




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM



def 							_A       (						lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Any      ):
     """simple docstring"""

     lowerCAmelCase__          =			AutoConfig.from_pretrained(lowerCAmelCase_      )
     lowerCAmelCase__          =			FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCAmelCase_      )
     lowerCAmelCase__          =			checkpoints.load_tax_checkpoint(lowerCAmelCase_      )

     lowerCAmelCase__          =			"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

     if config.model_type == "t5":
          lowerCAmelCase__          =			"SelfAttention"
     if config.model_type == "longt5" and config.encoder_attention_type == "local":
          lowerCAmelCase__          =			"LocalSelfAttention"
     elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
          lowerCAmelCase__          =			"TransientGlobalSelfAttention"
     else:
          raise ValueError(
              "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"
              " attribute with a value from ['local', 'transient-global]."      )

     # Encoder
     for layer_index in range(config.num_layers      ):
          lowerCAmelCase__          =			F'layers_{str(lowerCAmelCase_      )}'

          # Self-Attention
          lowerCAmelCase__          =			tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
          lowerCAmelCase__          =			tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
          lowerCAmelCase__          =			tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
          lowerCAmelCase__          =			tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

          # Global input layer norm
          if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
               lowerCAmelCase__          =			tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

          # Layer Normalization
          lowerCAmelCase__          =			tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

          if split_mlp_wi:
               lowerCAmelCase__          =			tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
               lowerCAmelCase__          =			tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
          else:
               lowerCAmelCase__          =			tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

          lowerCAmelCase__          =			tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          lowerCAmelCase__          =			tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          lowerCAmelCase__          =			flax_model.params["encoder"]["block"][str(lowerCAmelCase_      )]["layer"]
          lowerCAmelCase__          =			tax_attention_key
          lowerCAmelCase__          =			tax_attention_out
          lowerCAmelCase__          =			tax_attention_query
          lowerCAmelCase__          =			tax_attention_value

          lowerCAmelCase__          =			tax_attention_layer_norm

          # Global input layer norm
          if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
               lowerCAmelCase__          =			tax_global_layer_norm

          if split_mlp_wi:
               lowerCAmelCase__          =			tax_mlp_wi_a
               lowerCAmelCase__          =			tax_mlp_wi_a
          else:
               lowerCAmelCase__          =			tax_mlp_wi

          lowerCAmelCase__          =			tax_mlp_wo
          lowerCAmelCase__          =			tax_mlp_layer_norm

          lowerCAmelCase__          =			flax_model_encoder_layer_block

     # Only for layer 0:
     lowerCAmelCase__          =			tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
     lowerCAmelCase__          =			tax_encoder_rel_embedding

     # Side/global relative position_bias + layer norm
     if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
          lowerCAmelCase__          =			tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
          lowerCAmelCase__          =			tax_encoder_global_rel_embedding

     # Assigning
     lowerCAmelCase__          =			tax_model["target"]["encoder"]["encoder_norm"]["scale"]
     lowerCAmelCase__          =			tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_layers      ):
          lowerCAmelCase__          =			F'layers_{str(lowerCAmelCase_      )}'

          # Self-Attention
          lowerCAmelCase__          =			tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
          lowerCAmelCase__          =			tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
          lowerCAmelCase__          =			tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
          lowerCAmelCase__          =			tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

          # Layer Normalization
          lowerCAmelCase__          =			tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
              "scale"
          ]

          # Encoder-Decoder-Attention
          lowerCAmelCase__          =			tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
          lowerCAmelCase__          =			tax_enc_dec_attention_module["key"]["kernel"]
          lowerCAmelCase__          =			tax_enc_dec_attention_module["out"]["kernel"]
          lowerCAmelCase__          =			tax_enc_dec_attention_module["query"]["kernel"]
          lowerCAmelCase__          =			tax_enc_dec_attention_module["value"]["kernel"]

