'''
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
'''
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
ops = {'<': operator.lt, '<=': operator.le, '==': operator.eq, '!=': operator.ne, '>=': operator.ge, '>': operator.gt}
def _compare_versions(op, got_ver, want_ver, requirement, pkg, hint):
    if got_ver is None or want_ver is None: raise ValueError(f'Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider reinstalling {pkg}.')
    if not ops[op](version.parse(got_ver), version.parse(want_ver)): raise ImportError(f'{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}')
def require_version(requirement: str, hint: Optional[str]=None) -> None:
    """Args:"""
    hint = f'\n{hint}' if hint is not None else ''
    if re.match('^[\\w_\\-\\d]+$', requirement): pkg, op, want_ver = (requirement, None, None)
    else:
        match = re.findall('^([^!=<>\\s]+)([\\s!=<>]{1,2}.+)', requirement)
        if not match: raise ValueError(f'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but got {requirement}')
        pkg, want_full = match[0]
        want_range = want_full.split(',')
        wanted = {}
        for w in want_range:
            match = re.findall('^([\\s!=<>]{1,2})(.+)', w)
            if not match: raise ValueError(f'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but got {requirement}')
            op, want_ver = match[0]
            wanted[op] = want_ver
            if op not in ops: raise ValueError(f'{requirement}: need one of {list(ops.keys())}, but got {op}')
    if pkg == 'python':
        got_ver = '.'.join([str(x) for x in sys.version_info[:3]])
        for op, want_ver in wanted.items(): _compare_versions(op, got_ver, want_ver, requirement, pkg, hint)
        return
    try: got_ver = importlib.metadata.version(pkg)
    except importlib.metadata.PackageNotFoundError: raise importlib.metadata.PackageNotFoundError(f"The '{requirement}' distribution was not found and is required by this application. {hint}")
    if want_ver is not None:
        for op, want_ver in wanted.items(): _compare_versions(op, got_ver, want_ver, requirement, pkg, hint)
def require_version_core(requirement):
    hint = "Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"
    return require_version(requirement, hint)
'''
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
'''
