






'''simple docstring'''

import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
     import bitsandbytes as bnb

from copy import deepcopy


SCREAMING_SNAKE_CASE__											=     logging.getLogger(__name__)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase = None	,		__UpperCamelCase = None	,		__UpperCamelCase = None	,		__UpperCamelCase = None	,		__UpperCamelCase = None	,		__UpperCamelCase = False	,		)->				str:
      UpperCamelCase       						=      bnb_quantization_config.load_in_abit
      UpperCamelCase       						=      bnb_quantization_config.load_in_abit

      if load_in_abit and not is_abit_bnb_available():
            raise ImportError(
                """You have a version of `bitsandbytes` that is not compatible with 8bit quantization,"""
                """ make sure you have the latest version of `bitsandbytes` installed."""					)
      if load_in_abit and not is_abit_bnb_available():
            raise ValueError(
                """You have a version of `bitsandbytes` that is not compatible with 4bit quantization,"""
                """make sure you have the latest version of `bitsandbytes` installed."""					)

      UpperCamelCase       						=      []
      # custom device map
      if isinstance(__UpperCamelCase	,		__UpperCamelCase					) and len(device_map.keys()					) > 1:
            UpperCamelCase       						=      [key for key, value in device_map.items() if value in ["""disk""", """cpu"""]]

      # We keep some modules such as the lm_head in their original dtype for numerical stability reasons
      if bnb_quantization_config.skip_modules is None:
            UpperCamelCase       						=      get_keys_to_not_convert(__UpperCamelCase					)

      # add cpu modules to skip modules only for 4-bit modules
      if load_in_abit:
            bnb_quantization_config.skip_modules.extend(__UpperCamelCase					)
      UpperCamelCase       						=      bnb_quantization_config.skip_modules

      # We add the modules we want to keep in full precision
      if bnb_quantization_config.keep_in_fpaa_modules is None:
            UpperCamelCase       						=      []
      UpperCamelCase       						=      bnb_quantization_config.keep_in_fpaa_modules
      modules_to_not_convert.extend(__UpperCamelCase					)

      # compatibility with peft
      UpperCamelCase       						=      load_in_abit
      UpperCamelCase       						=      load_in_abit

      UpperCamelCase       						=      get_parameter_device(__UpperCamelCase					)
      if model_device.type != "meta":
            # quantization of an already loaded model
            logger.warning(
                """It is not recommended to quantize a loaded model. """
                """The model should be instantiated under the `init_empty_weights` context manager."""					)
            UpperCamelCase       						=      replace_with_bnb_layers(__UpperCamelCase	,		__UpperCamelCase	,		modules_to_not_convert=__UpperCamelCase					)
            # convert param to the right dtype
            UpperCamelCase       						=      bnb_quantization_config.torch_dtype
            for name, param in model.state_dict().items():
                  if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules					):
                        param.to(torch.floataa					)
                        if param.dtype != torch.floataa:
                              UpperCamelCase       						=      name.replace(""".weight"""	,		""""""					).replace(""".bias"""	,		""""""					)
                              UpperCamelCase       						=      getattr(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)
                              if param is not None:
                                    param.to(torch.floataa					)
                  elif torch.is_floating_point(__UpperCamelCase					):
                        param.to(__UpperCamelCase					)
            if model_device.type == "cuda":
                  # move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
                  model.cuda(torch.cuda.current_device()					)
                  torch.cuda.empty_cache()
            elif torch.cuda.is_available():
                  model.to(torch.cuda.current_device()					)
            else:
                  raise RuntimeError("""No GPU found. A GPU is needed for quantization."""					)
            logger.info(
                F"The model device type is {model_device.type}. However, cuda is needed for quantization."
                """We move the model to cuda."""					)
            return model

      elif weights_location is None:
            raise RuntimeError(
                F"`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} "					)

      else:
            with init_empty_weights():
                  UpperCamelCase       						=      replace_with_bnb_layers(
                      __UpperCamelCase	,		__UpperCamelCase	,		modules_to_not_convert=__UpperCamelCase					)

            UpperCamelCase       						=      get_quantized_model_device_map(
                __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		max_memory=__UpperCamelCase	,		no_split_module_classes=__UpperCamelCase	,		)
            if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
                  UpperCamelCase       						=      True

