

'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







@slow
@require_torch_gpu
class 				A__       (			unittest.TestCase    ):





  def 		A    (						self :      Any					)      ->       Dict:





       '''simple docstring'''






       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





  def 		A    (						self :      List[str]					)      ->       List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE				       =StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'					)
       _SCREAMING_SNAKE_CASE				       =sd_pipe.to(A__					)
       sd_pipe.set_progress_bar_config(disable=A__					)

       sd_pipe.set_scheduler('sample_euler'					)

       _SCREAMING_SNAKE_CASE				       ='''A painting of a squirrel eating a burger'''
       _SCREAMING_SNAKE_CASE				       =torch.manual_seed(0					)
       _SCREAMING_SNAKE_CASE				       =sd_pipe([prompt]				,   generator=A__				,   guidance_scale=9.0				,   num_inference_steps=20				,   output_type='np'					)

       _SCREAMING_SNAKE_CASE				       =output.images

       _SCREAMING_SNAKE_CASE				       =image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       _SCREAMING_SNAKE_CASE				       =np.array([0.04_47, 0.04_92, 0.04_68, 0.04_08, 0.03_83, 0.04_08, 0.03_54, 0.03_80, 0.03_39]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2





  def 		A    (						self :      Dict					)      ->       Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE				       =StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'					)
       _SCREAMING_SNAKE_CASE				       =sd_pipe.to(A__					)
       sd_pipe.set_progress_bar_config(disable=A__					)

       sd_pipe.set_scheduler('sample_euler'					)

       _SCREAMING_SNAKE_CASE				       ='''A painting of a squirrel eating a burger'''
       _SCREAMING_SNAKE_CASE				       =torch.manual_seed(0					)
       _SCREAMING_SNAKE_CASE				       =sd_pipe([prompt]				,   generator=A__				,   guidance_scale=9.0				,   num_inference_steps=20				,   output_type='np'					)

       _SCREAMING_SNAKE_CASE				       =output.images

       _SCREAMING_SNAKE_CASE				       =image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       _SCREAMING_SNAKE_CASE				       =np.array([0.12_37, 0.13_20, 0.14_38, 0.13_59, 0.13_90, 0.11_32, 0.12_77, 0.11_75, 0.11_12]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 5e-1





  def 		A    (						self :      int					)      ->       Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE				       =StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'					)
       _SCREAMING_SNAKE_CASE				       =sd_pipe.to(A__					)
       sd_pipe.set_progress_bar_config(disable=A__					)

       sd_pipe.set_scheduler('sample_dpmpp_2m'					)

       _SCREAMING_SNAKE_CASE				       ='''A painting of a squirrel eating a burger'''
       _SCREAMING_SNAKE_CASE				       =torch.manual_seed(0					)
       _SCREAMING_SNAKE_CASE				       =sd_pipe(
           [prompt]				,   generator=A__				,   guidance_scale=7.5				,   num_inference_steps=15				,   output_type='np'				,   use_karras_sigmas=A__				,   )

       _SCREAMING_SNAKE_CASE				       =output.images

       _SCREAMING_SNAKE_CASE				       =image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       _SCREAMING_SNAKE_CASE				       =np.array(
           [0.11_38_16_89, 0.12_11_29_21, 0.1_38_94_57, 0.12_54_96_06, 0.1_24_49_64, 0.10_83_15_17, 0.11_56_28_66, 0.10_86_78_16, 0.10_49_90_48]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2








from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_UpperCamelCase			:	List[str]								=HfArgumentParser(InitializationArguments)
_UpperCamelCase			:	Dict								=parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_UpperCamelCase			:	List[Any]								=AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_UpperCamelCase			:	str								={
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
_UpperCamelCase			:	int								=AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_UpperCamelCase			:	List[str]								=AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)



