

import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase      :    List[Any]						       =    logging.get_logger()







@dataclass
class 			__magic_name__					:
			SCREAMING_SNAKE_CASE							     =		4_2
			SCREAMING_SNAKE_CASE							     =		field(default_factory=lowerCAmelCase_   )
			SCREAMING_SNAKE_CASE							     =		field(default_factory=lowerCAmelCase_   )
			SCREAMING_SNAKE_CASE							     =		field(default_factory=lowerCAmelCase_   )





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      )     ->				Optional[int]:




						'''simple docstring'''
						__a						   =len(list(m.modules()      )      ) == 1 or isinstance(__snake_case      ,						nn.Convad      ) or isinstance(__snake_case      ,						nn.BatchNormad      )
						if has_not_submodules:
									self.traced.append(__snake_case      )
									__a						   =m





			def __call__(	self      ,						__snake_case      )     ->				Any:




						'''simple docstring'''
						for name, m in self.module.named_modules():
									self.handles.append(m.register_forward_hook(partial(self._forward_hook      ,						name=__snake_case      )      )      )
						self.module(__snake_case      )
						[x.remove() for x in self.handles]
						return self





			@property
			def 	__magic_name__					(	self      )     ->				Union[str, Any]:




						'''simple docstring'''
						return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()      )      ) > 0}







class 			__magic_name__					(    nn.Module   ):





			def __init__(	self      ,						__snake_case      )     ->				List[Any]:




						'''simple docstring'''
						super().__init__()

						__a						   =[]
						# - get the stem
						feature_blocks.append(('conv1', model.stem)      )
						# - get all the feature blocks
						for k, v in model.trunk_output.named_children():
									assert k.startswith('block'      ), f'Unexpected layer name {k}'
									__a						   =len(__snake_case      ) + 1
									feature_blocks.append((f'res{block_index}', v)      )

						__a						   =nn.ModuleDict(__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				List[Any]:




						'''simple docstring'''
						return get_trunk_forward_outputs(
						    __snake_case      ,						out_feat_keys=__snake_case      ,						feature_blocks=self._feature_blocks      ,						)







class 			__magic_name__					(    lowerCAmelCase_   ):





			def 	__magic_name__					(	self      )     ->				Optional[Any]:




						'''simple docstring'''
						return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]






def  UpperCamelCase_(							_snake_case     :						str			):




			"""simple docstring"""






			__a						   =RegNetConfig(depths=[2, 7, 17, 1] ,  hidden_sizes=[8, 8, 8, 8] ,  groups_width=8			)
			if "in1k" in model_name:
						__a						   =RegNetForImageClassification(_snake_case			)
			else:
						__a						   =RegNetModel(_snake_case			)
			# create from model (with small weights)
			__a						   =FakeRegNetVisslWrapper(
			    RegNet(FakeRegNetParams(depth=27 ,  group_width=1010 ,  w_a=1744 ,  w_a=620.83 ,  w_m=2.52			)			)			)

			with torch.no_grad():
						__a						   =from_model.eval()
						__a						   =our_model.eval()

						__a						   =torch.randn((1, 3, 32, 32)			)
						# trace both
						__a						   =Tracker(_snake_case			)
						__a						   =dest_tracker(_snake_case			).parametrized

						pprint(dest_tracker.nameamodule			)
						__a						   =Tracker(_snake_case			)
						__a						   =src_tracker(_snake_case			).parametrized

			# convert the keys -> module dict to keys -> params
			def to_params_dict(_snake_case     :						str			):
						__a						   =OrderedDict()
						for name, module in dict_with_modules.items():
									for param_name, param in module.state_dict().items():
												__a						   =param
						return params_dict

			__a						   ={}

			__a						   =to_params_dict(_snake_case			)
			__a						   =to_params_dict(_snake_case			)

			for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items() ,  dst_state_dict.items()			):
						__a						   =dest_key
						logger.info(F'{src_key} -> {dest_key}'			)
			# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
			if "in1k" in model_name:
						__a						   ='classifier.1.weight'
						__a						   ='classifier.1.bias'

			return from_to_ours_keys






def  UpperCamelCase_(							_snake_case     :						Path ,  _snake_case     :						str = None ,  _snake_case     :						bool = True			):




			"""simple docstring"""






			__a						   ='imagenet-1k-id2label.json'
			__a						   =1000

			__a						   ='huggingface/label-files'
			__a						   =num_labels
			__a						   =json.load(open(cached_download(hf_hub_url(_snake_case ,  _snake_case ,  repo_type='dataset'			)			) ,  'r'			)			)
			__a						   ={int(_snake_case			): v for k, v in idalabel.items()}

