
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


__magic_name__            =  logging.getLogger(__name__)





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase = None    ,		__lowerCAmelCase = None    ,		__lowerCAmelCase = None    ,		__lowerCAmelCase = None    ,		__lowerCAmelCase = None    ,		__lowerCAmelCase = False    ,		):
				snake_case__						=				bnb_quantization_config.load_in_abit
				snake_case__						=				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."							)

				snake_case__						=				[]
				# custom device map
				if isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							) and len(device_map.keys()							) > 1:
								snake_case__						=				[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:
								snake_case__						=				get_keys_to_not_convert(__lowerCAmelCase							)

				# add cpu modules to skip modules only for 4-bit modules
				if load_in_abit:
								bnb_quantization_config.skip_modules.extend(__lowerCAmelCase							)
				snake_case__						=				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:
								snake_case__						=				[]
				snake_case__						=				bnb_quantization_config.keep_in_fpaa_modules
				modules_to_not_convert.extend(__lowerCAmelCase							)

				# compatibility with peft
				snake_case__						=				load_in_abit
				snake_case__						=				load_in_abit

				snake_case__						=				get_parameter_device(__lowerCAmelCase							)
				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."							)
								snake_case__						=				replace_with_bnb_layers(__lowerCAmelCase    ,		__lowerCAmelCase    ,		modules_to_not_convert=__lowerCAmelCase							)
								# convert param to the right dtype
								snake_case__						=				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:
																				snake_case__						=				name.replace(".weight"    ,		""							).replace(".bias"    ,		""							)
																				snake_case__						=				getattr(__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							)
																				if param is not None:
																								param.to(torch.floataa							)
												elif torch.is_floating_point(__lowerCAmelCase							):
																param.to(__lowerCAmelCase							)
								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():
												snake_case__						=				replace_with_bnb_layers(
												    __lowerCAmelCase    ,		__lowerCAmelCase    ,		modules_to_not_convert=__lowerCAmelCase							)

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

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

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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase=None    ,		__lowerCAmelCase=None    ,		__lowerCAmelCase=None							):
				if device_map is None:
								if torch.cuda.is_available():
												snake_case__						=				{"": 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(__lowerCAmelCase    ,		__lowerCAmelCase							):
								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'."							)

								snake_case__						=				{}
								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							)
								    }							)

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

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

								snake_case__						=				max_memory
								snake_case__						=				infer_auto_device_map(__lowerCAmelCase    ,		**__lowerCAmelCase							)

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

								snake_case__						=				{
								    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(
																				    "\n                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n                        these modules in `torch_dtype`, you need to pass a custom `device_map` to\n                        `load_and_quantize_model`. Check\n                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n                        for more details.\n                        "							)
																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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase=None    ,		__lowerCAmelCase=None							):

				if modules_to_not_convert is None:
								snake_case__						=				[]

				snake_case__						,					snake_case__						=				_replace_with_bnb_layers(
				    __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							)
				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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase=None    ,		__lowerCAmelCase=None    ,		):
				snake_case__						=				False
				for name, module in model.named_children():
								if current_key_name is None:
												snake_case__						=				[]
								current_key_name.append(__lowerCAmelCase							)
								if isinstance(__lowerCAmelCase    ,		nn.Linear							) and name not in modules_to_not_convert:
												# Check if the current key is not in the `modules_to_not_convert`
												snake_case__						=				".".join(__lowerCAmelCase							)
												snake_case__						=				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:
																				snake_case__						=				False
																				break
												if proceed:
																# Load bnb module with empty weight and replace ``nn.Linear` module
																if bnb_quantization_config.load_in_abit:
																				snake_case__						=				bnb.nn.LinearabitLt(
																				    module.in_features    ,		module.out_features    ,		module.bias is not None    ,		has_fpaa_weights=__lowerCAmelCase    ,		threshold=bnb_quantization_config.llm_inta_threshold    ,		)
																elif bnb_quantization_config.load_in_abit:
																				snake_case__						=				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"							)
																snake_case__						=				module.weight.data
																if module.bias is not None:
																				snake_case__						=				module.bias.data
																bnb_module.requires_grad_(__lowerCAmelCase							)
																setattr(__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							)
																snake_case__						=				True
								if len(list(module.children()							)							) > 0:
												snake_case__						,					snake_case__						=				_replace_with_bnb_layers(
												    __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							)
												snake_case__						=				has_been_replaced | _has_been_replaced
								# Remove the last key for recursion
								current_key_name.pop(-1							)
				return model, has_been_replaced





