



"""simple docstring"""

import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


lowercase__					:						List[str]												=			logging.getLogger()




@unittest.skip("""Temporarily disable the doc tests.""")
@require_torch
@require_tf
@slow
class  _UpperCAmelCase (    unittest.TestCase):

 def 		_snake_case      (  self	: Tuple						,  lowercase_	: Path						,  lowercase_	: Union[str, None] = None						,  lowercase_	: Union[List[str], None] = None						,  lowercase_	: Union[str, List[str], None] = None						,  lowercase_	: bool = True						,  ):
     snake_case_				:  Any       =			[file for file in os.listdir(lowercase_       ) if os.path.isfile(os.path.join(lowercase_						,  lowercase_       )       )]

     if identifier is not None:
         snake_case_				:  Dict       =			[file for file in files if identifier in file]

     if n_identifier is not None:
         if isinstance(lowercase_						,  lowercase_       ):
             for n_ in n_identifier:
                 snake_case_				:  Any       =			[file for file in files if n_ not in file]
         else:
             snake_case_				:  Dict       =			[file for file in files if n_identifier not in file]

     snake_case_				:  str       =			ignore_files or []
     ignore_files.append('''__init__.py'''       )
     snake_case_				:  Tuple       =			[file for file in files if file not in ignore_files]

     for file in files:
         # Open all files
         print('''Testing'''						,  lowercase_       )

         if only_modules:
             snake_case_				:  Tuple       =			file.split('''.'''       )[0]
             try:
                 snake_case_				:  List[str]       =			getattr(lowercase_						,  lowercase_       )
                 snake_case_				:  List[str]       =			doctest.DocTestSuite(lowercase_       )
                 snake_case_				:  Tuple       =			unittest.TextTestRunner().run(lowercase_       )
                 self.assertIs(len(result.failures       )						,  0       )
             except AttributeError:
                 logger.info(f"{module_identifier} is not a module."       )
         else:
             snake_case_				:  int       =			doctest.testfile(str('''..''' / directory / file       )						,  optionflags=doctest.ELLIPSIS       )
             self.assertIs(result.failed						,  0       )

 def 		_snake_case      (  self	: Any       ):
     snake_case_				:  Any       =			Path('''src/transformers'''       )
     snake_case_				:  List[Any]       =			'''modeling'''
     snake_case_				:  Dict       =			[
         '''modeling_ctrl.py''',
         '''modeling_tf_ctrl.py''',
     ]
     self.analyze_directory(lowercase_						,  identifier=lowercase_						,  ignore_files=lowercase_       )

 def 		_snake_case      (  self	: Union[str, Any]       ):
     snake_case_				:  List[Any]       =			Path('''src/transformers'''       )
     snake_case_				:  Optional[Any]       =			'''tokenization'''
     self.analyze_directory(lowercase_						,  identifier=lowercase_       )

 def 		_snake_case      (  self	: Union[str, Any]       ):
     snake_case_				:  List[Any]       =			Path('''src/transformers'''       )
     snake_case_				:  Dict       =			'''configuration'''
     self.analyze_directory(lowercase_						,  identifier=lowercase_       )

 def 		_snake_case      (  self	: Any       ):
     snake_case_				:  Tuple       =			Path('''src/transformers'''       )
     snake_case_				:  Union[str, Any]       =			['''configuration''', '''modeling''', '''tokenization''']
     self.analyze_directory(lowercase_						,  n_identifier=lowercase_       )






 def 		_snake_case      (  self	: Optional[int]       ):
     snake_case_				:  Tuple       =			Path('''docs/source'''       )
     snake_case_				:  int       =			['''favicon.ico''']
     self.analyze_directory(lowercase_						,  ignore_files=lowercase_						,  only_modules=lowercase_       )










