



"""simple docstring"""






import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


lowerCAmelCase__      =    logging.get_logger(__name__)






def 	snake_case_  (				A_ :		Tuple,    A_ :		Optional[Any],    A_ :		Optional[int],    A_ :		List[Any]=False						):


       '''simple docstring'''





       try:
              import torch  # noqa: F401
       except ImportError:
              logger.error(
                  '''Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'''
                  ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
                  ''' instructions.'''						)
              raise

       if not is_sharded:
              _lowerCamelCase						:    Optional[int]         =			os.path.abspath(A_						)
              logger.info(F'''Loading PyTorch weights from {pt_path}'''						)

              _lowerCamelCase						:    str         =			torch.load(A_,    map_location='''cpu'''						)
              logger.info(F'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()						):,} parameters.'''						)

              _lowerCamelCase						:    List[str]         =			convert_pytorch_state_dict_to_flax(A_,    A_						)
       else:
              # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
              _lowerCamelCase						:    Optional[int]         =			convert_pytorch_sharded_state_dict_to_flax(A_,    A_						)
       return flax_state_dict






def 	snake_case_  (				A_ :		Tuple[str],    A_ :		np.ndarray,    A_ :		Dict[str, jnp.ndarray],    A_ :		str,    ):


       '''simple docstring'''





       def is_key_or_prefix_key_in_dict(A_ :		Tuple[str]						) -> bool:
              return len(set(A_						) & {key, (model_prefix,) + key}						) > 0

       # layer norm
       _lowerCamelCase						:    Any         =			pt_tuple_key[:-1] + ('''scale''',)
       if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(A_						):
              return renamed_pt_tuple_key, pt_tensor

       # batch norm layer mean
       _lowerCamelCase						:    Any         =			pt_tuple_key[:-1] + ('''mean''',)
       if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(A_						):
              return renamed_pt_tuple_key, pt_tensor

       # batch norm layer var
       _lowerCamelCase						:    List[str]         =			pt_tuple_key[:-1] + ('''var''',)
       if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(A_						):
              return renamed_pt_tuple_key, pt_tensor

       # embedding
       _lowerCamelCase						:    int         =			pt_tuple_key[:-1] + ('''embedding''',)
       if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(A_						):
              return renamed_pt_tuple_key, pt_tensor

       # conv layer
       _lowerCamelCase						:    Union[str, Any]         =			pt_tuple_key[:-1] + ('''kernel''',)
       if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(A_						):
              _lowerCamelCase						:    Tuple         =			pt_tensor.transpose(2,    3,    1,    0						)
              return renamed_pt_tuple_key, pt_tensor

       # linear layer
       _lowerCamelCase						:    Union[str, Any]         =			pt_tuple_key[:-1] + ('''kernel''',)
       if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(A_						):
              _lowerCamelCase						:    Optional[int]         =			pt_tensor.T
              return renamed_pt_tuple_key, pt_tensor

       # old PyTorch layer norm weight
       _lowerCamelCase						:    Any         =			pt_tuple_key[:-1] + ('''weight''',)
       if pt_tuple_key[-1] == "gamma":
              return renamed_pt_tuple_key, pt_tensor

       # old PyTorch layer norm bias
       _lowerCamelCase						:    Union[str, Any]         =			pt_tuple_key[:-1] + ('''bias''',)
       if pt_tuple_key[-1] == "beta":
              return renamed_pt_tuple_key, pt_tensor

       # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
       _lowerCamelCase						:    List[Any]         =			None
       if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
              _lowerCamelCase						:    List[str]         =			pt_tuple_key[-2] + '''_g'''
       elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
              _lowerCamelCase						:    Optional[int]         =			pt_tuple_key[-2] + '''_v'''
       if name is not None:
              _lowerCamelCase						:    int         =			pt_tuple_key[:-3] + (name,)
              return renamed_pt_tuple_key, pt_tensor

       return pt_tuple_key, pt_tensor






def 	snake_case_  (				A_ :		Union[str, Any],    A_ :		Tuple						):


