





'''simple docstring'''


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 							UpperCAmelCase_  (						):
   lowercase_      :int         =			argparse.ArgumentParser()
   parser.add_argument(
       "-m"     ,"--pretrained_model_name_or_path"     ,type=__a     ,default=__a     ,required=__a     ,help="Path to pretrained model or model identifier from huggingface.co/models."     ,)
   parser.add_argument(
       "-c"     ,"--caption"     ,type=__a     ,default="robotic cat with wings"     ,help="Text used to generate images."     ,)
   parser.add_argument(
       "-n"     ,"--images_num"     ,type=__a     ,default=4     ,help="How much images to generate."     ,)
   parser.add_argument(
       "-s"     ,"--seed"     ,type=__a     ,default=42     ,help="Seed for random process."     ,)
   parser.add_argument(
       "-ci"     ,"--cuda_id"     ,type=__a     ,default=0     ,help="cuda_id."     ,)
   lowercase_      :Dict         =			parser.parse_args()
   return args
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : List[Any]     ,__lowerCamelCase : List[str]			):

   if not len(__a			) == rows * cols:
      raise ValueError("The specified number of rows and columns are not correct."			)

   lowercase_      :Tuple         =			imgs[0].size
   lowercase_      :Tuple         =			Image.new("RGB"     ,size=(cols * w, rows * h)			)
   lowercase_      :str         =			grid.size

   for i, img in enumerate(__a			):
      grid.paste(__a     ,box=(i % cols * w, i // cols * h)			)
   return grid
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[Any]     ,__lowerCamelCase : Optional[int]="robotic cat with wings"     ,__lowerCamelCase : Any=7.5     ,__lowerCamelCase : Any=50     ,__lowerCamelCase : int=1     ,__lowerCamelCase : List[str]=42     ,):
   lowercase_      :int         =			torch.Generator(pipeline.device			).manual_seed(__a			)
   lowercase_      :Optional[int]         =			pipeline(
       __a     ,guidance_scale=__a     ,num_inference_steps=__a     ,generator=__a     ,num_images_per_prompt=__a     ,).images
   lowercase_      :List[str]         =			int(math.sqrt(__a			)			)
   lowercase_      :Any         =			image_grid(__a     ,rows=_rows     ,cols=num_images_per_prompt // _rows			)
   return grid, images


lowerCAmelCase :					List[str] 							=parse_args()
# Load models and create wrapper for stable diffusion
lowerCAmelCase :					Optional[Any] 							=CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
lowerCAmelCase :					str 							=CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
lowerCAmelCase :					Tuple 							=AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
lowerCAmelCase :					Union[str, Any] 							=UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')

lowerCAmelCase :					Union[str, Any] 							=StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowerCAmelCase :					Union[str, Any] 							=lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
 lowerCAmelCase :					Union[str, Any] 							=load(args.pretrained_model_name_or_path, model=unet)
 unet.eval()
 setattr(pipeline, '''unet''', unet)
else:
 lowerCAmelCase :					List[Any] 							=unet.to(torch.device('''cuda''', args.cuda_id))
lowerCAmelCase :					List[str] 							=pipeline.to(unet.device)
lowerCAmelCase							,		lowerCAmelCase :					Optional[int] 							=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 :					Optional[Any] 							=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)))









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase :					Tuple 							=logging.get_logger(__name__)

lowerCAmelCase :					str 							={
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}
class 				a_       (    _lowerCAmelCase							):
     __A								=      "cvt"





     def __init__(			self		: Tuple     ,				lowercase		: str=3     ,				lowercase		: str=[7, 3, 3]     ,				lowercase		: List[str]=[4, 2, 2]     ,				lowercase		: Dict=[2, 1, 1]     ,				lowercase		: int=[64, 192, 384]     ,				lowercase		: Dict=[1, 3, 6]     ,				lowercase		: Dict=[1, 2, 10]     ,				lowercase		: Any=[4.0, 4.0, 4.0]     ,				lowercase		: Tuple=[0.0, 0.0, 0.0]     ,				lowercase		: List[str]=[0.0, 0.0, 0.0]     ,				lowercase		: List[str]=[0.0, 0.0, 0.1]     ,				lowercase		: Any=[True, True, True]     ,				lowercase		: Any=[False, False, True]     ,				lowercase		: Optional[Any]=["dw_bn", "dw_bn", "dw_bn"]     ,				lowercase		: int=[3, 3, 3]     ,				lowercase		: str=[1, 1, 1]     ,				lowercase		: List[Any]=[2, 2, 2]     ,				lowercase		: Tuple=[1, 1, 1]     ,				lowercase		: Optional[Any]=[1, 1, 1]     ,				lowercase		: str=0.02     ,				lowercase		: str=1e-1_2     ,				**lowercase		: str     ,				):





        """simple docstring"""




        super().__init__(**lowercase						)
        lowercase_      :List[Any]         =			num_channels
        lowercase_      :Union[str, Any]         =			patch_sizes
        lowercase_      :Tuple         =			patch_stride
        lowercase_      :List[Any]         =			patch_padding
        lowercase_      :List[Any]         =			embed_dim
        lowercase_      :Union[str, Any]         =			num_heads
        lowercase_      :Any         =			depth
        lowercase_      :str         =			mlp_ratio
        lowercase_      :List[str]         =			attention_drop_rate
        lowercase_      :List[Any]         =			drop_rate
        lowercase_      :Union[str, Any]         =			drop_path_rate
        lowercase_      :Any         =			qkv_bias
        lowercase_      :Dict         =			cls_token
        lowercase_      :int         =			qkv_projection_method
        lowercase_      :Union[str, Any]         =			kernel_qkv
        lowercase_      :Optional[Any]         =			padding_kv
        lowercase_      :Optional[Any]         =			stride_kv
        lowercase_      :Dict         =			padding_q
        lowercase_      :Any         =			stride_q
        lowercase_      :Dict         =			initializer_range
        lowercase_      :Optional[Any]         =			layer_norm_eps