            UpperCamelCase       						=      any(x in list(device_map.values()					) for x in ["""cpu""", """disk"""]					)

            load_checkpoint_in_model(
                __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		dtype=bnb_quantization_config.torch_dtype	,		offload_folder=__UpperCamelCase	,		offload_state_dict=__UpperCamelCase	,		keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules	,		offload_abit_bnb=load_in_abit and offload	,		)
            return dispatch_model(__UpperCamelCase	,		device_map=__UpperCamelCase	,		offload_dir=__UpperCamelCase					)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		__UpperCamelCase=None	,		__UpperCamelCase=None					)->				Union[str, Any]:
      if device_map is None:
            if torch.cuda.is_available():
                  UpperCamelCase       						=      {"""""": torch.cuda.current_device()}
            else:
                  raise RuntimeError("""No GPU found. A GPU is needed for quantization."""					)
            logger.info("""The device_map was not initialized.""" """Setting device_map to `{'':torch.cuda.current_device()}`."""					)

      if isinstance(__UpperCamelCase	,		__UpperCamelCase					):
            if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
                  raise ValueError(
                      """If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or """
                      """'sequential'."""					)

            UpperCamelCase       						=      {}
            special_dtypes.update(
                {
                    name: bnb_quantization_config.torch_dtype
                    for name, _ in model.named_parameters()
                    if any(m in name for m in bnb_quantization_config.skip_modules					)
                }					)
            special_dtypes.update(
                {
                    name: torch.floataa
                    for name, _ in model.named_parameters()
                    if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules					)
                }					)

            UpperCamelCase       						=      {}
            UpperCamelCase       						=      special_dtypes
            UpperCamelCase       						=      no_split_module_classes
            UpperCamelCase       						=      bnb_quantization_config.target_dtype

            # get max_memory for each device.
            if device_map != "sequential":
                  UpperCamelCase       						=      get_balanced_memory(
                      __UpperCamelCase	,		low_zero=(device_map == """balanced_low_0""")	,		max_memory=__UpperCamelCase	,		**__UpperCamelCase	,		)

            UpperCamelCase       						=      max_memory
            UpperCamelCase       						=      infer_auto_device_map(__UpperCamelCase	,		**__UpperCamelCase					)

      if isinstance(__UpperCamelCase	,		__UpperCamelCase					):
            # check if don't have any quantized module on the cpu
            UpperCamelCase       						=      bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

            UpperCamelCase       						=      {
                key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
            }
            for device in ["cpu", "disk"]:
                  if device in device_map_without_some_modules.values():
                        if bnb_quantization_config.load_in_abit:
                              raise ValueError(
                                  """
                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
                        these modules in `torch_dtype`, you need to pass a custom `device_map` to
                        `load_and_quantize_model`. Check
                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
                        for more details.
                        """					)
                        else:
                              logger.info(
                                  """Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit"""					)
            del device_map_without_some_modules
      return device_map







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		__UpperCamelCase=None					)->				List[Any]:

      if modules_to_not_convert is None:
            UpperCamelCase       						=      []

      UpperCamelCase						,UpperCamelCase       						=      _replace_with_bnb_layers(
          __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)
      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."""
                """ this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers."""
                """ Please double check your model architecture, or submit an issue on github if you think this is"""
                """ a bug."""					)
      return model







