


'''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      UpperCAmelCase__   (      )	->						List[str]:
     __lowerCamelCase  :      int														=						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  :      Union[str, Any]														=						parser.parse_args()
     return args



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any] ,       UpperCAmelCase_       :							List[str] ,       UpperCAmelCase_       :							Tuple				)	->						Optional[Any]:
     if not len(UpperCAmelCase_				) == rows * cols:
          raise ValueError('The specified number of rows and columns are not correct.'				)

     __lowerCamelCase							,			__lowerCamelCase  :      Tuple														=						imgs[0].size
     __lowerCamelCase  :      Any														=						Image.new('RGB' ,       size=(cols * w, rows * h)				)
     __lowerCamelCase							,			__lowerCamelCase  :      Dict														=						grid.size

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



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


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

A__						:   Any				=    StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
A__						:   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""")):
      A__						:   Optional[Any]				=    load(args.pretrained_model_name_or_path, model=unet)
      unet.eval()
      setattr(pipeline, """unet""", unet)
else:
      A__						:   List[Any]				=    unet.to(torch.device("""cuda""", args.cuda_id))
A__						:   str				=    pipeline.to(unet.device)
A__		,   A__						:   int				=    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()))))
A__						:   Union[str, 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)))








"""simple docstring"""


import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test




class  __lowerCamelCase  ( unittest.TestCase			):




				@parameterized.expand([(None,), ('''foo.json''',)]   )
				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  GenerationConfig(
							    do_sample=UpperCAmelCase							,						temperature=0.7							,						length_penalty=1.0							,						bad_words_ids=[[1, 2, 3], [4, 5]]							,						)
							with tempfile.TemporaryDirectory() as tmp_dir:
										config.save_pretrained(UpperCAmelCase							,						config_name=UpperCAmelCase   )
										lowerCamelCase_					=  GenerationConfig.from_pretrained(UpperCAmelCase							,						config_name=UpperCAmelCase   )

							# Checks parameters that were specified
							self.assertEqual(loaded_config.do_sample							,						UpperCAmelCase   )
							self.assertEqual(loaded_config.temperature							,						0.7   )
							self.assertEqual(loaded_config.length_penalty							,						1.0   )
							self.assertEqual(loaded_config.bad_words_ids							,						[[1, 2, 3], [4, 5]]   )

							# Checks parameters that were not specified (defaults)
							self.assertEqual(loaded_config.top_k							,						50   )
							self.assertEqual(loaded_config.max_length							,						20   )
							self.assertEqual(loaded_config.max_time							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AutoConfig.from_pretrained('''gpt2'''   )
							lowerCamelCase_					=  GenerationConfig.from_model_config(UpperCAmelCase   )
							lowerCamelCase_					=  GenerationConfig()

							# The generation config has loaded a few non-default parameters from the model config
							self.assertNotEqual(UpperCAmelCase							,						UpperCAmelCase   )

							# One of those parameters is eos_token_id -- check if it matches
							self.assertNotEqual(generation_config_from_model.eos_token_id							,						default_generation_config.eos_token_id   )
							self.assertEqual(generation_config_from_model.eos_token_id							,						model_config.eos_token_id   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  GenerationConfig()
							lowerCamelCase_					=  {
							    '''max_new_tokens''': 1024,
							    '''foo''': '''bar''',
							}
							lowerCamelCase_					=  copy.deepcopy(UpperCAmelCase   )
							lowerCamelCase_					=  generation_config.update(**UpperCAmelCase   )

							# update_kwargs was not modified (no side effects)
							self.assertEqual(UpperCAmelCase							,						UpperCAmelCase   )

							# update_kwargs was used to update the config on valid attributes
							self.assertEqual(generation_config.max_new_tokens							,						1024   )

							# `.update()` returns a dictionary of unused kwargs
							self.assertEqual(UpperCAmelCase							,						{'''foo''': '''bar'''}   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  GenerationConfig()
							lowerCamelCase_					=  '''bar'''

							with tempfile.TemporaryDirectory('''test-generation-config'''   ) as tmp_dir:
										generation_config.save_pretrained(UpperCAmelCase   )

										lowerCamelCase_					=  GenerationConfig.from_pretrained(UpperCAmelCase   )
							# update_kwargs was used to update the config on valid attributes
							self.assertEqual(new_config.foo							,						'''bar'''   )

							lowerCamelCase_					=  GenerationConfig.from_model_config(UpperCAmelCase   )
							assert not hasattr(UpperCAmelCase							,						'''foo'''   )  # no new kwargs should be initialized if from config





