



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

from utils import logger






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						List[Any]						,  __UpperCAmelCase     :						Dict						,  __UpperCAmelCase     :						Tuple					)      ->			str:
							UpperCAmelCase_=       params

							UpperCAmelCase_=       np.array(__UpperCAmelCase					)
							UpperCAmelCase_=       np.array([len(__UpperCAmelCase					) 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     :						List[Any]						,  __UpperCAmelCase     :						int					)      ->			Optional[int]:
							return (self.token_ids[index], self.lengths[index])




	def __len__(     self     :						str					)      ->			Any:
							return len(self.lengths					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			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   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Optional[int]:
							UpperCAmelCase_=       self.params.max_model_input_size
							UpperCAmelCase_=       self.lengths > max_len
							logger.info(F"""Splitting {sum(__UpperCAmelCase					)} too long sequences."""					)

							def divide_chunks(__UpperCAmelCase     :						Union[str, Any]						,  __UpperCAmelCase     :						Optional[Any]					):
													return [l[i : i + n] for i in range(0						,  len(__UpperCAmelCase					)						,  __UpperCAmelCase					)]

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

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

							UpperCAmelCase_=       np.array(__UpperCAmelCase					)
							UpperCAmelCase_=       np.array(__UpperCAmelCase					)




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




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




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Tuple:
							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   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						Optional[int]					)      ->			List[Any]:
							UpperCAmelCase_=       [t[0] for t in batch]
							UpperCAmelCase_=       [t[1] for t in batch]
							assert len(__UpperCAmelCase					) == len(__UpperCAmelCase					)

							# Max for paddings
							UpperCAmelCase_=       max(__UpperCAmelCase					)

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

							UpperCAmelCase_=       torch.tensor(tk_					)  # (bs, max_seq_len_)
							UpperCAmelCase_=       torch.tensor(__UpperCAmelCase					)  # (bs)
							return tk_t, lg_t









import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 		lowercase						(			snake_case__			,   unittest.TestCase):






	"""simple docstring"""


	a__				: Tuple		       =							KandinskyInpaintPipeline
	a__				: Tuple		       =							["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
	a__				: str		       =							[
	    "prompt",
	    "negative_prompt",
	    "image_embeds",
	    "negative_image_embeds",
	    "image",
	    "mask_image",
	]
	a__				: List[Any]		       =							[
	    "generator",
	    "height",
	    "width",
	    "latents",
	    "guidance_scale",
	    "negative_prompt",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	a__				: Union[str, Any]		       =							False




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Any:
							return 32




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Union[str, Any]:
							return 32




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Optional[Any]:
							return self.time_input_dim




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Tuple:
							return self.time_input_dim * 4




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			List[str]:
							return 100




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			List[str]:
							UpperCAmelCase_=       XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""					)
							return tokenizer




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			List[str]:
							torch.manual_seed(0					)
							UpperCAmelCase_=       MCLIPConfig(
							    numDims=self.cross_attention_dim						,  transformerDimensions=self.text_embedder_hidden_size						,  hidden_size=self.text_embedder_hidden_size						,  intermediate_size=37						,  num_attention_heads=4						,  num_hidden_layers=5						,  vocab_size=1_005						,  )

							UpperCAmelCase_=       MultilingualCLIP(__UpperCAmelCase					)
							UpperCAmelCase_=       text_encoder.eval()

							return text_encoder




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Dict:
							torch.manual_seed(0					)

							UpperCAmelCase_=       {
							    """in_channels""": 9,
							    # Out channels is double in channels because predicts mean and variance
							    """out_channels""": 8,
							    """addition_embed_type""": """text_image""",
							    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
							    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
							    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
							    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
							    """layers_per_block""": 1,
							    """encoder_hid_dim""": self.text_embedder_hidden_size,
							    """encoder_hid_dim_type""": """text_image_proj""",
							    """cross_attention_dim""": self.cross_attention_dim,
							    """attention_head_dim""": 4,
							    """resnet_time_scale_shift""": """scale_shift""",
							    """class_embed_type""": None,
							}

							UpperCAmelCase_=       UNetaDConditionModel(**__UpperCAmelCase					)
							return model




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			str:
							return {
							    "block_out_channels": [32, 64],
							    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 12,
							    "out_channels": 3,
							    "up_block_types": [
							        "AttnUpDecoderBlock2D",
							        "UpDecoderBlock2D",
							    ],
							    "vq_embed_dim": 4,
							}




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Optional[int]:
							torch.manual_seed(0					)
							UpperCAmelCase_=       VQModel(**self.dummy_movq_kwargs					)
							return model




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Optional[int]:
							UpperCAmelCase_=       self.dummy_text_encoder
							UpperCAmelCase_=       self.dummy_tokenizer
							UpperCAmelCase_=       self.dummy_unet
							UpperCAmelCase_=       self.dummy_movq

