
"""simple docstring"""





import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__       =			logging.get_logger()
@dataclass
class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		__lowercase							:  nn.Module
		__lowercase							:  List[nn.Module]						   =		field(default_factory=__a )
		__lowercase							:  list						   =		field(default_factory=__a )




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = len(list(m.modules())) == 1 or isinstance(lowerCAmelCase__							,  nn.Convad) or isinstance(lowerCAmelCase__							,  nn.BatchNormad)
				if has_not_submodules:
						self.traced.append(lowerCAmelCase__)




		def __call__(     self							,  lowerCAmelCase__):
				for m in self.module.modules():
						self.handles.append(m.register_forward_hook(self._forward_hook))
				self.module(lowerCAmelCase__)
				[x.remove() for x in self.handles]
				return self




		@property
		def 	snake_case_     (     self):
				# check the len of the state_dict keys to see if we have learnable params
				return list(filter(lambda lowerCAmelCase__: len(list(x.state_dict().keys())) > 0							,  self.traced))



@dataclass
class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		__lowercase							:  nn.Module
		__lowercase							:  nn.Module
		__lowercase							:  int						   =		0
		__lowercase							:  List						   =		field(default_factory=__a )
		__lowercase							:  List						   =		field(default_factory=__a )




		def __call__(     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = Tracker(self.dest)(lowerCAmelCase__).parametrized
				__SCREAMING_SNAKE_CASE            = Tracker(self.src)(lowerCAmelCase__).parametrized

				__SCREAMING_SNAKE_CASE            = list(filter(lambda lowerCAmelCase__: type(lowerCAmelCase__) not in self.src_skip							,  lowerCAmelCase__))
				__SCREAMING_SNAKE_CASE            = list(filter(lambda lowerCAmelCase__: type(lowerCAmelCase__) not in self.dest_skip							,  lowerCAmelCase__))

				if len(lowerCAmelCase__) != len(lowerCAmelCase__):
						raise Exception(
						    f"Numbers of operations are different. Source module has {len(lowerCAmelCase__)} operations while"
						    f" destination module has {len(lowerCAmelCase__)}.")

				for dest_m, src_m in zip(lowerCAmelCase__							,  lowerCAmelCase__):
						dest_m.load_state_dict(src_m.state_dict())
						if self.verbose == 1:
								print(f"Transfered from={src_m} to={dest_m}")



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_ = True				):
		print(f"Converting {name}..."				)
		with torch.no_grad():
				__SCREAMING_SNAKE_CASE            = timm.create_model(UpperCamelCase_  ,				pretrained=UpperCamelCase_				).eval()
				__SCREAMING_SNAKE_CASE            = ResNetForImageClassification(UpperCamelCase_				).eval()
				__SCREAMING_SNAKE_CASE            = ModuleTransfer(src=UpperCamelCase_  ,				dest=UpperCamelCase_				)
				__SCREAMING_SNAKE_CASE            = torch.randn((1, 3, 224, 224)				)
				module_transfer(UpperCamelCase_				)

		assert torch.allclose(from_model(UpperCamelCase_				)  ,				our_model(UpperCamelCase_				).logits				), "The model logits don't match the original one."

		__SCREAMING_SNAKE_CASE            = f"resnet{'-'.join(name.split('resnet'				)				)}"
		print(UpperCamelCase_				)

		if push_to_hub:
				our_model.push_to_hub(
				    repo_path_or_name=save_directory / checkpoint_name  ,				commit_message="""Add model"""  ,				use_temp_dir=UpperCamelCase_  ,				)

				# we can use the convnext one
				__SCREAMING_SNAKE_CASE            = AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""				)
				image_processor.push_to_hub(
				    repo_path_or_name=save_directory / checkpoint_name  ,				commit_message="""Add image processor"""  ,				use_temp_dir=UpperCamelCase_  ,				)

				print(f"Pushed {checkpoint_name}"				)



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_ = None  ,				UpperCamelCase_ = True				):
		__SCREAMING_SNAKE_CASE            = """imagenet-1k-id2label.json"""
		__SCREAMING_SNAKE_CASE            = 1000
		__SCREAMING_SNAKE_CASE            = (1, num_labels)

		__SCREAMING_SNAKE_CASE            = """huggingface/label-files"""
		__SCREAMING_SNAKE_CASE            = num_labels
		__SCREAMING_SNAKE_CASE            = json.load(open(hf_hub_download(UpperCamelCase_  ,				UpperCamelCase_  ,				repo_type="""dataset"""				)  ,				"""r"""				)				)
		__SCREAMING_SNAKE_CASE            = {int(UpperCamelCase_				): v for k, v in idalabel.items()}

		__SCREAMING_SNAKE_CASE            = idalabel
		__SCREAMING_SNAKE_CASE            = {v: k for k, v in idalabel.items()}

		__SCREAMING_SNAKE_CASE            = partial(UpperCamelCase_  ,				num_labels=UpperCamelCase_  ,				idalabel=UpperCamelCase_  ,				labelaid=UpperCamelCase_				)