       '''simple docstring'''





       _lowerCamelCase						:    List[Any]         =			{k: v.numpy() for k, v in pt_state_dict.items()}

       _lowerCamelCase						:    List[Any]         =			flax_model.base_model_prefix

       # use params dict if the model contains batch norm layers
       if "params" in flax_model.params:
              _lowerCamelCase						:    Optional[int]         =			flax_model.params['''params''']
       else:
              _lowerCamelCase						:    str         =			flax_model.params
       _lowerCamelCase						:    int         =			flatten_dict(A_						)

       # add batch_stats keys,values to dict
       if "batch_stats" in flax_model.params:
              _lowerCamelCase						:    Tuple         =			flatten_dict(flax_model.params['''batch_stats''']						)
              random_flax_state_dict.update(A_						)

       _lowerCamelCase						:    Optional[int]         =			{}

       _lowerCamelCase						:    Optional[int]         =			(model_prefix not in flax_model_params) and (
           model_prefix in {k.split('''.'''						)[0] for k in pt_state_dict.keys()}
       )
       _lowerCamelCase						:    Optional[Any]         =			(model_prefix in flax_model_params) and (
           model_prefix not in {k.split('''.'''						)[0] for k in pt_state_dict.keys()}
       )

       # Need to change some parameters name to match Flax names
       for pt_key, pt_tensor in pt_state_dict.items():
              _lowerCamelCase						:    Optional[Any]         =			tuple(pt_key.split('''.'''						)						)

              # remove base model prefix if necessary
              _lowerCamelCase						:    List[Any]         =			pt_tuple_key[0] == model_prefix
              if load_model_with_head_into_base_model and has_base_model_prefix:
                     _lowerCamelCase						:    str         =			pt_tuple_key[1:]

              # Correctly rename weight parameters
              _lowerCamelCase      ,   _lowerCamelCase						:    Tuple         =			rename_key_and_reshape_tensor(
                  A_,    A_,    A_,    A_						)

              # add model prefix if necessary
              _lowerCamelCase						:    Any         =			(model_prefix,) + flax_key in random_flax_state_dict
              if load_base_model_into_model_with_head and require_base_model_prefix:
                     _lowerCamelCase						:    Any         =			(model_prefix,) + flax_key

              if flax_key in random_flax_state_dict:
                     if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                            raise ValueError(
                                F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
                                F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''						)

        # add batch stats if the model contains batchnorm layers
              if "batch_stats" in flax_model.params:
                     if "mean" in flax_key[-1] or "var" in flax_key[-1]:
                            _lowerCamelCase						:    Dict         =			jnp.asarray(A_						)
                            continue
                     # remove num_batches_tracked key
                     if "num_batches_tracked" in flax_key[-1]:
                            flax_state_dict.pop(A_,    A_						)
                            continue

                     # also add unexpected weight so that warning is thrown
                     _lowerCamelCase						:    List[Any]         =			jnp.asarray(A_						)

              else:
                     # also add unexpected weight so that warning is thrown
                     _lowerCamelCase						:    Union[str, Any]         =			jnp.asarray(A_						)

       return unflatten_dict(A_						)






def 	snake_case_  (				A_ :		str,    A_ :		Dict						):


       '''simple docstring'''





       import torch

       # Load the index
       _lowerCamelCase						:    Dict         =			{}
       for shard_file in shard_filenames:
              # load using msgpack utils
              _lowerCamelCase						:    Union[str, Any]         =			torch.load(A_						)
              _lowerCamelCase						:    str         =			{k: v.numpy() for k, v in pt_state_dict.items()}

              _lowerCamelCase						:    Optional[int]         =			flax_model.base_model_prefix

              # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
              if "batch_stats" in flax_model.params:
                     _lowerCamelCase						:    Any         =			flax_model.params['''params''']

                     _lowerCamelCase						:    Union[str, Any]         =			flatten_dict(A_						)
                     random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']						)						)
              else:
                     _lowerCamelCase						:    Tuple         =			flax_model.params
                     _lowerCamelCase						:    str         =			flatten_dict(A_						)

