

'''simple docstring'''





import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType





class 		__SCREAMING_SNAKE_CASE							:







				def __init__(       self     :		Dict		,					UpperCAmelCase__     :		Union[str, Any]=2		,					UpperCAmelCase__     :		str=3		,					UpperCAmelCase__     :		Optional[int]=64		,					UpperCAmelCase__     :		Union[str, Any]=None   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=np.random.default_rng(UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=length
										lowercase :							List[Any]    		=rng.normal(size=(length,)   ).astype(np.floataa   )
										lowercase :							Dict    		=a * self.x + b + rng.normal(scale=0.1		,					size=(length,)   ).astype(np.floataa   )







				def __len__(       self     :		Tuple   ):
										'''simple docstring'''
										return self.length







				def __getitem__(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		Any   ):
										'''simple docstring'''
										return {"x": self.x[i], "y": self.y[i]}





class 		__SCREAMING_SNAKE_CASE							(			torch.nn.Module      ):







				def __init__(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		List[Any]=0		,					UpperCAmelCase__     :		List[Any]=0		,					UpperCAmelCase__     :		str=False   ):
										'''simple docstring'''
										super().__init__()
										lowercase :							Any    		=torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
										lowercase :							List[str]    		=torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
										lowercase :							List[Any]    		=True







				def  lowerCamelCase_	(       self     :		str		,					UpperCAmelCase__     :		Any=None   ):
										'''simple docstring'''
										if self.first_batch:
																print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''   )
																lowercase :							Any    		=False
										return x * self.a[0] + self.b[0]





class 		__SCREAMING_SNAKE_CASE							(			torch.nn.Module      ):







				def __init__(       self     :		List[Any]		,					UpperCAmelCase__     :		Optional[Any]=0		,					UpperCAmelCase__     :		int=0		,					UpperCAmelCase__     :		Optional[Any]=False   ):
										'''simple docstring'''
										super().__init__()
										lowercase :							Optional[int]    		=torch.nn.Parameter(torch.tensor(UpperCAmelCase__   ).float()   )
										lowercase :							int    		=torch.nn.Parameter(torch.tensor(UpperCAmelCase__   ).float()   )
										lowercase :							Tuple    		=True







				def  lowerCamelCase_	(       self     :		List[Any]		,					UpperCAmelCase__     :		Optional[Any]=None   ):
										'''simple docstring'''
										if self.first_batch:
																print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''   )
																lowercase :							List[Any]    		=False
										return x * self.a + self.b






def 				_lowerCAmelCase			(   __magic_name__						:     Tuple   , __magic_name__						:     int = 16						)				->  Union[str, Any]:
						from datasets import load_dataset
						from transformers import AutoTokenizer

						lowercase :							Dict    		=AutoTokenizer.from_pretrained('''bert-base-cased'''						)
						lowercase :							Optional[int]    		={'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
						lowercase :							Dict    		=load_dataset('''csv'''   , data_files=__magic_name__						)
						lowercase :							int    		=datasets['''train'''].unique('''label'''						)

						lowercase :							List[str]    		={v: i for i, v in enumerate(__magic_name__						)}

						def tokenize_function(__magic_name__						:     Dict						):
												# max_length=None => use the model max length (it's actually the default)
												lowercase :							Dict    		=tokenizer(
												    examples['''sentence1''']   , examples['''sentence2''']   , truncation=__magic_name__   , max_length=__magic_name__   , padding='''max_length'''						)
												if "label" in examples:
																		lowercase :							List[Any]    		=[label_to_id[l] for l in examples['''label''']]
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						lowercase :							Optional[int]    		=datasets.map(
						    __magic_name__   , batched=__magic_name__   , remove_columns=['''sentence1''', '''sentence2''', '''label''']   , )

						def collate_fn(__magic_name__						:     Any						):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												if accelerator.distributed_type == DistributedType.TPU:
																		return tokenizer.pad(__magic_name__   , padding='''max_length'''   , max_length=128   , return_tensors='''pt'''						)
												return tokenizer.pad(__magic_name__   , padding='''longest'''   , return_tensors='''pt'''						)

						# Instantiate dataloaders.
						lowercase :							Union[str, Any]    		=DataLoader(tokenized_datasets['''train''']   , shuffle=__magic_name__   , collate_fn=__magic_name__   , batch_size=2						)
						lowercase :							Tuple    		=DataLoader(tokenized_datasets['''validation''']   , shuffle=__magic_name__   , collate_fn=__magic_name__   , batch_size=1						)

						return train_dataloader, eval_dataloader












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

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int  ,     _UpperCAmelCase					:    List[Any]  ,     _UpperCAmelCase					:    Optional[int]			):
 if not len(_UpperCAmelCase			) == 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(_UpperCAmelCase			):
  grid.paste(_UpperCAmelCase  ,     box=(i % cols * w, i // cols * h)			)
 return grid

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Any  ,     _UpperCAmelCase					:    Union[str, Any]="robotic cat with wings"  ,     _UpperCAmelCase					:    Optional[int]=7.5  ,     _UpperCAmelCase					:    Dict=50  ,     _UpperCAmelCase					:    Tuple=1  ,     _UpperCAmelCase					:    int=42  ,     ):
 lowerCAmelCase    				= torch.Generator(pipeline.device			).manual_seed(_UpperCAmelCase			)
 lowerCAmelCase    				= pipeline(
     _UpperCAmelCase  ,     guidance_scale=_UpperCAmelCase  ,     num_inference_steps=_UpperCAmelCase  ,     generator=_UpperCAmelCase  ,     num_images_per_prompt=_UpperCAmelCase  ,     ).images
 lowerCAmelCase    				= int(math.sqrt(_UpperCAmelCase			)			)
 lowerCAmelCase    				= image_grid(_UpperCAmelCase  ,     rows=_rows  ,     cols=num_images_per_prompt // _rows			)
 return grid, images


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

__UpperCamelCase     :						Tuple						       =  StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__UpperCamelCase     :						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''')):
       __UpperCamelCase     :						Dict						       =  load(args.pretrained_model_name_or_path, model=unet)
       unet.eval()
       setattr(pipeline, '''unet''', unet)
else:
       __UpperCamelCase     :						Dict						       =  unet.to(torch.device('''cuda''', args.cuda_id))
__UpperCamelCase     :						Optional[Any]						       =  pipeline.to(unet.device)
__UpperCamelCase       ,__UpperCamelCase     :						List[Any]						       =  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()))))
__UpperCamelCase     :						int						       =  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)))





