





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


snake_case_     :       Optional[Any]						=  logging.get_logger(__name__)
logging.set_verbosity_info()





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			str,			SCREAMING_SNAKE_CASE__      :			str   ) ->	Tuple:
						if "xprophetnet" in prophetnet_checkpoint_path:
												UpperCAmelCase_			:      str     						=  XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      List[str]     						=  XLMProphetNetForConditionalGeneration.from_pretrained(
												    SCREAMING_SNAKE_CASE__,			output_loading_info=SCREAMING_SNAKE_CASE__   )
						else:
												UpperCAmelCase_			:      Tuple     						=  ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      Optional[int]     						=  ProphetNetForConditionalGeneration.from_pretrained(
												    SCREAMING_SNAKE_CASE__,			output_loading_info=SCREAMING_SNAKE_CASE__   )

						UpperCAmelCase_			:      Any     						=  ['''key_proj''', '''value_proj''', '''query_proj''']

						UpperCAmelCase_			:      Dict     						=  {
						    '''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"]:
												UpperCAmelCase_			:      Union[str, Any]     						=  key.split('''.'''   )

												if attributes[0] == "lm_head":
																		UpperCAmelCase_			:      Tuple     						=  prophet
																		UpperCAmelCase_			:      Optional[int]     						=  prophet_old
												else:
																		UpperCAmelCase_			:      Optional[int]     						=  prophet.prophetnet
																		UpperCAmelCase_			:      Optional[Any]     						=  prophet_old.model

												UpperCAmelCase_			:      Any     						=  False
												for attribute in attributes:
																		if attribute in mapping:
																								UpperCAmelCase_			:      Union[str, Any]     						=  mapping[attribute]
																								if not hasattr(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ) and len(SCREAMING_SNAKE_CASE__   ) > 0:
																														UpperCAmelCase_			:      str     						=  attribute
																		elif hasattr(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ):
																								UpperCAmelCase_			:      Dict     						=  attribute

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

																								elif attribute == "key_proj":
																														UpperCAmelCase_			:      str     						=  nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]   )
																														UpperCAmelCase_			:      List[Any]     						=  nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]   )
																								elif attribute == "value_proj":
																														UpperCAmelCase_			:      Any     						=  nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]   )
																														UpperCAmelCase_			:      str     						=  nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]   )
																								UpperCAmelCase_			:      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."
																								UpperCAmelCase_			:      List[str]     						=  nn.Parameter(old_model.embed_positions.weight[:512, :]   )
																								UpperCAmelCase_			:      Optional[int]     						=  True
																								break

																		if attribute.isdigit():
																								UpperCAmelCase_			:      Any     						=  model[int(SCREAMING_SNAKE_CASE__   )]
																								UpperCAmelCase_			:      Optional[Any]     						=  old_model[int(SCREAMING_SNAKE_CASE__   )]
																		else:
																								UpperCAmelCase_			:      Tuple     						=  getattr(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )

																								if old_attribute == "":
																														UpperCAmelCase_			:      Dict     						=  old_model
																								else:
																														if not hasattr(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ):
																																				raise ValueError(F"""{old_model} does not have {old_attribute}"""   )
																														UpperCAmelCase_			:      Union[str, Any]     						=  getattr(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )

												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(SCREAMING_SNAKE_CASE__   )


if __name__ == "__main__":
					snake_case_     :       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."
					)
					snake_case_     :       Union[str, Any]						=  parser.parse_args()
					convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)












'''simple docstring'''







import sys
import turtle





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			tuple[float, float]   ) ->	tuple[float, float]:
						return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			int,			) ->	None:
						my_pen.up()
						my_pen.goto(vertexa[0],			vertexa[1]   )
						my_pen.down()
						my_pen.goto(vertexa[0],			vertexa[1]   )
						my_pen.goto(vertexa[0],			vertexa[1]   )
						my_pen.goto(vertexa[0],			vertexa[1]   )

						if depth == 0:
												return

						triangle(SCREAMING_SNAKE_CASE__,			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			depth - 1   )
						triangle(SCREAMING_SNAKE_CASE__,			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			depth - 1   )
						triangle(SCREAMING_SNAKE_CASE__,			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			depth - 1   )


if __name__ == "__main__":
					if len(sys.argv) != 2:
										raise ValueError(
										    "Correct format for using this script: "
										    "python fractals.py <int:depth_for_fractal>"
										)
					snake_case_     :       Any						=  turtle.Turtle()
					my_pen.ht()
					my_pen.speed(5)
					my_pen.pencolor("red")

					snake_case_     :       Tuple						=  [(-1_75, -1_25), (0, 1_75), (1_75, -1_25)]  # vertices of triangle
					triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))





