




import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class  lowerCamelCase     (		unittest.TestCase    ):






			'''simple docstring'''





			@property
			def 				UpperCAmelCase__							(			self      : Union[str, Any]	)    -> Dict:
								__magic_name__				:						List[Any]     				=				1
								__magic_name__				:						List[Any]     				=				3
								__magic_name__				:						Optional[int]     				=				8
								__magic_name__				:						Optional[Any]     				=				8

								__magic_name__				:						List[str]     				=				torch.rand((batch_size, num_channels, height, width)	)

								return sample

			def 				UpperCAmelCase__							(			self      : str							,    lowerCamelCase_      : Tuple	)    -> int:
								if isinstance(image[0]							,    PIL.Image.Image	):
													return np.stack([np.array(lowerCamelCase_	) for i in image]							,    axis=0	)
								elif isinstance(lowerCamelCase_							,    torch.Tensor	):
													return image.cpu().numpy().transpose(0							,    2							,    3							,    1	)
								return image

			def 				UpperCAmelCase__							(			self      : Union[str, Any]	)    -> List[str]:
								__magic_name__				:						str     				=				VaeImageProcessor(do_resize=lowerCamelCase_							,    do_normalize=lowerCamelCase_	)

								__magic_name__				:						Optional[Any]     				=				self.dummy_sample
								__magic_name__				:						Optional[int]     				=				self.to_np(lowerCamelCase_	)

								for output_type in ["pt", "np", "pil"]:
													__magic_name__				:						Tuple     				=				image_processor.postprocess(
													    image_processor.preprocess(lowerCamelCase_	)							,    output_type=lowerCamelCase_							,    )
													__magic_name__				:						Any     				=				self.to_np(lowerCamelCase_	)
													__magic_name__				:						Tuple     				=				(input_np * 255).round() if output_type == '''pil''' else input_np
													assert (
													    np.abs(in_np - out_np	).max() < 1E-6
													), F'''decoded output does not match input for output_type {output_type}'''

			def 				UpperCAmelCase__							(			self      : Tuple	)    -> List[Any]:
								__magic_name__				:						Optional[Any]     				=				VaeImageProcessor(do_resize=lowerCamelCase_							,    do_normalize=lowerCamelCase_	)
								__magic_name__				:						str     				=				self.dummy_sample.cpu().numpy().transpose(0							,    2							,    3							,    1	)

								for output_type in ["pt", "np", "pil"]:
													__magic_name__				:						List[Any]     				=				image_processor.postprocess(image_processor.preprocess(lowerCamelCase_	)							,    output_type=lowerCamelCase_	)

													__magic_name__				:						List[Any]     				=				self.to_np(lowerCamelCase_	)
													__magic_name__				:						List[Any]     				=				(input_np * 255).round() if output_type == '''pil''' else input_np
													assert (
													    np.abs(in_np - out_np	).max() < 1E-6
													), F'''decoded output does not match input for output_type {output_type}'''

			def 				UpperCAmelCase__							(			self      : Optional[Any]	)    -> str:
								__magic_name__				:						Optional[Any]     				=				VaeImageProcessor(do_resize=lowerCamelCase_							,    do_normalize=lowerCamelCase_	)

								__magic_name__				:						Optional[Any]     				=				self.dummy_sample.cpu().numpy().transpose(0							,    2							,    3							,    1	)
								__magic_name__				:						Dict     				=				image_processor.numpy_to_pil(lowerCamelCase_	)

								for output_type in ["pt", "np", "pil"]:
													__magic_name__				:						Any     				=				image_processor.postprocess(image_processor.preprocess(lowerCamelCase_	)							,    output_type=lowerCamelCase_	)
													for i, o in zip(lowerCamelCase_							,    lowerCamelCase_	):
																		__magic_name__				:						List[str]     				=				np.array(lowerCamelCase_	)
																		__magic_name__				:						List[Any]     				=				self.to_np(lowerCamelCase_	) if output_type == '''pil''' else (self.to_np(lowerCamelCase_	) * 255).round()
																		assert (
																		    np.abs(in_np - out_np	).max() < 1E-6
																		), F'''decoded output does not match input for output_type {output_type}'''

			def 				UpperCAmelCase__							(			self      : Union[str, Any]	)    -> Any:
								__magic_name__				:						Optional[int]     				=				VaeImageProcessor(do_resize=lowerCamelCase_							,    do_normalize=lowerCamelCase_	)

