





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


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Dict:
    if "xprophetnet" in prophetnet_checkpoint_path:
        lowerCamelCase__    :      Union[str, Any]					 =		XLMProphetNetForConditionalGenerationOld.from_pretrained(_UpperCAmelCase )
        lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		XLMProphetNetForConditionalGeneration.from_pretrained(
            _UpperCAmelCase						,	output_loading_info=_UpperCAmelCase )
    else:
        lowerCamelCase__    :      str					 =		ProphetNetForConditionalGenerationOld.from_pretrained(_UpperCAmelCase )
        lowerCamelCase__ ,							lowerCamelCase__    :      int					 =		ProphetNetForConditionalGeneration.from_pretrained(
            _UpperCAmelCase						,	output_loading_info=_UpperCAmelCase )

    lowerCamelCase__    :      List[str]					 =		['key_proj', 'value_proj', 'query_proj']

    lowerCamelCase__    :      Union[str, Any]					 =		{
        '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__    :      Optional[Any]					 =		key.split('.' )

        if attributes[0] == "lm_head":
            lowerCamelCase__    :      Optional[Any]					 =		prophet
            lowerCamelCase__    :      Union[str, Any]					 =		prophet_old
        else:
            lowerCamelCase__    :      int					 =		prophet.prophetnet
            lowerCamelCase__    :      Optional[int]					 =		prophet_old.model

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

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

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

            if attribute.isdigit():
                lowerCamelCase__    :      Optional[Any]					 =		model[int(_UpperCAmelCase )]
                lowerCamelCase__    :      List[str]					 =		old_model[int(_UpperCAmelCase )]
            else:
                lowerCamelCase__    :      int					 =		getattr(_UpperCAmelCase						,	_UpperCAmelCase )

                if old_attribute == "":
                    lowerCamelCase__    :      Optional[int]					 =		old_model
                else:
                    if not hasattr(_UpperCAmelCase						,	_UpperCAmelCase ):
                        raise ValueError(F"""{old_model} does not have {old_attribute}""" )
                    lowerCamelCase__    :      Optional[int]					 =		getattr(_UpperCAmelCase						,	_UpperCAmelCase )

        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(_UpperCAmelCase )


if __name__ == "__main__":
    _UpperCAmelCase				:       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."""
    )
    _UpperCAmelCase				:       str	   =				parser.parse_args()
    convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)












from __future__ import annotations

from typing import Any


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     None:
    create_state_space_tree(_UpperCAmelCase						,	[]						,	0 )


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     None:

    if index == len(_UpperCAmelCase ):
        print(_UpperCAmelCase )
        return

    create_state_space_tree(_UpperCAmelCase						,	_UpperCAmelCase						,	index + 1 )
    current_subsequence.append(sequence[index] )
    create_state_space_tree(_UpperCAmelCase						,	_UpperCAmelCase						,	index + 1 )
    current_subsequence.pop()


if __name__ == "__main__":
    _UpperCAmelCase				:       list[Any]	   =				[3, 1, 2, 4]
    generate_all_subsequences(seq)

    seq.clear()
    seq.extend(["""A""", """B""", """C"""])
    generate_all_subsequences(seq)





