
"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class      _UpperCAmelCase				(					_snake_case		,					unittest.TestCase):
    __lowercase							: Union[str, Any]  =					UnCLIPImageVariationPipeline
    __lowercase							: Optional[int]  =					IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
    __lowercase							: List[Any]  =					IMAGE_VARIATION_BATCH_PARAMS

    __lowercase							: Any  =					[
        """generator""",
        """return_dict""",
        """decoder_num_inference_steps""",
        """super_res_num_inference_steps""",
    ]
    __lowercase							: str  =					False
    @property
    def 					lowerCamelCase__	(    self     ):
      return 32
    @property
    def 					lowerCamelCase__	(    self     ):
      return 32
    @property
    def 					lowerCamelCase__	(    self     ):
      return self.time_input_dim
    @property
    def 					lowerCamelCase__	(    self     ):
      return self.time_input_dim * 4
    @property
    def 					lowerCamelCase__	(    self     ):
      return 1_00
    @property
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						str						=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )
      return tokenizer
    @property
    def 					lowerCamelCase__	(    self     ):
      torch.manual_seed(0     )
      _snake_case     :						Optional[int]						=   CLIPTextConfig(
          bos_token_id=0						,    eos_token_id=2						,    hidden_size=self.text_embedder_hidden_size						,    projection_dim=self.text_embedder_hidden_size						,    intermediate_size=37						,    layer_norm_eps=1E-05						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=10_00						,    )
      return CLIPTextModelWithProjection(snake_case_     )
    @property
    def 					lowerCamelCase__	(    self     ):
      torch.manual_seed(0     )
      _snake_case     :						Optional[int]						=   CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size						,    projection_dim=self.text_embedder_hidden_size						,    num_hidden_layers=5						,    num_attention_heads=4						,    image_size=32						,    intermediate_size=37						,    patch_size=1						,    )
      return CLIPVisionModelWithProjection(snake_case_     )
    @property
    def 					lowerCamelCase__	(    self     ):
      torch.manual_seed(0     )

      _snake_case     :						int						=   {
          "clip_embeddings_dim": self.text_embedder_hidden_size,
          "time_embed_dim": self.time_embed_dim,
          "cross_attention_dim": self.cross_attention_dim,
      }

      _snake_case     :						int						=   UnCLIPTextProjModel(**snake_case_     )
      return model
    @property
    def 					lowerCamelCase__	(    self     ):
      torch.manual_seed(0     )

      _snake_case     :						Union[str, Any]						=   {
          "sample_size": 32,
          # RGB in channels
          "in_channels": 3,
          # Out channels is double in channels because predicts mean and variance
          "out_channels": 6,
          "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,
          "cross_attention_dim": self.cross_attention_dim,
          "attention_head_dim": 4,
          "resnet_time_scale_shift": "scale_shift",
          "class_embed_type": "identity",
      }

      _snake_case     :						Any						=   UNetaDConditionModel(**snake_case_     )
      return model
    @property
    def 					lowerCamelCase__	(    self     ):
      return {
          "sample_size": 64,
          "layers_per_block": 1,
          "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
          "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
          "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
          "in_channels": 6,
          "out_channels": 3,
      }
    @property
    def 					lowerCamelCase__	(    self     ):
      torch.manual_seed(0     )

      _snake_case     :						Tuple						=   UNetaDModel(**self.dummy_super_res_kwargs     )
      return model
    @property
    def 					lowerCamelCase__	(    self     ):
      # seeded differently to get different unet than `self.dummy_super_res_first`
      torch.manual_seed(1     )

      _snake_case     :						List[str]						=   UNetaDModel(**self.dummy_super_res_kwargs     )
      return model
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Any						=   self.dummy_decoder
      _snake_case     :						Union[str, Any]						=   self.dummy_text_proj
      _snake_case     :						Union[str, Any]						=   self.dummy_text_encoder
      _snake_case     :						List[str]						=   self.dummy_tokenizer
      _snake_case     :						Union[str, Any]						=   self.dummy_super_res_first
      _snake_case     :						str						=   self.dummy_super_res_last

