"""simple docstring"""


import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()


class 							a					(		lowerCAmelCase_						,				unittest.TestCase ):
    _snake_case  :	Optional[Any]  				=				PriorTransformer
    _snake_case  :	Optional[Any]  				=				'hidden_states'


    @property
    def   lowerCAmelCase_	(       self						:	Any       ):
      _UpperCAmelCase    			=   4
      _UpperCAmelCase    			=   8
      _UpperCAmelCase    			=   7

      _UpperCAmelCase    			=   floats_tensor((batch_size, embedding_dim)       ).to(__lowerCAmelCase       )

      _UpperCAmelCase    			=   floats_tensor((batch_size, embedding_dim)       ).to(__lowerCAmelCase       )
      _UpperCAmelCase    			=   floats_tensor((batch_size, num_embeddings, embedding_dim)       ).to(__lowerCAmelCase       )

      return {
          "hidden_states": hidden_states,
          "timestep": 2,
          "proj_embedding": proj_embedding,
          "encoder_hidden_states": encoder_hidden_states,
      }


    def   lowerCAmelCase_	(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	Optional[Any]=0       ):
      torch.manual_seed(__lowerCAmelCase       )
      _UpperCAmelCase    			=   4
      _UpperCAmelCase    			=   8
      _UpperCAmelCase    			=   7

      _UpperCAmelCase    			=   torch.randn((batch_size, embedding_dim)       ).to(__lowerCAmelCase       )

      _UpperCAmelCase    			=   torch.randn((batch_size, embedding_dim)       ).to(__lowerCAmelCase       )
      _UpperCAmelCase    			=   torch.randn((batch_size, num_embeddings, embedding_dim)       ).to(__lowerCAmelCase       )

      return {
          "hidden_states": hidden_states,
          "timestep": 2,
          "proj_embedding": proj_embedding,
          "encoder_hidden_states": encoder_hidden_states,
      }


    @property
    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      return (4, 8)


    @property
    def   lowerCAmelCase_	(       self						:	int       ):
      return (4, 8)


    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   {
          """num_attention_heads""": 2,
          """attention_head_dim""": 4,
          """num_layers""": 2,
          """embedding_dim""": 8,
          """num_embeddings""": 7,
          """additional_embeddings""": 4,
      }
      _UpperCAmelCase    			=   self.dummy_input
      return init_dict, inputs_dict


    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase			,					_UpperCAmelCase    			=   PriorTransformer.from_pretrained(
          """hf-internal-testing/prior-dummy""" ,					output_loading_info=__lowerCAmelCase       )
      self.assertIsNotNone(__lowerCAmelCase       )
      self.assertEqual(len(loading_info["""missing_keys"""]       ) ,					0       )

      model.to(__lowerCAmelCase       )
      _UpperCAmelCase    			=   model(**self.dummy_input       )[0]

      assert hidden_states is not None, "Make sure output is not None"


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      _UpperCAmelCase			,					_UpperCAmelCase    			=   self.prepare_init_args_and_inputs_for_common()

      _UpperCAmelCase    			=   self.model_class(**__lowerCAmelCase       )
      _UpperCAmelCase    			=   inspect.signature(model.forward       )
      # signature.parameters is an OrderedDict => so arg_names order is deterministic
      _UpperCAmelCase    			=   [*signature.parameters.keys()]

      _UpperCAmelCase    			=   ["""hidden_states""", """timestep"""]
      self.assertListEqual(arg_names[:2] ,					__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      _UpperCAmelCase    			=   PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""       )
      _UpperCAmelCase    			=   model.to(__lowerCAmelCase       )

      if hasattr(__lowerCAmelCase ,					"""set_default_attn_processor"""       ):
        model.set_default_attn_processor()

      _UpperCAmelCase    			=   self.get_dummy_seed_input()

      with torch.no_grad():
        _UpperCAmelCase    			=   model(**__lowerCAmelCase       )[0]

      _UpperCAmelCase    			=   output[0, :5].flatten().cpu()
      print(__lowerCAmelCase       )

      # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
      # the expected output slices are not the same for CPU and GPU.
      _UpperCAmelCase    			=   torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]       )
      self.assertTrue(torch_all_close(__lowerCAmelCase ,					__lowerCAmelCase ,					rtol=1e-2       )       )



@slow
class 							a					(		unittest.TestCase ):


    def   lowerCAmelCase_	(       self						:	str ,					__lowerCAmelCase						:	Optional[int]=1 ,					__lowerCAmelCase						:	int=768 ,					__lowerCAmelCase						:	List[str]=77 ,					__lowerCAmelCase						:	int=0       ):
      torch.manual_seed(__lowerCAmelCase       )
      _UpperCAmelCase    			=   batch_size
      _UpperCAmelCase    			=   embedding_dim
      _UpperCAmelCase    			=   num_embeddings

      _UpperCAmelCase    			=   torch.randn((batch_size, embedding_dim)       ).to(__lowerCAmelCase       )

      _UpperCAmelCase    			=   torch.randn((batch_size, embedding_dim)       ).to(__lowerCAmelCase       )
      _UpperCAmelCase    			=   torch.randn((batch_size, num_embeddings, embedding_dim)       ).to(__lowerCAmelCase       )

      return {
          "hidden_states": hidden_states,
          "timestep": 2,
          "proj_embedding": proj_embedding,
          "encoder_hidden_states": encoder_hidden_states,
      }


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


    @parameterized.expand(
        [
            # fmt: off
            [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
            [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
            # fmt: on
        ]       )
    def   lowerCAmelCase_	(       self						:	Tuple ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	List[Any]       ):
      _UpperCAmelCase    			=   PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior""" ,					subfolder="""prior"""       )
      model.to(__lowerCAmelCase       )
      _UpperCAmelCase    			=   self.get_dummy_seed_input(seed=__lowerCAmelCase       )

      with torch.no_grad():
        _UpperCAmelCase    			=   model(**__lowerCAmelCase       )[0]

      assert list(sample.shape       ) == [1, 768]

      _UpperCAmelCase    			=   sample[0, :8].flatten().cpu()
      print(__lowerCAmelCase       )
      _UpperCAmelCase    			=   torch.tensor(__lowerCAmelCase       )

      assert torch_all_close(__lowerCAmelCase ,					__lowerCAmelCase ,					atol=1e-3       )

"""simple docstring"""


from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase = False		):







  """simple docstring"""
  if radian_mode:
    return [magnitude * cos(lowercase		), magnitude * sin(lowercase		)]
  return [magnitude * cos(radians(lowercase		)		), magnitude * sin(radians(lowercase		)		)]






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase = 10**-1		):







  """simple docstring"""
  _UpperCAmelCase    			=   cross(lowercase ,lowercase		)
  _UpperCAmelCase    			=   sum(lowercase		)
  return abs(lowercase		) < eps


if __name__ == "__main__":
    # Test to check if it works
    UpperCAmelCase__			=    array(
        [
            polar_force(718.4, 1_8_0 - 3_0),
            polar_force(879.54, 4_5),
            polar_force(1_0_0, -9_0),
        ]
    )

    UpperCAmelCase__			=    array([[0, 0], [0, 0], [0, 0]])

    assert in_static_equilibrium(forces, location)

    # Problem 1 in image_data/2D_problems.jpg
    UpperCAmelCase__			=    array(
        [
            polar_force(3_0 * 9.81, 1_5),
            polar_force(2_1_5, 1_8_0 - 4_5),
            polar_force(2_6_4, 9_0 - 3_0),
        ]
    )

    UpperCAmelCase__			=    array([[0, 0], [0, 0], [0, 0]])

    assert in_static_equilibrium(forces, location)

    # Problem in image_data/2D_problems_1.jpg
    UpperCAmelCase__			=    array([[0, -2_0_0_0], [0, -1_2_0_0], [0, 1_5_6_0_0], [0, -1_2_4_0_0]])

    UpperCAmelCase__			=    array([[0, 0], [6, 0], [1_0, 0], [1_2, 0]])

    assert in_static_equilibrium(forces, location)

    import doctest

    doctest.testmod()
