


'''simple docstring'''




import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_      :		List[str]          =		logging.get_logger(__name__)


def    UpperCAmelCase_  (       A		,	A		,	A      ):
   '''simple docstring'''





   _a					:  Tuple						=  os.path.abspath(A      )
   logger.info(f'''Converting TensorFlow checkpoint from {tf_path}'''      )
   # Load weights from TF model
   _a					:  List[Any]						=  tf.train.list_variables(A      )
   _a					:  Optional[int]						=  []
   _a					:  List[str]						=  []
   _a					:  Optional[Any]						=  []
   for full_name, shape in init_vars:
      # logger.info(f"Loading TF weight {name} with shape {shape}")
      _a					:  Union[str, Any]						=  full_name.split('/'      )
      if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
         logger.info(f'''Skipping non-model layer {full_name}'''      )
         continue
      if "optimizer" in full_name:
         logger.info(f'''Skipping optimization layer {full_name}'''      )
         continue
      if name[0] == "model":
         # ignore initial 'model'
         _a					:  Union[str, Any]						=  name[1:]
      # figure out how many levels deep the name is
      _a					:  Tuple						=  0
      for _name in name:
         if _name.startswith('layer_with_weights'      ):
            depth += 1
         else:
            break
      layer_depth.append(A      )
      # read data
      _a					:  Union[str, Any]						=  tf.train.load_variable(A		,	A      )
      names.append('/'.join(A      )      )
      arrays.append(A      )
   logger.info(f'''Read a total of {len(A      ):,} layers'''      )

   # Sanity check
   if len(set(A      )      ) != 1:
      raise ValueError(f'''Found layer names with different depths (layer depth {list(set(A      )      )})'''      )
   _a					:  Dict						=  list(set(A      )      )[0]
   if layer_depth != 1:
      raise ValueError(
          'The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'
          ' heads.'      )

   # convert layers
   logger.info('Converting weights...'      )
   for full_name, array in zip(A		,	A      ):
      _a					:  Optional[int]						=  full_name.split('/'      )
      _a					:  Dict						=  model
      _a					:  Union[str, Any]						=  []
      for i, m_name in enumerate(A      ):
         if m_name == ".ATTRIBUTES":
            # variable names end with .ATTRIBUTES/VARIABLE_VALUE
            break
         if m_name.startswith('layer_with_weights'      ):
            _a					:  Dict						=  int(m_name.split('-'      )[-1]      )
            if layer_num <= 2:
               # embedding layers
               # layer_num 0: word_embeddings
               # layer_num 1: position_embeddings
               # layer_num 2: token_type_embeddings
               continue
            elif layer_num == 3:
               # embedding LayerNorm
               trace.extend(['embeddings', 'LayerNorm']      )
               _a					:  Tuple						=  getattr(A		,	'embeddings'      )
               _a					:  str						=  getattr(A		,	'LayerNorm'      )
            elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
               # encoder layers
               trace.extend(['encoder', 'layer', str(layer_num - 4      )]      )
               _a					:  List[str]						=  getattr(A		,	'encoder'      )
               _a					:  Dict						=  getattr(A		,	'layer'      )
               _a					:  str						=  pointer[layer_num - 4]
            elif layer_num == config.num_hidden_layers + 4:
               # pooler layer
               trace.extend(['pooler', 'dense']      )
               _a					:  Optional[Any]						=  getattr(A		,	'pooler'      )
               _a					:  Any						=  getattr(A		,	'dense'      )
         elif m_name == "embeddings":
            trace.append('embeddings'      )
            _a					:  Dict						=  getattr(A		,	'embeddings'      )
            if layer_num == 0:
               trace.append('word_embeddings'      )
               _a					:  Dict						=  getattr(A		,	'word_embeddings'      )
            elif layer_num == 1:
               trace.append('position_embeddings'      )
               _a					:  Optional[Any]						=  getattr(A		,	'position_embeddings'      )
            elif layer_num == 2:
               trace.append('token_type_embeddings'      )
               _a					:  str						=  getattr(A		,	'token_type_embeddings'      )
            else:
               raise ValueError(f'''Unknown embedding layer with name {full_name}'''      )
            trace.append('weight'      )
            _a					:  Optional[int]						=  getattr(A		,	'weight'      )
         elif m_name == "_attention_layer":
            # self-attention layer
            trace.extend(['attention', 'self']      )
            _a					:  str						=  getattr(A		,	'attention'      )
            _a					:  List[Any]						=  getattr(A		,	'self'      )
         elif m_name == "_attention_layer_norm":
            # output attention norm
            trace.extend(['attention', 'output', 'LayerNorm']      )
            _a					:  str						=  getattr(A		,	'attention'      )
            _a					:  Tuple						=  getattr(A		,	'output'      )
            _a					:  Optional[int]						=  getattr(A		,	'LayerNorm'      )
         elif m_name == "_attention_output_dense":
            # output attention dense
            trace.extend(['attention', 'output', 'dense']      )
            _a					:  str						=  getattr(A		,	'attention'      )
            _a					:  Optional[Any]						=  getattr(A		,	'output'      )
            _a					:  Union[str, Any]						=  getattr(A		,	'dense'      )
         elif m_name == "_output_dense":
            # output dense
            trace.extend(['output', 'dense']      )
            _a					:  Tuple						=  getattr(A		,	'output'      )
            _a					:  Tuple						=  getattr(A		,	'dense'      )
         elif m_name == "_output_layer_norm":
            # output dense
            trace.extend(['output', 'LayerNorm']      )
            _a					:  Union[str, Any]						=  getattr(A		,	'output'      )
            _a					:  str						=  getattr(A		,	'LayerNorm'      )
         elif m_name == "_key_dense":
            # attention key
            trace.append('key'      )
            _a					:  Optional[int]						=  getattr(A		,	'key'      )
         elif m_name == "_query_dense":
            # attention query
            trace.append('query'      )
            _a					:  Dict						=  getattr(A		,	'query'      )
         elif m_name == "_value_dense":
            # attention value
            trace.append('value'      )
            _a					:  str						=  getattr(A		,	'value'      )
         elif m_name == "_intermediate_dense":
            # attention intermediate dense
            trace.extend(['intermediate', 'dense']      )
            _a					:  Union[str, Any]						=  getattr(A		,	'intermediate'      )
            _a					:  int						=  getattr(A		,	'dense'      )
         elif m_name == "_output_layer_norm":
            # output layer norm
            trace.append('output'      )
            _a					:  str						=  getattr(A		,	'output'      )
         # weights & biases
         elif m_name in ["bias", "beta"]:
            trace.append('bias'      )
            _a					:  List[Any]						=  getattr(A		,	'bias'      )
         elif m_name in ["kernel", "gamma"]:
            trace.append('weight'      )
            _a					:  List[Any]						=  getattr(A		,	'weight'      )
         else:
            logger.warning(f'''Ignored {m_name}'''      )
        # for certain layers reshape is necessary
      _a					:  List[str]						=  '.'.join(A      )
      if re.match(r'(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'		,	A      ) or re.match(
          r'(\S+)\.attention\.output\.dense\.weight'		,	A      ):
         _a					:  Dict						=  array.reshape(pointer.data.shape      )
      if "kernel" in full_name:
         _a					:  int						=  array.transpose()
      if pointer.shape == array.shape:
         _a					:  Any						=  torch.from_numpy(A      )
      else:
         raise ValueError(
             f'''Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'''
             f''' {array.shape}'''      )
      logger.info(f'''Successfully set variable {full_name} to PyTorch layer {trace}'''      )
   return model


