
"""simple docstring"""




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

from utils import logger



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   params

											SCREAMING_SNAKE_CASE__						:       List[str]           =   np.array(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   np.array([len(SCREAMING_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   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											return (self.token_ids[index], self.lengths[index])



				def __len__(self    )					->		int:



											"""simple docstring"""




											return len(self.lengths    )



				def 	__magic_name__    (self    )					->		str:



											"""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 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.params.max_model_input_size
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.lengths > max_len
											logger.info(F'''Splitting {sum(SCREAMING_SNAKE_CASE__    )} too long sequences.'''    )

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

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

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

											SCREAMING_SNAKE_CASE__						:       Dict           =   np.array(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       int           =   np.array(SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   len(self    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.lengths > 11
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.token_ids[indices]
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.lengths[indices]
											SCREAMING_SNAKE_CASE__						:       str           =   len(self    )
											logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''    )



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




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



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [t[0] for t in batch]
											SCREAMING_SNAKE_CASE__						:       Tuple           =   [t[1] for t in batch]
											assert len(SCREAMING_SNAKE_CASE__    ) == len(SCREAMING_SNAKE_CASE__    )

											# Max for paddings
											SCREAMING_SNAKE_CASE__						:       int           =   max(SCREAMING_SNAKE_CASE__    )

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

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





"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class 				snake_case       ( __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = ShapEImgaImgPipeline
					snake_case__				    = ["image"]
					snake_case__				    = ["image"]
					snake_case__				    = [
					    "num_images_per_prompt",
					    "num_inference_steps",
					    "generator",
					    "latents",
					    "guidance_scale",
					    "frame_size",
					    "output_type",
					    "return_dict",
					]
					snake_case__				    = False







					@property
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									return 32







					@property
					def 		__lowerCAmelCase						(				self   :	str			):
									return 32







					@property
					def 		__lowerCAmelCase						(				self   :	int			):
									return self.time_input_dim * 4







					@property
					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									return 8







					@property
					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									torch.manual_seed(0			)
									UpperCAmelCase__             =							CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size    ,image_size=64    ,projection_dim=self.text_embedder_hidden_size    ,intermediate_size=37    ,num_attention_heads=4    ,num_channels=3    ,num_hidden_layers=5    ,patch_size=1    ,)

									UpperCAmelCase__             =							CLIPVisionModel(lowerCamelCase__			)
									return model







					@property
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							CLIPImageProcessor(
									    crop_size=224    ,do_center_crop=lowerCamelCase__    ,do_normalize=lowerCamelCase__    ,do_resize=lowerCamelCase__    ,image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]    ,image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]    ,resample=3    ,size=224    ,)

									return image_processor







					@property
					def 		__lowerCAmelCase						(				self   :	str			):
									torch.manual_seed(0			)

									UpperCAmelCase__             =							{
									    'num_attention_heads': 2,
									    'attention_head_dim': 16,
									    'embedding_dim': self.time_input_dim,
									    'num_embeddings': 32,
									    'embedding_proj_dim': self.text_embedder_hidden_size,
									    'time_embed_dim': self.time_embed_dim,
									    'num_layers': 1,
									    'clip_embed_dim': self.time_input_dim * 2,
									    'additional_embeddings': 0,
									    'time_embed_act_fn': 'gelu',
									    'norm_in_type': 'layer',
									    'embedding_proj_norm_type': 'layer',
									    'encoder_hid_proj_type': None,
									    'added_emb_type': None,
									}

									UpperCAmelCase__             =							PriorTransformer(**lowerCamelCase__			)
									return model







					@property
					def 		__lowerCAmelCase						(				self   :	Tuple			):
									torch.manual_seed(0			)

