"""simple docstring"""

import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


__lowercase    =				get_logger(__name__)


def    lowerCAmelCase			(__UpperCamelCase							:    str							,					__UpperCamelCase							:    str			):
							"""simple docstring"""


							if text.startswith(__UpperCamelCase			):
														return text[len(__UpperCamelCase			) :]
							return text  # or whatever


def    lowerCAmelCase			(__UpperCamelCase							:    Dict			):
							"""simple docstring"""


							return {remove_prefix(__UpperCamelCase							,					'''model.'''			): v for k, v in sd.items()}


def    lowerCAmelCase			(__UpperCamelCase							:    List[Dict[str, torch.Tensor]]			):
							"""simple docstring"""


							__UpperCamelCase							={}
							for k in state_dicts[0].keys():
														__UpperCamelCase							=[sd[k] for sd in state_dicts]
														__UpperCamelCase							=sum(__UpperCamelCase			) / len(__UpperCamelCase			)
														assert isinstance(__UpperCamelCase							,					torch.Tensor			)
														__UpperCamelCase							=new_t
							return new_sd


def    lowerCAmelCase			(__UpperCamelCase							:    str							,					__UpperCamelCase							:    str							,					__UpperCamelCase							:    str			):
							"""simple docstring"""


							__UpperCamelCase							=AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase			)
							if os.path.isfile(__UpperCamelCase			):
														__UpperCamelCase							=[pl_ckpt_path]
							else:
														assert os.path.isdir(__UpperCamelCase			)
														__UpperCamelCase							=list(Path(__UpperCamelCase			).glob('''*.ckpt'''			)			)
														assert ckpt_files, F"""could not find any ckpt files inside the {pl_ckpt_path} directory"""

							if len(__UpperCamelCase			) > 1:
														logger.info(F"""averaging the weights of {ckpt_files}"""			)

							__UpperCamelCase							=[sanitize(torch.load(__UpperCamelCase							,					map_location='''cpu'''			)['''state_dict''']			) for x in ckpt_files]
							__UpperCamelCase							=average_state_dicts(__UpperCamelCase			)

							__UpperCamelCase	,      __UpperCamelCase							=hf_model.load_state_dict(__UpperCamelCase							,					strict=__UpperCamelCase			)
							assert not missing, F"""missing keys: {missing}"""
							hf_model.save_pretrained(__UpperCamelCase			)
							try:
														__UpperCamelCase							=AutoTokenizer.from_pretrained(__UpperCamelCase			)
														tok.save_pretrained(__UpperCamelCase			)
							except Exception:
														pass
														# dont copy tokenizer if cant


if __name__ == "__main__":
				fire.Fire(convert_pl_to_hf)




"""simple docstring"""

import gc
import tempfile
import unittest

import numpy as np
import torch

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


__lowercase    =				False






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



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    List[str]				)				->				List[Any]:
													'''simple docstring'''



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





						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
													# remove text_unet
													pipe.remove_unused_weights()
													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							=load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)

													__UpperCamelCase							=torch.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt='''first prompt'''     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=2     , output_type='''numpy'''     , ).images

													with tempfile.TemporaryDirectory() as tmpdirname:
																				pipe.save_pretrained(UpperCamelCase__				)
																				__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCamelCase__				)

													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							=generator.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt='''first prompt'''     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , 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   UpperCAmelCase_ (				self							:    Optional[int]				)				->				Optional[int]:
													'''simple docstring'''



													__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
													pipe.remove_unused_weights()
													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							='''cyberpunk 2077'''
													__UpperCamelCase							=load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
													__UpperCamelCase							=torch.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt=UpperCamelCase__     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=50     , output_type='''numpy'''     , ).images

													__UpperCamelCase							=image[0, 253:256, 253:256, -1]

													assert image.shape == (1, 512, 512, 3)
													__UpperCamelCase							=np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]				)

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