def    UpperCAmelCase_  (       A		,	A		,	A      ):
   '''simple docstring'''





   logger.info(f'''Loading model based on config from {config_path}...'''      )
   _a					:  Tuple						=  BertConfig.from_json_file(A      )
   _a					:  Union[str, Any]						=  BertModel(A      )

   # Load weights from checkpoint
   logger.info(f'''Loading weights from checkpoint {tf_checkpoint_path}...'''      )
   load_tfa_weights_in_bert(A		,	A		,	A      )

   # Save pytorch-model
   logger.info(f'''Saving PyTorch model to {pytorch_dump_path}...'''      )
   torch.save(model.state_dict()		,	A      )


if __name__ == "__main__":
 UpperCAmelCase_      :		List[Any]          =		argparse.ArgumentParser()
 parser.add_argument(
     "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow 2.x checkpoint path."
 )
 parser.add_argument(
     "--bert_config_file",
     type=str,
     required=True,
     help="The config json file corresponding to the BERT model. This specifies the model architecture.",
 )
 parser.add_argument(
     "--pytorch_dump_path",
     type=str,
     required=True,
     help="Path to the output PyTorch model (must include filename).",
 )
 UpperCAmelCase_      :		int          =		parser.parse_args()
 convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






'''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


UpperCAmelCase_      :		List[str]          =		logging.get_logger(__name__)
logging.set_verbosity_info()


def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   if "xprophetnet" in prophetnet_checkpoint_path:
      _a					:  str						=  XLMProphetNetForConditionalGenerationOld.from_pretrained(A      )
      _a		,						_a					:  Any						=  XLMProphetNetForConditionalGeneration.from_pretrained(
          A		,	output_loading_info=A      )
   else:
      _a					:  Optional[Any]						=  ProphetNetForConditionalGenerationOld.from_pretrained(A      )
      _a		,						_a					:  Dict						=  ProphetNetForConditionalGeneration.from_pretrained(
          A		,	output_loading_info=A      )

   _a					:  Tuple						=  ['key_proj', 'value_proj', 'query_proj']

   _a					:  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"]:
      _a					:  Tuple						=  key.split('.'      )

      if attributes[0] == "lm_head":
         _a					:  str						=  prophet
         _a					:  Dict						=  prophet_old
      else:
         _a					:  Union[str, Any]						=  prophet.prophetnet
         _a					:  List[Any]						=  prophet_old.model

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

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

            elif attribute == "key_proj":
               _a					:  str						=  nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]      )
               _a					:  List[Any]						=  nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]      )
            elif attribute == "value_proj":
               _a					:  Optional[int]						=  nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]      )
               _a					:  str						=  nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]      )
            _a					:  int						=  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] == 5_1_2, "We want 512 position_embeddings."
            _a					:  Tuple						=  nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]      )
            _a					:  Any						=  True
            break

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

            if old_attribute == "":
               _a					:  List[str]						=  old_model
            else:
               if not hasattr(A		,	A      ):
                  raise ValueError(f'''{old_model} does not have {old_attribute}'''      )
               _a					:  Optional[int]						=  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__":
 UpperCAmelCase_      :		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."
 )
 UpperCAmelCase_      :		List[str]          =		parser.parse_args()
 convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)


