

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


lowercase_             = logging.get_logger(__name__)
logging.set_verbosity_info()
def   a   (    A__			:     str   ,     A__			:     str  )					->   Optional[int]:





      """simple docstring"""



      if "xprophetnet" in prophetnet_checkpoint_path:
            _lowercase			 =XLMProphetNetForConditionalGenerationOld.from_pretrained(A__  )
            _lowercase , _lowercase			 =XLMProphetNetForConditionalGeneration.from_pretrained(
                A__   ,     output_loading_info=A__  )
      else:
            _lowercase			 =ProphetNetForConditionalGenerationOld.from_pretrained(A__  )
            _lowercase , _lowercase			 =ProphetNetForConditionalGeneration.from_pretrained(
                A__   ,     output_loading_info=A__  )

      _lowercase			 =['key_proj', 'value_proj', 'query_proj']

      _lowercase			 ={
          '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"]:
            _lowercase			 =key.split('.'  )

            if attributes[0] == "lm_head":
                  _lowercase			 =prophet
                  _lowercase			 =prophet_old
            else:
                  _lowercase			 =prophet.prophetnet
                  _lowercase			 =prophet_old.model

            _lowercase			 =False
            for attribute in attributes:
                  if attribute in mapping:
                        _lowercase			 =mapping[attribute]
                        if not hasattr(A__   ,     A__  ) and len(A__  ) > 0:
                              _lowercase			 =attribute
                  elif hasattr(A__   ,     A__  ):
                        _lowercase			 =attribute

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

                        elif attribute == "key_proj":
                              _lowercase			 =nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]  )
                              _lowercase			 =nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]  )
                        elif attribute == "value_proj":
                              _lowercase			 =nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]  )
                              _lowercase			 =nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]  )
                        _lowercase			 =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."
                        _lowercase			 =nn.Parameter(old_model.embed_positions.weight[:512, :]  )
                        _lowercase			 =True
                        break

                  if attribute.isdigit():
                        _lowercase			 =model[int(A__  )]
                        _lowercase			 =old_model[int(A__  )]
                  else:
                        _lowercase			 =getattr(A__   ,     A__  )

                        if old_attribute == "":
                              _lowercase			 =old_model
                        else:
                              if not hasattr(A__   ,     A__  ):
                                    raise ValueError(F'''{old_model} does not have {old_attribute}'''  )
                              _lowercase			 =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__":
  lowercase_             = 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.'
  )
  lowercase_             = parser.parse_args()
  convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)





def   a   (    A__			:     Optional[int]  )					->   Tuple:





      """simple docstring"""



      _lowercase			 =[0] * len(A__  )
      _lowercase			 =[]
      _lowercase			 =[]
      _lowercase			 =0

      for values in graph.values():
            for i in values:
                  indegree[i] += 1

      for i in range(len(A__  )  ):
            if indegree[i] == 0:
                  queue.append(A__  )

      while queue:
            _lowercase			 =queue.pop(0  )
            cnt += 1
            topo.append(A__  )
            for x in graph[vertex]:
                  indegree[x] -= 1
                  if indegree[x] == 0:
                        queue.append(A__  )

      if cnt != len(A__  ):
            print('Cycle exists'  )
      else:
            print(A__  )


# Adjacency List of Graph
lowercase_             =     {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)