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		__UpperCamelCase=None	,		)->				int:
      UpperCamelCase       						=      False
      for name, module in model.named_children():
            if current_key_name is None:
                  UpperCamelCase       						=      []
            current_key_name.append(__UpperCamelCase					)
            if isinstance(__UpperCamelCase	,		nn.Linear					) and name not in modules_to_not_convert:
                  # Check if the current key is not in the `modules_to_not_convert`
                  UpperCamelCase       						=      """.""".join(__UpperCamelCase					)
                  UpperCamelCase       						=      True
                  for key in modules_to_not_convert:
                        if (
                            (key in current_key_name_str) and (key + "." in current_key_name_str)
                        ) or key == current_key_name_str:
                              UpperCamelCase       						=      False
                              break
                  if proceed:
                        # Load bnb module with empty weight and replace ``nn.Linear` module
                        if bnb_quantization_config.load_in_abit:
                              UpperCamelCase       						=      bnb.nn.LinearabitLt(
                                  module.in_features	,		module.out_features	,		module.bias is not None	,		has_fpaa_weights=__UpperCamelCase	,		threshold=bnb_quantization_config.llm_inta_threshold	,		)
                        elif bnb_quantization_config.load_in_abit:
                              UpperCamelCase       						=      bnb.nn.Linearabit(
                                  module.in_features	,		module.out_features	,		module.bias is not None	,		bnb_quantization_config.bnb_abit_compute_dtype	,		compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant	,		quant_type=bnb_quantization_config.bnb_abit_quant_type	,		)
                        else:
                              raise ValueError("""load_in_8bit and load_in_4bit can't be both False"""					)
                        UpperCamelCase       						=      module.weight.data
                        if module.bias is not None:
                              UpperCamelCase       						=      module.bias.data
                        bnb_module.requires_grad_(__UpperCamelCase					)
                        setattr(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)
                        UpperCamelCase       						=      True
            if len(list(module.children()					)					) > 0:
                  UpperCamelCase						,UpperCamelCase       						=      _replace_with_bnb_layers(
                      __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)
                  UpperCamelCase       						=      has_been_replaced | _has_been_replaced
            # Remove the last key for recursion
            current_key_name.pop(-1					)
      return model, has_been_replaced







def 					lowercase__				(   __UpperCamelCase					)->				Union[str, Any]:
      # Create a copy of the model
      with init_empty_weights():
            UpperCamelCase       						=      deepcopy(__UpperCamelCase					)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

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

      # Check if it is a base model
      UpperCamelCase       						=      False
      if hasattr(__UpperCamelCase	,		"""base_model_prefix"""					):
            UpperCamelCase       						=      not hasattr(__UpperCamelCase	,		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
      UpperCamelCase       						=      list(model.named_children()					)
      UpperCamelCase       						=      [list_modules[-1][0]]

      # add last module together with tied weights
      UpperCamelCase       						=      set(__UpperCamelCase					) - set(__UpperCamelCase					)
      UpperCamelCase       						=      list(set(__UpperCamelCase					)					) + list(__UpperCamelCase					)

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

      return filtered_module_names







def 					lowercase__				(   __UpperCamelCase					)->				str:
      for m in model.modules():
            if isinstance(__UpperCamelCase	,		bnb.nn.Linearabit					):
                  return True
      return False







def 					lowercase__				(   __UpperCamelCase					)->				Optional[int]:
      return next(parameter.parameters()					).device







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				Union[str, Any]:
      # if it is not quantized, we quantize and offload the quantized weights and the SCB stats
      if fpaa_statistics is None:
            set_module_tensor_to_device(__UpperCamelCase	,		__UpperCamelCase	,		0	,		dtype=__UpperCamelCase	,		value=__UpperCamelCase					)
            UpperCamelCase       						=      param_name
            UpperCamelCase       						=      model
            if "." in tensor_name:
                  UpperCamelCase       						=      tensor_name.split("""."""					)
                  for split in splits[:-1]:
                        UpperCamelCase       						=      getattr(__UpperCamelCase	,		__UpperCamelCase					)
                        if new_module is None:
                              raise ValueError(F"{module} has no attribute {split}."					)
                        UpperCamelCase       						=      new_module
                  UpperCamelCase       						=      splits[-1]
            # offload weights
            UpperCamelCase       						=      False
            offload_weight(module._parameters[tensor_name]	,		__UpperCamelCase	,		__UpperCamelCase	,		index=__UpperCamelCase					)
            if hasattr(module._parameters[tensor_name]	,		"""SCB"""					):
                  offload_weight(
                      module._parameters[tensor_name].SCB	,		param_name.replace("""weight"""	,		"""SCB"""					)	,		__UpperCamelCase	,		index=__UpperCamelCase	,		)
      else:
            offload_weight(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		index=__UpperCamelCase					)
            offload_weight(__UpperCamelCase	,		param_name.replace("""weight"""	,		"""SCB"""					)	,		__UpperCamelCase	,		index=__UpperCamelCase					)

      set_module_tensor_to_device(__UpperCamelCase	,		__UpperCamelCase	,		"""meta"""	,		dtype=__UpperCamelCase	,		value=torch.empty(*param.size()					)					)








'''simple docstring'''

import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand







def 					lowercase__				(   __UpperCamelCase					)->				str:
      return EnvironmentCommand()







def 					lowercase__				(   __UpperCamelCase					)->				str:
      return EnvironmentCommand(args.accelerate_config_file					)






class        a_		(    lowerCamelCase       ):