							UpperCAmelCase_=       DDIMScheduler(
							    num_train_timesteps=1_000						,  beta_schedule="""linear"""						,  beta_start=0.00_085						,  beta_end=0.012						,  clip_sample=__UpperCAmelCase						,  set_alpha_to_one=__UpperCAmelCase						,  steps_offset=1						,  prediction_type="""epsilon"""						,  thresholding=__UpperCAmelCase						,  )

							UpperCAmelCase_=       {
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}

							return components




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						Dict=0					)      ->			Dict:
							UpperCAmelCase_=       floats_tensor((1, self.cross_attention_dim)						,  rng=random.Random(__UpperCAmelCase					)					).to(__UpperCAmelCase					)
							UpperCAmelCase_=       floats_tensor((1, self.cross_attention_dim)						,  rng=random.Random(seed + 1					)					).to(__UpperCAmelCase					)
							# create init_image
							UpperCAmelCase_=       floats_tensor((1, 3, 64, 64)						,  rng=random.Random(__UpperCAmelCase					)					).to(__UpperCAmelCase					)
							UpperCAmelCase_=       image.cpu().permute(0						,  2						,  3						,  1					)[0]
							UpperCAmelCase_=       Image.fromarray(np.uinta(__UpperCAmelCase					)					).convert("""RGB"""					).resize((256, 256)					)
							# create mask
							UpperCAmelCase_=       np.ones((64, 64)						,  dtype=np.floataa					)
							UpperCAmelCase_=       0

							if str(__UpperCAmelCase					).startswith("""mps"""					):
													UpperCAmelCase_=       torch.manual_seed(__UpperCAmelCase					)
							else:
													UpperCAmelCase_=       torch.Generator(device=__UpperCAmelCase					).manual_seed(__UpperCAmelCase					)
							UpperCAmelCase_=       {
							    """prompt""": """horse""",
							    """image""": init_image,
							    """mask_image""": mask,
							    """image_embeds""": image_embeds,
							    """negative_image_embeds""": negative_image_embeds,
							    """generator""": generator,
							    """height""": 64,
							    """width""": 64,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 4.0,
							    """output_type""": """np""",
							}
							return inputs




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			List[str]:
							UpperCAmelCase_=       """cpu"""

							UpperCAmelCase_=       self.get_dummy_components()

							UpperCAmelCase_=       self.pipeline_class(**__UpperCAmelCase					)
							UpperCAmelCase_=       pipe.to(__UpperCAmelCase					)

							pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

							UpperCAmelCase_=       pipe(**self.get_dummy_inputs(__UpperCAmelCase					)					)
							UpperCAmelCase_=       output.images

							UpperCAmelCase_=       pipe(
							    **self.get_dummy_inputs(__UpperCAmelCase					)						,  return_dict=__UpperCAmelCase						,  )[0]

							UpperCAmelCase_=       image[0, -3:, -3:, -1]
							UpperCAmelCase_=       image_from_tuple[0, -3:, -3:, -1]

							print(F"""image.shape {image.shape}"""					)

							assert image.shape == (1, 64, 64, 3)

							UpperCAmelCase_=       np.array(
							    [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]					)

							assert (
							    np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
							), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2
							), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Any:
							super().test_inference_batch_single_identical(expected_max_diff=3E-3					)






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






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Any:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Optional[Any]:
							UpperCAmelCase_=       load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""					)

							UpperCAmelCase_=       load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""					)
							UpperCAmelCase_=       np.ones((768, 768)						,  dtype=np.floataa					)
							UpperCAmelCase_=       0

							UpperCAmelCase_=       """a hat"""

							UpperCAmelCase_=       KandinskyPriorPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-prior"""						,  torch_dtype=torch.floataa					)
							pipe_prior.to(__UpperCAmelCase					)

							UpperCAmelCase_=       KandinskyInpaintPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-inpaint"""						,  torch_dtype=torch.floataa					)
							UpperCAmelCase_=       pipeline.to(__UpperCAmelCase					)
							pipeline.set_progress_bar_config(disable=__UpperCAmelCase					)

							UpperCAmelCase_=       torch.Generator(device="""cpu"""					).manual_seed(0					)
							UpperCAmelCase_,       UpperCAmelCase_=       pipe_prior(
							    __UpperCAmelCase						,  generator=__UpperCAmelCase						,  num_inference_steps=5						,  negative_prompt=""""""						,  ).to_tuple()

							UpperCAmelCase_=       pipeline(
							    __UpperCAmelCase						,  image=__UpperCAmelCase						,  mask_image=__UpperCAmelCase						,  image_embeds=__UpperCAmelCase						,  negative_image_embeds=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  num_inference_steps=100						,  height=768						,  width=768						,  output_type="""np"""						,  )

							UpperCAmelCase_=       output.images[0]

							assert image.shape == (768, 768, 3)

							assert_mean_pixel_difference(__UpperCAmelCase						,  __UpperCAmelCase					)




