




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

from utils import logger


class 							__UpperCamelCase			( _UpperCAmelCase  ):





				'''simple docstring'''



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_						):
											lowerCAmelCase   =		params

											lowerCAmelCase   =		np.array(A_						)
											lowerCAmelCase   =		np.array([len(A_						) 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       , UpperCAmelCase_						):
											return (self.token_ids[index], self.lengths[index])



				def __len__( self						):
											return len(self.lengths						)



				def       __snake_case		( self						):
											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       __snake_case		( self						):
											lowerCAmelCase   =		self.params.max_model_input_size
											lowerCAmelCase   =		self.lengths > max_len
											logger.info(F"""Splitting {sum(A_						)} too long sequences."""						)

											def divide_chunks(UpperCAmelCase_       , UpperCAmelCase_						):
																		return [l[i : i + n] for i in range(0       , len(A_						)       , A_						)]

											lowerCAmelCase   =		[]
											lowerCAmelCase   =		[]
											if self.params.mlm:
																		lowerCAmelCase   ,     lowerCAmelCase   =		self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
											else:
																		lowerCAmelCase   ,     lowerCAmelCase   =		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:
																									lowerCAmelCase   =		[]
																									for sub_s in divide_chunks(seq_       , max_len - 2						):
																																if sub_s[0] != cls_id:
																																							lowerCAmelCase   =		np.insert(A_       , 0       , A_						)
																																if sub_s[-1] != sep_id:
																																							lowerCAmelCase   =		np.insert(A_       , len(A_						)       , A_						)
																																assert len(A_						) <= max_len
																																assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																																sub_seqs.append(A_						)

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

											lowerCAmelCase   =		np.array(A_						)
											lowerCAmelCase   =		np.array(A_						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		len(self						)
											lowerCAmelCase   =		self.lengths > 11
											lowerCAmelCase   =		self.token_ids[indices]
											lowerCAmelCase   =		self.lengths[indices]
											lowerCAmelCase   =		len(self						)
											logger.info(F"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""						)



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



				def       __snake_case		( self						):
											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       __snake_case		( self       , UpperCAmelCase_						):
											lowerCAmelCase   =		[t[0] for t in batch]
											lowerCAmelCase   =		[t[1] for t in batch]
											assert len(A_						) == len(A_						)

											# Max for paddings
											lowerCAmelCase   =		max(A_						)

											# Pad token ids
											if self.params.mlm:
																		lowerCAmelCase   =		self.params.special_tok_ids['''pad_token''']
											else:
																		lowerCAmelCase   =		self.params.special_tok_ids['''unk_token''']
											lowerCAmelCase   =		[list(t.astype(A_						)						) + [pad_idx] * (max_seq_len_ - len(A_						)) for t in token_ids]
											assert len(tk_						) == len(A_						)
											assert all(len(A_						) == max_seq_len_ for t in tk_						)

											lowerCAmelCase   =		torch.tensor(tk_						)  # (bs, max_seq_len_)
											lowerCAmelCase   =		torch.tensor(A_						)  # (bs)
											return tk_t, lg_t





import torch

from diffusers import StableDiffusionPipeline


UpperCAmelCase_						="""path-to-your-trained-model"""
UpperCAmelCase_						=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

UpperCAmelCase_						="""A photo of sks dog in a bucket"""
UpperCAmelCase_						=pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")



