

'''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						:  Any							       =				logging.get_logger(__name__)
logging.set_verbosity_info()


def       snake_case   (	snake_case      : str					,		snake_case      : str			) ->  List[str]:


    """simple docstring"""


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

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

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

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

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

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

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

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

                if old_attribute == "":
                    lowerCAmelCase 						=							old_model
                else:
                    if not hasattr(snake_case					,		snake_case			):
                        raise ValueError(F'{old_model} does not have {old_attribute}'			)
                    lowerCAmelCase 						=							getattr(snake_case					,		snake_case			)

        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(snake_case			)


if __name__ == "__main__":
     _UpperCamelCase						:  Union[str, 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						:  int							       =				parser.parse_args()
     convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)








'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase						:  Optional[int]							       =				logging.get_logger(__name__)

_UpperCamelCase						:  Tuple							       =				{
    "google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     '''canine'''

  def __init__(				self      , _SCREAMING_SNAKE_CASE=7_68      , _SCREAMING_SNAKE_CASE=12      , _SCREAMING_SNAKE_CASE=12      , _SCREAMING_SNAKE_CASE=30_72      , _SCREAMING_SNAKE_CASE="gelu"      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=1_63_84      , _SCREAMING_SNAKE_CASE=16      , _SCREAMING_SNAKE_CASE=0.02      , _SCREAMING_SNAKE_CASE=1e-12      , _SCREAMING_SNAKE_CASE=0      , _SCREAMING_SNAKE_CASE=0Xe0_00      , _SCREAMING_SNAKE_CASE=0Xe0_01      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=8      , _SCREAMING_SNAKE_CASE=1_63_84      , _SCREAMING_SNAKE_CASE=1_28      , **_SCREAMING_SNAKE_CASE      , ):





      '''simple docstring'''

      super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE      , bos_token_id=_SCREAMING_SNAKE_CASE      , eos_token_id=_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							max_position_embeddings
      lowerCAmelCase 						=							hidden_size
      lowerCAmelCase 						=							num_hidden_layers
      lowerCAmelCase 						=							num_attention_heads
      lowerCAmelCase 						=							intermediate_size
      lowerCAmelCase 						=							hidden_act
      lowerCAmelCase 						=							hidden_dropout_prob
      lowerCAmelCase 						=							attention_probs_dropout_prob
      lowerCAmelCase 						=							initializer_range
      lowerCAmelCase 						=							type_vocab_size
      lowerCAmelCase 						=							layer_norm_eps

      # Character config:
      lowerCAmelCase 						=							downsampling_rate
      lowerCAmelCase 						=							upsampling_kernel_size
      lowerCAmelCase 						=							num_hash_functions
      lowerCAmelCase 						=							num_hash_buckets
      lowerCAmelCase 						=							local_transformer_stride





