import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = SwinConfig()
       lowerCAmelCase__					     = swin_name.split('_'     )

       lowerCAmelCase__					     = name_split[1]
       lowerCAmelCase__					     = int(name_split[4]     )
       lowerCAmelCase__					     = int(name_split[3][-1]     )

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

       if "in22k" in swin_name:
              lowerCAmelCase__					     = 2_1_8_4_1
       else:
              lowerCAmelCase__					     = 1_0_0_0
              lowerCAmelCase__					     = 'huggingface/label-files'
              lowerCAmelCase__					     = 'imagenet-1k-id2label.json'
              lowerCAmelCase__					     = json.load(open(hf_hub_download(lowerCAmelCase__     ,  lowerCAmelCase__     ,  repo_type='dataset'     )     ,  'r'     )     )
              lowerCAmelCase__					     = {int(lowerCAmelCase__     ): v for k, v in idalabel.items()}
              lowerCAmelCase__					     = idalabel
              lowerCAmelCase__					     = {v: k for k, v in idalabel.items()}

       lowerCAmelCase__					     = img_size
       lowerCAmelCase__					     = num_classes
       lowerCAmelCase__					     = embed_dim
       lowerCAmelCase__					     = depths
       lowerCAmelCase__					     = num_heads
       lowerCAmelCase__					     = window_size

       return config






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       if "patch_embed.proj" in name:
              lowerCAmelCase__					     = name.replace('patch_embed.proj'     ,  'embeddings.patch_embeddings.projection'     )
       if "patch_embed.norm" in name:
              lowerCAmelCase__					     = name.replace('patch_embed.norm'     ,  'embeddings.norm'     )
       if "layers" in name:
              lowerCAmelCase__					     = 'encoder.' + name
       if "attn.proj" in name:
              lowerCAmelCase__					     = name.replace('attn.proj'     ,  'attention.output.dense'     )
       if "attn" in name:
              lowerCAmelCase__					     = name.replace('attn'     ,  'attention.self'     )
       if "norm1" in name:
              lowerCAmelCase__					     = name.replace('norm1'     ,  'layernorm_before'     )
       if "norm2" in name:
              lowerCAmelCase__					     = name.replace('norm2'     ,  'layernorm_after'     )
       if "mlp.fc1" in name:
              lowerCAmelCase__					     = name.replace('mlp.fc1'     ,  'intermediate.dense'     )
       if "mlp.fc2" in name:
              lowerCAmelCase__					     = name.replace('mlp.fc2'     ,  'output.dense'     )

       if name == "norm.weight":
              lowerCAmelCase__					     = 'layernorm.weight'
       if name == "norm.bias":
              lowerCAmelCase__					     = 'layernorm.bias'

       if "head" in name:
              lowerCAmelCase__					     = name.replace('head'     ,  'classifier'     )
       else:
              lowerCAmelCase__					     = 'swin.' + name

       return name






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       for key in orig_state_dict.copy().keys():
              lowerCAmelCase__					     = orig_state_dict.pop(lowerCAmelCase__     )

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

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

       return orig_state_dict






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = timm.create_model(lowerCAmelCase__     ,  pretrained=lowerCAmelCase__     )
       timm_model.eval()

       lowerCAmelCase__					     = get_swin_config(lowerCAmelCase__     )
       lowerCAmelCase__					     = SwinForImageClassification(lowerCAmelCase__     )
       model.eval()

       lowerCAmelCase__					     = convert_state_dict(timm_model.state_dict()     ,  lowerCAmelCase__     )
       model.load_state_dict(lowerCAmelCase__     )

       lowerCAmelCase__					     = 'http://images.cocodataset.org/val2017/000000039769.jpg'

       lowerCAmelCase__					     = AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'     ,  '-'     )     )     )
       lowerCAmelCase__					     = Image.open(requests.get(lowerCAmelCase__     ,  stream=lowerCAmelCase__     ).raw     )
       lowerCAmelCase__					     = image_processor(images=lowerCAmelCase__     ,  return_tensors='pt'     )

       lowerCAmelCase__					     = timm_model(inputs['pixel_values']     )
       lowerCAmelCase__					     = model(**lowerCAmelCase__     ).logits

