

'''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  (					):






 """simple docstring"""



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


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



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

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

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


def 	_A  (					A__  ,       A__="robotic cat with wings"  ,       A__=7.5  ,       A__=50  ,       A__=1  ,       A__=42  ,       ):






 """simple docstring"""



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




'''simple docstring'''






import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  def __init__(   self     :  Optional[Any]      ,lowercase__     :  int      ,lowercase__     :  List[str]=None      ,lowercase__     :  Any=True      ,lowercase__     :  Union[str, Any]=None      ,**lowercase__     :  Dict    ):
   __lowercase													=				parent
   __lowercase													=				config_class
   __lowercase													=				has_text_modality
   __lowercase													=				kwargs
   __lowercase													=				common_properties






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.config_class(**self.inputs_dict    )
   __lowercase													=				(
       ['''hidden_size''', '''num_attention_heads''', '''num_hidden_layers''']
       if self.common_properties is None
       else self.common_properties
   )

   # Add common fields for text models
   if self.has_text_modality:
    common_properties.extend(['''vocab_size''']    )

   # Test that config has the common properties as getters
   for prop in common_properties:
    self.parent.assertTrue(hasattr(lowercase__      ,lowercase__    )      ,msg=F"`{prop}` does not exist"    )

   # Test that config has the common properties as setter
   for idx, name in enumerate(lowercase__    ):
    try:
     setattr(lowercase__      ,lowercase__      ,lowercase__    )
     self.parent.assertEqual(
         getattr(lowercase__      ,lowercase__    )      ,lowercase__      ,msg=F"`{name} value {idx} expected, but was {getattr(lowercase__      ,lowercase__    )}"    )
    except NotImplementedError:
     # Some models might not be able to implement setters for common_properties
     # In that case, a NotImplementedError is raised
     pass

        # Test if config class can be called with Config(prop_name=..)
   for idx, name in enumerate(lowercase__    ):
    try:
     __lowercase													=				self.config_class(**{name: idx}    )
     self.parent.assertEqual(
         getattr(lowercase__      ,lowercase__    )      ,lowercase__      ,msg=F"`{name} value {idx} expected, but was {getattr(lowercase__      ,lowercase__    )}"    )
    except NotImplementedError:
     # Some models might not be able to implement setters for common_properties
     # In that case, a NotImplementedError is raised
     pass






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				self.config_class(**self.inputs_dict    )
   __lowercase													=				json.loads(config.to_json_string()    )
   for key, value in self.inputs_dict.items():
    self.parent.assertEqual(obj[key]      ,lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				self.config_class(**self.inputs_dict    )

   with tempfile.TemporaryDirectory() as tmpdirname:
    __lowercase													=				os.path.join(lowercase__      ,'''config.json'''    )
    config_first.to_json_file(lowercase__    )
    __lowercase													=				self.config_class.from_json_file(lowercase__    )

   self.parent.assertEqual(config_second.to_dict()      ,config_first.to_dict()    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   __lowercase													=				self.config_class(**self.inputs_dict    )

   with tempfile.TemporaryDirectory() as tmpdirname:
    config_first.save_pretrained(lowercase__    )
    __lowercase													=				self.config_class.from_pretrained(lowercase__    )

   self.parent.assertEqual(config_second.to_dict()      ,config_first.to_dict()    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.config_class(**self.inputs_dict    )

   __lowercase													=				'''test'''
   with tempfile.TemporaryDirectory() as tmpdirname:
    __lowercase													=				os.path.join(lowercase__      ,lowercase__    )
    config_first.save_pretrained(lowercase__    )
    __lowercase													=				self.config_class.from_pretrained(lowercase__      ,subfolder=lowercase__    )

   self.parent.assertEqual(config_second.to_dict()      ,config_first.to_dict()    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   __lowercase													=				self.config_class(**self.inputs_dict      ,num_labels=5    )
   self.parent.assertEqual(len(config.idalabel    )      ,5    )
   self.parent.assertEqual(len(config.labelaid    )      ,5    )

   __lowercase													=				3
   self.parent.assertEqual(len(config.idalabel    )      ,3    )
   self.parent.assertEqual(len(config.labelaid    )      ,3    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   if self.config_class.is_composition:
    return
   __lowercase													=				self.config_class()
   self.parent.assertIsNotNone(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				copy.deepcopy(lowercase__    )
   __lowercase													=				self.config_class(**lowercase__    )
   __lowercase													=				[]
   for key, value in config_common_kwargs.items():
    if key == "torch_dtype":
     if not is_torch_available():
      continue
     else:
      import torch

      if config.torch_dtype != torch.floataa:
       wrong_values.append(('''torch_dtype''', config.torch_dtype, torch.floataa)    )
    elif getattr(lowercase__      ,lowercase__    ) != value:
     wrong_values.append((key, getattr(lowercase__      ,lowercase__    ), value)    )

   if len(lowercase__    ) > 0:
    __lowercase													=				'''\n'''.join([F"- {v[0]}: got {v[1]} instead of {v[2]}" for v in wrong_values]    )
    raise ValueError(F"The following keys were not properly set in the config:\n{errors}"    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   self.create_and_test_config_common_properties()
   self.create_and_test_config_to_json_string()
   self.create_and_test_config_to_json_file()
   self.create_and_test_config_from_and_save_pretrained()
   self.create_and_test_config_from_and_save_pretrained_subfolder()
   self.create_and_test_config_with_num_labels()
   self.check_config_can_be_init_without_params()
   self.check_config_arguments_init()