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  GenerationConfig()
							self.assertEqual(default_config.temperature							,						1.0   )
							self.assertEqual(default_config.do_sample							,						UpperCAmelCase   )
							self.assertEqual(default_config.num_beams							,						1   )

							lowerCamelCase_					=  GenerationConfig(
							    do_sample=UpperCAmelCase							,						temperature=0.7							,						length_penalty=1.0							,						bad_words_ids=[[1, 2, 3], [4, 5]]							,						)
							self.assertEqual(config.temperature							,						0.7   )
							self.assertEqual(config.do_sample							,						UpperCAmelCase   )
							self.assertEqual(config.num_beams							,						1   )

							with tempfile.TemporaryDirectory() as tmp_dir:
										config.save_pretrained(UpperCAmelCase   )
										lowerCamelCase_					=  GenerationConfig.from_pretrained(UpperCAmelCase							,						temperature=1.0   )

							self.assertEqual(loaded_config.temperature							,						1.0   )
							self.assertEqual(loaded_config.do_sample							,						UpperCAmelCase   )
							self.assertEqual(loaded_config.num_beams							,						1   )  # default value




@is_staging_test
class  __lowerCamelCase  ( unittest.TestCase			):




				@classmethod
				def        UpperCAmelCase__						(				cls   ):
							lowerCamelCase_					=  TOKEN
							HfFolder.save_token(UpperCAmelCase   )




				@classmethod
				def        UpperCAmelCase__						(				cls   ):
							try:
										delete_repo(token=cls._token							,						repo_id='''test-generation-config'''   )
							except HTTPError:
										pass

							try:
										delete_repo(token=cls._token							,						repo_id='''valid_org/test-generation-config-org'''   )
							except HTTPError:
										pass




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  GenerationConfig(
							    do_sample=UpperCAmelCase							,						temperature=0.7							,						length_penalty=1.0							,						)
							config.push_to_hub('''test-generation-config'''							,						use_auth_token=self._token   )

							lowerCamelCase_					=  GenerationConfig.from_pretrained(f"{USER}/test-generation-config"   )
							for k, v in config.to_dict().items():
										if k != "transformers_version":
													self.assertEqual(UpperCAmelCase							,						getattr(UpperCAmelCase							,						UpperCAmelCase   )   )

        # Reset repo
							delete_repo(token=self._token							,						repo_id='''test-generation-config'''   )

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
										config.save_pretrained(
										    UpperCAmelCase							,						repo_id='''test-generation-config'''							,						push_to_hub=UpperCAmelCase							,						use_auth_token=self._token   )

							lowerCamelCase_					=  GenerationConfig.from_pretrained(f"{USER}/test-generation-config"   )
							for k, v in config.to_dict().items():
										if k != "transformers_version":
													self.assertEqual(UpperCAmelCase							,						getattr(UpperCAmelCase							,						UpperCAmelCase   )   )





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  GenerationConfig(
							    do_sample=UpperCAmelCase							,						temperature=0.7							,						length_penalty=1.0							,						)
							config.push_to_hub('''valid_org/test-generation-config-org'''							,						use_auth_token=self._token   )

							lowerCamelCase_					=  GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''   )
							for k, v in config.to_dict().items():
										if k != "transformers_version":
													self.assertEqual(UpperCAmelCase							,						getattr(UpperCAmelCase							,						UpperCAmelCase   )   )

        # Reset repo
							delete_repo(token=self._token							,						repo_id='''valid_org/test-generation-config-org'''   )

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
										config.save_pretrained(
										    UpperCAmelCase							,						repo_id='''valid_org/test-generation-config-org'''							,						push_to_hub=UpperCAmelCase							,						use_auth_token=self._token   )

							lowerCamelCase_					=  GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''   )
							for k, v in config.to_dict().items():
										if k != "transformers_version":
													self.assertEqual(UpperCAmelCase							,						getattr(UpperCAmelCase							,						UpperCAmelCase   )   )