		__SCREAMING_SNAKE_CASE            = {
		    """resnet18""": ImageNetPreTrainedConfig(
		        depths=[2, 2, 2, 2]  ,				hidden_sizes=[64, 128, 256, 512]  ,				layer_type="""basic"""				),
		    """resnet26""": ImageNetPreTrainedConfig(
		        depths=[2, 2, 2, 2]  ,				hidden_sizes=[256, 512, 1024, 2048]  ,				layer_type="""bottleneck"""				),
		    """resnet34""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 6, 3]  ,				hidden_sizes=[64, 128, 256, 512]  ,				layer_type="""basic"""				),
		    """resnet50""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 6, 3]  ,				hidden_sizes=[256, 512, 1024, 2048]  ,				layer_type="""bottleneck"""				),
		    """resnet101""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 23, 3]  ,				hidden_sizes=[256, 512, 1024, 2048]  ,				layer_type="""bottleneck"""				),
		    """resnet152""": ImageNetPreTrainedConfig(
		        depths=[3, 8, 36, 3]  ,				hidden_sizes=[256, 512, 1024, 2048]  ,				layer_type="""bottleneck"""				),
		}

		if model_name:
				convert_weight_and_push(UpperCamelCase_  ,				names_to_config[model_name]  ,				UpperCamelCase_  ,				UpperCamelCase_				)
		else:
				for model_name, config in names_to_config.items():
						convert_weight_and_push(UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				)
		return config, expected_shape


if __name__ == "__main__":
							__magic_name__       =			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--model_name",
							    default=None,
							    type=str,
							    help=(
							        "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
							        " currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
							    ),
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path",
							    default=None,
							    type=Path,
							    required=True,
							    help="Path to the output PyTorch model directory.",
							)
							parser.add_argument(
							    "--push_to_hub",
							    default=True,
							    type=bool,
							    required=False,
							    help="If True, push model and image processor to the hub.",
							)

							__magic_name__       =			parser.parse_args()
							__magic_name__       =			args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)












from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


__UpperCAmelCase								=						logging.get_logger(__name__)  # pylint: disable=invalid-name

__UpperCAmelCase								=						'\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e-img2img"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"\n        >>> image = load_image(image_url).convert("RGB")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")\n        ```\n'



@dataclass
class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    Union[PIL.Image.Image, np.ndarray]





class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							Any:
							super().__init__()

							self.register_modules(
							    prior=_UpperCamelCase				,		image_encoder=_UpperCamelCase				,		image_processor=_UpperCamelCase				,		scheduler=_UpperCamelCase				,		renderer=_UpperCamelCase				,		)



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							List[Any]:
							if latents is None:
												UpperCAmelCase_    :      str            =						randn_tensor(_UpperCamelCase				,		generator=_UpperCamelCase				,		device=_UpperCamelCase				,		dtype=_UpperCamelCase   )
							else:
												if latents.shape != shape:
																	raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"   )
												UpperCAmelCase_    :      Tuple            =						latents.to(_UpperCamelCase   )

							UpperCAmelCase_    :      Tuple            =						latents * scheduler.init_noise_sigma
							return latents



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase=0   )	->							Union[str, Any]:
							if is_accelerate_available():
												from accelerate import cpu_offload
							else:
												raise ImportError('Please install accelerate via `pip install accelerate`'   )

							UpperCAmelCase_    :      int            =						torch.device(f"cuda:{gpu_id}"   )

							UpperCAmelCase_    :      int            =						[self.image_encoder, self.prior]
							for cpu_offloaded_model in models:
												if cpu_offloaded_model is not None:
																	cpu_offload(_UpperCamelCase				,		_UpperCamelCase   )



		@property
		def 					__UpperCAmelCase			(							self   )	->							int:
							if self.device != torch.device('meta'   ) or not hasattr(self.image_encoder				,		'_hf_hook'   ):
												return self.device
							for module in self.image_encoder.modules():
												if (
												    hasattr(_UpperCamelCase				,		'_hf_hook'   )
												    and hasattr(module._hf_hook				,		'execution_device'   )
												    and module._hf_hook.execution_device is not None
												):
																	return torch.device(module._hf_hook.execution_device   )
							return self.device



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							str:
							if isinstance(_UpperCamelCase				,		_UpperCamelCase   ) and isinstance(image[0]				,		torch.Tensor   ):
												UpperCAmelCase_    :      int            =						torch.cat(_UpperCamelCase				,		axis=0   ) if image[0].ndim == 4 else torch.stack(_UpperCamelCase				,		axis=0   )

							if not isinstance(_UpperCamelCase				,		torch.Tensor   ):
												UpperCAmelCase_    :      Optional[int]            =						self.image_processor(_UpperCamelCase				,		return_tensors='pt'   ).pixel_values[0].unsqueeze(0   )

							UpperCAmelCase_    :      Tuple            =						image.to(dtype=self.image_encoder.dtype				,		device=_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[Any]            =						self.image_encoder(_UpperCamelCase   )['last_hidden_state']
							UpperCAmelCase_    :      Union[str, Any]            =						image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

