



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__						(							):
				"""simple docstring"""







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

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[Any]				,     _SCREAMING_SNAKE_CASE       :			Optional[int]				,     _SCREAMING_SNAKE_CASE       :			Optional[Any]    ):
				"""simple docstring"""







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

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

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

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Union[str, Any]				,     _SCREAMING_SNAKE_CASE       :			int="robotic cat with wings"				,     _SCREAMING_SNAKE_CASE       :			Optional[int]=7.5				,     _SCREAMING_SNAKE_CASE       :			str=50				,     _SCREAMING_SNAKE_CASE       :			Tuple=1				,     _SCREAMING_SNAKE_CASE       :			int=42				,     ):
				"""simple docstring"""







				__a       =		torch.Generator(pipeline.device    ).manual_seed(_SCREAMING_SNAKE_CASE    )
				__a       =		pipeline(
				    _SCREAMING_SNAKE_CASE				,     guidance_scale=_SCREAMING_SNAKE_CASE				,     num_inference_steps=_SCREAMING_SNAKE_CASE				,     generator=_SCREAMING_SNAKE_CASE				,     num_images_per_prompt=_SCREAMING_SNAKE_CASE				,     ).images
				__a       =		int(math.sqrt(_SCREAMING_SNAKE_CASE    )    )
				__a       =		image_grid(_SCREAMING_SNAKE_CASE				,     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)))





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	torch.FloatTensor






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      lowerCamelCase__					):





							@register_to_config
							def __init__(   self		:		Tuple      ,				__lowercase		:		int = 16      ,				__lowercase		:		int = 88      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		int = 1      ,				__lowercase		:		float = 0.0      ,				__lowercase		:		int = 32      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		bool = False      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		str = "geglu"      ,				__lowercase		:		bool = True      ,				__lowercase		:		bool = True      ,				):

											'''simple docstring'''
											super().__init__()
											__a       =		num_attention_heads
											__a       =		attention_head_dim
											__a       =		num_attention_heads * attention_head_dim

											__a       =		in_channels

											__a       =		torch.nn.GroupNorm(num_groups=__lowercase      ,				num_channels=__lowercase      ,				eps=1E-6      ,				affine=__lowercase	)
											__a       =		nn.Linear(__lowercase      ,				__lowercase	)

											# 3. Define transformers blocks
											__a       =		nn.ModuleList(
											    [
											        BasicTransformerBlock(
											            __lowercase      ,				__lowercase      ,				__lowercase      ,				dropout=__lowercase      ,				cross_attention_dim=__lowercase      ,				activation_fn=__lowercase      ,				attention_bias=__lowercase      ,				double_self_attention=__lowercase      ,				norm_elementwise_affine=__lowercase      ,				)
											        for d in range(__lowercase	)
											    ]	)

											__a       =		nn.Linear(__lowercase      ,				__lowercase	)







							def 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		str=None      ,				__lowercase		:		List[Any]=None      ,				__lowercase		:		List[Any]=None      ,				__lowercase		:		int=1      ,				__lowercase		:		Union[str, Any]=None      ,				__lowercase		:		bool = True      ,				):

											'''simple docstring'''
											__a     ,					__a     ,					__a     ,					__a       =		hidden_states.shape
											__a       =		batch_frames // num_frames

											__a       =		hidden_states

											__a       =		hidden_states[None, :].reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
											__a       =		hidden_states.permute(0      ,				2      ,				1      ,				3      ,				4	)

											__a       =		self.norm(__lowercase	)
											__a       =		hidden_states.permute(0      ,				3      ,				4      ,				2      ,				1	).reshape(batch_size * height * width      ,				__lowercase      ,				__lowercase	)

											__a       =		self.proj_in(__lowercase	)

											# 2. Blocks
											for block in self.transformer_blocks:
															__a       =		block(
															    __lowercase      ,				encoder_hidden_states=__lowercase      ,				timestep=__lowercase      ,				cross_attention_kwargs=__lowercase      ,				class_labels=__lowercase      ,				)

											# 3. Output
											__a       =		self.proj_out(__lowercase	)
											__a       =		(
											    hidden_states[None, None, :]
											    .reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
											    .permute(0      ,				3      ,				4      ,				1      ,				2	)
											    .contiguous()
											)
											__a       =		hidden_states.reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)

											__a       =		hidden_states + residual

											if not return_dict:
															return (output,)

											return TransformerTemporalModelOutput(sample=__lowercase	)
