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



def A__  (			lowercase:     Union[str, Any],		lowercase:     int,		lowercase:     Union[str, Any]					)				->		Tuple:
       if not len(lowercase_					) == rows * cols:
              raise ValueError('The specified number of rows and columns are not correct.'					)

       A						:     Union[str, Any]         =imgs[0].size
       A						:     Optional[int]         =Image.new('RGB',		size=(cols * w, rows * h)					)
       A						:     Tuple         =grid.size

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



def A__  (			lowercase:     str,		lowercase:     Optional[Any]="robotic cat with wings",		lowercase:     Tuple=7.5,		lowercase:     Dict=50,		lowercase:     Optional[Any]=1,		lowercase:     Dict=42,		)				->		int:
       A						:     List[str]         =torch.Generator(pipeline.device					).manual_seed(lowercase_					)
       A						:     int         =pipeline(
           lowercase_,		guidance_scale=lowercase_,		num_inference_steps=lowercase_,		generator=lowercase_,		num_images_per_prompt=lowercase_,		).images
       A						:     Dict         =int(math.sqrt(lowercase_					)					)
       A						:     Optional[int]         =image_grid(lowercase_,		rows=_rows,		cols=num_images_per_prompt // _rows					)
       return grid, images


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

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







import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
    import torch

if is_tf_available():
    import tensorflow as tf



@require_vision
@require_torchvision
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Union[str, Any]:
         A						:     Dict         =tempfile.mkdtemp()
         A						:     int         =SamImageProcessor()
         A						:     Union[str, Any]         =SamProcessor(SCREAMING_SNAKE_CASE__     )
         processor.save_pretrained(self.tmpdirname     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]      ,	**SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				Optional[int]:
         return AutoProcessor.from_pretrained(self.tmpdirname      ,	**SCREAMING_SNAKE_CASE__     ).image_processor







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Any:
         shutil.rmtree(self.tmpdirname     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Optional[int]:
         A						:     str         =[np.random.randint(2_55      ,	size=(3, 30, 4_00)      ,	dtype=np.uinta     )]

         A						:     Optional[int]         =[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__      ,	0      ,	-1     )     ) for x in image_inputs]

         return image_inputs







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				Tuple:
         A						:     Optional[int]         =SamProcessor(image_processor=self.get_image_processor()     )
         processor.save_pretrained(self.tmpdirname     )

         A						:     str         =self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__      ,	padding_value=1.0     )

         A						:     Union[str, Any]         =SamProcessor.from_pretrained(self.tmpdirname      ,	do_normalize=SCREAMING_SNAKE_CASE__      ,	padding_value=1.0     )

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Optional[int]:
         A						:     Optional[Any]         =self.get_image_processor()

         A						:     Optional[Any]         =SamProcessor(image_processor=SCREAMING_SNAKE_CASE__     )

         A						:     Dict         =self.prepare_image_inputs()

         A						:     Optional[int]         =image_processor(SCREAMING_SNAKE_CASE__      ,	return_tensors='np'     )
         A						:     Optional[Any]         =processor(images=SCREAMING_SNAKE_CASE__      ,	return_tensors='np'     )

         input_feat_extract.pop('original_sizes'     )  # pop original_sizes as it is popped in the processor
         input_feat_extract.pop('reshaped_input_sizes'     )  # pop original_sizes as it is popped in the processor

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







  @require_torch
  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Any:
         A						:     str         =self.get_image_processor()

         A						:     Union[str, Any]         =SamProcessor(image_processor=SCREAMING_SNAKE_CASE__     )
         A						:     str         =[torch.ones((1, 3, 5, 5)     )]

         A						:     Optional[Any]         =[[17_64, 26_46]]

         A						:     List[Any]         =[[6_83, 10_24]]
         A						:     Union[str, Any]         =processor.post_process_masks(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
         self.assertEqual(masks[0].shape      ,	(1, 3, 17_64, 26_46)     )

         A						:     Any         =processor.post_process_masks(
             SCREAMING_SNAKE_CASE__      ,	torch.tensor(SCREAMING_SNAKE_CASE__     )      ,	torch.tensor(SCREAMING_SNAKE_CASE__     )     )
         self.assertEqual(masks[0].shape      ,	(1, 3, 17_64, 26_46)     )

         # should also work with np
         A						:     str         =[np.ones((1, 3, 5, 5)     )]
         A						:     int         =processor.post_process_masks(SCREAMING_SNAKE_CASE__      ,	np.array(SCREAMING_SNAKE_CASE__     )      ,	np.array(SCREAMING_SNAKE_CASE__     )     )

         self.assertEqual(masks[0].shape      ,	(1, 3, 17_64, 26_46)     )

         A						:     Any         =[[1, 0], [0, 1]]
         with self.assertRaises(SCREAMING_SNAKE_CASE__     ):
                A						:     Any         =processor.post_process_masks(SCREAMING_SNAKE_CASE__      ,	np.array(SCREAMING_SNAKE_CASE__     )      ,	np.array(SCREAMING_SNAKE_CASE__     )     )







@require_vision
@require_tf
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				str:
         A						:     Tuple         =tempfile.mkdtemp()
         A						:     Union[str, Any]         =SamImageProcessor()
         A						:     Union[str, Any]         =SamProcessor(SCREAMING_SNAKE_CASE__     )
         processor.save_pretrained(self.tmpdirname     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	**SCREAMING_SNAKE_CASE__    :					str     )    ->				Union[str, Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname      ,	**SCREAMING_SNAKE_CASE__     ).image_processor







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				List[str]:
         shutil.rmtree(self.tmpdirname     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Tuple:
         A						:     Optional[Any]         =[np.random.randint(2_55      ,	size=(3, 30, 4_00)      ,	dtype=np.uinta     )]