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
				# Create a copy of the model
				with init_empty_weights():
								snake_case__						=				deepcopy(__lowerCAmelCase							)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

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

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

				# add last module together with tied weights
				snake_case__						=				set(__lowerCAmelCase							) - set(__lowerCAmelCase							)
				snake_case__						=				list(set(__lowerCAmelCase							)							) + list(__lowerCAmelCase							)

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

				return filtered_module_names





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
				for m in model.modules():
								if isinstance(__lowerCAmelCase    ,		bnb.nn.Linearabit							):
												return True
				return False





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
				return next(parameter.parameters()							).device





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
				# 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(__lowerCAmelCase    ,		__lowerCAmelCase    ,		0    ,		dtype=__lowerCAmelCase    ,		value=__lowerCAmelCase							)
								snake_case__						=				param_name
								snake_case__						=				model
								if "." in tensor_name:
												snake_case__						=				tensor_name.split("."							)
												for split in splits[:-1]:
																snake_case__						=				getattr(__lowerCAmelCase    ,		__lowerCAmelCase							)
																if new_module is None:
																				raise ValueError(F"""{module} has no attribute {split}."""							)
																snake_case__						=				new_module
												snake_case__						=				splits[-1]
								# offload weights
								snake_case__						=				False
								offload_weight(module._parameters[tensor_name]    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		index=__lowerCAmelCase							)
								if hasattr(module._parameters[tensor_name]    ,		"SCB"							):
												offload_weight(
												    module._parameters[tensor_name].SCB    ,		param_name.replace("weight"    ,		"SCB"							)    ,		__lowerCAmelCase    ,		index=__lowerCAmelCase    ,		)
				else:
								offload_weight(__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		index=__lowerCAmelCase							)
								offload_weight(__lowerCAmelCase    ,		param_name.replace("weight"    ,		"SCB"							)    ,		__lowerCAmelCase    ,		index=__lowerCAmelCase							)

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__magic_name__            =  {
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  ['''CLIPTokenizerFast''']

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  ['''CLIPFeatureExtractor''']
     __magic_name__            =  ['''CLIPImageProcessor''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''CLIPModel''',
         '''CLIPPreTrainedModel''',
         '''CLIPTextModel''',
         '''CLIPTextModelWithProjection''',
         '''CLIPVisionModel''',
         '''CLIPVisionModelWithProjection''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFCLIPModel''',
         '''TFCLIPPreTrainedModel''',
         '''TFCLIPTextModel''',
         '''TFCLIPVisionModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         '''FlaxCLIPModel''',
         '''FlaxCLIPPreTrainedModel''',
         '''FlaxCLIPTextModel''',
         '''FlaxCLIPTextPreTrainedModel''',
         '''FlaxCLIPVisionModel''',
         '''FlaxCLIPVisionPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_clip import (
         CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
         CLIPConfig,
         CLIPOnnxConfig,
         CLIPTextConfig,
         CLIPVisionConfig,
     )
     from .processing_clip import CLIPProcessor
     from .tokenization_clip import CLIPTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_clip_fast import CLIPTokenizerFast

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_clip import CLIPFeatureExtractor
          from .image_processing_clip import CLIPImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_clip import (
              CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              CLIPModel,
              CLIPPreTrainedModel,
              CLIPTextModel,
              CLIPTextModelWithProjection,
              CLIPVisionModel,
              CLIPVisionModelWithProjection,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_clip import (
              TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFCLIPModel,
              TFCLIPPreTrainedModel,
              TFCLIPTextModel,
              TFCLIPVisionModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_clip import (
              FlaxCLIPModel,
              FlaxCLIPPreTrainedModel,
              FlaxCLIPTextModel,
              FlaxCLIPTextPreTrainedModel,
              FlaxCLIPVisionModel,
              FlaxCLIPVisionPreTrainedModel,
          )


else:
     import sys

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



