
'''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


a_     =  get_logger(__name__)

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






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

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






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

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






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

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






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

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

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

    snake_case_     ,	snake_case_	:      List[str]            =  hf_model.load_state_dict(__SCREAMING_SNAKE_CASE,						strict=__SCREAMING_SNAKE_CASE    )
    assert not missing, f'missing keys: {missing}'
    hf_model.save_pretrained(__SCREAMING_SNAKE_CASE    )
    try:
        snake_case_	:      int            =  AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE    )
        tok.save_pretrained(__SCREAMING_SNAKE_CASE    )
    except Exception:
        pass
        # dont copy tokenizer if cant


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






'''simple docstring'''
import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


a_     =  importlib.metadata.version("numpy")
a_     =  ".".join([str(x) for x in sys.version_info[:3]])






class 				UpperCAmelCase_			(		snake_case__   ):





 def     snake_case__						(      self):
     # lt + different version strings
     require_version_core("numpy<1000.4.5")
     require_version_core("numpy<1000.4")
     require_version_core("numpy<1000")

     # le
     require_version_core("numpy<=1000.4.5")
     require_version_core(F'numpy<={numpy_ver}')

     # eq
     require_version_core(F'numpy=={numpy_ver}')

     # ne
     require_version_core("numpy!=1000.4.5")

     # ge
     require_version_core("numpy>=1.0")
     require_version_core("numpy>=1.0.0")
     require_version_core(F'numpy>={numpy_ver}')

     # gt
     require_version_core("numpy>1.0.0")

     # mix
     require_version_core("numpy>1.0.0,<1000")

     # requirement w/o version
     require_version_core("numpy")

     # unmet requirements due to version conflict
     for req in ["numpy==1.0.0", "numpy>=1000.0.0", F'numpy<{numpy_ver}']:
         try:
             require_version_core(lowercase_)
         except ImportError as e:
             self.assertIn(F'{req} is required'	,    str(lowercase_))
             self.assertIn("but found"	,    str(lowercase_))

        # unmet requirements due to missing module
     for req in ["numpipypie>1", "numpipypie2"]:
         try:
             require_version_core(lowercase_)
         except importlib.metadata.PackageNotFoundError as e:
             self.assertIn(F'The \'{req}\' distribution was not found and is required by this application'	,    str(lowercase_))
             self.assertIn("Try: pip install transformers -U"	,    str(lowercase_))

        # bogus requirements formats:
        # 1. whole thing
     for req in ["numpy??1.0.0", "numpy1.0.0"]:
         try:
             require_version_core(lowercase_)
         except ValueError as e:
             self.assertIn("requirement needs to be in the pip package format"	,    str(lowercase_))
        # 2. only operators
     for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
         try:
             require_version_core(lowercase_)
         except ValueError as e:
             self.assertIn("need one of "	,    str(lowercase_))





 def     snake_case__						(      self):
     # matching requirement
     require_version("python>=3.6.0")

     # not matching requirements
     for req in ["python>9.9.9", "python<3.0.0"]:
         try:
             require_version_core(lowercase_)
         except ImportError as e:
             self.assertIn(F'{req} is required'	,    str(lowercase_))
             self.assertIn(F'but found python=={python_ver}'	,    str(lowercase_))




