

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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     A__(lowerCAmelCase__,   unittest.TestCase				):
						"""simple docstring"""






						_A  :							str     =       KandinskyVaaImgaImgPipeline
						_A  :							Dict     =       ["""image_embeds""", """negative_image_embeds""", """image"""]
						_A  :							int     =       [
						    """image_embeds""",
						    """negative_image_embeds""",
						    """image""",
						]
						_A  :							str     =       [
						    """generator""",
						    """height""",
						    """width""",
						    """strength""",
						    """guidance_scale""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						_A  :							int     =       False





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





						@property
						def        UpperCamelCase__ (  self  )			->      List[Any]:
									return 32





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





						@property
						def        UpperCamelCase__ (  self  )			->      Optional[Any]:
									return self.time_input_dim * 4





						@property
						def        UpperCamelCase__ (  self  )			->      List[Any]:
									return 100





						@property
						def        UpperCamelCase__ (  self  )			->      Optional[Any]:
									torch.manual_seed(0  )

									a_		:		List[str]      				=      {
									    '''in_channels''': 4,
									    # Out channels is double in channels because predicts mean and variance
									    '''out_channels''': 8,
									    '''addition_embed_type''': '''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''': '''image_proj''',
									    '''cross_attention_dim''': self.cross_attention_dim,
									    '''attention_head_dim''': 4,
									    '''resnet_time_scale_shift''': '''scale_shift''',
									    '''class_embed_type''': None,
									}

									a_		:		Any      				=      UNetaDConditionModel(**lowercase_  )
									return model





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





						def        UpperCamelCase__ (  self  )			->      Optional[int]:
									a_		:		Any      				=      self.dummy_unet
									a_		:		Optional[int]      				=      self.dummy_movq

									a_		:		str      				=      {
									    '''num_train_timesteps''': 1_000,
									    '''beta_schedule''': '''linear''',
									    '''beta_start''': 0.0_0_0_8_5,
									    '''beta_end''': 0.0_1_2,
									    '''clip_sample''': False,
									    '''set_alpha_to_one''': False,
									    '''steps_offset''': 0,
									    '''prediction_type''': '''epsilon''',
									    '''thresholding''': False,
									}

									a_		:		str      				=      DDIMScheduler(**lowercase_  )

									a_		:		Dict      				=      {
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''movq''': movq,
									}

									return components





						def        UpperCamelCase__ (  self   ,	_lowercase   ,	_lowercase=0  )			->      Any:
									a_		:		List[str]      				=      floats_tensor((1, self.text_embedder_hidden_size)   ,	rng=random.Random(lowercase_  )  ).to(lowercase_  )
									a_		:		Optional[int]      				=      floats_tensor((1, self.text_embedder_hidden_size)   ,	rng=random.Random(seed + 1  )  ).to(
									    lowercase_  )
									# create init_image
									a_		:		Union[str, Any]      				=      floats_tensor((1, 3, 64, 64)   ,	rng=random.Random(lowercase_  )  ).to(lowercase_  )
									a_		:		Union[str, Any]      				=      image.cpu().permute(0   ,	2   ,	3   ,	1  )[0]
									a_		:		int      				=      Image.fromarray(np.uinta(lowercase_  )  ).convert("""RGB"""  ).resize((256, 256)  )

									if str(lowercase_  ).startswith("""mps"""  ):
												a_		:		List[str]      				=      torch.manual_seed(lowercase_  )
									else:
												a_		:		Tuple      				=      torch.Generator(device=lowercase_  ).manual_seed(lowercase_  )
									a_		:		Optional[int]      				=      {
									    '''image''': init_image,
									    '''image_embeds''': image_embeds,
									    '''negative_image_embeds''': negative_image_embeds,
									    '''generator''': generator,
									    '''height''': 64,
									    '''width''': 64,
									    '''num_inference_steps''': 10,
									    '''guidance_scale''': 7.0,
									    '''strength''': 0.2,
									    '''output_type''': '''np''',
									}
									return inputs





						def        UpperCamelCase__ (  self  )			->      Any:
									a_		:		Dict      				=      '''cpu'''

									a_		:		str      				=      self.get_dummy_components()

									a_		:		List[Any]      				=      self.pipeline_class(**lowercase_  )
									a_		:		List[Any]      				=      pipe.to(lowercase_  )

									pipe.set_progress_bar_config(disable=lowercase_  )

									a_		:		str      				=      pipe(**self.get_dummy_inputs(lowercase_  )  )
									a_		:		Tuple      				=      output.images

									a_		:		Union[str, Any]      				=      pipe(
									    **self.get_dummy_inputs(lowercase_  )   ,	return_dict=lowercase_   ,	)[0]

									a_		:		str      				=      image[0, -3:, -3:, -1]
									a_		:		List[str]      				=      image_from_tuple[0, -3:, -3:, -1]

