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

from utils import logger





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				def __init__(						self		,     lowercase		,     lowercase ):



						"""simple docstring"""





						A_		:    int         =				params

						A_		:    Any         =				np.array(lowercase )
						A_		:    str         =				np.array([len(lowercase ) 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		,     lowercase ):



						"""simple docstring"""





						return (self.token_ids[index], self.lengths[index])
				def __len__(						self ):



						"""simple docstring"""





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



						"""simple docstring"""





						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 ):



						"""simple docstring"""





						A_		:    Any         =				self.params.max_model_input_size
						A_		:    Tuple         =				self.lengths > max_len
						logger.info(F'''Splitting {sum(lowercase )} too long sequences.''' )

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

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

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

						A_		:    List[str]         =				np.array(lowercase )
						A_		:    int         =				np.array(lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Any         =				len(self )
						A_		:    Tuple         =				self.lengths > 1_1
						A_		:    int         =				self.token_ids[indices]
						A_		:    Any         =				self.lengths[indices]
						A_		:    List[str]         =				len(self )
						logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.''' )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





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



						"""simple docstring"""





						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		,     lowercase ):



						"""simple docstring"""





						A_		:    Any         =				[t[0] for t in batch]
						A_		:    Optional[int]         =				[t[1] for t in batch]
						assert len(lowercase ) == len(lowercase )

						# Max for paddings
						A_		:    Any         =				max(lowercase )

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

						A_		:    Any         =				torch.tensor(tk_ )  # (bs, max_seq_len_)
						A_		:    Any         =				torch.tensor(lowercase )  # (bs)
						return tk_t, lg_t







import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    int         =				UNetaDModel(
						    block_out_channels=(3_2, 6_4)		,     layers_per_block=2		,     sample_size=3_2		,     in_channels=3		,     out_channels=3		,     down_block_types=('DownBlock2D', 'AttnDownBlock2D')		,     up_block_types=('AttnUpBlock2D', 'UpBlock2D')		,     )
						return model
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    Optional[Any]         =				VQModel(
						    block_out_channels=[3_2, 6_4]		,     in_channels=3		,     out_channels=3		,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,     latent_channels=3		,     )
						return model
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    List[Any]         =				CLIPTextConfig(
						    bos_token_id=0		,     eos_token_id=2		,     hidden_size=3_2		,     intermediate_size=3_7		,     layer_norm_eps=1E-05		,     num_attention_heads=4		,     num_hidden_layers=5		,     pad_token_id=1		,     vocab_size=1_0_0_0		,     )
						return CLIPTextModel(lowercase )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				self.dummy_uncond_unet
						A_		:    List[Any]         =				DDIMScheduler()
						A_		:    Any         =				self.dummy_vq_model

						A_		:    int         =				LDMPipeline(unet=lowercase		,     vqvae=lowercase		,     scheduler=lowercase )
						ldm.to(lowercase )
						ldm.set_progress_bar_config(disable=lowercase )

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    Dict         =				ldm(generator=lowercase		,     num_inference_steps=2		,     output_type='numpy' ).images

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    List[str]         =				ldm(generator=lowercase		,     num_inference_steps=2		,     output_type='numpy'		,     return_dict=lowercase )[0]

						A_		:    Union[str, Any]         =				image[0, -3:, -3:, -1]
						A_		:    int         =				image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)
						A_		:    List[str]         =				np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
						A_		:    str         =				1E-2 if torch_device != 'mps' else 3E-2

						assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance





@slow
@require_torch
class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
						ldm.to(lowercase )
						ldm.set_progress_bar_config(disable=lowercase )

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    List[str]         =				ldm(generator=lowercase		,     num_inference_steps=5		,     output_type='numpy' ).images

						A_		:    List[Any]         =				image[0, -3:, -3:, -1]

						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						A_		:    Tuple         =				np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
						A_		:    Dict         =				1E-2 if torch_device != 'mps' else 3E-2

						assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance






