

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(      )->						Optional[int]:



    """simple docstring"""






    UpperCamelCase_			  =						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=4_2			,						help="Seed for random process."			,						)
    parser.add_argument(
        "-ci"			,						"--cuda_id"			,						type=SCREAMING_SNAKE_CASE_			,						default=0			,						help="cuda_id."			,						)
    UpperCamelCase_			  =						parser.parse_args()
    return args




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Dict:



    """simple docstring"""






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

    UpperCamelCase_  ,       UpperCamelCase_			  =						imgs[0].size
    UpperCamelCase_			  =						Image.new("RGB"			,						size=(cols * w, rows * h)			)
    UpperCamelCase_  ,       UpperCamelCase_			  =						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_			,						SCREAMING_SNAKE_CASE_="robotic cat with wings"			,						SCREAMING_SNAKE_CASE_=7.5			,						SCREAMING_SNAKE_CASE_=5_0			,						SCREAMING_SNAKE_CASE_=1			,						SCREAMING_SNAKE_CASE_=4_2			,						)->						str:



    """simple docstring"""






    UpperCamelCase_			  =						torch.Generator(pipeline.device			).manual_seed(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						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
    UpperCamelCase_			  =						int(math.sqrt(SCREAMING_SNAKE_CASE_			)			)
    UpperCamelCase_			  =						image_grid(SCREAMING_SNAKE_CASE_			,						rows=_rows			,						cols=num_images_per_prompt // _rows			)
    return grid, images


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

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



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Dict										=      ["""image_processor""", """tokenizer"""]
     UpperCamelCase_	:str										=      """BridgeTowerImageProcessor"""
     UpperCamelCase_	:Tuple										=      ("""RobertaTokenizer""", """RobertaTokenizerFast""")






     def __init__(				self		,						_lowercase		,						_lowercase	)->     Any:
         super().__init__(_lowercase		,						_lowercase	)






     def __call__(				self		,						_lowercase		,						_lowercase = None		,						_lowercase = True		,						_lowercase = False		,						_lowercase = None		,						_lowercase = None		,						_lowercase = 0		,						_lowercase = None		,						_lowercase = None		,						_lowercase = None		,						_lowercase = False		,						_lowercase = False		,						_lowercase = False		,						_lowercase = False		,						_lowercase = True		,						_lowercase = None		,						**_lowercase		,						)->     BatchEncoding:
         UpperCamelCase_			  =						self.tokenizer(
             text=_lowercase		,						add_special_tokens=_lowercase		,						padding=_lowercase		,						truncation=_lowercase		,						max_length=_lowercase		,						stride=_lowercase		,						pad_to_multiple_of=_lowercase		,						return_token_type_ids=_lowercase		,						return_attention_mask=_lowercase		,						return_overflowing_tokens=_lowercase		,						return_special_tokens_mask=_lowercase		,						return_offsets_mapping=_lowercase		,						return_length=_lowercase		,						verbose=_lowercase		,						return_tensors=_lowercase		,						**_lowercase		,						)
         # add pixel_values + pixel_mask
         UpperCamelCase_			  =						self.image_processor(
             _lowercase		,						return_tensors=_lowercase		,						do_normalize=_lowercase		,						do_center_crop=_lowercase		,						**_lowercase	)
         encoding.update(_lowercase	)

         return encoding






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     Union[str, Any]:
         return self.tokenizer.batch_decode(*_lowercase		,						**_lowercase	)






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     List[Any]:
         return self.tokenizer.decode(*_lowercase		,						**_lowercase	)






     @property
     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						self.tokenizer.model_input_names
         UpperCamelCase_			  =						self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)