              _lowerCamelCase						:    Dict         =			(model_prefix not in flax_model_params) and (
                  model_prefix in {k.split('''.'''						)[0] for k in pt_state_dict.keys()}
              )
              _lowerCamelCase						:    Dict         =			(model_prefix in flax_model_params) and (
                  model_prefix not in {k.split('''.'''						)[0] for k in pt_state_dict.keys()}
              )
              # Need to change some parameters name to match Flax names
              for pt_key, pt_tensor in pt_state_dict.items():
                     _lowerCamelCase						:    int         =			tuple(pt_key.split('''.'''						)						)

                     # remove base model prefix if necessary
                     _lowerCamelCase						:    Tuple         =			pt_tuple_key[0] == model_prefix
                     if load_model_with_head_into_base_model and has_base_model_prefix:
                            _lowerCamelCase						:    List[Any]         =			pt_tuple_key[1:]

                     # Correctly rename weight parameters
                     _lowerCamelCase      ,   _lowerCamelCase						:    Dict         =			rename_key_and_reshape_tensor(
                         A_,    A_,    A_,    A_						)
                     # add model prefix if necessary
                     _lowerCamelCase						:    Optional[Any]         =			(model_prefix,) + flax_key in random_flax_state_dict
                     if load_base_model_into_model_with_head and require_base_model_prefix:
                            _lowerCamelCase						:    List[Any]         =			(model_prefix,) + flax_key

                     if flax_key in random_flax_state_dict:
                            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                                   raise ValueError(
                                       F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
                                       F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''						)

            # add batch stats if the model contains batchnorm layers
                     if "batch_stats" in flax_model.params:
                            if "mean" in flax_key[-1]:
                                   _lowerCamelCase						:    Dict         =			jnp.asarray(A_						)
                                   continue
                            if "var" in flax_key[-1]:
                                   _lowerCamelCase						:    int         =			jnp.asarray(A_						)
                                   continue
                            # remove num_batches_tracked key
                            if "num_batches_tracked" in flax_key[-1]:
                                   flax_state_dict.pop(A_,    A_						)
                                   continue

                            # also add unexpected weight so that warning is thrown
                            _lowerCamelCase						:    Tuple         =			jnp.asarray(A_						)

                     else:
                            # also add unexpected weight so that warning is thrown
                            _lowerCamelCase						:    Optional[int]         =			jnp.asarray(A_						)
       return unflatten_dict(A_						)






def 	snake_case_  (				A_ :		List[str],    A_ :		Dict						):


       '''simple docstring'''





       _lowerCamelCase						:    Dict         =			os.path.abspath(A_						)
       logger.info(F'''Loading Flax weights from {flax_checkpoint_path}'''						)

       # import correct flax class
       _lowerCamelCase						:    Union[str, Any]         =			getattr(A_,    '''Flax''' + model.__class__.__name__						)

       # load flax weight dict
       with open(A_,    '''rb'''						) as state_f:
              try:
                     _lowerCamelCase						:    Dict         =			from_bytes(A_,    state_f.read()						)
              except UnpicklingError:
                     raise EnvironmentError(F'''Unable to convert {flax_checkpoint_path} to Flax deserializable object. '''						)

       return load_flax_weights_in_pytorch_model(A_,    A_						)







def 	snake_case_  (				A_ :		int,    A_ :		List[Any]						):


       '''simple docstring'''





       try:
              import torch  # noqa: F401
       except ImportError:
              logger.error(
                  '''Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'''
                  ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
                  ''' instructions.'''						)
              raise

       # check if we have bf16 weights
       _lowerCamelCase						:    Union[str, Any]         =			flatten_dict(jax.tree_util.tree_map(lambda A_						: x.dtype == jnp.bfloataa,    A_						)						).values()
       if any(A_						):
              # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
              # and bf16 is not fully supported in PT yet.
              logger.warning(
                  '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
                  '''before loading those in PyTorch model.'''						)
              _lowerCamelCase						:    Optional[int]         =			jax.tree_util.tree_map(
                  lambda A_						: params.astype(np.floataa						) if params.dtype == jnp.bfloataa else params,    A_						)

