




import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


lowercase__    :					List[str]					=						get_logger(__name__)


def 						lowerCamelCase__   (		_A					,     _A							):







	'''simple docstring'''





	if text.startswith(_UpperCAmelCase							):
		return text[len(_UpperCAmelCase							) :]
	return text  # or whatever


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	return {remove_prefix(_UpperCAmelCase					,     "model."							): v for k, v in sd.items()}


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	snake_case_		   =					{}
	for k in state_dicts[0].keys():
		snake_case_		   =					[sd[k] for sd in state_dicts]
		snake_case_		   =					sum(_UpperCAmelCase							) / len(_UpperCAmelCase							)
		assert isinstance(_UpperCAmelCase					,     torch.Tensor							)
		snake_case_		   =					new_t
	return new_sd


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase							)
	if os.path.isfile(_UpperCAmelCase							):
		snake_case_		   =					[pl_ckpt_path]
	else:
		assert os.path.isdir(_UpperCAmelCase							)
		snake_case_		   =					list(Path(_UpperCAmelCase							).glob("*.ckpt"							)							)
		assert ckpt_files, f"could not find any ckpt files inside the {pl_ckpt_path} directory"

	if len(_UpperCAmelCase							) > 1:
		logger.info(f"averaging the weights of {ckpt_files}"							)

	snake_case_		   =					[sanitize(torch.load(_UpperCAmelCase					,     map_location="cpu"							)["state_dict"]							) for x in ckpt_files]
	snake_case_		   =					average_state_dicts(_UpperCAmelCase							)

	snake_case_		   =					hf_model.load_state_dict(_UpperCAmelCase					,     strict=_UpperCAmelCase							)
	assert not missing, f"missing keys: {missing}"
	hf_model.save_pretrained(_UpperCAmelCase							)
	try:
		snake_case_		   =					AutoTokenizer.from_pretrained(_UpperCAmelCase							)
		tok.save_pretrained(_UpperCAmelCase							)
	except Exception:
		pass
		# dont copy tokenizer if cant


if __name__ == "__main__":
		fire.Fire(convert_pl_to_hf)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowercase__    :					List[str]					=						{
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__    :					Optional[Any]					=						["XLMProphetNetTokenizer"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__    :					List[Any]					=						[
		    "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "XLMProphetNetDecoder",
		    "XLMProphetNetEncoder",
		    "XLMProphetNetForCausalLM",
		    "XLMProphetNetForConditionalGeneration",
		    "XLMProphetNetModel",
		    "XLMProphetNetPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_xlm_prophetnet import (
				    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XLMProphetNetDecoder,
				    XLMProphetNetEncoder,
				    XLMProphetNetForCausalLM,
				    XLMProphetNetForConditionalGeneration,
				    XLMProphetNetModel,
				    XLMProphetNetPreTrainedModel,
				)

else:
		import sys

		lowercase__    :					Any					=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