  @staticmethod
  def 						A__		(   _SCREAMING_SNAKE_CASE   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      parser.add_parser("""env"""   )
        download_parser.set_defaults(func=_SCREAMING_SNAKE_CASE   )
        download_parser.add_argument(
            """--accelerate-config_file"""    ,	default=_SCREAMING_SNAKE_CASE    ,	help="""The accelerate config file to use for the default values in the launching script."""    ,	)
        download_parser.set_defaults(func=_SCREAMING_SNAKE_CASE   )

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	*_SCREAMING_SNAKE_CASE   )						->   None:



        """simple docstring"""






        UpperCamelCase       						=      accelerate_config_file

  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      """not installed"""
        if is_safetensors_available():
              import safetensors

              UpperCamelCase       						=      safetensors.__version__
        elif importlib.util.find_spec("""safetensors"""   ) is not None:
              import safetensors

              UpperCamelCase       						=      F"{safetensors.__version__} but is ignored because of PyTorch version too old."

        UpperCamelCase       						=      """not installed"""
        UpperCamelCase       						=      UpperCamelCase       						=      """not found"""
        if is_accelerate_available():
              import accelerate
              from accelerate.commands.config import default_config_file, load_config_from_file

              UpperCamelCase       						=      accelerate.__version__
              # Get the default from the config file.
              if self._accelerate_config_file is not None or os.path.isfile(_SCREAMING_SNAKE_CASE   ):
                    UpperCamelCase       						=      load_config_from_file(self._accelerate_config_file   ).to_dict()

              UpperCamelCase       						=      (
                  """\n""".join([F"\t- {prop}: {val}" for prop, val in accelerate_config.items()]   )
                  if isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
                  else F"\t{accelerate_config}"
              )

        UpperCamelCase       						=      """not installed"""
        UpperCamelCase       						=      """NA"""
        if is_torch_available():
              import torch

              UpperCamelCase       						=      torch.__version__
              UpperCamelCase       						=      torch.cuda.is_available()

        UpperCamelCase       						=      """not installed"""
        UpperCamelCase       						=      """NA"""
        if is_tf_available():
              import tensorflow as tf

              UpperCamelCase       						=      tf.__version__
              try:
                    # deprecated in v2.1
                    UpperCamelCase       						=      tf.test.is_gpu_available()
              except AttributeError:
                    # returns list of devices, convert to bool
                    UpperCamelCase       						=      bool(tf.config.list_physical_devices("""GPU"""   )   )

        UpperCamelCase       						=      """not installed"""
        UpperCamelCase       						=      """not installed"""
        UpperCamelCase       						=      """not installed"""
        UpperCamelCase       						=      """NA"""
        if is_flax_available():
              import flax
              import jax
              import jaxlib

              UpperCamelCase       						=      flax.__version__
              UpperCamelCase       						=      jax.__version__
              UpperCamelCase       						=      jaxlib.__version__
              UpperCamelCase       						=      jax.lib.xla_bridge.get_backend().platform

        UpperCamelCase       						=      {
            """`transformers` version""": version,
            """Platform""": platform.platform(),
            """Python version""": platform.python_version(),
            """Huggingface_hub version""": huggingface_hub.__version__,
            """Safetensors version""": F"{safetensors_version}",
            """Accelerate version""": F"{accelerate_version}",
            """Accelerate config""": F"{accelerate_config_str}",
            """PyTorch version (GPU?)""": F"{pt_version} ({pt_cuda_available})",
            """Tensorflow version (GPU?)""": F"{tf_version} ({tf_cuda_available})",
            """Flax version (CPU?/GPU?/TPU?)""": F"{flax_version} ({jax_backend})",
            """Jax version""": F"{jax_version}",
            """JaxLib version""": F"{jaxlib_version}",
            """Using GPU in script?""": """<fill in>""",
            """Using distributed or parallel set-up in script?""": """<fill in>""",
        }

        print("""\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"""   )
        print(self.format_dict(_SCREAMING_SNAKE_CASE   )   )

        return info


  @staticmethod
  def 						A__		(   _SCREAMING_SNAKE_CASE   )						->   Dict:



        """simple docstring"""






        return "\n".join([F"- {prop}: {val}" for prop, val in d.items()]   ) + "\n"
