import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class     a_			(						unittest.TestCase						):






 '''simple docstring'''






 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        lowerCAmelCase__					     = tempfile.mkdtemp()

        lowerCAmelCase__					     = BlipImageProcessor()
        lowerCAmelCase__					     = BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel')

        lowerCAmelCase__					     = BlipProcessor(lowercase__      ,   lowercase__)

        processor.save_pretrained(self.tmpdirname)




 def 		__snake_case						(      self :  Optional[int]      ,   **lowercase__ :  Any):







        '''simple docstring'''



        return AutoProcessor.from_pretrained(self.tmpdirname      ,   **lowercase__).tokenizer




 def 		__snake_case						(      self :  List[str]      ,   **lowercase__ :  str):







        '''simple docstring'''



        return AutoProcessor.from_pretrained(self.tmpdirname      ,   **lowercase__).image_processor




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        shutil.rmtree(self.tmpdirname)




 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = [np.random.randint(255      ,   size=(3, 30, 400)      ,   dtype=np.uinta)]

        lowerCAmelCase__					     = [Image.fromarray(np.moveaxis(lowercase__      ,   0      ,   -1)) for x in image_inputs]

        return image_inputs




 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        lowerCAmelCase__					     = BlipProcessor(tokenizer=self.get_tokenizer()      ,   image_processor=self.get_image_processor())
        processor.save_pretrained(self.tmpdirname)

        lowerCAmelCase__					     = self.get_tokenizer(bos_token='(BOS)'      ,   eos_token='(EOS)')
        lowerCAmelCase__					     = self.get_image_processor(do_normalize=lowercase__      ,   padding_value=1.0)

        lowerCAmelCase__					     = BlipProcessor.from_pretrained(
            self.tmpdirname      ,   bos_token='(BOS)'      ,   eos_token='(EOS)'      ,   do_normalize=lowercase__      ,   padding_value=1.0)

        self.assertEqual(processor.tokenizer.get_vocab()      ,   tokenizer_add_kwargs.get_vocab())
        self.assertIsInstance(processor.tokenizer      ,   lowercase__)

        self.assertEqual(processor.image_processor.to_json_string()      ,   image_processor_add_kwargs.to_json_string())
        self.assertIsInstance(processor.image_processor      ,   lowercase__)




 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        lowerCAmelCase__					     = self.get_image_processor()
        lowerCAmelCase__					     = self.get_tokenizer()

        lowerCAmelCase__					     = BlipProcessor(tokenizer=lowercase__      ,   image_processor=lowercase__)

        lowerCAmelCase__					     = self.prepare_image_inputs()

        lowerCAmelCase__					     = image_processor(lowercase__      ,   return_tensors='np')
        lowerCAmelCase__					     = processor(images=lowercase__      ,   return_tensors='np')

        for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()      ,   input_processor[key].sum()      ,   delta=1e-2)




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.get_image_processor()
        lowerCAmelCase__					     = self.get_tokenizer()

        lowerCAmelCase__					     = BlipProcessor(tokenizer=lowercase__      ,   image_processor=lowercase__)

        lowerCAmelCase__					     = 'lower newer'

        lowerCAmelCase__					     = processor(text=lowercase__)

        lowerCAmelCase__					     = tokenizer(lowercase__      ,   return_token_type_ids=lowercase__)

        for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]      ,   encoded_processor[key])




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        lowerCAmelCase__					     = self.get_image_processor()
        lowerCAmelCase__					     = self.get_tokenizer()

        lowerCAmelCase__					     = BlipProcessor(tokenizer=lowercase__      ,   image_processor=lowercase__)

        lowerCAmelCase__					     = 'lower newer'
        lowerCAmelCase__					     = self.prepare_image_inputs()

        lowerCAmelCase__					     = processor(text=lowercase__      ,   images=lowercase__)

        self.assertListEqual(list(inputs.keys())      ,   ['pixel_values', 'input_ids', 'attention_mask'])

        # test if it raises when no input is passed
        with pytest.raises(lowercase__):
               processor()




 def 		__snake_case						(      self :  int):







        '''simple docstring'''



        lowerCAmelCase__					     = self.get_image_processor()
        lowerCAmelCase__					     = self.get_tokenizer()

        lowerCAmelCase__					     = BlipProcessor(tokenizer=lowercase__      ,   image_processor=lowercase__)

        lowerCAmelCase__					     = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        lowerCAmelCase__					     = processor.batch_decode(lowercase__)
        lowerCAmelCase__					     = tokenizer.batch_decode(lowercase__)

        self.assertListEqual(lowercase__      ,   lowercase__)




 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.get_image_processor()
        lowerCAmelCase__					     = self.get_tokenizer()

        lowerCAmelCase__					     = BlipProcessor(tokenizer=lowercase__      ,   image_processor=lowercase__)

        lowerCAmelCase__					     = 'lower newer'
        lowerCAmelCase__					     = self.prepare_image_inputs()

        lowerCAmelCase__					     = processor(text=lowercase__      ,   images=lowercase__)

        # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
        self.assertListEqual(list(inputs.keys())      ,   ['pixel_values', 'input_ids', 'attention_mask'])

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)))