       _lowerCamelCase						:    Dict         =			flatten_dict(A_						)
       _lowerCamelCase						:    Tuple         =			pt_model.state_dict()

       _lowerCamelCase						:    Any         =			(pt_model.base_model_prefix in flax_state) and (
           pt_model.base_model_prefix not in {k.split('''.'''						)[0] for k in pt_model_dict.keys()}
       )
       _lowerCamelCase						:    List[str]         =			(pt_model.base_model_prefix not in flax_state) and (
           pt_model.base_model_prefix in {k.split('''.'''						)[0] for k in pt_model_dict.keys()}
       )

       # keep track of unexpected & missing keys
       _lowerCamelCase						:    Optional[int]         =			[]
       _lowerCamelCase						:    Any         =			set(pt_model_dict.keys()						)

       for flax_key_tuple, flax_tensor in flax_state_dict.items():
              _lowerCamelCase						:    int         =			flax_key_tuple[0] == pt_model.base_model_prefix
              _lowerCamelCase						:    Optional[Any]         =			'''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple						) in pt_model_dict

              # adapt flax_key to prepare for loading from/to base model only
              if load_model_with_head_into_base_model and has_base_model_prefix:
                     _lowerCamelCase						:    int         =			flax_key_tuple[1:]
              elif load_base_model_into_model_with_head and require_base_model_prefix:
                     _lowerCamelCase						:    Any         =			(pt_model.base_model_prefix,) + flax_key_tuple

              # rename flax weights to PyTorch format
              if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(A_						) not in pt_model_dict:
                     # conv layer
                     _lowerCamelCase						:    str         =			flax_key_tuple[:-1] + ('''weight''',)
                     _lowerCamelCase						:    List[str]         =			jnp.transpose(A_,    (3, 2, 0, 1)						)
              elif flax_key_tuple[-1] == "kernel" and ".".join(A_						) not in pt_model_dict:
                     # linear layer
                     _lowerCamelCase						:    Optional[int]         =			flax_key_tuple[:-1] + ('''weight''',)
                     _lowerCamelCase						:    Dict         =			flax_tensor.T
              elif flax_key_tuple[-1] in ["scale", "embedding"]:
                     _lowerCamelCase						:    str         =			flax_key_tuple[:-1] + ('''weight''',)

              # adding batch stats from flax batch norm to pt
              elif "mean" in flax_key_tuple[-1]:
                     _lowerCamelCase						:    Tuple         =			flax_key_tuple[:-1] + ('''running_mean''',)
              elif "var" in flax_key_tuple[-1]:
                     _lowerCamelCase						:    Optional[Any]         =			flax_key_tuple[:-1] + ('''running_var''',)

              if "batch_stats" in flax_state:
                     _lowerCamelCase						:    str         =			'''.'''.join(flax_key_tuple[1:]						)  # Remove the params/batch_stats header
              else:
                     _lowerCamelCase						:    Tuple         =			'''.'''.join(A_						)

              # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
              _lowerCamelCase						:    Any         =			{}
              # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
              for key in pt_model_dict:
                     _lowerCamelCase						:    Optional[int]         =			key.split('''.'''						)
                     _lowerCamelCase						:    Any         =			None
                     if key_components[-3::2] == ["parametrizations", "original0"]:
                            _lowerCamelCase						:    List[Any]         =			key_components[-2] + '''_g'''
                     elif key_components[-3::2] == ["parametrizations", "original1"]:
                            _lowerCamelCase						:    Union[str, Any]         =			key_components[-2] + '''_v'''
                     if name is not None:
                            _lowerCamelCase						:    Optional[Any]         =			key_components[:-3] + [name]
                            _lowerCamelCase						:    int         =			'''.'''.join(A_						)
                            _lowerCamelCase						:    Tuple         =			key

              if flax_key in special_pt_names:
                     _lowerCamelCase						:    Any         =			special_pt_names[flax_key]