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

			__a						   =partial(_snake_case ,  num_labels=_snake_case ,  idalabel=_snake_case ,  labelaid=_snake_case			)

			__a						   ={
			    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
			        depths=[2, 7, 17, 1] ,  hidden_sizes=[2020, 4040, 11110, 28280] ,  groups_width=1010			),
			    # finetuned on imagenet
			    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
			        depths=[2, 7, 17, 1] ,  hidden_sizes=[2020, 4040, 11110, 28280] ,  groups_width=1010			),
			}

			# add seer weights logic
			def load_using_classy_vision(_snake_case     :						str			) -> Tuple[Dict, Dict]:
						__a						   =torch.hub.load_state_dict_from_url(_snake_case ,  model_dir=str(_snake_case			) ,  map_location='cpu'			)
						# check if we have a head, if yes add it
						__a						   =files['classy_state_dict']['base_model']['model']
						return model_state_dict["trunk"], model_state_dict["heads"]

			__a						   ={
			    'regnet-y-10b-seer': partial(
			        _snake_case ,  'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch' ,  ),
			    'regnet-y-10b-seer-in1k': partial(
			        _snake_case ,  'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch' ,  ),
			}

			__a						   =get_from_to_our_keys(_snake_case			)

			if not (save_directory / F'{model_name}.pth').exists():
						logger.info('Loading original state_dict.'			)
						__a   ,					__a						   =names_to_from_model[model_name]()
						__a						   =from_state_dict_trunk
						if "in1k" in model_name:
									# add the head
									__a						   ={**from_state_dict_trunk, **from_state_dict_head}
						logger.info('Done!'			)

						__a						   ={}

						__a						   =list(from_state_dict.keys()			)
						__a						   =r'\.block.-part.'
						# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
						for key in from_state_dict.keys():
									# remove the weird "block[0,1]-part" from the key
									__a						   =re.sub(_snake_case ,  '' ,  _snake_case			)
									# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
									__a						   =from_to_ours_keys[src_key]
									# store the parameter with our key
									__a						   =from_state_dict[key]
									not_used_keys.remove(_snake_case			)
						# check that all keys have been updated
						assert len(_snake_case			) == 0, F'Some keys where not used {",".join(_snake_case			)}'

						logger.info(F'The following keys were not used: {",".join(_snake_case			)}'			)

						# save our state dict to disk
						torch.save(_snake_case ,  save_directory / F'{model_name}.pth'			)

						del converted_state_dict
			else:
						logger.info('The state_dict was already stored on disk.'			)
			if push_to_hub:
						logger.info(F'Token is {os.environ["HF_TOKEN"]}'			)
						logger.info('Loading our model.'			)
						# create our model
						__a						   =names_to_config[model_name]
						__a						   =RegNetModel
						if "in1k" in model_name:
									__a						   =RegNetForImageClassification
						__a						   =our_model_func(_snake_case			)
						# place our model to the meta device (so remove all the weights)
						our_model.to(torch.device('meta'			)			)
						logger.info('Loading state_dict in our model.'			)
						# load state dict
						__a						   =our_model.state_dict().keys()
						PreTrainedModel._load_pretrained_model_low_mem(
						    _snake_case ,  _snake_case ,  [save_directory / F'{model_name}.pth']			)
						logger.info('Finally, pushing!'			)
						# push it to hub
						our_model.push_to_hub(
						    repo_path_or_name=save_directory / model_name ,  commit_message='Add model' ,  output_dir=save_directory / model_name ,  )
						__a						   =384
						# we can use the convnext one
						__a						   =AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' ,  size=_snake_case			)
						image_processor.push_to_hub(
						    repo_path_or_name=save_directory / model_name ,  commit_message='Add image processor' ,  output_dir=save_directory / model_name ,  )


if __name__ == "__main__":
						_lowerCAmelCase      :    int						       =    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 regnet* architecture,"
						        " currently: regnetx-*, regnety-*. 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.",
						)

						_lowerCAmelCase      :    str						       =    parser.parse_args()

						_lowerCAmelCase      :    Path						       =    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 logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


_lowerCAmelCase      :    str						       =    getLogger(__name__)


_lowerCAmelCase      :    int						       =    4
_lowerCAmelCase      :    Union[str, Any]						       =    7

_lowerCAmelCase      :    Optional[Any]						       =    {
    "float16": torch.floataa,
    "float32": torch.floataa,
}






def  UpperCamelCase_(							_snake_case     :						Union[str, Any]			):




			"""simple docstring"""






			if isinstance(_snake_case ,  torch.Tensor			):
						return image
			elif isinstance(_snake_case ,  PIL.Image.Image			):
						__a						   =[image]

			if isinstance(image[0] ,  PIL.Image.Image			):
						__a   ,					__a						   =image[0].size
						__a   ,					__a						   =(x - x % 64 for x in (w, h))  # resize to integer multiple of 32

