




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


lowerCamelCase__			:       int         =							logging.getLogger(__name__)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Any      ,      __UpperCAmelCase						:     Optional[int]      ,      __UpperCAmelCase						:     str = None      ,      __UpperCAmelCase						:     List[str] = None      ,      __UpperCAmelCase						:     Optional[int] = None      ,      __UpperCAmelCase						:     List[str] = None      ,      __UpperCAmelCase						:     Tuple = None      ,      __UpperCAmelCase						:     Any = False      ,      ) ->			Dict:
						SCREAMING_SNAKE_CASE_     						=      bnb_quantization_config.load_in_abit
						SCREAMING_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.'							)

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

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

						# compatibility with peft
						SCREAMING_SNAKE_CASE_     						=      load_in_abit
						SCREAMING_SNAKE_CASE_     						=      load_in_abit

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

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

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

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

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]      ,      __UpperCAmelCase						:     Dict      ,      __UpperCAmelCase						:     str=None      ,      __UpperCAmelCase						:     int=None      ,      __UpperCAmelCase						:     Tuple=None							) ->			Optional[int]:






						if device_map is None:
												if torch.cuda.is_available():
																		SCREAMING_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(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_							):
												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\'.'							)

												SCREAMING_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							)
												    }							)

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

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

												SCREAMING_SNAKE_CASE_     						=      max_memory
												SCREAMING_SNAKE_CASE_     						=      infer_auto_device_map(SCREAMING_SNAKE_CASE_      ,      **SCREAMING_SNAKE_CASE_							)

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

												SCREAMING_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  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[Any]      ,      __UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     Optional[Any]=None      ,      __UpperCAmelCase						:     Any=None							) ->			Tuple:






						if modules_to_not_convert is None:
												SCREAMING_SNAKE_CASE_     						=      []

						SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      _replace_with_bnb_layers(
						    SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_							)
						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  UpperCAmelCase_							(				__UpperCAmelCase						:     List[str]      ,      __UpperCAmelCase						:     Union[str, Any]      ,      __UpperCAmelCase						:     int=None      ,      __UpperCAmelCase						:     str=None      ,      ) ->			List[str]:
						SCREAMING_SNAKE_CASE_     						=      False
						for name, module in model.named_children():
												if current_key_name is None:
																		SCREAMING_SNAKE_CASE_     						=      []
												current_key_name.append(SCREAMING_SNAKE_CASE_							)
												if isinstance(SCREAMING_SNAKE_CASE_      ,      nn.Linear							) and name not in modules_to_not_convert:
																		# Check if the current key is not in the `modules_to_not_convert`
																		SCREAMING_SNAKE_CASE_     						=      '.'.join(SCREAMING_SNAKE_CASE_							)
																		SCREAMING_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:
																														SCREAMING_SNAKE_CASE_     						=      False
																														break
																		if proceed:
																								# Load bnb module with empty weight and replace ``nn.Linear` module
																								if bnb_quantization_config.load_in_abit:
																														SCREAMING_SNAKE_CASE_     						=      bnb.nn.LinearabitLt(
																														    module.in_features      ,      module.out_features      ,      module.bias is not None      ,      has_fpaa_weights=SCREAMING_SNAKE_CASE_      ,      threshold=bnb_quantization_config.llm_inta_threshold      ,      )
																								elif bnb_quantization_config.load_in_abit:
																														SCREAMING_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'							)
																								SCREAMING_SNAKE_CASE_     						=      module.weight.data
																								if module.bias is not None:
																														SCREAMING_SNAKE_CASE_     						=      module.bias.data
																								bnb_module.requires_grad_(SCREAMING_SNAKE_CASE_							)
																								setattr(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_							)
																								SCREAMING_SNAKE_CASE_     						=      True
												if len(list(module.children()							)							) > 0:
																		SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      _replace_with_bnb_layers(
																		    SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_							)
																		SCREAMING_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  UpperCAmelCase_							(				__UpperCAmelCase						:     int							) ->			Union[str, Any]:






						with init_empty_weights():
												SCREAMING_SNAKE_CASE_     						=      deepcopy(SCREAMING_SNAKE_CASE_							)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

