






'''simple docstring'''
import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm
  import torch_xla.debug.metrics as met






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):




 def __init__(      self	:							List[str]					,      *__lowercase	:							Any					,      __lowercase	:							Optional[Any]=None					,      __lowercase	:							int=None					,      **__lowercase	:							str      )	->						List[str]:
       super().__init__(*__lowercase					,      **__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=eval_examples
       SCREAMING_SNAKE_CASE__     :				Dict									=post_process_function




 def      __magic_name__	(      self	:							Optional[int]					,      __lowercase	:							Optional[Dataset] = None					,      __lowercase	:							str=None					,      __lowercase	:							Optional[List[str]] = None					,      __lowercase	:							str = "eval"					,      **__lowercase	:							List[Any]					,      )	->						Dict[str, float]:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=gen_kwargs.copy()
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=(
           gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''      ) is not None else self.args.generation_max_length
       )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=(
           gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''      ) is not None else self.args.generation_num_beams
       )
       SCREAMING_SNAKE_CASE__     :				List[str]									=gen_kwargs

       SCREAMING_SNAKE_CASE__     :				Any									=self.eval_dataset if eval_dataset is None else eval_dataset
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.get_eval_dataloader(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.eval_examples if eval_examples is None else eval_examples

       # Temporarily disable metric computation, we will do it in the loop here.
       SCREAMING_SNAKE_CASE__     :				int									=self.compute_metrics
       SCREAMING_SNAKE_CASE__     :				Any									=None
       SCREAMING_SNAKE_CASE__     :				str									=time.time()
       SCREAMING_SNAKE_CASE__     :				List[Any]									=self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
             SCREAMING_SNAKE_CASE__     :				Tuple									=eval_loop(
                 __lowercase					,      description='''Evaluation'''					,      prediction_loss_only=True if compute_metrics is None else None					,      ignore_keys=__lowercase					,      metric_key_prefix=__lowercase					,      )
       finally:
             SCREAMING_SNAKE_CASE__     :				Any									=compute_metrics
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self.args.eval_batch_size * self.args.world_size
       if F"{metric_key_prefix}_jit_compilation_time" in output.metrics:
             start_time += output.metrics[F"{metric_key_prefix}_jit_compilation_time"]
       output.metrics.update(
           speed_metrics(
               __lowercase					,      __lowercase					,      num_samples=output.num_samples					,      num_steps=math.ceil(output.num_samples / total_batch_size      )					,      )      )

       if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
             # Only the main node write the results by default
             SCREAMING_SNAKE_CASE__     :				List[str]									=self.post_process_function(__lowercase					,      __lowercase					,      __lowercase      )
             SCREAMING_SNAKE_CASE__     :				Any									=self.compute_metrics(__lowercase      )

             # Prefix all keys with metric_key_prefix + '_'
             for key in list(metrics.keys()      ):
                   if not key.startswith(F"{metric_key_prefix}_"      ):
                         SCREAMING_SNAKE_CASE__     :				str									=metrics.pop(__lowercase      )

             metrics.update(output.metrics      )
       else:
             SCREAMING_SNAKE_CASE__     :				Any									=output.metrics

       if self.args.should_log:
             # Only the main node log the results by default
             self.log(__lowercase      )

       if self.args.tpu_metrics_debug or self.args.debug:
             # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
             xm.master_print(met.metrics_report()      )

       SCREAMING_SNAKE_CASE__     :				List[str]									=self.callback_handler.on_evaluate(self.args					,      self.state					,      self.control					,      __lowercase      )
       return metrics




 def      __magic_name__	(      self	:							Any					,      __lowercase	:							Any					,      __lowercase	:							List[str]					,      __lowercase	:							Optional[int]=None					,      __lowercase	:							str = "test"					,      **__lowercase	:							List[Any]      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				str									=gen_kwargs.copy()

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.get_test_dataloader(__lowercase      )

       # Temporarily disable metric computation, we will do it in the loop here.
       SCREAMING_SNAKE_CASE__     :				int									=self.compute_metrics
       SCREAMING_SNAKE_CASE__     :				int									=None
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=time.time()
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=eval_loop(
                 __lowercase					,      description='''Prediction'''					,      prediction_loss_only=True if compute_metrics is None else None					,      ignore_keys=__lowercase					,      metric_key_prefix=__lowercase					,      )
       finally:
             SCREAMING_SNAKE_CASE__     :				Tuple									=compute_metrics

       SCREAMING_SNAKE_CASE__     :				str									=self.args.eval_batch_size * self.args.world_size
       if F"{metric_key_prefix}_jit_compilation_time" in output.metrics:
             start_time += output.metrics[F"{metric_key_prefix}_jit_compilation_time"]
       output.metrics.update(
           speed_metrics(
               __lowercase					,      __lowercase					,      num_samples=output.num_samples					,      num_steps=math.ceil(output.num_samples / total_batch_size      )					,      )      )
       if self.post_process_function is None or self.compute_metrics is None:
             return output

