





import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration






def 							_lowerCAmelCase (  _a   :					Dict			)	->			List[str]:
     lowerCAmelCase_					:       List[str]    =		[
         """encoder.version""",
         """decoder.version""",
         """model.encoder.version""",
         """model.decoder.version""",
         """decoder.output_projection.weight""",
         """_float_tensor""",
         """encoder.embed_positions._float_tensor""",
         """decoder.embed_positions._float_tensor""",
     ]
     for k in ignore_keys:
          state_dict.pop(_a					,   _a			)






def 							_lowerCAmelCase (  _a   :					Optional[Any]			)	->			int:
     lowerCAmelCase_					:       str    =		list(s_dict.keys()			)
     for key in keys:
          if "transformer_layers" in key:
               lowerCAmelCase_					:       int    =		s_dict.pop(_a			)
          elif "subsample" in key:
               lowerCAmelCase_					:       Tuple    =		s_dict.pop(_a			)






def 							_lowerCAmelCase (  _a   :					List[str]			)	->			str:
     lowerCAmelCase_					:       Any    =		emb.weight.shape
     lowerCAmelCase_					:       List[Any]    =		nn.Linear(_a					,   _a					,   bias=_a			)
     lowerCAmelCase_					:       int    =		emb.weight.data
     return lin_layer






def 							_lowerCAmelCase (  _a   :					Optional[int]					,   _a   :					Optional[int]			)	->			Dict:
     lowerCAmelCase_					:       Dict    =		torch.load(_a					,   map_location="""cpu"""			)
     lowerCAmelCase_					:       List[Any]    =		mam_aaa["""args"""]
     lowerCAmelCase_					:       Dict    =		mam_aaa["""model"""]
     lowerCAmelCase_					:       Optional[int]    =		state_dict["""decoder.output_projection.weight"""]

     remove_ignore_keys_(_a			)
     rename_keys(_a			)

     lowerCAmelCase_					:       Dict    =		state_dict["""decoder.embed_tokens.weight"""].shape[0]

     lowerCAmelCase_					:       str    =		args.share_decoder_input_output_embed

     lowerCAmelCase_					:       int    =		[int(_a			) for i in args.conv_kernel_sizes.split(""","""			)]
     lowerCAmelCase_					:       Tuple    =		SpeechaTextConfig(
         vocab_size=_a					,   max_source_positions=args.max_source_positions					,   max_target_positions=args.max_target_positions					,   encoder_layers=args.encoder_layers					,   decoder_layers=args.decoder_layers					,   encoder_attention_heads=args.encoder_attention_heads					,   decoder_attention_heads=args.decoder_attention_heads					,   encoder_ffn_dim=args.encoder_ffn_embed_dim					,   decoder_ffn_dim=args.decoder_ffn_embed_dim					,   d_model=args.encoder_embed_dim					,   dropout=args.dropout					,   attention_dropout=args.attention_dropout					,   activation_dropout=args.activation_dropout					,   activation_function="""relu"""					,   num_conv_layers=len(_a			)					,   conv_channels=args.conv_channels					,   conv_kernel_sizes=_a					,   input_feat_per_channel=args.input_feat_per_channel					,   input_channels=args.input_channels					,   tie_word_embeddings=_a					,   num_beams=5					,   max_length=2_00					,   use_cache=_a					,   decoder_start_token_id=2					,   early_stopping=_a					,   )

     lowerCAmelCase_					:       List[str]    =		SpeechaTextForConditionalGeneration(_a			)
     lowerCAmelCase_					:       int    =		model.model.load_state_dict(_a					,   strict=_a			)
     if len(_a			) > 0 and not set(_a			) <= {
         "encoder.embed_positions.weights",
         "decoder.embed_positions.weights",
     }:
          raise ValueError(
              """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
              F' but all the following weights are missing {missing}'			)

     if tie_embeds:
          lowerCAmelCase_					:       int    =		make_linear_from_emb(model.model.decoder.embed_tokens			)
     else:
          lowerCAmelCase_					:       str    =		lm_head_weights

     model.save_pretrained(_a			)


if __name__ == "__main__":
      UpperCAmelCase_					:   int 							=						argparse.ArgumentParser()
      # Required parameters
      parser.add_argument("""--fairseq_path""", type=str, help="""Path to the fairseq model (.pt) file.""")
      parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
      UpperCAmelCase_					:   Tuple 							=						parser.parse_args()
      convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)







UpperCAmelCase_					:   str 							=						"""Alexander Joslin"""

import operator as op

from .stack import Stack






def 							_lowerCAmelCase (  _a   :					str			)	->			int:
     lowerCAmelCase_					:       Any    =		{"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}

     lowerCAmelCase_					:       Stack[int]    =		Stack()
     lowerCAmelCase_					:       Stack[str]    =		Stack()

     for i in equation:
          if i.isdigit():
               # RULE 1
               operand_stack.push(int(_a			)			)
          elif i in operators:
               # RULE 2
               operator_stack.push(_a			)
          elif i == ")":
               # RULE 4
               lowerCAmelCase_					:       Optional[int]    =		operator_stack.peek()
               operator_stack.pop()
               lowerCAmelCase_					:       Union[str, Any]    =		operand_stack.peek()
               operand_stack.pop()
               lowerCAmelCase_					:       List[str]    =		operand_stack.peek()
               operand_stack.pop()

               lowerCAmelCase_					:       Dict    =		operators[opr](_a					,   _a			)
               operand_stack.push(_a			)

    # RULE 5
     return operand_stack.peek()


if __name__ == "__main__":
      UpperCAmelCase_					:   Dict 							=						"""(5 + ((4 * 2) * (2 + 3)))"""
      # answer = 45
      print(F"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")