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

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

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

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

						return filtered_module_names

def  UpperCAmelCase_							(				__UpperCAmelCase						:     str							) ->			Tuple:






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

def  UpperCAmelCase_							(				__UpperCAmelCase						:     str							) ->			int:
						return next(parameter.parameters()							).device





def  UpperCAmelCase_							(				__UpperCAmelCase						:     Union[str, Any]      ,      __UpperCAmelCase						:     Union[str, Any]      ,      __UpperCAmelCase						:     List[Any]      ,      __UpperCAmelCase						:     List[str]      ,      __UpperCAmelCase						:     Dict      ,      __UpperCAmelCase						:     Union[str, Any]      ,      __UpperCAmelCase						:     Union[str, Any]							) ->			Optional[int]:






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

						set_module_tensor_to_device(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      'meta'      ,      dtype=SCREAMING_SNAKE_CASE_      ,      value=torch.empty(*param.size()							)							)





import argparse
import os
import re


lowerCamelCase__			:       str         =							'src/transformers'

# Pattern that looks at the indentation in a line.
lowerCamelCase__			:       Tuple         =							re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCamelCase__			:       str         =							re.compile(r'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowerCamelCase__			:       int         =							re.compile(r'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
lowerCamelCase__			:       int         =							re.compile(r'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowerCamelCase__			:       int         =							re.compile(r'\[([^\]]+)\]')

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]							) ->			Any:
      SCREAMING_SNAKE_CASE_     						=      _re_indent.search(__UpperCAmelCase							)
      return "" if search is None else search.groups()[0]

def  UpperCAmelCase_							(				__UpperCAmelCase						:     int      ,      __UpperCAmelCase						:     Optional[int]=""      ,      __UpperCAmelCase						:     Optional[int]=None      ,      __UpperCAmelCase						:     Dict=None							) ->			Dict:
      SCREAMING_SNAKE_CASE_     						=      0
      SCREAMING_SNAKE_CASE_     						=      code.split('\n'							)
      if start_prompt is not None:
            while not lines[index].startswith(__UpperCAmelCase							):
                  index += 1
            SCREAMING_SNAKE_CASE_     						=      ['\n'.join(lines[:index]							)]
      else:
            SCREAMING_SNAKE_CASE_     						=      []

      # We split into blocks until we get to the `end_prompt` (or the end of the block).
      SCREAMING_SNAKE_CASE_     						=      [lines[index]]
      index += 1
      while index < len(__UpperCAmelCase							) and (end_prompt is None or not lines[index].startswith(__UpperCAmelCase							)):
            if len(lines[index]							) > 0 and get_indent(lines[index]							) == indent_level:
                  if len(__UpperCAmelCase							) > 0 and get_indent(current_block[-1]							).startswith(indent_level + ' '							):
                        current_block.append(lines[index]							)
                        blocks.append('\n'.join(__UpperCAmelCase							)							)
                        if index < len(__UpperCAmelCase							) - 1:
                              SCREAMING_SNAKE_CASE_     						=      [lines[index + 1]]
                              index += 1
                        else:
                              SCREAMING_SNAKE_CASE_     						=      []
                  else:
                        blocks.append('\n'.join(__UpperCAmelCase							)							)
                        SCREAMING_SNAKE_CASE_     						=      [lines[index]]
            else:
                  current_block.append(lines[index]							)
            index += 1

      # Adds current block if it's nonempty.
      if len(__UpperCAmelCase							) > 0:
            blocks.append('\n'.join(__UpperCAmelCase							)							)

      # Add final block after end_prompt if provided.
      if end_prompt is not None and index < len(__UpperCAmelCase							):
            blocks.append('\n'.join(lines[index:]							)							)

      return blocks

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Dict							) ->			Any:

      def _inner(__UpperCAmelCase						:     Tuple							):
            return key(__UpperCAmelCase							).lower().replace('_'      ,      ''							)

      return _inner

def  UpperCAmelCase_							(				__UpperCAmelCase						:     List[str]      ,      __UpperCAmelCase						:     Union[str, Any]=None							) ->			Dict:

