





'''simple docstring'''

import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger

class    UpperCAmelCase			(    UpperCAmelCase_	):




		def __init__(self   :						List[str]				,      A__   :						Optional[int]				,      A__   :						List[str]					)       ->			List[Any]:
						lowercase    =			params

						lowercase    =			np.array(_snake_case					)
						lowercase    =			np.array([len(_snake_case					) for t in data]					)

						self.check()
						self.remove_long_sequences()
						self.remove_empty_sequences()
						self.remove_unknown_sequences()
						self.check()
						self.print_statistics()




		def __getitem__(self   :						str				,      A__   :						str					)       ->			Union[str, Any]:
						return (self.token_ids[index], self.lengths[index])




		def __len__(self   :						Union[str, Any]					)       ->			str:
						return len(self.lengths					)




		def 					UpperCAmelCase__ (self   :						Union[str, Any]					)       ->			List[Any]:
						assert len(self.token_ids					) == len(self.lengths					)
						assert all(self.lengths[i] == len(self.token_ids[i]					) for i in range(len(self.lengths					)					)					)




		def 					UpperCAmelCase__ (self   :						str					)       ->			Optional[int]:
						lowercase    =			self.params.max_model_input_size
						lowercase    =			self.lengths > max_len
						logger.info(f'Splitting {sum(_snake_case					)} too long sequences.'					)

						def divide_chunks(A__   :						List[Any]				,      A__   :						Optional[int]					):
										return [l[i : i + n] for i in range(0				,      len(_snake_case					)				,      _snake_case					)]

						lowercase    =			[]
						lowercase    =			[]
						if self.params.mlm:
										lowercase ,				lowercase    =			self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
						else:
										lowercase ,				lowercase    =			self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]

						for seq_, len_ in zip(self.token_ids				,      self.lengths					):
										assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
										if len_ <= max_len:
														new_tok_ids.append(seq_					)
														new_lengths.append(len_					)
										else:
														lowercase    =			[]
														for sub_s in divide_chunks(seq_				,      max_len - 2					):
																		if sub_s[0] != cls_id:
																						lowercase    =			np.insert(_snake_case				,      0				,      _snake_case					)
																		if sub_s[-1] != sep_id:
																						lowercase    =			np.insert(_snake_case				,      len(_snake_case					)				,      _snake_case					)
																		assert len(_snake_case					) <= max_len
																		assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																		sub_seqs.append(_snake_case					)

														new_tok_ids.extend(_snake_case					)
														new_lengths.extend([len(_snake_case					) for l in sub_seqs]					)

						lowercase    =			np.array(_snake_case					)
						lowercase    =			np.array(_snake_case					)




		def 					UpperCAmelCase__ (self   :						List[Any]					)       ->			Union[str, Any]:
						lowercase    =			len(self					)
						lowercase    =			self.lengths > 1_1
						lowercase    =			self.token_ids[indices]
						lowercase    =			self.lengths[indices]
						lowercase    =			len(self					)
						logger.info(f'Remove {init_size - new_size} too short (<=11 tokens) sequences.'					)




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


						if "unk_token" not in self.params.special_tok_ids:
										return
						else:
										lowercase    =			self.params.special_tok_ids["unk_token"]
						lowercase    =			len(self					)
						lowercase    =			np.array([np.count_nonzero(a == unk_token_id					) for a in self.token_ids]					)
						lowercase    =			(unk_occs / self.lengths) < 0.5
						lowercase    =			self.token_ids[indices]
						lowercase    =			self.lengths[indices]
						lowercase    =			len(self					)
						logger.info(f'Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'					)




		def 					UpperCAmelCase__ (self   :						str					)       ->			Tuple:


						if not self.params.is_master:
										return
						logger.info(f'{len(self					)} sequences'					)
						# data_len = sum(self.lengths)
						# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
						# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

						# unk_idx = self.params.special_tok_ids['unk_token']
						# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
						# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')





		def 					UpperCAmelCase__ (self   :						Optional[int]				,      A__   :						List[str]					)       ->			Dict:
						lowercase    =			[t[0] for t in batch]
						lowercase    =			[t[1] for t in batch]
						assert len(_snake_case					) == len(_snake_case					)

						# Max for paddings
						lowercase    =			max(_snake_case					)

						# Pad token ids
						if self.params.mlm:
										lowercase    =			self.params.special_tok_ids["pad_token"]
						else:
										lowercase    =			self.params.special_tok_ids["unk_token"]
						lowercase    =			[list(t.astype(_snake_case					)					) + [pad_idx] * (max_seq_len_ - len(_snake_case					)) for t in token_ids]
						assert len(tk_					) == len(_snake_case					)
						assert all(len(_snake_case					) == max_seq_len_ for t in tk_					)

						lowercase    =			torch.tensor(tk_					)  # (bs, max_seq_len_)
						lowercase    =			torch.tensor(_snake_case					)  # (bs)
						return tk_t, lg_t









import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			A__	  =     OmegaConf.load(__UpperCamelCase       )
			A__	  =     torch.load(__UpperCamelCase			,						map_location='cpu'       )['model']
			A__	  =     list(state_dict.keys()       )

			# extract state_dict for VQVAE
			A__	  =     {}
			A__	  =     'first_stage_model.'
			for key in keys:
						if key.startswith(__UpperCamelCase       ):
									A__	  =     state_dict[key]

    # extract state_dict for UNetLDM
			A__	  =     {}
			A__	  =     'model.diffusion_model.'
			for key in keys:
						if key.startswith(__UpperCamelCase       ):
									A__	  =     state_dict[key]

			A__	  =     config.model.params.first_stage_config.params
			A__	  =     config.model.params.unet_config.params

			A__	  =     VQModel(**__UpperCamelCase       ).eval()
			vqvae.load_state_dict(__UpperCamelCase       )

			A__	  =     UNetLDMModel(**__UpperCamelCase       ).eval()
			unet.load_state_dict(__UpperCamelCase       )

			A__	  =     DDIMScheduler(
			    timesteps=config.model.params.timesteps			,						beta_schedule='scaled_linear'			,						beta_start=config.model.params.linear_start			,						beta_end=config.model.params.linear_end			,						clip_sample=__UpperCamelCase			,						)

			A__	  =     LDMPipeline(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			pipeline.save_pretrained(__UpperCamelCase       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   argparse.ArgumentParser()
							parser.add_argument('''--checkpoint_path''', type=str, required=True)
							parser.add_argument('''--config_path''', type=str, required=True)
							parser.add_argument('''--output_path''', type=str, required=True)
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()

							convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)




