



import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__snake_case    :		Any  =False



class 	lowerCamelCase__      (							unittest.TestCase):




		'''simple docstring'''



		pass



@nightly
@require_torch_gpu
class 	lowerCamelCase__      (							unittest.TestCase):




		'''simple docstring'''



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''		,torch_dtype=torch.floataa			)
						pipe.to(__lowerCamelCase			)
						pipe.set_progress_bar_config(disable=__lowerCamelCase			)

						lowerCAmelCase__					:    Optional[Any]												=					load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''			)

						lowerCAmelCase__					:    List[Any]												=					torch.manual_seed(0			)
						lowerCAmelCase__					:    List[str]												=					pipe.dual_guided(
						    prompt='''first prompt'''		,image=__lowerCamelCase		,text_to_image_strength=0.75		,generator=__lowerCamelCase		,guidance_scale=7.5		,num_inference_steps=2		,output_type='''numpy'''		,).images

						with tempfile.TemporaryDirectory() as tmpdirname:
										pipe.save_pretrained(__lowerCamelCase			)
										lowerCAmelCase__					:    Tuple												=					VersatileDiffusionPipeline.from_pretrained(__lowerCamelCase		,torch_dtype=torch.floataa			)
						pipe.to(__lowerCamelCase			)
						pipe.set_progress_bar_config(disable=__lowerCamelCase			)

						lowerCAmelCase__					:    Union[str, Any]												=					generator.manual_seed(0			)
						lowerCAmelCase__					:    Optional[Any]												=					pipe.dual_guided(
						    prompt='''first prompt'''		,image=__lowerCamelCase		,text_to_image_strength=0.75		,generator=__lowerCamelCase		,guidance_scale=7.5		,num_inference_steps=2		,output_type='''numpy'''		,).images

						assert np.abs(image - new_image			).sum() < 1e-5, "Models don't have the same forward pass"



		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''		,torch_dtype=torch.floataa			)
						pipe.to(__lowerCamelCase			)
						pipe.set_progress_bar_config(disable=__lowerCamelCase			)

						lowerCAmelCase__					:    Union[str, Any]												=					'''cyberpunk 2077'''
						lowerCAmelCase__					:    Union[str, Any]												=					load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''			)
						lowerCAmelCase__					:    int												=					torch.manual_seed(0			)
						lowerCAmelCase__					:    int												=					pipe.dual_guided(
						    prompt=__lowerCamelCase		,image=__lowerCamelCase		,text_to_image_strength=0.75		,generator=__lowerCamelCase		,guidance_scale=7.5		,num_inference_steps=50		,output_type='''numpy'''		,).images

						lowerCAmelCase__					:    Dict												=					image[0, 2_53:2_56, 2_53:2_56, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						lowerCAmelCase__					:    Any												=					np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]			)

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

						lowerCAmelCase__					:    Dict												=					'''A painting of a squirrel eating a burger '''
						lowerCAmelCase__					:    int												=					torch.manual_seed(0			)
						lowerCAmelCase__					:    Tuple												=					pipe.text_to_image(
						    prompt=__lowerCamelCase		,generator=__lowerCamelCase		,guidance_scale=7.5		,num_inference_steps=50		,output_type='''numpy'''			).images

						lowerCAmelCase__					:    List[Any]												=					image[0, 2_53:2_56, 2_53:2_56, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						lowerCAmelCase__					:    List[Any]												=					np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]			)

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

						lowerCAmelCase__					:    str												=					pipe.image_variation(__lowerCamelCase		,generator=__lowerCamelCase		,output_type='''numpy'''			).images

						lowerCAmelCase__					:    Optional[Any]												=					image[0, 2_53:2_56, 2_53:2_56, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						lowerCAmelCase__					:    Union[str, Any]												=					np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]			)

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






from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
__snake_case    :		Dict  =HfArgumentParser(InitializationArguments)
__snake_case    :		Tuple  =parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
__snake_case    :		Optional[int]  =AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
__snake_case    :		List[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)
__snake_case    :		List[Any]  =AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
__snake_case    :		int  =AutoModelForCausalLM.from_config(config)

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