"""simple docstring"""

import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
     import bitsandbytes as bnb
     import torch
     import torch.nn as nn

     from ..pytorch_utils import ConvaD

if is_accelerate_available():
     from accelerate import init_empty_weights
     from accelerate.utils import find_tied_parameters

lowercase__					:						Optional[int]												=			logging.get_logger(__name__)




def __lowercase    (							_a	,    _a	,    _a	,    _a=None	,    _a=None					):
    # Recurse if needed
    if "." in tensor_name:
        snake_case_				:  Union[str, Any]       =			tensor_name.split('''.'''					)
        for split in splits[:-1]:
            snake_case_				:  Any       =			getattr(_a	,    _a					)
            if new_module is None:
                raise ValueError(f"{module} has no attribute {split}."					)
            snake_case_				:  int       =			new_module
        snake_case_				:  str       =			splits[-1]

    if tensor_name not in module._parameters and tensor_name not in module._buffers:
        raise ValueError(f"{module} does not have a parameter or a buffer named {tensor_name}."					)
    snake_case_				:  Tuple       =			tensor_name in module._buffers
    snake_case_				:  Optional[int]       =			getattr(_a	,    _a					)

    if old_value.device == torch.device('''meta'''					) and device not in ["meta", torch.device('''meta'''					)] and value is None:
        raise ValueError(f"{tensor_name} is on the meta device, we need a `value` to put in on {device}."					)

    snake_case_				:  Optional[Any]       =			False
    snake_case_				:  List[Any]       =			False
    if is_buffer or not is_bitsandbytes_available():
        snake_case_				:  Optional[Any]       =			False
        snake_case_				:  Tuple       =			False
    else:
        snake_case_				:  Tuple       =			hasattr(bnb.nn	,    '''Params4bit'''					) and isinstance(module._parameters[tensor_name]	,    bnb.nn.Paramsabit					)
        snake_case_				:  int       =			isinstance(module._parameters[tensor_name]	,    bnb.nn.IntaParams					)

    if is_abit or is_abit:
        snake_case_				:  List[str]       =			module._parameters[tensor_name]
        if param.device.type != "cuda":
            if value is None:
                snake_case_				:  Any       =			old_value.to(_a					)
            elif isinstance(_a	,    torch.Tensor					):
                snake_case_				:  str       =			value.to('''cpu'''					)
                if value.dtype == torch.inta:
                    snake_case_				:  List[Any]       =			version.parse(importlib.metadata.version('''bitsandbytes'''					)					) > version.parse(
                        '''0.37.2'''					)
                    if not is_abit_serializable:
                        raise ValueError(
                            '''Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. '''
                            '''Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.'''					)
            else:
                snake_case_				:  Tuple       =			torch.tensor(_a	,    device='''cpu'''					)

            # Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
            # Since weights are saved in the correct "orientation", we skip transposing when loading.
            if issubclass(module.source_cls	,    _a					) and fpaa_statistics is None:
                snake_case_				:  Any       =			new_value.T

            snake_case_				:  Tuple       =			old_value.__dict__
            if is_abit:
                snake_case_				:  Tuple       =			bnb.nn.IntaParams(_a	,    requires_grad=_a	,    **_a					).to(_a					)
            elif is_abit:
                snake_case_				:  Any       =			bnb.nn.Paramsabit(_a	,    requires_grad=_a	,    **_a					).to(_a					)

            snake_case_				:  Union[str, Any]       =			new_value
            if fpaa_statistics is not None:
                setattr(module.weight	,    '''SCB'''	,    fpaa_statistics.to(_a					)					)

    else:
        if value is None:
            snake_case_				:  Dict       =			old_value.to(_a					)
        elif isinstance(_a	,    torch.Tensor					):
            snake_case_				:  Dict       =			value.to(_a					)
        else:
            snake_case_				:  str       =			torch.tensor(_a	,    device=_a					)

        if is_buffer:
            snake_case_				:  Optional[int]       =			new_value
        else:
            snake_case_				:  Optional[Any]       =			nn.Parameter(_a	,    requires_grad=old_value.requires_grad					)
            snake_case_				:  List[Any]       =			new_value




def __lowercase    (							_a	,    _a=None	,    _a=None	,    _a=None	,    _a=False					):
    for name, module in model.named_children():
        if current_key_name is None:
            snake_case_				:  List[str]       =			[]
        current_key_name.append(_a					)