              if flax_key in pt_model_dict:
                     if flax_tensor.shape != pt_model_dict[flax_key].shape:
                            raise ValueError(
                                F'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
                                F'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'''						)
                     else:
                            # add weight to pytorch dict
                            _lowerCamelCase						:    Optional[Any]         =			np.asarray(A_						) if not isinstance(A_,    np.ndarray						) else flax_tensor
                            _lowerCamelCase						:    Optional[int]         =			torch.from_numpy(A_						)
                            # remove from missing keys
                            missing_keys.remove(A_						)
              else:
                     # weight is not expected by PyTorch model
                     unexpected_keys.append(A_						)

       pt_model.load_state_dict(A_						)

       # re-transform missing_keys to list
       _lowerCamelCase						:    Tuple         =			list(A_						)

       if len(A_						) > 0:
              logger.warning(
                  '''Some weights of the Flax model were not used when initializing the PyTorch model'''
                  F''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
                  F''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
                  ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
                  F''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
                  ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
                  ''' FlaxBertForSequenceClassification model).'''						)
       else:
              logger.warning(F'''All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'''						)
       if len(A_						) > 0:
              logger.warning(
                  F'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
                  F''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
                  ''' use it for predictions and inference.'''						)
       else:
              logger.warning(
                  F'''All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'''
                  '''If your task is similar to the task the model of the checkpoint was trained on, '''
                  F'''you can already use {pt_model.__class__.__name__} for predictions without further training.'''						)

       return pt_model







"""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


lowerCAmelCase__      =    logging.get_logger(__name__)
logging.set_verbosity_info()






def 	snake_case_  (				A_ :		str,    A_ :		str						):


       '''simple docstring'''





       if "xprophetnet" in prophetnet_checkpoint_path:
              _lowerCamelCase						:    Any         =			XLMProphetNetForConditionalGenerationOld.from_pretrained(A_						)
              _lowerCamelCase      ,   _lowerCamelCase						:    List[str]         =			XLMProphetNetForConditionalGeneration.from_pretrained(
                  A_,    output_loading_info=A_						)
       else:
              _lowerCamelCase						:    str         =			ProphetNetForConditionalGenerationOld.from_pretrained(A_						)
              _lowerCamelCase      ,   _lowerCamelCase						:    Any         =			ProphetNetForConditionalGeneration.from_pretrained(
                  A_,    output_loading_info=A_						)

       _lowerCamelCase						:    Optional[Any]         =			['''key_proj''', '''value_proj''', '''query_proj''']

       _lowerCamelCase						:    List[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						:    Union[str, Any]         =			key.split('''.'''						)

              if attributes[0] == "lm_head":
                     _lowerCamelCase						:    str         =			prophet
                     _lowerCamelCase						:    List[Any]         =			prophet_old
              else:
                     _lowerCamelCase						:    Optional[int]         =			prophet.prophetnet
                     _lowerCamelCase						:    Optional[Any]         =			prophet_old.model

              _lowerCamelCase						:    Any         =			False
              for attribute in attributes:
                     if attribute in mapping:
                            _lowerCamelCase						:    Optional[int]         =			mapping[attribute]
                            if not hasattr(A_,    A_						) and len(A_						) > 0:
                                   _lowerCamelCase						:    int         =			attribute
                     elif hasattr(A_,    A_						):
                            _lowerCamelCase						:    int         =			attribute

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

                            elif attribute == "key_proj":
                                   _lowerCamelCase						:    int         =			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]						)
                                   _lowerCamelCase						:    str         =			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]						)
                            elif attribute == "value_proj":
                                   _lowerCamelCase						:    Tuple         =			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]						)
                                   _lowerCamelCase						:    str         =			nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]						)
                            _lowerCamelCase						:    Dict         =			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_12, "We want 512 position_embeddings."
                            _lowerCamelCase						:    List[str]         =			nn.Parameter(old_model.embed_positions.weight[:5_12, :]						)
                            _lowerCamelCase						:    Optional[Any]         =			True
                            break

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

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


