





import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class SCREAMING_SNAKE_CASE_						(					__lowerCamelCase					):

						'''simple docstring'''





						__magic_name__:			List[str]       =       ""
						__magic_name__:			List[str]       =       (
						    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
						)
						__magic_name__:			Optional[Any]       =       None  # compression type in fsspec. ex: "gzip"
						__magic_name__:			Union[str, Any]       =       None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz






						def __init__(      self      :      Optional[Any]		,   _A      :      str = ""		,   _A      :      Optional[str] = None		,   _A      :      Optional[dict] = None		,   **_A      :      Dict	)							->    str:







							"""simple docstring"""


							super().__init__(self		,   **UpperCAmelCase_	)
							# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
							snake_case_		:			int											=	fsspec.open(
							    UpperCAmelCase_		,   mode='rb'		,   protocol=UpperCAmelCase_		,   compression=self.compression		,   client_kwargs={
							        'requote_redirect_url': False,  # see https://github.com/huggingface/datasets/pull/5459
							        'trust_env': True,  # Enable reading proxy env variables.
							        **(target_options or {}).pop('client_kwargs'		,   {}	),  # To avoid issues if it was already passed.
							    }		,   **(target_options or {})		,   )
							snake_case_		:			Tuple											=	os.path.basename(self.file.path.split('::'	)[0]	)
							snake_case_		:			Union[str, Any]											=	(
							    self.compressed_name[: self.compressed_name.rindex('.'	)]
							    if '.' in self.compressed_name
							    else self.compressed_name
							)
							snake_case_		:			str											=	None






						@classmethod
						def UpperCAmelCase_				(      cls      :      Optional[int]		,   _A      :      Dict	)							->    Tuple:







							"""simple docstring"""


							return super()._strip_protocol(UpperCAmelCase_	).lstrip('/'	)






						def UpperCAmelCase_				(      self      :      Any	)							->    List[Any]:







							"""simple docstring"""


							if self.dir_cache is None:
								snake_case_		:			Tuple											=	{**self.file.fs.info(self.file.path	), 'name': self.uncompressed_name}
								snake_case_		:			Dict											=	{f['name']: f}






						def UpperCAmelCase_				(      self      :      str		,   _A      :      str	)							->    List[str]:







							"""simple docstring"""


							return self.file.open().read()






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      str		,   _A      :      str = "rb"		,   _A      :      Any=None		,   _A      :      Optional[int]=True		,   _A      :      Any=None		,   **_A      :      str		,   )							->    Tuple:







							"""simple docstring"""


							snake_case_		:			str											=	self._strip_protocol(UpperCAmelCase_	)
							if mode != "rb":
								raise ValueError(F"""Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\'"""	)
							return self.file.open()
class SCREAMING_SNAKE_CASE_						(					__lowerCamelCase					):

						'''simple docstring'''





						__magic_name__:			Tuple       =       "bz2"
						__magic_name__:			Tuple       =       "bz2"
						__magic_name__:			List[Any]       =       ".bz2"
class SCREAMING_SNAKE_CASE_						(					__lowerCamelCase					):

						'''simple docstring'''





						__magic_name__:			int       =       "gzip"
						__magic_name__:			List[Any]       =       "gzip"
						__magic_name__:			Tuple       =       ".gz"
class SCREAMING_SNAKE_CASE_						(					__lowerCamelCase					):

						'''simple docstring'''





						__magic_name__:			List[Any]       =       "lz4"
						__magic_name__:			List[Any]       =       "lz4"
						__magic_name__:			List[Any]       =       ".lz4"
class SCREAMING_SNAKE_CASE_						(					__lowerCamelCase					):

						'''simple docstring'''





						__magic_name__:			Union[str, Any]       =       "xz"
						__magic_name__:			Optional[Any]       =       "xz"
						__magic_name__:			int       =       ".xz"
class SCREAMING_SNAKE_CASE_						(					__lowerCamelCase					):

						'''simple docstring'''