      _snake_case     :						int						=   UnCLIPScheduler(
          variance_type="learned_range"						,    prediction_type="epsilon"						,    num_train_timesteps=10_00						,    )

      _snake_case     :						Any						=   UnCLIPScheduler(
          variance_type="fixed_small_log"						,    prediction_type="epsilon"						,    num_train_timesteps=10_00						,    )

      _snake_case     :						Any						=   CLIPImageProcessor(crop_size=32						,    size=32     )

      _snake_case     :						Optional[Any]						=   self.dummy_image_encoder

      return {
          "decoder": decoder,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "text_proj": text_proj,
          "feature_extractor": feature_extractor,
          "image_encoder": image_encoder,
          "super_res_first": super_res_first,
          "super_res_last": super_res_last,
          "decoder_scheduler": decoder_scheduler,
          "super_res_scheduler": super_res_scheduler,
      }
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_=0						,    snake_case_=True     ):
      _snake_case     :						int						=   floats_tensor((1, 3, 32, 32)						,    rng=random.Random(snake_case_     )     ).to(snake_case_     )
      if str(snake_case_     ).startswith("mps"     ):
        _snake_case     :						List[Any]						=   torch.manual_seed(snake_case_     )
      else:
        _snake_case     :						int						=   torch.Generator(device=snake_case_     ).manual_seed(snake_case_     )

      if pil_image:
        _snake_case     :						Optional[int]						=   input_image * 0.5 + 0.5
        _snake_case     :						Union[str, Any]						=   input_image.clamp(0						,    1     )
        _snake_case     :						List[str]						=   input_image.cpu().permute(0						,    2						,    3						,    1     ).float().numpy()
        _snake_case     :						Optional[int]						=   DiffusionPipeline.numpy_to_pil(snake_case_     )[0]

      return {
          "image": input_image,
          "generator": generator,
          "decoder_num_inference_steps": 2,
          "super_res_num_inference_steps": 2,
          "output_type": "np",
      }
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[str]						=   "cpu"

      _snake_case     :						List[Any]						=   self.get_dummy_components()

      _snake_case     :						Any						=   self.pipeline_class(**snake_case_     )
      _snake_case     :						Tuple						=   pipe.to(snake_case_     )

      pipe.set_progress_bar_config(disable=snake_case_     )

      _snake_case     :						List[Any]						=   self.get_dummy_inputs(snake_case_						,    pil_image=snake_case_     )

      _snake_case     :						Any						=   pipe(**snake_case_     )
      _snake_case     :						int						=   output.images

      _snake_case     :						Optional[Any]						=   self.get_dummy_inputs(snake_case_						,    pil_image=snake_case_     )

      _snake_case     :						Optional[Any]						=   pipe(
          **snake_case_						,    return_dict=snake_case_						,    )[0]

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

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

      _snake_case     :						List[str]						=   np.array(
          [
              0.9997,
              0.0002,
              0.9997,
              0.9997,
              0.9969,
              0.0023,
              0.9997,
              0.9969,
              0.9970,
          ]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						int						=   "cpu"

      _snake_case     :						str						=   self.get_dummy_components()

      _snake_case     :						Union[str, Any]						=   self.pipeline_class(**snake_case_     )
      _snake_case     :						List[Any]						=   pipe.to(snake_case_     )

      pipe.set_progress_bar_config(disable=snake_case_     )

      _snake_case     :						int						=   self.get_dummy_inputs(snake_case_						,    pil_image=snake_case_     )

      _snake_case     :						List[str]						=   pipe(**snake_case_     )
      _snake_case     :						Optional[Any]						=   output.images

      _snake_case     :						int						=   self.get_dummy_inputs(snake_case_						,    pil_image=snake_case_     )

      _snake_case     :						Optional[int]						=   pipe(
          **snake_case_						,    return_dict=snake_case_						,    )[0]

      _snake_case     :						Optional[int]						=   image[0, -3:, -3:, -1]
      _snake_case     :						Tuple						=   image_from_tuple[0, -3:, -3:, -1]

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

      _snake_case     :						Any						=   np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[str]						=   "cpu"

      _snake_case     :						str						=   self.get_dummy_components()

