




import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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 ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Tuple:
  lowerCAmelCase__				:  str		       =     [tensor.shape for tensor in tensor_list]
  return all(shape == shapes[0] for shape in shapes[1:]      )






class  _lowerCAmelCase     (  _lowercase    ,			_lowercase    ,			_lowercase    ,			unittest.TestCase	):
      A__		     =		StableDiffusionLatentUpscalePipeline
      A__		     =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
          'height',
          'width',
          'cross_attention_kwargs',
          'negative_prompt_embeds',
          'prompt_embeds',
      }
      A__		     =		PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
      A__		     =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      A__		     =		frozenset(
          []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      A__		     =		frozenset([]	)

      A__		     =		True

      @property
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[str]		       =     1
        lowerCAmelCase__				:  Any		       =     4
        lowerCAmelCase__				:  Optional[int]		       =     (16, 16)

        lowerCAmelCase__				:  Union[str, Any]		       =     floats_tensor((batch_size, num_channels) + sizes      ,				rng=random.Random(0		)		).to(__UpperCAmelCase		)
        return image

      def 	__magic_name__(   self		):
        torch.manual_seed(0		)
        lowerCAmelCase__				:  List[str]		       =     UNetaDConditionModel(
            act_fn='''gelu'''      ,				attention_head_dim=8      ,				norm_num_groups=__UpperCAmelCase      ,				block_out_channels=[32, 32, 64, 64]      ,				time_cond_proj_dim=160      ,				conv_in_kernel=1      ,				conv_out_kernel=1      ,				cross_attention_dim=32      ,				down_block_types=(
                '''KDownBlock2D''',
                '''KCrossAttnDownBlock2D''',
                '''KCrossAttnDownBlock2D''',
                '''KCrossAttnDownBlock2D''',
            )      ,				in_channels=8      ,				mid_block_type=__UpperCAmelCase      ,				only_cross_attention=__UpperCAmelCase      ,				out_channels=5      ,				resnet_time_scale_shift='''scale_shift'''      ,				time_embedding_type='''fourier'''      ,				timestep_post_act='''gelu'''      ,				up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')      ,				)
        lowerCAmelCase__				:  Any		       =     AutoencoderKL(
            block_out_channels=[32, 32, 64, 64]      ,				in_channels=3      ,				out_channels=3      ,				down_block_types=[
                '''DownEncoderBlock2D''',
                '''DownEncoderBlock2D''',
                '''DownEncoderBlock2D''',
                '''DownEncoderBlock2D''',
            ]      ,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,				latent_channels=4      ,				)
        lowerCAmelCase__				:  Union[str, Any]		       =     EulerDiscreteScheduler(prediction_type='''sample'''		)
        lowerCAmelCase__				:  Optional[Any]		       =     CLIPTextConfig(
            bos_token_id=0      ,				eos_token_id=2      ,				hidden_size=32      ,				intermediate_size=37      ,				layer_norm_eps=1e-05      ,				num_attention_heads=4      ,				num_hidden_layers=5      ,				pad_token_id=1      ,				vocab_size=1000      ,				hidden_act='''quick_gelu'''      ,				projection_dim=512      ,				)
        lowerCAmelCase__				:  Dict		       =     CLIPTextModel(__UpperCAmelCase		)
        lowerCAmelCase__				:  List[Any]		       =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

        lowerCAmelCase__				:  str		       =     {
            '''unet''': model.eval(),
            '''vae''': vae.eval(),
            '''scheduler''': scheduler,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
        }

        return components

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=0		):
        if str(__UpperCAmelCase		).startswith('''mps'''		):
          lowerCAmelCase__				:  Dict		       =     torch.manual_seed(__UpperCAmelCase		)
        else:
          lowerCAmelCase__				:  str		       =     torch.Generator(device=__UpperCAmelCase		).manual_seed(__UpperCAmelCase		)
        lowerCAmelCase__				:  Any		       =     {
            '''prompt''': '''A painting of a squirrel eating a burger''',
            '''image''': self.dummy_image.cpu(),
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''output_type''': '''numpy''',
        }
        return inputs