						__a						   =[np.array(i.resize((w, h)			)			)[None, :] for i in image]
						__a						   =np.concatenate(_snake_case ,  axis=0			)
						__a						   =np.array(_snake_case			).astype(np.floataa			) / 255.0
						__a						   =image.transpose(0 ,  3 ,  1 ,  2			)
						__a						   =2.0 * image - 1.0
						__a						   =torch.from_numpy(_snake_case			)
			elif isinstance(image[0] ,  torch.Tensor			):
						__a						   =torch.cat(_snake_case ,  dim=0			)

			return image






class 			__magic_name__					(    lowerCAmelCase_   ):





			def __init__(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case = 350      ,						)     ->				List[str]:




						'''simple docstring'''
						super().__init__(
						    vae=__snake_case      ,						text_encoder=__snake_case      ,						tokenizer=__snake_case      ,						unet=__snake_case      ,						low_res_scheduler=__snake_case      ,						scheduler=__snake_case      ,						safety_checker=__snake_case      ,						feature_extractor=__snake_case      ,						watermarker=__snake_case      ,						max_noise_level=__snake_case      ,						)





			def __call__(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case = 75      ,						__snake_case = 9.0      ,						__snake_case = 20      ,						__snake_case = None      ,						__snake_case = 1      ,						__snake_case = 0.0      ,						__snake_case = None      ,						__snake_case = None      ,						__snake_case = None      ,						__snake_case = None      ,						__snake_case = "pil"      ,						__snake_case = True      ,						__snake_case = None      ,						__snake_case = 1      ,						)     ->				str:




						'''simple docstring'''
						self.check_inputs(__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      )

						# 2. Define call parameters
						if prompt is not None and isinstance(__snake_case      ,						__snake_case      ):
									__a						   =1
						elif prompt is not None and isinstance(__snake_case      ,						__snake_case      ):
									__a						   =len(__snake_case      )
						else:
									__a						   =prompt_embeds.shape[0]

						__a						   =self._execution_device
						# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
						# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
						# corresponds to doing no classifier free guidance.
						__a						   =guidance_scale > 1.0

						# 3. Encode input prompt
						__a						   =self._encode_prompt(
						    __snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						prompt_embeds=__snake_case      ,						negative_prompt_embeds=__snake_case      ,						)

						__a						   =ORT_TO_PT_TYPE[str(text_embeddings.dtype      )]

						# 4. Preprocess image
						__a						   =preprocess(__snake_case      )
						__a						   =image.cpu()

						# 5. set timesteps
						self.scheduler.set_timesteps(__snake_case      ,						device=__snake_case      )
						__a						   =self.scheduler.timesteps

						# 5. Add noise to image
						__a						   =torch.tensor([noise_level]      ,						dtype=torch.long      ,						device=__snake_case      )
						__a						   =torch.randn(image.shape      ,						generator=__snake_case      ,						device=__snake_case      ,						dtype=__snake_case      )
						__a						   =self.low_res_scheduler.add_noise(__snake_case      ,						__snake_case      ,						__snake_case      )

						__a						   =2 if do_classifier_free_guidance else 1
						__a						   =np.concatenate([image] * batch_multiplier * num_images_per_prompt      )
						__a						   =np.concatenate([noise_level] * image.shape[0]      )

						# 6. Prepare latent variables
						__a   ,					__a						   =image.shape[2:]
						__a						   =self.prepare_latents(
						    batch_size * num_images_per_prompt      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						)

						# 7. Check that sizes of image and latents match
						__a						   =image.shape[1]
						if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
									raise ValueError(
									    'Incorrect configuration settings! The config of `pipeline.unet` expects'
									    f' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'
									    f' `num_channels_image`: {num_channels_image} '
									    f' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'
									    ' `pipeline.unet` or your `image` input.'      )

						# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
						__a						   =self.prepare_extra_step_kwargs(__snake_case      ,						__snake_case      )

						__a						   =next(
						    (input.type for input in self.unet.model.get_inputs() if input.name == 'timestep')      ,						'tensor(float)'      )
						__a						   =ORT_TO_NP_TYPE[timestep_dtype]

						# 9. Denoising loop
						__a						   =len(__snake_case      ) - num_inference_steps * self.scheduler.order
						with self.progress_bar(total=__snake_case      ) as progress_bar:
									for i, t in enumerate(__snake_case      ):
												# expand the latents if we are doing classifier free guidance
												__a						   =np.concatenate([latents] * 2      ) if do_classifier_free_guidance else latents

												# concat latents, mask, masked_image_latents in the channel dimension
												__a						   =self.scheduler.scale_model_input(__snake_case      ,						__snake_case      )
												__a						   =np.concatenate([latent_model_input, image]      ,						axis=1      )