         A						:     Any         =[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__      ,	0      ,	-1     )     ) for x in image_inputs]

         return image_inputs







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				List[str]:
         A						:     Optional[Any]         =SamProcessor(image_processor=self.get_image_processor()     )
         processor.save_pretrained(self.tmpdirname     )

         A						:     Optional[Any]         =self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__      ,	padding_value=1.0     )

         A						:     Dict         =SamProcessor.from_pretrained(self.tmpdirname      ,	do_normalize=SCREAMING_SNAKE_CASE__      ,	padding_value=1.0     )

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				Any:
         A						:     Any         =self.get_image_processor()

         A						:     Any         =SamProcessor(image_processor=SCREAMING_SNAKE_CASE__     )

         A						:     int         =self.prepare_image_inputs()

         A						:     Tuple         =image_processor(SCREAMING_SNAKE_CASE__      ,	return_tensors='np'     )
         A						:     List[Any]         =processor(images=SCREAMING_SNAKE_CASE__      ,	return_tensors='np'     )

         input_feat_extract.pop('original_sizes'     )  # pop original_sizes as it is popped in the processor
         input_feat_extract.pop('reshaped_input_sizes'     )  # pop reshaped_input_sizes as it is popped in the processor

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







  @require_tf
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Tuple:
         A						:     int         =self.get_image_processor()

         A						:     Any         =SamProcessor(image_processor=SCREAMING_SNAKE_CASE__     )
         A						:     Optional[int]         =[tf.ones((1, 3, 5, 5)     )]

         A						:     Tuple         =[[17_64, 26_46]]

         A						:     Union[str, Any]         =[[6_83, 10_24]]
         A						:     int         =processor.post_process_masks(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	return_tensors='tf'     )
         self.assertEqual(masks[0].shape      ,	(1, 3, 17_64, 26_46)     )

         A						:     List[Any]         =processor.post_process_masks(
             SCREAMING_SNAKE_CASE__      ,	tf.convert_to_tensor(SCREAMING_SNAKE_CASE__     )      ,	tf.convert_to_tensor(SCREAMING_SNAKE_CASE__     )      ,	return_tensors='tf'      ,	)
         self.assertEqual(masks[0].shape      ,	(1, 3, 17_64, 26_46)     )

         # should also work with np
         A						:     Any         =[np.ones((1, 3, 5, 5)     )]
         A						:     Optional[Any]         =processor.post_process_masks(
             SCREAMING_SNAKE_CASE__      ,	np.array(SCREAMING_SNAKE_CASE__     )      ,	np.array(SCREAMING_SNAKE_CASE__     )      ,	return_tensors='tf'     )

         self.assertEqual(masks[0].shape      ,	(1, 3, 17_64, 26_46)     )

         A						:     Any         =[[1, 0], [0, 1]]
         with self.assertRaises(tf.errors.InvalidArgumentError     ):
                A						:     List[str]         =processor.post_process_masks(
                    SCREAMING_SNAKE_CASE__      ,	np.array(SCREAMING_SNAKE_CASE__     )      ,	np.array(SCREAMING_SNAKE_CASE__     )      ,	return_tensors='tf'     )







@require_vision
@require_torchvision
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Union[str, Any]:
         A						:     Optional[int]         =tempfile.mkdtemp()
         A						:     Union[str, Any]         =SamImageProcessor()
         A						:     Dict         =SamProcessor(SCREAMING_SNAKE_CASE__     )
         processor.save_pretrained(self.tmpdirname     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	**SCREAMING_SNAKE_CASE__    :					List[str]     )    ->				Any:
         return AutoProcessor.from_pretrained(self.tmpdirname      ,	**SCREAMING_SNAKE_CASE__     ).image_processor







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				Any:
         shutil.rmtree(self.tmpdirname     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				Tuple:
         A						:     Any         =[np.random.randint(2_55      ,	size=(3, 30, 4_00)      ,	dtype=np.uinta     )]

         A						:     Tuple         =[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__      ,	0      ,	-1     )     ) for x in image_inputs]

         return image_inputs







  @is_pt_tf_cross_test
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				List[str]:
         A						:     Optional[Any]         =self.get_image_processor()

         A						:     Dict         =SamProcessor(image_processor=SCREAMING_SNAKE_CASE__     )
         A						:     Optional[int]         =np.random.randint(0      ,	2      ,	size=(1, 3, 5, 5)     ).astype(np.floataa     )
         A						:     Optional[int]         =[tf.convert_to_tensor(SCREAMING_SNAKE_CASE__     )]
         A						:     Union[str, Any]         =[torch.tensor(SCREAMING_SNAKE_CASE__     )]

         A						:     int         =[[17_64, 26_46]]

         A						:     int         =[[6_83, 10_24]]
         A						:     Dict         =processor.post_process_masks(
             SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	return_tensors='tf'     )
         A						:     Optional[Any]         =processor.post_process_masks(
             SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     )

         self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()     )     )







  @is_pt_tf_cross_test
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Any:
         A						:     Union[str, Any]         =self.get_image_processor()

         A						:     int         =SamProcessor(image_processor=SCREAMING_SNAKE_CASE__     )

         A						:     int         =self.prepare_image_inputs()

         A						:     List[Any]         =image_processor(SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     )['pixel_values'].numpy()
         A						:     Tuple         =processor(images=SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     )['pixel_values'].numpy()

         A						:     Optional[int]         =image_processor(SCREAMING_SNAKE_CASE__      ,	return_tensors='tf'     )['pixel_values'].numpy()
         A						:     Dict         =processor(images=SCREAMING_SNAKE_CASE__      ,	return_tensors='tf'     )['pixel_values'].numpy()

         self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )     )
         self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )     )
         self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )     )