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Any		       =     '''cpu'''

        lowerCAmelCase__				:  Any		       =     self.get_dummy_components()
        lowerCAmelCase__				:  Optional[int]		       =     self.pipeline_class(**__UpperCAmelCase		)
        pipe.to(__UpperCAmelCase		)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

        lowerCAmelCase__				:  Optional[Any]		       =     self.get_dummy_inputs(__UpperCAmelCase		)
        lowerCAmelCase__				:  Optional[int]		       =     pipe(**__UpperCAmelCase		).images
        lowerCAmelCase__				:  Dict		       =     image[0, -3:, -3:, -1]

        self.assertEqual(image.shape      ,				(1, 256, 256, 3)		)
        lowerCAmelCase__				:  Optional[Any]		       =     np.array(
            [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]		)
        lowerCAmelCase__				:  List[Any]		       =     np.abs(image_slice.flatten() - expected_slice		).max()
        self.assertLessEqual(__UpperCAmelCase      ,				1e-3		)

      def 	__magic_name__(   self		):
        super().test_attention_slicing_forward_pass(expected_max_diff=7e-3		)

      def 	__magic_name__(   self		):
        super().test_cpu_offload_forward_pass(expected_max_diff=3e-3		)

      def 	__magic_name__(   self		):
        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3		)

      def 	__magic_name__(   self		):
        super().test_inference_batch_single_identical(expected_max_diff=7e-3		)

      def 	__magic_name__(   self		):
        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3		)

      def 	__magic_name__(   self		):
        super().test_save_load_local(expected_max_difference=3e-3		)

      def 	__magic_name__(   self		):
        super().test_save_load_optional_components(expected_max_difference=3e-3		)







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     [
            '''DDIMScheduler''',
            '''DDPMScheduler''',
            '''PNDMScheduler''',
            '''HeunDiscreteScheduler''',
            '''EulerAncestralDiscreteScheduler''',
            '''KDPM2DiscreteScheduler''',
            '''KDPM2AncestralDiscreteScheduler''',
            '''DPMSolverSDEScheduler''',
        ]
        lowerCAmelCase__				:  int		       =     self.get_dummy_components()
        lowerCAmelCase__				:  List[Any]		       =     self.pipeline_class(**__UpperCAmelCase		)

        # make sure that PNDM does not need warm-up
        pipe.scheduler.register_to_config(skip_prk_steps=__UpperCAmelCase		)

        pipe.to(__UpperCAmelCase		)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)
        lowerCAmelCase__				:  Any		       =     self.get_dummy_inputs(__UpperCAmelCase		)
        lowerCAmelCase__				:  Union[str, Any]		       =     2

        lowerCAmelCase__				:  int		       =     []
        for scheduler_enum in KarrasDiffusionSchedulers:
          if scheduler_enum.name in skip_schedulers:
            # no sigma schedulers are not supported
            # no schedulers
            continue

          lowerCAmelCase__				:  List[str]		       =     getattr(__UpperCAmelCase      ,				scheduler_enum.name		)
          lowerCAmelCase__				:  List[Any]		       =     scheduler_cls.from_config(pipe.scheduler.config		)
          lowerCAmelCase__				:  str		       =     pipe(**__UpperCAmelCase		)[0]
          outputs.append(__UpperCAmelCase		)

        assert check_same_shape(__UpperCAmelCase		)






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

      def 	__magic_name__(   self		):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Dict		       =     torch.manual_seed(33		)

        lowerCAmelCase__				:  str		       =     StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''      ,				torch_dtype=torch.floataa		)
        pipe.to('''cuda'''		)

        lowerCAmelCase__				:  Any		       =     StableDiffusionLatentUpscalePipeline.from_pretrained(
            '''stabilityai/sd-x2-latent-upscaler'''      ,				torch_dtype=torch.floataa		)
        upscaler.to('''cuda'''		)

        lowerCAmelCase__				:  Union[str, Any]		       =     '''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

        lowerCAmelCase__				:  int		       =     pipe(__UpperCAmelCase      ,				generator=__UpperCAmelCase      ,				output_type='''latent'''		).images

        lowerCAmelCase__				:  List[str]		       =     upscaler(
            prompt=__UpperCAmelCase      ,				image=__UpperCAmelCase      ,				num_inference_steps=20      ,				guidance_scale=0      ,				generator=__UpperCAmelCase      ,				output_type='''np'''      ,				).images[0]

        lowerCAmelCase__				:  Optional[int]		       =     load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'''		)
        assert np.abs((expected_image - image).mean()		) < 5e-2