          # Layer Normalization
          lowerCAmelCase__          =			tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

          # MLP
          if split_mlp_wi:
               lowerCAmelCase__          =			tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
               lowerCAmelCase__          =			tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
          else:
               lowerCAmelCase__          =			tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

          lowerCAmelCase__          =			tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          lowerCAmelCase__          =			tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          lowerCAmelCase__          =			flax_model.params["decoder"]["block"][str(lowerCAmelCase_      )]["layer"]
          lowerCAmelCase__          =			tax_attention_key
          lowerCAmelCase__          =			tax_attention_out
          lowerCAmelCase__          =			tax_attention_query
          lowerCAmelCase__          =			tax_attention_value

          lowerCAmelCase__          =			tax_pre_attention_layer_norm

          lowerCAmelCase__          =			tax_enc_dec_attention_key
          lowerCAmelCase__          =			tax_enc_dec_attention_out
          lowerCAmelCase__          =			tax_enc_dec_attention_query
          lowerCAmelCase__          =			tax_enc_dec_attention_value

          lowerCAmelCase__          =			tax_cross_layer_norm

          if split_mlp_wi:
               lowerCAmelCase__          =			tax_mlp_wi_a
               lowerCAmelCase__          =			tax_mlp_wi_a
          else:
               lowerCAmelCase__          =			tax_mlp_wi

          lowerCAmelCase__          =			tax_mlp_wo

          lowerCAmelCase__          =			txa_mlp_layer_norm

          lowerCAmelCase__          =			flax_model_decoder_layer_block

     # Decoder Normalization
     lowerCAmelCase__          =			tax_model["target"]["decoder"]["decoder_norm"]["scale"]
     lowerCAmelCase__          =			txa_decoder_norm

     # Only for layer 0:
     lowerCAmelCase__          =			tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
     lowerCAmelCase__          =			tax_decoder_rel_embedding

     # Token Embeddings
     lowerCAmelCase__          =			tax_model["target"]["token_embedder"]["embedding"]
     lowerCAmelCase__          =			txa_token_embeddings

     # LM Head (only in v1.1 and LongT5 checkpoints)
     if "logits_dense" in tax_model["target"]["decoder"]:
          lowerCAmelCase__          =			tax_model["target"]["decoder"]["logits_dense"]["kernel"]

     flax_model.save_pretrained(lowerCAmelCase_      )
     print("T5X Model was sucessfully converted!"      )


if __name__ == "__main__":
     UpperCamelCase								=					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
     )
     parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
     parser.add_argument(
         '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
     )
     UpperCamelCase								=					parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






from __future__ import annotations

UpperCamelCase								=					[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]



def 							_A       (						lowerCAmelCase_					: list[list[int]]       ,    lowerCAmelCase_					: list[int]       ,    lowerCAmelCase_					: list[int]       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: list[list[int]]       ,    ):
     """simple docstring"""

     lowerCAmelCase__          =			[
         [0 for col in range(len(grid[0]      )      )] for row in range(len(lowerCAmelCase_      )      )
     ]  # the reference grid
     lowerCAmelCase__          =			1
     lowerCAmelCase__          =			[
         [0 for col in range(len(grid[0]      )      )] for row in range(len(lowerCAmelCase_      )      )
     ]  # the action grid

     lowerCAmelCase__          =			init[0]
     lowerCAmelCase__          =			init[1]
     lowerCAmelCase__          =			0
     lowerCAmelCase__          =			g + heuristic[x][y]  # cost from starting cell to destination cell
     lowerCAmelCase__          =			[[f, g, x, y]]

     lowerCAmelCase__          =			False  # flag that is set when search is complete
     lowerCAmelCase__          =			False  # flag set if we can't find expand

     while not found and not resign:
          if len(lowerCAmelCase_      ) == 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()
               lowerCAmelCase__          =			cell.pop()
               lowerCAmelCase__          =			next_cell[2]
               lowerCAmelCase__          =			next_cell[3]
               lowerCAmelCase__          =			next_cell[1]

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

     lowerCAmelCase__          =			[]
     for i in range(len(lowerCAmelCase_      )      ):
          path.append(invpath[len(lowerCAmelCase_      ) - 1 - i]      )
     return path, action


if __name__ == "__main__":
     UpperCamelCase								=					[
         [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],
     ]

     UpperCamelCase								=					[0, 0]
     # all coordinates are given in format [y,x]
     UpperCamelCase								=					[len(grid) - 1, len(grid[0]) - 1]
     UpperCamelCase								=					1

     # the cost map which pushes the path closer to the goal
     UpperCamelCase								=					[[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])):
               UpperCamelCase								=					abs(i - goal[0]) + abs(j - goal[1])
               if grid[i][j] == 1:
                    # added extra penalty in the heuristic map
                    UpperCamelCase								=					99

     UpperCamelCase						,  UpperCamelCase								=					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])
