							# create a imagenet -> id dictionary for easier use
							__lowercase		       =			{}
							if idalabel is not None:
								for key, value in idalabel.items():
									for label in value.split(","	):
										__lowercase		       =			int(lowerCamelCase	)
								__lowercase		       =			dict(sorted(self.labels.items()	)	)



						def     _snake_case	(	self	:							int							,      lowerCamelCase	:							Union[str, List[str]]	):






							'''simple docstring'''



							if not isinstance(lowerCamelCase							,      lowerCamelCase	):
								__lowercase		       =			list(lowerCamelCase	)

							for l in label:
								if l not in self.labels:
									raise ValueError(
									    f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""	)

							return [self.labels[l] for l in label]



						@torch.no_grad()
						def __call__(	self	:							Any							,      lowerCamelCase	:							List[int]							,      lowerCamelCase	:							float = 4.0							,      lowerCamelCase	:							Optional[Union[torch.Generator, List[torch.Generator]]] = None							,      lowerCamelCase	:							int = 50							,      lowerCamelCase	:							Optional[str] = "pil"							,      lowerCamelCase	:							bool = True							,      ):






							'''simple docstring'''



							__lowercase		       =			len(lowerCamelCase	)
							__lowercase		       =			self.transformer.config.sample_size
							__lowercase		       =			self.transformer.config.in_channels

							__lowercase		       =			randn_tensor(
							    shape=(batch_size, latent_channels, latent_size, latent_size)							,      generator=lowerCamelCase							,      device=self.device							,      dtype=self.transformer.dtype							,      )
							__lowercase		       =			torch.cat([latents] * 2	) if guidance_scale > 1 else latents

							__lowercase		       =			torch.tensor(lowerCamelCase							,      device=self.device	).reshape(-1	)
							__lowercase		       =			torch.tensor([1_000] * batch_size							,      device=self.device	)
							__lowercase		       =			torch.cat([class_labels, class_null]							,      0	) if guidance_scale > 1 else class_labels

							# set step values
							self.scheduler.set_timesteps(lowerCamelCase	)

							for t in self.progress_bar(self.scheduler.timesteps	):
								if guidance_scale > 1:
									__lowercase		       =			latent_model_input[: len(lowerCamelCase	) // 2]
									__lowercase		       =			torch.cat([half, half]							,      dim=0	)
								__lowercase		       =			self.scheduler.scale_model_input(lowerCamelCase							,      lowerCamelCase	)

								__lowercase		       =			t
								if not torch.is_tensor(lowerCamelCase	):
									# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
									# This would be a good case for the `match` statement (Python 3.10+)
									__lowercase		       =			latent_model_input.device.type == "mps"
									if isinstance(lowerCamelCase							,      lowerCamelCase	):
										__lowercase		       =			torch.floataa if is_mps else torch.floataa
									else:
										__lowercase		       =			torch.intaa if is_mps else torch.intaa
									__lowercase		       =			torch.tensor([timesteps]							,      dtype=lowerCamelCase							,      device=latent_model_input.device	)
								elif len(timesteps.shape	) == 0:
									__lowercase		       =			timesteps[None].to(latent_model_input.device	)
								# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
								__lowercase		       =			timesteps.expand(latent_model_input.shape[0]	)
								# predict noise model_output
								__lowercase		       =			self.transformer(
								    lowerCamelCase							,      timestep=lowerCamelCase							,      class_labels=lowerCamelCase	).sample

								# perform guidance
								if guidance_scale > 1:
									__lowercase       ,    __lowercase		       =			noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
									__lowercase       ,    __lowercase		       =			torch.split(lowerCamelCase							,      len(lowerCamelCase	) // 2							,      dim=0	)

									__lowercase		       =			uncond_eps + guidance_scale * (cond_eps - uncond_eps)
									__lowercase		       =			torch.cat([half_eps, half_eps]							,      dim=0	)

									__lowercase		       =			torch.cat([eps, rest]							,      dim=1	)

								# learned sigma
								if self.transformer.config.out_channels // 2 == latent_channels:
									__lowercase       ,    __lowercase		       =			torch.split(lowerCamelCase							,      lowerCamelCase							,      dim=1	)
								else:
									__lowercase		       =			noise_pred

								# compute previous image: x_t -> x_t-1
								__lowercase		       =			self.scheduler.step(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	).prev_sample

							if guidance_scale > 1:
								__lowercase       ,    __lowercase		       =			latent_model_input.chunk(2							,      dim=0	)
							else:
								__lowercase		       =			latent_model_input

							__lowercase		       =			1 / self.vae.config.scaling_factor * latents
							__lowercase		       =			self.vae.decode(lowerCamelCase	).sample

							__lowercase		       =			(samples / 2 + 0.5).clamp(0							,      1	)

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							__lowercase		       =			samples.cpu().permute(0							,      2							,      3							,      1	).float().numpy()

							if output_type == "pil":
								__lowercase		       =			self.numpy_to_pil(lowerCamelCase	)

							if not return_dict:
								return (samples,)

							return ImagePipelineOutput(images=lowerCamelCase	)








import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			SwinvaConfig()
	__lowercase		       =			swinva_name.split("_"   )

	__lowercase		       =			name_split[1]
	if "to" in name_split[3]:
		__lowercase		       =			int(name_split[3][-3:]   )
	else:
		__lowercase		       =			int(name_split[3]   )
	if "to" in name_split[2]:
		__lowercase		       =			int(name_split[2][-2:]   )
	else:
		__lowercase		       =			int(name_split[2][6:]   )

	if model_size == "tiny":
		__lowercase		       =			9_6
		__lowercase		       =			(2, 2, 6, 2)
		__lowercase		       =			(3, 6, 1_2, 2_4)
	elif model_size == "small":
		__lowercase		       =			9_6
		__lowercase		       =			(2, 2, 1_8, 2)
		__lowercase		       =			(3, 6, 1_2, 2_4)
	elif model_size == "base":
		__lowercase		       =			1_2_8
		__lowercase		       =			(2, 2, 1_8, 2)
		__lowercase		       =			(4, 8, 1_6, 3_2)
	else:
		__lowercase		       =			1_9_2
		__lowercase		       =			(2, 2, 1_8, 2)
		__lowercase		       =			(6, 1_2, 2_4, 4_8)

	if "to" in swinva_name:
		__lowercase		       =			(1_2, 1_2, 1_2, 6)

	if ("22k" in swinva_name) and ("to" not in swinva_name):
		__lowercase		       =			2_1_8_4_1
		__lowercase		       =			"huggingface/label-files"
		__lowercase		       =			"imagenet-22k-id2label.json"
		__lowercase		       =			json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"   )  ,	"r"   )   )
		__lowercase		       =			{int(_SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
		__lowercase		       =			idalabel
		__lowercase		       =			{v: k for k, v in idalabel.items()}

	else:
		__lowercase		       =			1_0_0_0
		__lowercase		       =			"huggingface/label-files"
		__lowercase		       =			"imagenet-1k-id2label.json"
		__lowercase		       =			json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"   )  ,	"r"   )   )
		__lowercase		       =			{int(_SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
		__lowercase		       =			idalabel
		__lowercase		       =			{v: k for k, v in idalabel.items()}

	__lowercase		       =			img_size
	__lowercase		       =			num_classes
	__lowercase		       =			embed_dim
	__lowercase		       =			depths
	__lowercase		       =			num_heads
	__lowercase		       =			window_size