									UpperCAmelCase__             =							{
									    'param_shapes': (
									        (self.renderer_dim, 93),
									        (self.renderer_dim, 8),
									        (self.renderer_dim, 8),
									        (self.renderer_dim, 8),
									    ),
									    'd_latent': self.time_input_dim,
									    'd_hidden': self.renderer_dim,
									    'n_output': 12,
									    'background': (
									        0.1,
									        0.1,
									        0.1,
									    ),
									}
									UpperCAmelCase__             =							ShapERenderer(**lowerCamelCase__			)
									return model







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							self.dummy_prior
									UpperCAmelCase__             =							self.dummy_image_encoder
									UpperCAmelCase__             =							self.dummy_image_processor
									UpperCAmelCase__             =							self.dummy_renderer

									UpperCAmelCase__             =							HeunDiscreteScheduler(
									    beta_schedule='exp'    ,num_train_timesteps=1_024    ,prediction_type='sample'    ,use_karras_sigmas=lowerCamelCase__    ,clip_sample=lowerCamelCase__    ,clip_sample_range=1.0    ,)
									UpperCAmelCase__             =							{
									    'prior': prior,
									    'image_encoder': image_encoder,
									    'image_processor': image_processor,
									    'renderer': renderer,
									    'scheduler': scheduler,
									}

									return components







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	str=0			):
									UpperCAmelCase__             =							floats_tensor((1, 3, 64, 64)    ,rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)

									if str(lowerCamelCase__			).startswith('mps'			):
													UpperCAmelCase__             =							torch.manual_seed(lowerCamelCase__			)
									else:
													UpperCAmelCase__             =							torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
									UpperCAmelCase__             =							{
									    'image': input_image,
									    'generator': generator,
									    'num_inference_steps': 1,
									    'frame_size': 32,
									    'output_type': 'np',
									}
									return inputs







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									UpperCAmelCase__             =							'cpu'

									UpperCAmelCase__             =							self.get_dummy_components()

									UpperCAmelCase__             =							self.pipeline_class(**lowerCamelCase__			)
									UpperCAmelCase__             =							pipe.to(lowerCamelCase__			)

									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							pipe(**self.get_dummy_inputs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							output.images[0]
									UpperCAmelCase__             =							image[0, -3:, -3:, -1]

									assert image.shape == (20, 32, 32, 3)

									UpperCAmelCase__             =							np.array(
									    [
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									    ]			)

									assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
									self._test_inference_batch_consistent(batch_sizes=[1, 2]			)







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							torch_device == 'cpu'
									UpperCAmelCase__             =							True
									self._test_inference_batch_single_identical(
									    batch_size=2    ,test_max_difference=lowerCamelCase__    ,relax_max_difference=lowerCamelCase__    ,)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							self.get_dummy_components()
									UpperCAmelCase__             =							self.pipeline_class(**lowerCamelCase__			)
									UpperCAmelCase__             =							pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							1
									UpperCAmelCase__             =							2

									UpperCAmelCase__             =							self.get_dummy_inputs(lowerCamelCase__			)

									for key in inputs.keys():
													if key in self.batch_params:
																	UpperCAmelCase__             =							batch_size * [inputs[key]]

									UpperCAmelCase__             =							pipe(**lowerCamelCase__    ,num_images_per_prompt=lowerCamelCase__			)[0]

									assert images.shape[0] == batch_size * num_images_per_prompt






@slow
@require_torch_gpu
class 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

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







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'			)
									UpperCAmelCase__             =							load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/shap_e/test_shap_e_img2img_out.npy'			)
									UpperCAmelCase__             =							ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'			)
									UpperCAmelCase__             =							pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							torch.Generator(device=lowerCamelCase__			).manual_seed(0			)

									UpperCAmelCase__             =							pipe(
									    lowerCamelCase__    ,generator=lowerCamelCase__    ,guidance_scale=3.0    ,num_inference_steps=64    ,frame_size=64    ,output_type='np'    ,).images[0]

									assert images.shape == (20, 64, 64, 3)

									assert_mean_pixel_difference(lowerCamelCase__    ,lowerCamelCase__			)



