
"""simple docstring"""
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class      _UpperCAmelCase				(					__A		,					__A):
    @register_to_config
    def __init__(    self						,    *,
        snake_case_ = 4						,    snake_case_ = 7_68						,    snake_case_						,    snake_case_						,    ):
      super().__init__()

      _snake_case     :						List[str]						=   nn.Parameter(torch.zeros(UpperCamelCase__     )     )

      # parameters for additional clip time embeddings
      _snake_case     :						Any						=   nn.Linear(UpperCamelCase__						,    UpperCamelCase__     )
      _snake_case     :						str						=   nn.Linear(UpperCamelCase__						,    UpperCamelCase__     )

      # parameters for encoder hidden states
      _snake_case     :						str						=   clip_extra_context_tokens
      _snake_case     :						str						=   nn.Linear(
          UpperCamelCase__						,    self.clip_extra_context_tokens * cross_attention_dim     )
      _snake_case     :						Tuple						=   nn.Linear(UpperCamelCase__						,    UpperCamelCase__     )
      _snake_case     :						Union[str, Any]						=   nn.LayerNorm(UpperCamelCase__     )


    def 					lowerCamelCase__	(    self						,    *, snake_case_						,    snake_case_						,    snake_case_						,    snake_case_     ):
      if do_classifier_free_guidance:
        # Add the classifier free guidance embeddings to the image embeddings
        _snake_case     :						Union[str, Any]						=   image_embeddings.shape[0]
        _snake_case     :						List[str]						=   self.learned_classifier_free_guidance_embeddings.unsqueeze(0     )
        _snake_case     :						Union[str, Any]						=   classifier_free_guidance_embeddings.expand(
            UpperCamelCase__						,    -1     )
        _snake_case     :						Union[str, Any]						=   torch.cat([classifier_free_guidance_embeddings, image_embeddings]						,    dim=0     )

      # The image embeddings batch size and the text embeddings batch size are equal
      assert image_embeddings.shape[0] == prompt_embeds.shape[0]

      _snake_case     :						Union[str, Any]						=   prompt_embeds.shape[0]

      # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
      # adding CLIP embeddings to the existing timestep embedding, ...
      _snake_case     :						List[Any]						=   self.embedding_proj(UpperCamelCase__     )
      _snake_case     :						Optional[Any]						=   self.clip_image_embeddings_project_to_time_embeddings(UpperCamelCase__     )
      _snake_case     :						Tuple						=   time_projected_image_embeddings + time_projected_prompt_embeds

      # ... and by projecting CLIP embeddings into four
      # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
      _snake_case     :						Optional[Any]						=   self.clip_extra_context_tokens_proj(UpperCamelCase__     )
      _snake_case     :						int						=   clip_extra_context_tokens.reshape(UpperCamelCase__						,    -1						,    self.clip_extra_context_tokens     )
      _snake_case     :						Any						=   clip_extra_context_tokens.permute(0						,    2						,    1     )

      _snake_case     :						Union[str, Any]						=   self.encoder_hidden_states_proj(UpperCamelCase__     )
      _snake_case     :						Optional[Any]						=   self.text_encoder_hidden_states_norm(UpperCamelCase__     )
      _snake_case     :						Any						=   torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]						,    dim=1     )

      return text_encoder_hidden_states, additive_clip_time_embeddings

"""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()