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

									a_		:		Dict      				=      np.array(
									    [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3]  )
									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()}'''







@slow
@require_torch_gpu
class     A__(unittest.TestCase				):
						"""simple docstring"""






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





						def        UpperCamelCase__ (  self  )			->      int:
									a_		:		int      				=      load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/kandinskyv22/kandinskyv22_img2img_frog.npy"""  )

									a_		:		Any      				=      load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""  )
									a_		:		Any      				=      '''A red cartoon frog, 4k'''

									a_		:		Any      				=      KandinskyVaaPriorPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-2-prior"""   ,	torch_dtype=torch.floataa  )
									pipe_prior.to(lowercase_  )

									a_		:		Any      				=      KandinskyVaaImgaImgPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-2-decoder"""   ,	torch_dtype=torch.floataa  )
									a_		:		Any      				=      pipeline.to(lowercase_  )

									pipeline.set_progress_bar_config(disable=lowercase_  )

									a_		:		Tuple      				=      torch.Generator(device="""cpu"""  ).manual_seed(0  )
									a_		:		List[Any]      				=      pipe_prior(
									    lowercase_   ,	generator=lowercase_   ,	num_inference_steps=5   ,	negative_prompt=""""""   ,	).to_tuple()

									a_		:		Dict      				=      pipeline(
									    image=lowercase_   ,	image_embeds=lowercase_   ,	negative_image_embeds=lowercase_   ,	generator=lowercase_   ,	num_inference_steps=100   ,	height=768   ,	width=768   ,	strength=0.2   ,	output_type="""np"""   ,	)

									a_		:		Optional[Any]      				=      output.images[0]

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

									assert_mean_pixel_difference(lowercase_   ,	lowercase_  )







"""simple docstring"""

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

from utils import logger




class        _UpperCAmelCase       (			lowerCAmelCase__):

 def __init__(     self		:       Optional[int]  ,     lowercase_		:       str  ,     lowercase_		:       int				):
     snake_case_ :     Dict				=	params

     snake_case_ :     Union[str, Any]				=	np.array(lowercase_				)
     snake_case_ :     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		:       Dict  ,     lowercase_		:       Union[str, Any]				):
     return (self.token_ids[index], self.lengths[index])

 def __len__(     self		:       List[Any]				):
     return len(self.lengths				)

 def 						_snake_case					(     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 						_snake_case					(     self		:       Tuple				):
     snake_case_ :     str				=	self.params.max_model_input_size
     snake_case_ :     Dict				=	self.lengths > max_len
     logger.info(f"Splitting {sum(lowercase_				)} too long sequences."				)

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

     snake_case_ :     Tuple				=	[]
     snake_case_ :     Any				=	[]
     if self.params.mlm:
         snake_case_,	snake_case_ :     Union[str, Any]				=	self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
     else:
         snake_case_,	snake_case_ :     Dict				=	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_ :     Any				=	[]
             for sub_s in divide_chunks(seq_  ,     max_len - 2				):
                 if sub_s[0] != cls_id:
                     snake_case_ :     Dict				=	np.insert(lowercase_  ,     0  ,     lowercase_				)
                 if sub_s[-1] != sep_id:
                     snake_case_ :     Tuple				=	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]				)

     snake_case_ :     List[str]				=	np.array(lowercase_				)
     snake_case_ :     Optional[Any]				=	np.array(lowercase_				)

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

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

 def 						_snake_case					(     self		:       Dict				):
     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 						_snake_case					(     self		:       List[str]  ,     lowercase_		:       Dict				):
     snake_case_ :     Optional[int]				=	[t[0] for t in batch]
     snake_case_ :     str				=	[t[1] for t in batch]
     assert len(lowercase_				) == len(lowercase_				)

     # Max for paddings
     snake_case_ :     str				=	max(lowercase_				)

     # Pad token ids
     if self.params.mlm:
         snake_case_ :     Tuple				=	self.params.special_tok_ids['''pad_token''']
     else:
         snake_case_ :     Dict				=	self.params.special_tok_ids['''unk_token''']
     snake_case_ :     Any				=	[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_				)

     snake_case_ :     str				=	torch.tensor(tk_				)  # (bs, max_seq_len_)
     snake_case_ :     Optional[int]				=	torch.tensor(lowercase_				)  # (bs)
     return tk_t, lg_t





