


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

from utils import logger





class       A    (   A_ ):


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

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


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


							def 			_A       (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 			_A       (self					):
									__lowercase=			self.params.max_model_input_size
									__lowercase=			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					)]

									__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(lowerCAmelCase      ,				0      ,				lowerCAmelCase					)
															if sub_s[-1] != sep_id:
																	__lowercase=			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]					)

									__lowercase=			np.array(lowerCAmelCase					)
									__lowercase=			np.array(lowerCAmelCase					)


							def 			_A       (self					):
									__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 			_A       (self					):
									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 			_A       (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 			_A       (self      ,				lowerCAmelCase					):
									__lowercase=			[t[0] for t in batch]
									__lowercase=			[t[1] for t in batch]
									assert len(lowerCAmelCase					) == len(lowerCAmelCase					)

									# Max for paddings
									__lowercase=			max(lowerCAmelCase					)

									# 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(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_					)

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










import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()





class       A    (   A_					,				unittest.TestCase ):
							UpperCamelCase_   :							Any       =PriorTransformer
							UpperCamelCase_   :							List[str]       ='''hidden_states'''


							@property
							def 			_A       (self					):
									__lowercase=			4
									__lowercase=			8
									__lowercase=			7

									__lowercase=			floats_tensor((batch_size, embedding_dim)					).to(lowerCAmelCase					)

									__lowercase=			floats_tensor((batch_size, embedding_dim)					).to(lowerCAmelCase					)
									__lowercase=			floats_tensor((batch_size, num_embeddings, embedding_dim)					).to(lowerCAmelCase					)

									return {
									    "hidden_states": hidden_states,
									    "timestep": 2,
									    "proj_embedding": proj_embedding,
									    "encoder_hidden_states": encoder_hidden_states,
									}


							def 			_A       (self      ,				lowerCAmelCase=0					):
									torch.manual_seed(lowerCAmelCase					)
									__lowercase=			4
									__lowercase=			8
									__lowercase=			7

									__lowercase=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)

									__lowercase=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)
									__lowercase=			torch.randn((batch_size, num_embeddings, embedding_dim)					).to(lowerCAmelCase					)

									return {
									    "hidden_states": hidden_states,
									    "timestep": 2,
									    "proj_embedding": proj_embedding,
									    "encoder_hidden_states": encoder_hidden_states,
									}


							@property
							def 			_A       (self					):
									return (4, 8)


							@property
							def 			_A       (self					):
									return (4, 8)


							def 			_A       (self					):
									__lowercase=			{
									    'num_attention_heads': 2,
									    'attention_head_dim': 4,
									    'num_layers': 2,
									    'embedding_dim': 8,
									    'num_embeddings': 7,
									    'additional_embeddings': 4,
									}
									__lowercase=			self.dummy_input
									return init_dict, inputs_dict


							def 			_A       (self					):
									__lowercase,							__lowercase=			PriorTransformer.from_pretrained(
									    'hf-internal-testing/prior-dummy'      ,				output_loading_info=lowerCAmelCase					)
									self.assertIsNotNone(lowerCAmelCase					)
									self.assertEqual(len(loading_info['missing_keys']					)      ,				0					)

									model.to(lowerCAmelCase					)
									__lowercase=			model(**self.dummy_input					)[0]

									assert hidden_states is not None, "Make sure output is not None"


							def 			_A       (self					):
									__lowercase,							__lowercase=			self.prepare_init_args_and_inputs_for_common()

									__lowercase=			self.model_class(**lowerCAmelCase					)
									__lowercase=			inspect.signature(model.forward					)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									__lowercase=			[*signature.parameters.keys()]

									__lowercase=			['hidden_states', 'timestep']
									self.assertListEqual(arg_names[:2]      ,				lowerCAmelCase					)







							def 			_A       (self					):
									__lowercase=			PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'					)
									__lowercase=			model.to(lowerCAmelCase					)

									if hasattr(lowerCAmelCase      ,				'set_default_attn_processor'					):
											model.set_default_attn_processor()

									__lowercase=			self.get_dummy_seed_input()

									with torch.no_grad():
											__lowercase=			model(**lowerCAmelCase					)[0]

									__lowercase=			output[0, :5].flatten().cpu()
									print(lowerCAmelCase					)

									# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
									# the expected output slices are not the same for CPU and GPU.
									__lowercase=			torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]					)
									self.assertTrue(torch_all_close(lowerCAmelCase      ,				lowerCAmelCase      ,				rtol=1E-2					)					)





@slow
class       A    (   unittest.TestCase ):


							def 			_A       (self      ,				lowerCAmelCase=1      ,				lowerCAmelCase=7_6_8      ,				lowerCAmelCase=7_7      ,				lowerCAmelCase=0					):
									torch.manual_seed(lowerCAmelCase					)
									__lowercase=			batch_size
									__lowercase=			embedding_dim
									__lowercase=			num_embeddings

									__lowercase=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)

									__lowercase=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)
									__lowercase=			torch.randn((batch_size, num_embeddings, embedding_dim)					).to(lowerCAmelCase					)

									return {
									    "hidden_states": hidden_states,
									    "timestep": 2,
									    "proj_embedding": proj_embedding,
									    "encoder_hidden_states": encoder_hidden_states,
									}


							def 			_A       (self					):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







							@parameterized.expand(
							    [
							        # fmt: off
							        [1_3, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
							        [3_7, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
							        # fmt: on
							    ]					)
							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									__lowercase=			PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'      ,				subfolder='prior'					)
									model.to(lowerCAmelCase					)
									__lowercase=			self.get_dummy_seed_input(seed=lowerCAmelCase					)

									with torch.no_grad():
											__lowercase=			model(**lowerCAmelCase					)[0]

									assert list(sample.shape					) == [1, 7_6_8]

									__lowercase=			sample[0, :8].flatten().cpu()
									print(lowerCAmelCase					)
									__lowercase=			torch.tensor(lowerCAmelCase					)

									assert torch_all_close(lowerCAmelCase      ,				lowerCAmelCase      ,				atol=1E-3					)