							UpperCAmelCase_    :      List[str]            =						image_embeds.repeat_interleave(_UpperCamelCase				,		dim=0   )

							if do_classifier_free_guidance:
												UpperCAmelCase_    :      Dict            =						torch.zeros_like(_UpperCamelCase   )

												# For classifier free guidance, we need to do two forward passes.
												# Here we concatenate the unconditional and text embeddings into a single batch
												# to avoid doing two forward passes
												UpperCAmelCase_    :      Optional[int]            =						torch.cat([negative_image_embeds, image_embeds]   )

							return image_embeds



		@torch.no_grad()
		@replace_example_docstring(_UpperCamelCase   )
		def __call__(							self				,		_UpperCamelCase				,		_UpperCamelCase = 1				,		_UpperCamelCase = 2_5				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = 4.0				,		_UpperCamelCase = 6_4				,		_UpperCamelCase = "pil"				,		_UpperCamelCase = True				,		)	->							Union[str, Any]:

							if isinstance(_UpperCamelCase				,		PIL.Image.Image   ):
												UpperCAmelCase_    :      Tuple            =						1
							elif isinstance(_UpperCamelCase				,		torch.Tensor   ):
												UpperCAmelCase_    :      str            =						image.shape[0]
							elif isinstance(_UpperCamelCase				,		_UpperCamelCase   ) and isinstance(image[0]				,		(torch.Tensor, PIL.Image.Image)   ):
												UpperCAmelCase_    :      Optional[int]            =						len(_UpperCamelCase   )
							else:
												raise ValueError(
												    f"`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(_UpperCamelCase   )}"   )

							UpperCAmelCase_    :      Tuple            =						self._execution_device

							UpperCAmelCase_    :      str            =						batch_size * num_images_per_prompt

							UpperCAmelCase_    :      str            =						guidance_scale > 1.0
							UpperCAmelCase_    :      str            =						self._encode_image(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )

							# prior

							self.scheduler.set_timesteps(_UpperCamelCase				,		device=_UpperCamelCase   )
							UpperCAmelCase_    :      int            =						self.scheduler.timesteps

							UpperCAmelCase_    :      int            =						self.prior.config.num_embeddings
							UpperCAmelCase_    :      Any            =						self.prior.config.embedding_dim

							UpperCAmelCase_    :      List[str]            =						self.prepare_latents(
							    (batch_size, num_embeddings * embedding_dim)				,		image_embeds.dtype				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		self.scheduler				,		)

							# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
							UpperCAmelCase_    :      List[Any]            =						latents.reshape(latents.shape[0]				,		_UpperCamelCase				,		_UpperCamelCase   )

							for i, t in enumerate(self.progress_bar(_UpperCamelCase   )   ):
												# expand the latents if we are doing classifier free guidance
												UpperCAmelCase_    :      Tuple            =						torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
												UpperCAmelCase_    :      Optional[Any]            =						self.scheduler.scale_model_input(_UpperCamelCase				,		_UpperCamelCase   )

												UpperCAmelCase_    :      int            =						self.prior(
												    _UpperCamelCase				,		timestep=_UpperCamelCase				,		proj_embedding=_UpperCamelCase				,		).predicted_image_embedding

												# remove the variance
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						noise_pred.split(
												    scaled_model_input.shape[2]				,		dim=2   )  # batch_size, num_embeddings, embedding_dim

												if do_classifier_free_guidance is not None:
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						noise_pred.chunk(2   )
																	UpperCAmelCase_    :      List[Any]            =						noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

												UpperCAmelCase_    :      List[str]            =						self.scheduler.step(
												    _UpperCamelCase				,		timestep=_UpperCamelCase				,		sample=_UpperCamelCase				,		).prev_sample

							if output_type == "latent":
												return ShapEPipelineOutput(images=_UpperCamelCase   )

							UpperCAmelCase_    :      List[Any]            =						[]
							for i, latent in enumerate(_UpperCamelCase   ):
												print()
												UpperCAmelCase_    :      List[str]            =						self.renderer.decode(
												    latent[None, :]				,		_UpperCamelCase				,		size=_UpperCamelCase				,		ray_batch_size=4_0_9_6				,		n_coarse_samples=6_4				,		n_fine_samples=1_2_8				,		)

												images.append(_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[int]            =						torch.stack(_UpperCamelCase   )

							if output_type not in ["np", "pil"]:
												raise ValueError(f"Only the output types `pil` and `np` are supported not output_type={output_type}"   )

							UpperCAmelCase_    :      Dict            =						images.cpu().numpy()

							if output_type == "pil":
												UpperCAmelCase_    :      List[str]            =						[self.numpy_to_pil(_UpperCamelCase   ) for image in images]

							# Offload last model to CPU
							if hasattr(self				,		'final_offload_hook'   ) and self.final_offload_hook is not None:
												self.final_offload_hook.offload()

							if not return_dict:
												return (images,)

							return ShapEPipelineOutput(images=_UpperCamelCase   )