												# timestep to tensor
												__a						   =np.array([t]      ,						dtype=__snake_case      )

												# predict the noise residual
												__a						   =self.unet(
												    sample=__snake_case      ,						timestep=__snake_case      ,						encoder_hidden_states=__snake_case      ,						class_labels=noise_level.astype(np.intaa      )      ,						)[0]

												# perform guidance
												if do_classifier_free_guidance:
															__a   ,					__a						   =np.split(__snake_case      ,						2      )
												__a						   =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

												# compute the previous noisy sample x_t -> x_t-1
												__a						   =self.scheduler.step(
												    torch.from_numpy(__snake_case      )      ,						__snake_case      ,						__snake_case      ,						**__snake_case      ).prev_sample

												# call the callback, if provided
												if i == len(__snake_case      ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
															progress_bar.update()
															if callback is not None and i % callback_steps == 0:
																		callback(__snake_case      ,						__snake_case      ,						__snake_case      )

        # 10. Post-processing
						__a						   =self.decode_latents(latents.float()      )

						# 11. Convert to PIL
						if output_type == "pil":
									__a						   =self.numpy_to_pil(__snake_case      )

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				int:




						'''simple docstring'''
						__a						   =1 / 0.0_8333 * latents
						__a						   =self.vae(latent_sample=__snake_case      )[0]
						__a						   =np.clip(image / 2 + 0.5      ,						0      ,						1      )
						__a						   =image.transpose((0, 2, 3, 1)      )
						return image





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case = None      ,						__snake_case = None      ,						)     ->				List[str]:




						'''simple docstring'''
						if prompt is not None and isinstance(__snake_case      ,						__snake_case      ):
									__a						   =1
						elif prompt is not None and isinstance(__snake_case      ,						__snake_case      ):
									__a						   =len(__snake_case      )
						else:
									__a						   =prompt_embeds.shape[0]

						if prompt_embeds is None:
									__a						   =self.tokenizer(
									    __snake_case      ,						padding='max_length'      ,						max_length=self.tokenizer.model_max_length      ,						truncation=__snake_case      ,						return_tensors='pt'      ,						)
									__a						   =text_inputs.input_ids
									__a						   =self.tokenizer(__snake_case      ,						padding='longest'      ,						return_tensors='pt'      ).input_ids

									if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
									    __snake_case      ,						__snake_case      ):
												__a						   =self.tokenizer.batch_decode(
												    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]      )
												logger.warning(
												    'The following part of your input was truncated because CLIP can only handle sequences up to'
												    f' {self.tokenizer.model_max_length} tokens: {removed_text}'      )

									# no positional arguments to text_encoder
									__a						   =self.text_encoder(
									    input_ids=text_input_ids.int().to(__snake_case      )      ,						)
									__a						   =prompt_embeds[0]

						__a   ,					__a   ,					__a						   =prompt_embeds.shape
						# duplicate text embeddings for each generation per prompt, using mps friendly method
						__a						   =prompt_embeds.repeat(1      ,						__snake_case      )
						__a						   =prompt_embeds.reshape(bs_embed * num_images_per_prompt      ,						__snake_case      ,						-1      )

						# get unconditional embeddings for classifier free guidance
						if do_classifier_free_guidance and negative_prompt_embeds is None:
									__a						   =42
									if negative_prompt is None:
												__a						   =[''] * batch_size
									elif type(__snake_case      ) is not type(__snake_case      ):
												raise TypeError(
												    f'`negative_prompt` should be the same type to `prompt`, but got {type(__snake_case      )} !='
												    f' {type(__snake_case      )}.'      )
									elif isinstance(__snake_case      ,						__snake_case      ):
												__a						   =[negative_prompt]
									elif batch_size != len(__snake_case      ):
												raise ValueError(
												    f'`negative_prompt`: {negative_prompt} has batch size {len(__snake_case      )}, but `prompt`:'
												    f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
												    ' the batch size of `prompt`.'      )
									else:
												__a						   =negative_prompt

									__a						   =text_input_ids.shape[-1]
									__a						   =self.tokenizer(
									    __snake_case      ,						padding='max_length'      ,						max_length=__snake_case      ,						truncation=__snake_case      ,						return_tensors='pt'      ,						)

									# if hasattr(uncond_input, "attention_mask"):
									#     attention_mask = uncond_input.attention_mask.to(device)
									# else:
									#     attention_mask = None

									__a						   =self.text_encoder(
									    input_ids=uncond_input.input_ids.int().to(__snake_case      )      ,						)
									__a						   =uncond_embeddings[0]

						if do_classifier_free_guidance:
									__a						   =uncond_embeddings.shape[1]
									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
									__a						   =uncond_embeddings.repeat(1      ,						__snake_case      )
									__a						   =uncond_embeddings.reshape(batch_size * num_images_per_prompt      ,						__snake_case      ,						-1      )

									# 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
									__a						   =np.concatenate([uncond_embeddings, prompt_embeds]      )

						return prompt_embeds