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Any		       =     torch.manual_seed(33		)

        lowerCAmelCase__				:  Any		       =     StableDiffusionLatentUpscalePipeline.from_pretrained(
            '''stabilityai/sd-x2-latent-upscaler'''      ,				torch_dtype=torch.floataa		)
        upscaler.to('''cuda'''		)

        lowerCAmelCase__				:  List[str]		       =     '''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

        lowerCAmelCase__				:  Union[str, Any]		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'''		)

        lowerCAmelCase__				:  Any		       =     upscaler(
            prompt=__UpperCAmelCase      ,				image=__UpperCAmelCase      ,				num_inference_steps=20      ,				guidance_scale=0      ,				generator=__UpperCAmelCase      ,				output_type='''np'''      ,				).images[0]

        lowerCAmelCase__				:  List[str]		       =     load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'''		)
        assert np.abs((expected_image - image).max()		) < 5e-2







import numpy as np






class  _lowerCAmelCase     :

      def __init__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     (0, 0)
        lowerCAmelCase__				:  Optional[int]		       =     None
        lowerCAmelCase__				:  Optional[Any]		       =     0
        lowerCAmelCase__				:  Optional[int]		       =     0
        lowerCAmelCase__				:  Tuple		       =     0

      def __eq__(   self      ,				__UpperCAmelCase		):
        return self.position == cell.position







      def 	__magic_name__(   self		):
        print(self.position		)






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase=(5, 5)		):
        lowerCAmelCase__				:  List[str]		       =     np.zeros(__UpperCAmelCase		)
        lowerCAmelCase__				:  List[Any]		       =     world_size[0]
        lowerCAmelCase__				:  str		       =     world_size[1]

      def 	__magic_name__(   self		):
        print(self.w		)







      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     [
            (-1, -1),
            (-1, 0),
            (-1, 1),
            (0, -1),
            (0, 1),
            (1, -1),
            (1, 0),
            (1, 1),
        ]
        lowerCAmelCase__				:  Tuple		       =     cell.position[0]
        lowerCAmelCase__				:  Optional[int]		       =     cell.position[1]
        lowerCAmelCase__				:  Any		       =     []
        for n in neughbour_cord:
          lowerCAmelCase__				:  int		       =     current_x + n[0]
          lowerCAmelCase__				:  Dict		       =     current_y + n[1]
          if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
            lowerCAmelCase__				:  Optional[int]		       =     Cell()
            lowerCAmelCase__				:  Optional[int]		       =     (x, y)
            lowerCAmelCase__				:  Any		       =     cell
            neighbours.append(__UpperCAmelCase		)
        return neighbours



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Union[str, Any]:
  lowerCAmelCase__				:  int		       =     []
  lowerCAmelCase__				:  int		       =     []
  _open.append(UpperCamelCase      )

  while _open:
    lowerCAmelCase__				:  Dict		       =     np.argmin([n.f for n in _open]      )
    lowerCAmelCase__				:  Dict		       =     _open[min_f]
    _closed.append(_open.pop(UpperCamelCase      )      )
    if current == goal:
      break
    for n in world.get_neigbours(UpperCamelCase      ):
      for c in _closed:
        if c == n:
          continue
      lowerCAmelCase__				:  Any		       =     current.g + 1
      lowerCAmelCase__       ,			lowerCAmelCase__				:  int		       =     n.position
      lowerCAmelCase__       ,			lowerCAmelCase__				:  Union[str, Any]		       =     goal.position
      lowerCAmelCase__				:  Tuple		       =     (ya - ya) ** 2 + (xa - xa) ** 2
      lowerCAmelCase__				:  Optional[Any]		       =     n.h + n.g

      for c in _open:
        if c == n and c.f < n.f:
          continue
      _open.append(UpperCamelCase      )
  lowerCAmelCase__				:  List[Any]		       =     []
  while current.parent is not None:
    path.append(current.position      )
    lowerCAmelCase__				:  List[Any]		       =     current.parent
  path.append(current.position      )
  return path[::-1]


if __name__ == "__main__":
     lowerCAmelCase_					    = Gridworld()
     # Start position and goal
     lowerCAmelCase_					    = Cell()
     lowerCAmelCase_					    = (0, 0)
     lowerCAmelCase_					    = Cell()
     lowerCAmelCase_					    = (4, 4)
     print(F"""path from {start.position} to {goal.position}""")
     lowerCAmelCase_					    = astar(world, start, goal)
     #   Just for visual reasons.
     for i in s:
          lowerCAmelCase_					    = 1
     print(world.w)