      # If no key is provided, we use a noop.
      def noop(__UpperCAmelCase						:     Dict							):
            return x

      if key is None:
            SCREAMING_SNAKE_CASE_     						=      noop
      # Constants are all uppercase, they go first.
      SCREAMING_SNAKE_CASE_     						=      [obj for obj in objects if key(__UpperCAmelCase							).isupper()]
      # Classes are not all uppercase but start with a capital, they go second.
      SCREAMING_SNAKE_CASE_     						=      [obj for obj in objects if key(__UpperCAmelCase							)[0].isupper() and not key(__UpperCAmelCase							).isupper()]
      # Functions begin with a lowercase, they go last.
      SCREAMING_SNAKE_CASE_     						=      [obj for obj in objects if not key(__UpperCAmelCase							)[0].isupper()]

      SCREAMING_SNAKE_CASE_     						=      ignore_underscore(__UpperCAmelCase							)
      return sorted(__UpperCAmelCase      ,      key=__UpperCAmelCase							) + sorted(__UpperCAmelCase      ,      key=__UpperCAmelCase							) + sorted(__UpperCAmelCase      ,      key=__UpperCAmelCase							)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Tuple							) ->			List[Any]:

      # This inner function sort imports between [ ].
      def _replace(__UpperCAmelCase						:     Optional[Any]							):
            SCREAMING_SNAKE_CASE_     						=      match.groups()[0]
            if "," not in imports:
                  return f"[{imports}]"
            SCREAMING_SNAKE_CASE_     						=      [part.strip().replace('"'      ,      ''							) for part in imports.split(','							)]
            # We will have a final empty element if the line finished with a comma.
            if len(keys[-1]							) == 0:
                  SCREAMING_SNAKE_CASE_     						=      keys[:-1]
            return "[" + ", ".join([f"\"{k}\"" for k in sort_objects(__UpperCAmelCase							)]							) + "]"

      SCREAMING_SNAKE_CASE_     						=      import_statement.split('\n'							)
      if len(__UpperCAmelCase							) > 3:
            # Here we have to sort internal imports that are on several lines (one per name):
            # key: [
            #     "object1",
            #     "object2",
            #     ...
            # ]

            # We may have to ignore one or two lines on each side.
            SCREAMING_SNAKE_CASE_     						=      2 if lines[1].strip() == '[' else 1
            SCREAMING_SNAKE_CASE_     						=      [(i, _re_strip_line.search(__UpperCAmelCase							).groups()[0]) for i, line in enumerate(lines[idx:-idx]							)]
            SCREAMING_SNAKE_CASE_     						=      sort_objects(__UpperCAmelCase      ,      key=lambda __UpperCAmelCase							: x[1]							)
            SCREAMING_SNAKE_CASE_     						=      [lines[x[0] + idx] for x in sorted_indices]
            return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]							)
      elif len(__UpperCAmelCase							) == 3:
            # Here we have to sort internal imports that are on one separate line:
            # key: [
            #     "object1", "object2", ...
            # ]
            if _re_bracket_content.search(lines[1]							) is not None:
                  SCREAMING_SNAKE_CASE_     						=      _re_bracket_content.sub(_replace      ,      lines[1]							)
            else:
                  SCREAMING_SNAKE_CASE_     						=      [part.strip().replace('"'      ,      ''							) for part in lines[1].split(','							)]
                  # We will have a final empty element if the line finished with a comma.
                  if len(keys[-1]							) == 0:
                        SCREAMING_SNAKE_CASE_     						=      keys[:-1]
                  SCREAMING_SNAKE_CASE_     						=      get_indent(lines[1]							) + ', '.join([f"\"{k}\"" for k in sort_objects(__UpperCAmelCase							)]							)
            return "\n".join(__UpperCAmelCase							)
      else:
            # Finally we have to deal with imports fitting on one line
            SCREAMING_SNAKE_CASE_     						=      _re_bracket_content.sub(_replace      ,      __UpperCAmelCase							)
            return import_statement

