





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

from utils import logger
class      UpperCamelCase_						(					_lowerCamelCase   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Union[str, Any]:
										_snake_case													=			params

										_snake_case													=			np.array(lowerCAmelCase_					)
										_snake_case													=			np.array([len(lowerCAmelCase_					) 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			,  lowerCAmelCase_					)						->							Tuple:
										return (self.token_ids[index], self.lengths[index])




					def __len__(						self					)						->							Optional[Any]:
										return len(self.lengths					)




					def 		lowerCAmelCase     (						self					)						->							Tuple:
										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 		lowerCAmelCase     (						self					)						->							Tuple:
										_snake_case													=			self.params.max_model_input_size
										_snake_case													=			self.lengths > max_len
										logger.info(F'''Splitting {sum(lowerCAmelCase_					)} too long sequences.'''					)

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

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

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

										_snake_case													=			np.array(lowerCAmelCase_					)
										_snake_case													=			np.array(lowerCAmelCase_					)




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




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




					def 		lowerCAmelCase     (						self					)						->							List[Any]:
										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 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Tuple:
										_snake_case													=			[t[0] for t in batch]
										_snake_case													=			[t[1] for t in batch]
										assert len(lowerCAmelCase_					) == len(lowerCAmelCase_					)

										# Max for paddings
										_snake_case													=			max(lowerCAmelCase_					)

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

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










import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class      UpperCamelCase_						:




					@property
					def 		lowerCAmelCase     (						self					)						->							int:
										return self.get_dummy_input()




					@property
					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										if self.block_type == "down":
															return (4, 32, 16, 16)
										elif self.block_type == "mid":
															return (4, 32, 32, 32)
										elif self.block_type == "up":
															return (4, 32, 64, 64)

										raise ValueError(F'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'''					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_=True			,  lowerCAmelCase_=False			,  lowerCAmelCase_=False			,  lowerCAmelCase_=False			,  )						->							List[str]:
										_snake_case													=			4
										_snake_case													=			32
										_snake_case													=			(32, 32)

										_snake_case													=			torch.manual_seed(0					)
										_snake_case													=			torch.device(lowerCAmelCase_					)
										_snake_case													=			(batch_size, num_channels) + sizes
										_snake_case													=			randn_tensor(lowerCAmelCase_			,  generator=lowerCAmelCase_			,  device=lowerCAmelCase_					)
										_snake_case													=			{'hidden_states': hidden_states}

										if include_temb:
															_snake_case													=			128
															_snake_case													=			randn_tensor((batch_size, temb_channels)			,  generator=lowerCAmelCase_			,  device=lowerCAmelCase_					)

										if include_res_hidden_states_tuple:
															_snake_case													=			torch.manual_seed(1					)
															_snake_case													=			(randn_tensor(lowerCAmelCase_			,  generator=lowerCAmelCase_			,  device=lowerCAmelCase_					),)

										if include_encoder_hidden_states:
															_snake_case													=			floats_tensor((batch_size, 32, 32)					).to(lowerCAmelCase_					)

										if include_skip_sample:
															_snake_case													=			randn_tensor(((batch_size, 3) + sizes)			,  generator=lowerCAmelCase_			,  device=lowerCAmelCase_					)

										return dummy_input




					def 		lowerCAmelCase     (						self					)						->							Tuple:
										_snake_case													=			{
										    'in_channels': 32,
										    'out_channels': 32,
										    'temb_channels': 128,
										}
										if self.block_type == "up":
															_snake_case													=			32

										if self.block_type == "mid":
															init_dict.pop('out_channels'					)

										_snake_case													=			self.dummy_input
										return init_dict, inputs_dict




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Optional[int]:
										_snake_case							,       _snake_case													=			self.prepare_init_args_and_inputs_for_common()
										_snake_case													=			self.block_class(**lowerCAmelCase_					)
										unet_block.to(lowerCAmelCase_					)
										unet_block.eval()

										with torch.no_grad():
															_snake_case													=			unet_block(**lowerCAmelCase_					)

										if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					):
															_snake_case													=			output[0]

										self.assertEqual(output.shape			,  self.output_shape					)

										_snake_case													=			output[0, -1, -3:, -3:]
										_snake_case													=			torch.tensor(lowerCAmelCase_					).to(lowerCAmelCase_					)
										assert torch_all_close(output_slice.flatten()			,  lowerCAmelCase_			,  atol=5E-3					)






					@unittest.skipIf(torch_device == 'mps'			,  'Training is not supported in mps'					)
					def 		lowerCAmelCase     (						self					)						->							Tuple:
										_snake_case							,       _snake_case													=			self.prepare_init_args_and_inputs_for_common()
										_snake_case													=			self.block_class(**lowerCAmelCase_					)
										model.to(lowerCAmelCase_					)
										model.train()
										_snake_case													=			model(**lowerCAmelCase_					)

										if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					):
															_snake_case													=			output[0]

										_snake_case													=			torch.device(lowerCAmelCase_					)
										_snake_case													=			randn_tensor(output.shape			,  device=lowerCAmelCase_					)
										_snake_case													=			torch.nn.functional.mse_loss(lowerCAmelCase_			,  lowerCAmelCase_					)
										loss.backward()



