


"""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


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




def 	snake_case		(  UpperCamelCase__							:    List[str]      ,					UpperCamelCase__							:    Tuple     ) ->   Tuple:
      if "xprophetnet" in prophetnet_checkpoint_path:
            lowerCamelCase       :       List[str]   =						XLMProphetNetForConditionalGenerationOld.from_pretrained(A__     )
            lowerCamelCase       :       int   =						XLMProphetNetForConditionalGeneration.from_pretrained(
                A__      ,					output_loading_info=A__     )
      else:
            lowerCamelCase       :       List[str]   =						ProphetNetForConditionalGenerationOld.from_pretrained(A__     )
            lowerCamelCase       :       Any   =						ProphetNetForConditionalGeneration.from_pretrained(
                A__      ,					output_loading_info=A__     )

      lowerCamelCase       :       str   =						['key_proj', 'value_proj', 'query_proj']

      lowerCamelCase       :       List[str]   =						{
          '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"]:
            lowerCamelCase       :       Any   =						key.split("""."""     )

            if attributes[0] == "lm_head":
                  lowerCamelCase       :       Tuple   =						prophet
                  lowerCamelCase       :       Any   =						prophet_old
            else:
                  lowerCamelCase       :       Dict   =						prophet.prophetnet
                  lowerCamelCase       :       Tuple   =						prophet_old.model

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

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

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

                  if attribute.isdigit():
                        lowerCamelCase       :       Union[str, Any]   =						model[int(A__     )]
                        lowerCamelCase       :       List[Any]   =						old_model[int(A__     )]
                  else:
                        lowerCamelCase       :       Tuple   =						getattr(A__      ,					A__     )

                        if old_attribute == "":
                              lowerCamelCase       :       List[str]   =						old_model
                        else:
                              if not hasattr(A__      ,					A__     ):
                                    raise ValueError(F'{old_model} does not have {old_attribute}'     )
                              lowerCamelCase       :       List[Any]   =						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__":
    __lowerCamelCase   :Dict						     =		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.'
    )
    __lowerCamelCase   :List[Any]						     =		parser.parse_args()
    convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)








from __future__ import annotations

lowerCAmelCase__			:      Union[str, Any]								=[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]






def 						a__			(				A__,      A__,      A__,      A__,      A__,      ):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					[
			    [0 for col in range(len(grid[0]				)				)] for row in range(len(A__				)				)
			]  # the reference grid
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					1
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[
			    [0 for col in range(len(grid[0]				)				)] for row in range(len(A__				)				)
			]  # the action grid

			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					init[0]
			SCREAMING_SNAKE_CASE_		:     List[str]					=					init[1]
			SCREAMING_SNAKE_CASE_		:     Dict					=					0
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					g + heuristic[x][y]  # cost from starting cell to destination cell
			SCREAMING_SNAKE_CASE_		:     List[str]					=					[[f, g, x, y]]

			SCREAMING_SNAKE_CASE_		:     Dict					=					False  # flag that is set when search is complete
			SCREAMING_SNAKE_CASE_		:     Tuple					=					False  # flag set if we can't find expand

			while not found and not resign:
						if len(A__				) == 0:
									raise ValueError('Algorithm is unable to find solution'				)
						else:  # to choose the least costliest action so as to move closer to the goal
									cell.sort()
									cell.reverse()
									SCREAMING_SNAKE_CASE_		:     Any					=					cell.pop()
									SCREAMING_SNAKE_CASE_		:     Any					=					next_cell[2]
									SCREAMING_SNAKE_CASE_		:     str					=					next_cell[3]
									SCREAMING_SNAKE_CASE_		:     Tuple					=					next_cell[1]

									if x == goal[0] and y == goal[1]:
												SCREAMING_SNAKE_CASE_		:     Dict					=					True
									else:
												for i in range(len(A__				)				):  # to try out different valid actions
															SCREAMING_SNAKE_CASE_		:     Optional[int]					=					x + DIRECTIONS[i][0]
															SCREAMING_SNAKE_CASE_		:     List[Any]					=					y + DIRECTIONS[i][1]
															if xa >= 0 and xa < len(A__				) and ya >= 0 and ya < len(grid[0]				):
																		if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																					SCREAMING_SNAKE_CASE_		:     List[Any]					=					g + cost
																					SCREAMING_SNAKE_CASE_		:     List[Any]					=					ga + heuristic[xa][ya]
																					cell.append([fa, ga, xa, ya]				)
																					SCREAMING_SNAKE_CASE_		:     int					=					1
																					SCREAMING_SNAKE_CASE_		:     int					=					i
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[]
			SCREAMING_SNAKE_CASE_		:     List[str]					=					goal[0]
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					goal[1]
			invpath.append([x, y]				)  # we get the reverse path from here
			while x != init[0] or y != init[1]:
						SCREAMING_SNAKE_CASE_		:     Any					=					x - DIRECTIONS[action[x][y]][0]
						SCREAMING_SNAKE_CASE_		:     int					=					y - DIRECTIONS[action[x][y]][1]
						SCREAMING_SNAKE_CASE_		:     int					=					xa
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					ya
						invpath.append([x, y]				)

			SCREAMING_SNAKE_CASE_		:     List[Any]					=					[]
			for i in range(len(A__				)				):
						path.append(invpath[len(A__				) - 1 - i]				)
			return path, action


if __name__ == "__main__":
				lowerCAmelCase__			:      Union[str, Any]								=[
				    [0, 1, 0, 0, 0, 0],
				    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
				    [0, 1, 0, 0, 0, 0],
				    [0, 1, 0, 0, 1, 0],
				    [0, 0, 0, 0, 1, 0],
				]

				lowerCAmelCase__			:      Any								=[0, 0]
				# all coordinates are given in format [y,x]
				lowerCAmelCase__			:      List[str]								=[len(grid) - 1, len(grid[0]) - 1]
				lowerCAmelCase__			:      str								=1

				# the cost map which pushes the path closer to the goal
				lowerCAmelCase__			:      Union[str, Any]								=[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
				for i in range(len(grid)):
								for j in range(len(grid[0])):
												lowerCAmelCase__			:      List[str]								=abs(i - goal[0]) + abs(j - goal[1])
												if grid[i][j] == 1:
																# added extra penalty in the heuristic map
																lowerCAmelCase__			:      List[str]								=99

				lowerCAmelCase__							,lowerCAmelCase__			:      Any								=search(grid, init, goal, cost, heuristic)

				print('ACTION MAP')
				for i in range(len(action)):
								print(action[i])

				for i in range(len(path)):
								print(path[i])