	return config



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	if "patch_embed.proj" in name:
		__lowercase		       =			name.replace("patch_embed.proj"  ,	"embeddings.patch_embeddings.projection"   )
	if "patch_embed.norm" in name:
		__lowercase		       =			name.replace("patch_embed.norm"  ,	"embeddings.norm"   )
	if "layers" in name:
		__lowercase		       =			"encoder." + name
	if "attn.proj" in name:
		__lowercase		       =			name.replace("attn.proj"  ,	"attention.output.dense"   )
	if "attn" in name:
		__lowercase		       =			name.replace("attn"  ,	"attention.self"   )
	if "norm1" in name:
		__lowercase		       =			name.replace("norm1"  ,	"layernorm_before"   )
	if "norm2" in name:
		__lowercase		       =			name.replace("norm2"  ,	"layernorm_after"   )
	if "mlp.fc1" in name:
		__lowercase		       =			name.replace("mlp.fc1"  ,	"intermediate.dense"   )
	if "mlp.fc2" in name:
		__lowercase		       =			name.replace("mlp.fc2"  ,	"output.dense"   )
	if "q_bias" in name:
		__lowercase		       =			name.replace("q_bias"  ,	"query.bias"   )
	if "k_bias" in name:
		__lowercase		       =			name.replace("k_bias"  ,	"key.bias"   )
	if "v_bias" in name:
		__lowercase		       =			name.replace("v_bias"  ,	"value.bias"   )
	if "cpb_mlp" in name:
		__lowercase		       =			name.replace("cpb_mlp"  ,	"continuous_position_bias_mlp"   )
	if name == "norm.weight":
		__lowercase		       =			"layernorm.weight"
	if name == "norm.bias":
		__lowercase		       =			"layernorm.bias"

	if "head" in name:
		__lowercase		       =			name.replace("head"  ,	"classifier"   )
	else:
		__lowercase		       =			"swinv2." + name

	return name



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	for key in orig_state_dict.copy().keys():
		__lowercase		       =			orig_state_dict.pop(_SCREAMING_SNAKE_CASE   )

		if "mask" in key:
			continue
		elif "qkv" in key:
			__lowercase		       =			key.split("."   )
			__lowercase		       =			int(key_split[1]   )
			__lowercase		       =			int(key_split[3]   )
			__lowercase		       =			model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

			if "weight" in key:
				__lowercase		       =			val[:dim, :]
				__lowercase		       =			val[dim : dim * 2, :]
				__lowercase		       =			val[-dim:, :]
			else:
				__lowercase		       =			val[:dim]
				__lowercase		       =			val[
				    dim : dim * 2
				]
				__lowercase		       =			val[-dim:]
		else:
			__lowercase		       =			val

	return orig_state_dict



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			timm.create_model(_SCREAMING_SNAKE_CASE  ,	pretrained=_SCREAMING_SNAKE_CASE   )
	timm_model.eval()

	__lowercase		       =			get_swinva_config(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			SwinvaForImageClassification(_SCREAMING_SNAKE_CASE   )
	model.eval()

	__lowercase		       =			convert_state_dict(timm_model.state_dict()  ,	_SCREAMING_SNAKE_CASE   )
	model.load_state_dict(_SCREAMING_SNAKE_CASE   )

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

	__lowercase		       =			AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"  ,	"-"   )   )   )
	__lowercase		       =			Image.open(requests.get(_SCREAMING_SNAKE_CASE  ,	stream=_SCREAMING_SNAKE_CASE   ).raw   )
	__lowercase		       =			image_processor(images=_SCREAMING_SNAKE_CASE  ,	return_tensors="pt"   )

	__lowercase		       =			timm_model(inputs["pixel_values"]   )
	__lowercase		       =			model(**_SCREAMING_SNAKE_CASE   ).logits

	assert torch.allclose(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	atol=1E-3   )

	print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""   )
	model.save_pretrained(_SCREAMING_SNAKE_CASE   )

	print(F"""Saving image processor to {pytorch_dump_folder_path}"""   )
	image_processor.save_pretrained(_SCREAMING_SNAKE_CASE   )

	model.push_to_hub(
	    repo_path_or_name=Path(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )  ,	organization="nandwalritik"  ,	commit_message="Add model"  ,	)


if __name__ == "__main__":
	snake_case__					:     Union[str, Any]    		=   argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--swinv2_name""",
	    default="""swinv2_tiny_patch4_window8_256""",
	    type=str,
	    help="""Name of the Swinv2 timm model you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
	)

	snake_case__					:     str    		=   parser.parse_args()
	convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)