        if (isinstance(_a	,    nn.Linear					) or isinstance(_a	,    _a					)) and name not in modules_to_not_convert:
            # Check if the current key is not in the `modules_to_not_convert`
            if not any(key in '''.'''.join(_a					) for key in modules_to_not_convert					):
                with init_empty_weights():
                    if isinstance(_a	,    _a					):
                        snake_case_,			snake_case_				:  List[Any]       =			module.weight.shape
                    else:
                        snake_case_				:  Dict       =			module.in_features
                        snake_case_				:  Tuple       =			module.out_features

                    if quantization_config.quantization_method() == "llm_int8":
                        snake_case_				:  str       =			bnb.nn.LinearabitLt(
                            _a	,    _a	,    module.bias is not None	,    has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight	,    threshold=quantization_config.llm_inta_threshold	,    )
                        snake_case_				:  str       =			True
                    else:
                        if (
                            quantization_config.llm_inta_skip_modules is not None
                            and name in quantization_config.llm_inta_skip_modules
                        ):
                            pass
                        else:
                            snake_case_				:  Union[str, Any]       =			bnb.nn.Linearabit(
                                _a	,    _a	,    module.bias is not None	,    quantization_config.bnb_abit_compute_dtype	,    compress_statistics=quantization_config.bnb_abit_use_double_quant	,    quant_type=quantization_config.bnb_abit_quant_type	,    )
                            snake_case_				:  List[Any]       =			True
                    # Store the module class in case we need to transpose the weight later
                    snake_case_				:  str       =			type(_a					)
                    # Force requires grad to False to avoid unexpected errors
                    model._modules[name].requires_grad_(_a					)
        if len(list(module.children()					)					) > 0:
            snake_case_,			snake_case_				:  Optional[int]       =			_replace_with_bnb_linear(
                _a	,    _a	,    _a	,    _a	,    has_been_replaced=_a	,    )
        # Remove the last key for recursion
        current_key_name.pop(-1					)
    return model, has_been_replaced




def __lowercase    (							_a	,    _a=None	,    _a=None	,    _a=None					):
    snake_case_				:  Any       =			['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
    snake_case_,			snake_case_				:  List[Any]       =			_replace_with_bnb_linear(
        _a	,    _a	,    _a	,    _a					)

    if not has_been_replaced:
        logger.warning(
            '''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
            ''' Please double check your model architecture, or submit an issue on github if you think this is'''
            ''' a bug.'''					)

    return model




def __lowercase    (							*_a	,    **_a					):
    warnings.warn(
        '''`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead'''	,    _a	,    )
    return replace_with_bnb_linear(*_a	,    **_a					)




def __lowercase    (							*_a	,    **_a					):
    warnings.warn(
        '''`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead'''	,    _a	,    )
    return set_module_quantized_tensor_to_device(*_a	,    **_a					)






def __lowercase    (							_a					):
    snake_case_				:  List[str]       =			deepcopy(_a					)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
    tied_model.tie_weights()

    snake_case_				:  Optional[Any]       =			find_tied_parameters(_a					)
    # For compatibility with Accelerate < 0.18
    if isinstance(_a	,    _a					):
        snake_case_				:  Any       =			sum(list(tied_params.values()					)	,    []					) + list(tied_params.keys()					)
    else:
        snake_case_				:  str       =			sum(_a	,    []					)
    snake_case_				:  str       =			len(_a					) > 0

    # Check if it is a base model
    snake_case_				:  Dict       =			not hasattr(_a	,    model.base_model_prefix					)

    # Ignore this for base models (BertModel, GPT2Model, etc.)
    if (not has_tied_params) and is_base_model:
        return []

    # otherwise they have an attached head
    snake_case_				:  Dict       =			list(model.named_children()					)
    snake_case_				:  List[Any]       =			[list_modules[-1][0]]

    # add last module together with tied weights
    snake_case_				:  Optional[int]       =			set(_a					) - set(_a					)
    snake_case_				:  List[Any]       =			list(set(_a					)					) + list(_a					)

    # remove ".weight" from the keys
    snake_case_				:  Any       =			['''.weight''', '''.bias''']
    snake_case_				:  List[str]       =			[]
    for name in list_untouched:
        for name_to_remove in names_to_remove:
            if name_to_remove in name:
                snake_case_				:  List[str]       =			name.replace(_a	,    ''''''					)
        filtered_module_names.append(_a					)

    return filtered_module_names