								__magic_name__				:						Union[str, Any]     				=				self.dummy_sample
								__magic_name__				:						Union[str, Any]     				=				input_pt_ad.squeeze(0	)

								__magic_name__				:						str     				=				image_processor.postprocess(
								    image_processor.preprocess(lowerCamelCase_	)							,    output_type='''np'''							,    )
								__magic_name__				:						Tuple     				=				image_processor.postprocess(
								    image_processor.preprocess(lowerCamelCase_	)							,    output_type='''np'''							,    )

								__magic_name__				:						List[Any]     				=				self.to_np(self.dummy_sample	)
								__magic_name__				:						Tuple     				=				input_np_ad.squeeze(0	)

								__magic_name__				:						int     				=				image_processor.postprocess(
								    image_processor.preprocess(lowerCamelCase_	)							,    output_type='''np'''							,    )
								__magic_name__				:						Union[str, Any]     				=				image_processor.postprocess(
								    image_processor.preprocess(lowerCamelCase_	)							,    output_type='''np'''							,    )

								assert np.abs(out_pt_ad - out_pt_ad	).max() < 1E-6
								assert np.abs(out_np_ad - out_np_ad	).max() < 1E-6


			def 				UpperCAmelCase__							(			self      : Optional[int]	)    -> str:
								__magic_name__				:						int     				=				VaeImageProcessor(do_resize=lowerCamelCase_							,    do_normalize=lowerCamelCase_	)

								__magic_name__				:						List[Any]     				=				self.dummy_sample
								__magic_name__				:						List[Any]     				=				list(lowerCamelCase_	)

								__magic_name__				:						Dict     				=				image_processor.postprocess(
								    image_processor.preprocess(lowerCamelCase_	)							,    output_type='''np'''							,    )

								__magic_name__				:						str     				=				image_processor.postprocess(
								    image_processor.preprocess(lowerCamelCase_	)							,    output_type='''np'''							,    )

								__magic_name__				:						Dict     				=				self.to_np(self.dummy_sample	)
								list(lowerCamelCase_	)

								__magic_name__				:						int     				=				image_processor.postprocess(
								    image_processor.preprocess(lowerCamelCase_	)							,    output_type='''np'''							,    )

								__magic_name__				:						List[Any]     				=				image_processor.postprocess(
								    image_processor.preprocess(lowerCamelCase_	)							,    output_type='''np'''							,    )

								assert np.abs(out_pt_ad - out_pt_list	).max() < 1E-6
								assert np.abs(out_np_ad - out_np_list	).max() < 1E-6









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


__lowerCamelCase :    Any      						=      get_logger(__name__)







def 		lowercase__ (		__A:						str				,__A:						str					):





					'''simple docstring'''






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







def 		lowercase__ (		__A:						List[str]					):





					'''simple docstring'''






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







def 		lowercase__ (		__A:						List[Dict[str, torch.Tensor]]					):





					'''simple docstring'''






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







def 		lowercase__ (		__A:						str				,__A:						str				,__A:						str					):





					'''simple docstring'''






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

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

					__magic_name__				:						Union[str, Any]     				=				[sanitize(torch.load(__A				,map_location='''cpu'''					)['''state_dict''']					) for x in ckpt_files]
					__magic_name__				:						Dict     				=				average_state_dicts(__A					)

					__magic_name__				,     __magic_name__				:						Union[str, Any]     				=				hf_model.load_state_dict(__A				,strict=__A					)
					assert not missing, F'''missing keys: {missing}'''
					hf_model.save_pretrained(__A					)
					try:
										__magic_name__				:						Optional[Any]     				=				AutoTokenizer.from_pretrained(__A					)
										tok.save_pretrained(__A					)
					except Exception:
										pass
										# dont copy tokenizer if cant


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



