
"""simple docstring"""
import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_a      :			Any					=							logging.get_logger(__name__)
logging.set_verbosity_info()
def 				a__   (   a   :				str       ,     a   :				str		):

  """simple docstring"""







  if "xprophetnet" in prophetnet_checkpoint_path:
    _snake_case     :						Optional[int]						=   XLMProphetNetForConditionalGenerationOld.from_pretrained(a		)
    _snake_case     :						Optional[int]						=   XLMProphetNetForConditionalGeneration.from_pretrained(
        a       ,     output_loading_info=a		)
  else:
    _snake_case     :						Optional[Any]						=   ProphetNetForConditionalGenerationOld.from_pretrained(a		)
    _snake_case     :						List[Any]						=   ProphetNetForConditionalGeneration.from_pretrained(
        a       ,     output_loading_info=a		)

  _snake_case     :						Dict						=   ["key_proj", "value_proj", "query_proj"]

  _snake_case     :						Optional[Any]						=   {
      "self_attn": "ngram_self_attn",
      "cross_attn": "encoder_attn",
      "cross_attn_layer_norm": "encoder_attn_layer_norm",
      "feed_forward_layer_norm": "final_layer_norm",
      "feed_forward": "",
      "intermediate": "fc1",
      "output": "fc2",
      "key_proj": "k_proj",
      "query_proj": "q_proj",
      "value_proj": "v_proj",
      "word_embeddings": "embed_tokens",
      "embeddings_layer_norm": "emb_layer_norm",
      "relative_pos_embeddings": "relative_linear",
      "ngram_embeddings": "ngram_input_embed",
      "position_embeddings": "embed_positions",
  }

  for key in loading_info["missing_keys"]:
    _snake_case     :						Optional[int]						=   key.split("."		)

    if attributes[0] == "lm_head":
      _snake_case     :						Optional[int]						=   prophet
      _snake_case     :						List[str]						=   prophet_old
    else:
      _snake_case     :						int						=   prophet.prophetnet
      _snake_case     :						int						=   prophet_old.model

    _snake_case     :						Dict						=   False
    for attribute in attributes:
      if attribute in mapping:
        _snake_case     :						Tuple						=   mapping[attribute]
        if not hasattr(a       ,     a		) and len(a		) > 0:
          _snake_case     :						Tuple						=   attribute
      elif hasattr(a       ,     a		):
        _snake_case     :						List[Any]						=   attribute

      if attribute == "weight":
        assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
        _snake_case     :						List[str]						=   old_model.weight
        logger.info(f'{attribute} is initialized.'		)
        _snake_case     :						Optional[int]						=   True
        break
      elif attribute == "bias":
        assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
        _snake_case     :						int						=   old_model.bias
        logger.info(f'{attribute} is initialized'		)
        _snake_case     :						Union[str, Any]						=   True
        break
      elif attribute in special_keys and hasattr(a       ,     "in_proj_weight"		):
        _snake_case     :						List[Any]						=   old_model.in_proj_weight.shape[0] // 3
        _snake_case     :						List[str]						=   getattr(a       ,     a		)
        param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
        param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
        if attribute == "query_proj":
          _snake_case     :						Union[str, Any]						=   nn.Parameter(old_model.in_proj_weight[:embed_dim, :]		)
          _snake_case     :						List[str]						=   nn.Parameter(old_model.in_proj_bias[:embed_dim]		)

        elif attribute == "key_proj":
          _snake_case     :						Tuple						=   nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]		)
          _snake_case     :						str						=   nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]		)
        elif attribute == "value_proj":
          _snake_case     :						Optional[int]						=   nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]		)
          _snake_case     :						Optional[int]						=   nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]		)
        _snake_case     :						Union[str, Any]						=   True
        break
      elif attribute == "position_embeddings":
        assert (
            model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
        ), "Hidden size has to match"
        assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
        _snake_case     :						List[Any]						=   nn.Parameter(old_model.embed_positions.weight[:512, :]		)
        _snake_case     :						Dict						=   True
        break

      if attribute.isdigit():
        _snake_case     :						Optional[int]						=   model[int(a		)]
        _snake_case     :						List[str]						=   old_model[int(a		)]
      else:
        _snake_case     :						Optional[Any]						=   getattr(a       ,     a		)

        if old_attribute == "":
          _snake_case     :						int						=   old_model
        else:
          if not hasattr(a       ,     a		):
            raise ValueError(f'{old_model} does not have {old_attribute}'		)
          _snake_case     :						str						=   getattr(a       ,     a		)

    if not is_key_init:
      raise ValueError(f'{key} was not correctly initialized!'		)

  print(f'Saving model to {pytorch_dump_folder_path}'		)
  prophet.save_pretrained(a		)


if __name__ == "__main__":
    _a      :			List[Any]					=							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--prophetnet_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    _a      :			int					=							parser.parse_args()
    convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)

"""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()