       SCREAMING_SNAKE_CASE__     :				Tuple									=self.post_process_function(__lowercase					,      __lowercase					,      __lowercase					,      '''predict'''      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=self.compute_metrics(__lowercase      )

       # Prefix all keys with metric_key_prefix + '_'
       for key in list(metrics.keys()      ):
             if not key.startswith(F"{metric_key_prefix}_"      ):
                   SCREAMING_SNAKE_CASE__     :				Dict									=metrics.pop(__lowercase      )
       metrics.update(output.metrics      )
       return PredictionOutput(predictions=predictions.predictions					,      label_ids=predictions.label_ids					,      metrics=__lowercase      )







'''simple docstring'''
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      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				unittest.TestCase				):
 snake_case_  							=							KandinskyVaaImgaImgPipeline
 snake_case_  							=							["""image_embeds""", """negative_image_embeds""", """image"""]
 snake_case_  							=							[
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
 ]
 snake_case_  							=							[
     """generator""",
     """height""",
     """width""",
     """strength""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 snake_case_  							=							False




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						Tuple:
       return 32




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						str:
       return 32




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




 @property
 def      __magic_name__	(      self	:							List[Any]      )	->						int:
       return self.time_input_dim * 4




 @property
 def      __magic_name__	(      self	:							Tuple      )	->						Optional[int]:
       return 1_00




 @property
 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Tuple:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={
           '''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,
       }

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=UNetaDConditionModel(**__lowercase      )
       return model




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




 def      __magic_name__	(      self	:							str      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.dummy_unet
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.dummy_movq

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={
           '''num_train_timesteps''': 10_00,
           '''beta_schedule''': '''linear''',
           '''beta_start''': 0.00085,
           '''beta_end''': 0.012,
           '''clip_sample''': False,
           '''set_alpha_to_one''': False,
           '''steps_offset''': 0,
           '''prediction_type''': '''epsilon''',
           '''thresholding''': False,
       }

       SCREAMING_SNAKE_CASE__     :				str									=DDIMScheduler(**__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Any									={
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components




 def      __magic_name__	(      self	:							str					,      __lowercase	:							Optional[Any]					,      __lowercase	:							Any=0      )	->						int:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=floats_tensor((1, self.text_embedder_hidden_size)					,      rng=random.Random(__lowercase      )      ).to(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=floats_tensor((1, self.text_embedder_hidden_size)					,      rng=random.Random(seed + 1      )      ).to(
           __lowercase      )
       # create init_image
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=floats_tensor((1, 3, 64, 64)					,      rng=random.Random(__lowercase      )      ).to(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Dict									=image.cpu().permute(0					,      2					,      3					,      1      )[0]
       SCREAMING_SNAKE_CASE__     :				Any									=Image.fromarray(np.uinta(__lowercase      )      ).convert('''RGB'''      ).resize((2_56, 2_56)      )

       if str(__lowercase      ).startswith('''mps'''      ):
             SCREAMING_SNAKE_CASE__     :				Dict									=torch.manual_seed(__lowercase      )
       else:
             SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				str									={
           '''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      __magic_name__	(      self	:							int      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									='''cpu'''

       SCREAMING_SNAKE_CASE__     :				Tuple									=self.get_dummy_components()

       SCREAMING_SNAKE_CASE__     :				Dict									=self.pipeline_class(**__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=pipe.to(__lowercase      )

       pipe.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Dict									=pipe(**self.get_dummy_inputs(__lowercase      )      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=output.images

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

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

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

       SCREAMING_SNAKE_CASE__     :				Tuple									=np.array(
           [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263]      )
       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      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




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




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				str									=load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_img2img_frog.npy'''      )

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									='''A red cartoon frog, 4k'''

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=KandinskyVaaPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-prior'''					,      torch_dtype=torch.floataa      )
       pipe_prior.to(__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Any									=KandinskyVaaImgaImgPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-decoder'''					,      torch_dtype=torch.floataa      )
       SCREAMING_SNAKE_CASE__     :				Dict									=pipeline.to(__lowercase      )

       pipeline.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Generator(device='''cpu'''      ).manual_seed(0      )
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[str]									=pipe_prior(
           __lowercase					,      generator=__lowercase					,      num_inference_steps=5					,      negative_prompt=''''''					,      ).to_tuple()

       SCREAMING_SNAKE_CASE__     :				List[Any]									=pipeline(
           image=__lowercase					,      image_embeds=__lowercase					,      negative_image_embeds=__lowercase					,      generator=__lowercase					,      num_inference_steps=1_00					,      height=7_68					,      width=7_68					,      strength=0.2					,      output_type='''np'''					,      )

       SCREAMING_SNAKE_CASE__     :				int									=output.images[0]

       assert image.shape == (7_68, 7_68, 3)

       assert_mean_pixel_difference(__lowercase					,      __lowercase      )