       assert torch.allclose(lowerCAmelCase__     ,  lowerCAmelCase__     ,  atol=1e-3     )

       print(F"""Saving model {swin_name} 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__":
    lowerCAmelCase__     						=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--swin_name',
        default='swin_tiny_patch4_window7_224',
        type=str,
        help='Name of the Swin 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.'
    )

    lowerCAmelCase__     						=			parser.parse_args()
    convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)

import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel






def 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = argparse.ArgumentParser()
       parser.add_argument(
           '-m'     ,  '--pretrained_model_name_or_path'     ,  type=lowerCAmelCase__     ,  default=lowerCAmelCase__     ,  required=lowerCAmelCase__     ,  help='Path to pretrained model or model identifier from huggingface.co/models.'     ,  )
       parser.add_argument(
           '-c'     ,  '--caption'     ,  type=lowerCAmelCase__     ,  default='robotic cat with wings'     ,  help='Text used to generate images.'     ,  )
       parser.add_argument(
           '-n'     ,  '--images_num'     ,  type=lowerCAmelCase__     ,  default=4     ,  help='How much images to generate.'     ,  )
       parser.add_argument(
           '-s'     ,  '--seed'     ,  type=lowerCAmelCase__     ,  default=4_2     ,  help='Seed for random process.'     ,  )
       parser.add_argument(
           '-ci'     ,  '--cuda_id'     ,  type=lowerCAmelCase__     ,  default=0     ,  help='cuda_id.'     ,  )
       lowerCAmelCase__					     = parser.parse_args()
       return args






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       if not len(lowerCAmelCase__     ) == rows * cols:
              raise ValueError('The specified number of rows and columns are not correct.'     )

       lowerCAmelCase__     , lowerCAmelCase__					     = imgs[0].size
       lowerCAmelCase__					     = Image.new('RGB'     ,  size=(cols * w, rows * h)     )
       lowerCAmelCase__     , lowerCAmelCase__					     = grid.size

       for i, img in enumerate(lowerCAmelCase__     ):
              grid.paste(lowerCAmelCase__     ,  box=(i % cols * w, i // cols * h)     )
       return grid






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__="robotic cat with wings"     ,  lowerCAmelCase__=7.5     ,  lowerCAmelCase__=5_0     ,  lowerCAmelCase__=1     ,  lowerCAmelCase__=4_2     ,  ):
       lowerCAmelCase__					     = torch.Generator(pipeline.device     ).manual_seed(lowerCAmelCase__     )
       lowerCAmelCase__					     = pipeline(
           lowerCAmelCase__     ,  guidance_scale=lowerCAmelCase__     ,  num_inference_steps=lowerCAmelCase__     ,  generator=lowerCAmelCase__     ,  num_images_per_prompt=lowerCAmelCase__     ,  ).images
       lowerCAmelCase__					     = int(math.sqrt(lowerCAmelCase__     )     )
       lowerCAmelCase__					     = image_grid(lowerCAmelCase__     ,  rows=_rows     ,  cols=num_images_per_prompt // _rows     )
       return grid, images


lowerCAmelCase__     						=			parse_args()
# Load models and create wrapper for stable diffusion
lowerCAmelCase__     						=			CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
lowerCAmelCase__     						=			CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
lowerCAmelCase__     						=			AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
lowerCAmelCase__     						=			UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')

lowerCAmelCase__     						=			StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowerCAmelCase__     						=			lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
    lowerCAmelCase__     						=			load(args.pretrained_model_name_or_path, model=unet)
    unet.eval()
    setattr(pipeline, 'unet', unet)
else:
    lowerCAmelCase__     						=			unet.to(torch.device('cuda', args.cuda_id))
lowerCAmelCase__     						=			pipeline.to(unet.device)
lowerCAmelCase__						,		lowerCAmelCase__     						=			generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '{}.png'.format('_'.join(args.caption.split()))))
lowerCAmelCase__     						=			os.path.join(args.pretrained_model_name_or_path, '_'.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
    image.save(os.path.join(dirname, '{}.png'.format(idx + 1)))
