




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      :						str							=  logging.get_logger(__name__)
logging.set_verbosity_info()







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			int:



       """simple docstring"""

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

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

       A__ 					=       {
           '''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__ 					=       key.split('''.'''		)

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

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

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

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

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

                            if old_attribute == "":
                                   A__ 					=       old_model
                            else:
                                   if not hasattr(lowercase_					,							lowercase_		):
                                          raise ValueError(f"""{old_model} does not have {old_attribute}"""		)
                                   A__ 					=       getattr(lowercase_					,							lowercase_		)

              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(lowercase_		)


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      :						Any							=  parser.parse_args()
  convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)









"""simple docstring"""


import re




def        lowercase__     (       lowercase_      )       ->		bool:




      """simple docstring"""

      _UpperCamelCase   :						List[Any]    =							re.compile(
          r"^(?:0|94|\+94|0{2}94)" r"7(0|1|2|4|5|6|7|8)" r"(-| |)" r"\d{7}$"      )

      return bool(re.search(lowercase_		,lowercase_      )      )


if __name__ == "__main__":
  lowerCamelCase__						= "0094702343221"

  print(is_sri_lankan_phone_number(phone))