						__magic_name__:			Dict       =       "zstd"
						__magic_name__:			Any       =       "zstd"
						__magic_name__:			List[str]       =       ".zst"






						def __init__(      self      :      Optional[int]		,   _A      :      str		,   _A      :      str = "rb"		,   _A      :      Optional[str] = None		,   _A      :      Optional[dict] = None		,   _A      :      int = DEFAULT_BLOCK_SIZE		,   **_A      :      Dict		,   )							->    Dict:







							"""simple docstring"""


							super().__init__(
							    fo=UpperCAmelCase_		,   mode=UpperCAmelCase_		,   target_protocol=UpperCAmelCase_		,   target_options=UpperCAmelCase_		,   block_size=UpperCAmelCase_		,   **UpperCAmelCase_		,   )
							# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
							#
							# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
							#     out.close = close
							# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
							#
							# see https://github.com/intake/filesystem_spec/issues/725
							snake_case_		:			str											=	self.file.__enter__
							class SCREAMING_SNAKE_CASE_						:

													'''simple docstring'''





													def __init__(      self      :      Optional[int]		,   _A      :      Dict	)							->    str:







														"""simple docstring"""


														snake_case_		:			str											=	file_






													def __enter__(      self      :      str	)							->    Union[str, Any]:







														"""simple docstring"""


														self._file.__enter__()
														return self






													def __exit__(      self      :      Optional[int]		,   *_A      :      Dict		,   **_A      :      Any	)							->    Dict:







														"""simple docstring"""


														self._file.__exit__(*UpperCAmelCase_		,   **UpperCAmelCase_	)






													def __iter__(      self      :      Tuple	)							->    List[Any]:







														"""simple docstring"""


														return iter(self._file	)






													def UpperCAmelCase_				(      self      :      Any	)							->    Union[str, Any]:







														"""simple docstring"""


														return next(self._file	)






													def __getattr__(      self      :      str		,   _A      :      Tuple	)							->    List[Any]:







														"""simple docstring"""


														return getattr(self._file		,   UpperCAmelCase_	)

							def fixed_enter(*_A      :      List[str]		,   **_A      :      Dict	):
								return WrappedFile(_enter(*UpperCAmelCase_		,   **UpperCAmelCase_	)	)

							snake_case_		:			str											=	fixed_enter








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


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


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	if "xprophetnet" in prophetnet_checkpoint_path:
		snake_case_		:			int											=	XLMProphetNetForConditionalGenerationOld.from_pretrained(__a   )
		snake_case_						,snake_case_		:			List[Any]											=	XLMProphetNetForConditionalGeneration.from_pretrained(
		    __a   , output_loading_info=__a   )
	else:
		snake_case_		:			List[str]											=	ProphetNetForConditionalGenerationOld.from_pretrained(__a   )
		snake_case_						,snake_case_		:			Dict											=	ProphetNetForConditionalGeneration.from_pretrained(
		    __a   , output_loading_info=__a   )

	snake_case_		:			str											=	['key_proj', 'value_proj', 'query_proj']

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

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

		snake_case_		:			Union[str, Any]											=	False
		for attribute in attributes:
			if attribute in mapping:
				snake_case_		:			Optional[Any]											=	mapping[attribute]
				if not hasattr(__a   , __a   ) and len(__a   ) > 0:
					snake_case_		:			List[Any]											=	attribute
			elif hasattr(__a   , __a   ):
				snake_case_		:			List[Any]											=	attribute

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

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

			if attribute.isdigit():
				snake_case_		:			Any											=	model[int(__a   )]
				snake_case_		:			Any											=	old_model[int(__a   )]
			else:
				snake_case_		:			str											=	getattr(__a   , __a   )

				if old_attribute == "":
					snake_case_		:			Tuple											=	old_model
				else:
					if not hasattr(__a   , __a   ):
						raise ValueError(f"""{old_model} does not have {old_attribute}"""   )
					snake_case_		:			List[str]											=	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__":
						_SCREAMING_SNAKE_CASE      	=		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."""
						)
						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)