      _snake_case     :						Tuple						=   self.pipeline_class(**snake_case_     )
      _snake_case     :						Optional[Any]						=   pipe.to(snake_case_     )

      pipe.set_progress_bar_config(disable=snake_case_     )

      _snake_case     :						Optional[Any]						=   self.get_dummy_inputs(snake_case_						,    pil_image=snake_case_     )
      _snake_case     :						Tuple						=   [
          pipeline_inputs["image"],
          pipeline_inputs["image"],
      ]

      _snake_case     :						int						=   pipe(**snake_case_     )
      _snake_case     :						Dict						=   output.images

      _snake_case     :						str						=   self.get_dummy_inputs(snake_case_						,    pil_image=snake_case_     )
      _snake_case     :						Tuple						=   [
          tuple_pipeline_inputs["image"],
          tuple_pipeline_inputs["image"],
      ]

      _snake_case     :						List[Any]						=   pipe(
          **snake_case_						,    return_dict=snake_case_						,    )[0]

      _snake_case     :						Optional[Any]						=   image[0, -3:, -3:, -1]
      _snake_case     :						Tuple						=   image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (2, 64, 64, 3)

      _snake_case     :						Optional[Any]						=   np.array(
          [
              0.9997,
              0.9989,
              0.0008,
              0.0021,
              0.9960,
              0.0018,
              0.0014,
              0.0002,
              0.9933,
          ]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Optional[int]						=   torch.device("cpu"     )






      class      _UpperCAmelCase				:
          __lowercase							: List[str]  =					1


      _snake_case     :						Any						=   self.get_dummy_components()

      _snake_case     :						List[Any]						=   self.pipeline_class(**snake_case_     )
      _snake_case     :						Union[str, Any]						=   pipe.to(snake_case_     )

      pipe.set_progress_bar_config(disable=snake_case_     )

      _snake_case     :						Dict						=   torch.Generator(device=snake_case_     ).manual_seed(0     )
      _snake_case     :						List[Any]						=   pipe.decoder.dtype
      _snake_case     :						int						=   1

      _snake_case     :						Optional[int]						=   (
          batch_size,
          pipe.decoder.config.in_channels,
          pipe.decoder.config.sample_size,
          pipe.decoder.config.sample_size,
      )
      _snake_case     :						List[str]						=   pipe.prepare_latents(
          snake_case_						,    dtype=snake_case_						,    device=snake_case_						,    generator=snake_case_						,    latents=snake_case_						,    scheduler=DummyScheduler()     )

      _snake_case     :						Optional[int]						=   (
          batch_size,
          pipe.super_res_first.config.in_channels // 2,
          pipe.super_res_first.config.sample_size,
          pipe.super_res_first.config.sample_size,
      )
      _snake_case     :						Optional[int]						=   pipe.prepare_latents(
          snake_case_						,    dtype=snake_case_						,    device=snake_case_						,    generator=snake_case_						,    latents=snake_case_						,    scheduler=DummyScheduler()     )

      _snake_case     :						Any						=   self.get_dummy_inputs(snake_case_						,    pil_image=snake_case_     )

      _snake_case     :						str						=   pipe(
          **snake_case_						,    decoder_latents=snake_case_						,    super_res_latents=snake_case_     ).images

      _snake_case     :						Optional[int]						=   self.get_dummy_inputs(snake_case_						,    pil_image=snake_case_     )
      # Don't pass image, instead pass embedding
      _snake_case     :						List[Any]						=   pipeline_inputs.pop("image"     )
      _snake_case     :						List[str]						=   pipe.image_encoder(snake_case_     ).image_embeds

      _snake_case     :						Dict						=   pipe(
          **snake_case_						,    decoder_latents=snake_case_						,    super_res_latents=snake_case_						,    image_embeddings=snake_case_						,    ).images

      # make sure passing text embeddings manually is identical
      assert np.abs(img_out_a - img_out_a     ).max() < 1E-4
    @skip_mps
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						str						=   torch_device == "cpu"

      # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
      _snake_case     :						Optional[int]						=   1E-2

      self._test_attention_slicing_forward_pass(
          test_max_difference=snake_case_						,    expected_max_diff=snake_case_     )
    @skip_mps
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Optional[Any]						=   torch_device == "cpu"
      _snake_case     :						Optional[Any]						=   True
      _snake_case     :						List[Any]						=   [
          "decoder_num_inference_steps",
          "super_res_num_inference_steps",
      ]

      self._test_inference_batch_single_identical(
          test_max_difference=snake_case_						,    relax_max_difference=snake_case_						,    additional_params_copy_to_batched_inputs=snake_case_						,    )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Union[str, Any]						=   [
          "decoder_num_inference_steps",
          "super_res_num_inference_steps",
      ]

      if torch_device == "mps":
        # TODO: MPS errors with larger batch sizes
        _snake_case     :						Dict						=   [2, 3]
        self._test_inference_batch_consistent(
            batch_sizes=snake_case_						,    additional_params_copy_to_batched_inputs=snake_case_						,    )
      else:
        self._test_inference_batch_consistent(
            additional_params_copy_to_batched_inputs=snake_case_     )
    @skip_mps
    def 					lowerCamelCase__	(    self     ):
      return super().test_dict_tuple_outputs_equivalent()
    @skip_mps
    def 					lowerCamelCase__	(    self     ):
      return super().test_save_load_local()


    @skip_mps
    def 					lowerCamelCase__	(    self     ):
      return super().test_save_load_optional_components()






@slow
@require_torch_gpu
class      _UpperCAmelCase				(					unittest.TestCase):
    def 					lowerCamelCase__	(    self     ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Dict						=   load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"     )
      _snake_case     :						Dict						=   load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/unclip/karlo_v1_alpha_cat_variation_fp16.npy"     )

      _snake_case     :						Any						=   UnCLIPImageVariationPipeline.from_pretrained(
          "kakaobrain/karlo-v1-alpha-image-variations"						,    torch_dtype=torch.floataa     )
      _snake_case     :						Union[str, Any]						=   pipeline.to(snake_case_     )
      pipeline.set_progress_bar_config(disable=snake_case_     )

      _snake_case     :						Dict						=   torch.Generator(device="cpu"     ).manual_seed(0     )
      _snake_case     :						Tuple						=   pipeline(
          snake_case_						,    generator=snake_case_						,    output_type="np"						,    )

      _snake_case     :						Tuple						=   output.images[0]

      assert image.shape == (2_56, 2_56, 3)

      assert_mean_pixel_difference(snake_case_						,    snake_case_						,    15     )

"""simple docstring"""
from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def 				a__   (   a   :				float       ,     a   :				float       ,     a   :				bool = False		):

  """simple docstring"""







  if radian_mode:
    return [magnitude * cos(a		), magnitude * sin(a		)]
  return [magnitude * cos(radians(a		)		), magnitude * sin(radians(a		)		)]
def 				a__   (   a   :				NDArray[floataa]       ,     a   :				NDArray[floataa]       ,     a   :				float = 10**-1		):

  """simple docstring"""







  _snake_case     :						NDArray[floataa]						=   cross(a       ,     a		)
  _snake_case     :						float						=   sum(a		)
  return abs(a		) < eps


if __name__ == "__main__":
    # Test to check if it works
    _a      :			Tuple					=							array(
        [
            polar_force(718.4, 180 - 30),
            polar_force(879.54, 45),
            polar_force(100, -90),
        ]
    )

    _a      :			NDArray[floataa]					=							array([[0, 0], [0, 0], [0, 0]])

    assert in_static_equilibrium(forces, location)

    # Problem 1 in image_data/2D_problems.jpg
    _a      :			List[Any]					=							array(
        [
            polar_force(30 * 9.81, 15),
            polar_force(215, 180 - 45),
            polar_force(264, 90 - 30),
        ]
    )

    _a      :			List[Any]					=							array([[0, 0], [0, 0], [0, 0]])

    assert in_static_equilibrium(forces, location)

    # Problem in image_data/2D_problems_1.jpg
    _a      :			List[str]					=							array([[0, -2_000], [0, -1_200], [0, 15_600], [0, -12_400]])

    _a      :			Optional[int]					=							array([[0, 0], [6, 0], [10, 0], [12, 0]])

    assert in_static_equilibrium(forces, location)

    import doctest

    doctest.testmod()

