
"""simple docstring"""
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		:						Union[str, Any] =					get_logger(__name__)


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


					'''simple docstring'''






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


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			Optional[int]:


					'''simple docstring'''
					return {remove_prefix(UpperCAmelCase_	,				"""model.""" ): v for k, v in sd.items()}


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			Any:


					'''simple docstring'''
					__a:						Optional[int]		    =					{}
					for k in state_dicts[0].keys():
										__a:						Tuple		    =					[sd[k] for sd in state_dicts]
										__a:						Union[str, Any]		    =					sum(UpperCAmelCase_ ) / len(UpperCAmelCase_ )
										assert isinstance(UpperCAmelCase_	,				torch.Tensor )
										__a:						Union[str, Any]		    =					new_t
					return new_sd


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			None:


					'''simple docstring'''
					__a:						List[str]		    =					AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_ )
					if os.path.isfile(UpperCAmelCase_ ):
										__a:						Optional[Any]		    =					[pl_ckpt_path]
					else:
										assert os.path.isdir(UpperCAmelCase_ )
										__a:						List[str]		    =					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}""" )

					__a:						List[Any]		    =					[sanitize(torch.load(UpperCAmelCase_	,				map_location="""cpu""" )["""state_dict"""] ) for x in ckpt_files]
					__a:						Optional[Any]		    =					average_state_dicts(UpperCAmelCase_ )

					__a:						str		    =					hf_model.load_state_dict(UpperCAmelCase_	,				strict=UpperCAmelCase_ )
					assert not missing, f"""missing keys: {missing}"""
					hf_model.save_pretrained(UpperCAmelCase_ )
					try:
										__a:						Union[str, Any]		    =					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)


"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase		:						List[str] =					{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						Tuple =					['LlamaTokenizer']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						Dict =					['LlamaTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						List[Any] =					[
			    'OpenLlamaForCausalLM',
			    'OpenLlamaModel',
			    'OpenLlamaPreTrainedModel',
			    'OpenLlamaForSequenceClassification',
			]


if TYPE_CHECKING:
			from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_open_llama import (
						    OpenLlamaForCausalLM,
						    OpenLlamaForSequenceClassification,
						    OpenLlamaModel,
						    OpenLlamaPreTrainedModel,
						)


else:
			import sys

			lowercase		:						Dict =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
