


'''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

lowerCamelCase							:   int  						=	logging.get_logger(__name__)

def _SCREAMING_SNAKE_CASE      (A  , A  , A  , A=None  , A=None       )       ->       int:






       """simple docstring"""







       if "." in tensor_name:
              lowercase__      			= tensor_name.split('''.'''       )
              for split in splits[:-1]:
                     lowercase__      			= getattr(A  , A       )
                     if new_module is None:
                            raise ValueError(f"{module} has no attribute {split}."       )
                     lowercase__      			= new_module
              lowercase__      			= 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}."       )
       lowercase__      			= tensor_name in module._buffers
       lowercase__      			= 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}."       )

       lowercase__      			= False
       lowercase__      			= False
       if is_buffer or not is_bitsandbytes_available():
              lowercase__      			= False
              lowercase__      			= False
       else:
              lowercase__      			= hasattr(bnb.nn  , '''Params4bit'''       ) and isinstance(module._parameters[tensor_name]  , bnb.nn.Paramsabit       )
              lowercase__      			= isinstance(module._parameters[tensor_name]  , bnb.nn.IntaParams       )

       if is_abit or is_abit:
              lowercase__      			= module._parameters[tensor_name]
              if param.device.type != "cuda":
                     if value is None:
                            lowercase__      			= old_value.to(A       )
                     elif isinstance(A  , torch.Tensor       ):
                            lowercase__      			= value.to('''cpu'''       )
                            if value.dtype == torch.inta:
                                   lowercase__      			= 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:
                            lowercase__      			= 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:
                            lowercase__      			= new_value.T

                     lowercase__      			= old_value.__dict__
                     if is_abit:
                            lowercase__      			= bnb.nn.IntaParams(A  , requires_grad=A  , **A       ).to(A       )
                     elif is_abit:
                            lowercase__      			= bnb.nn.Paramsabit(A  , requires_grad=A  , **A       ).to(A       )

                     lowercase__      			= new_value
                     if fpaa_statistics is not None:
                            setattr(module.weight  , '''SCB'''  , fpaa_statistics.to(A       )       )

       else:
              if value is None:
                     lowercase__      			= old_value.to(A       )
              elif isinstance(A  , torch.Tensor       ):
                     lowercase__      			= value.to(A       )
              else:
                     lowercase__      			= torch.tensor(A  , device=A       )

              if is_buffer:
                     lowercase__      			= new_value
              else:
                     lowercase__      			= nn.Parameter(A  , requires_grad=old_value.requires_grad       )
                     lowercase__      			= new_value

def _SCREAMING_SNAKE_CASE      (A  , A=None  , A=None  , A=None  , A=False       )       ->       Union[str, Any]:






       """simple docstring"""







       for name, module in model.named_children():
              if current_key_name is None:
                     lowercase__      			= []
              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       ):
                                          lowercase__			,lowercase__      			= module.weight.shape
                                   else:
                                          lowercase__      			= module.in_features
                                          lowercase__      			= module.out_features

                                   if quantization_config.quantization_method() == "llm_int8":
                                          lowercase__      			= 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  , )
                                          lowercase__      			= True
                                   else:
                                          if (
                                              quantization_config.llm_inta_skip_modules is not None
                                              and name in quantization_config.llm_inta_skip_modules
                                          ):
                                                 pass
                                          else:
                                                 lowercase__      			= 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  , )
                                                 lowercase__      			= True
                    # Store the module class in case we need to transpose the weight later
                                   lowercase__      			= type(A       )
                                   # Force requires grad to False to avoid unexpected errors
                                   model._modules[name].requires_grad_(A       )
              if len(list(module.children()       )       ) > 0:
                     lowercase__			,lowercase__      			= _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 _SCREAMING_SNAKE_CASE      (A  , A=None  , A=None  , A=None       )       ->       Optional[int]:






       """simple docstring"""







       lowercase__      			= ['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
       lowercase__			,lowercase__      			= _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 _SCREAMING_SNAKE_CASE      (*A  , **A       )       ->       List[str]:






       """simple docstring"""







       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 _SCREAMING_SNAKE_CASE      (*A  , **A       )       ->       Tuple:






       """simple docstring"""







       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 _SCREAMING_SNAKE_CASE      (A       )       ->       int:






       """simple docstring"""







       lowercase__      			= deepcopy(A       )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
       tied_model.tie_weights()

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

       # Check if it is a base model
       lowercase__      			= 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
       lowercase__      			= list(model.named_children()       )
       lowercase__      			= [list_modules[-1][0]]

       # add last module together with tied weights
       lowercase__      			= set(A       ) - set(A       )
       lowercase__      			= list(set(A       )       ) + list(A       )

       # remove ".weight" from the keys
       lowercase__      			= ['''.weight''', '''.bias''']
       lowercase__      			= []
       for name in list_untouched:
              for name_to_remove in names_to_remove:
                     if name_to_remove in name:
                            lowercase__      			= name.replace(A  , ''''''       )
              filtered_module_names.append(A       )

       return filtered_module_names









"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCAmelCase_   						=   {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   ['SpeechT5Tokenizer']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SpeechT5ForSpeechToText',
							    'SpeechT5ForSpeechToSpeech',
							    'SpeechT5ForTextToSpeech',
							    'SpeechT5Model',
							    'SpeechT5PreTrainedModel',
							    'SpeechT5HifiGan',
							]

if TYPE_CHECKING:
							from .configuration_speechta import (
							    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
							    SpeechTaConfig,
							    SpeechTaHifiGanConfig,
							)
							from .feature_extraction_speechta import SpeechTaFeatureExtractor
							from .processing_speechta import SpeechTaProcessor

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_speechta import SpeechTaTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_speechta import (
														    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SpeechTaForSpeechToSpeech,
														    SpeechTaForSpeechToText,
														    SpeechTaForTextToSpeech,
														    SpeechTaHifiGan,
														    SpeechTaModel,
														    SpeechTaPreTrainedModel,
														)

else:
							import sys

							lowerCAmelCase_   						=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