def  UpperCAmelCase_							(				__UpperCAmelCase						:     int      ,      __UpperCAmelCase						:     Any=True							) ->			Any:
      with open(__UpperCAmelCase      ,      encoding='utf-8'							) as f:
            SCREAMING_SNAKE_CASE_     						=      f.read()

      if "_import_structure" not in code:
            return

      # Blocks of indent level 0
      SCREAMING_SNAKE_CASE_     						=      split_code_in_indented_blocks(
          __UpperCAmelCase      ,      start_prompt='_import_structure = {'      ,      end_prompt='if TYPE_CHECKING:'							)

      # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
      for block_idx in range(1      ,      len(__UpperCAmelCase							) - 1							):
            # Check if the block contains some `_import_structure`s thingy to sort.
            SCREAMING_SNAKE_CASE_     						=      main_blocks[block_idx]
            SCREAMING_SNAKE_CASE_     						=      block.split('\n'							)

            # Get to the start of the imports.
            SCREAMING_SNAKE_CASE_     						=      0
            while line_idx < len(__UpperCAmelCase							) and "_import_structure" not in block_lines[line_idx]:
                  # Skip dummy import blocks
                  if "import dummy" in block_lines[line_idx]:
                        SCREAMING_SNAKE_CASE_     						=      len(__UpperCAmelCase							)
                  else:
                        line_idx += 1
            if line_idx >= len(__UpperCAmelCase							):
                  continue

            # Ignore beginning and last line: they don't contain anything.
            SCREAMING_SNAKE_CASE_     						=      '\n'.join(block_lines[line_idx:-1]							)
            SCREAMING_SNAKE_CASE_     						=      get_indent(block_lines[1]							)
            # Slit the internal block into blocks of indent level 1.
            SCREAMING_SNAKE_CASE_     						=      split_code_in_indented_blocks(__UpperCAmelCase      ,      indent_level=__UpperCAmelCase							)
            # We have two categories of import key: list or _import_structure[key].append/extend
            SCREAMING_SNAKE_CASE_     						=      _re_direct_key if '_import_structure = {' in block_lines[0] else _re_indirect_key
            # Grab the keys, but there is a trap: some lines are empty or just comments.
            SCREAMING_SNAKE_CASE_     						=      [(pattern.search(__UpperCAmelCase							).groups()[0] if pattern.search(__UpperCAmelCase							) is not None else None) for b in internal_blocks]
            # We only sort the lines with a key.
            SCREAMING_SNAKE_CASE_     						=      [(i, key) for i, key in enumerate(__UpperCAmelCase							) if key is not None]
            SCREAMING_SNAKE_CASE_     						=      [x[0] for x in sorted(__UpperCAmelCase      ,      key=lambda __UpperCAmelCase							: x[1]							)]

            # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
            SCREAMING_SNAKE_CASE_     						=      0
            SCREAMING_SNAKE_CASE_     						=      []
            for i in range(len(__UpperCAmelCase							)							):
                  if keys[i] is None:
                        reorderded_blocks.append(internal_blocks[i]							)
                  else:
                        SCREAMING_SNAKE_CASE_     						=      sort_objects_in_import(internal_blocks[sorted_indices[count]]							)
                        reorderded_blocks.append(__UpperCAmelCase							)
                        count += 1

        # And we put our main block back together with its first and last line.
            SCREAMING_SNAKE_CASE_     						=      '\n'.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]							)

      if code != "\n".join(__UpperCAmelCase							):
            if check_only:
                  return True
            else:
                  print(f"Overwriting {file}."							)
                  with open(__UpperCAmelCase      ,      'w'      ,      encoding='utf-8'							) as f:
                        f.write('\n'.join(__UpperCAmelCase							)							)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[Any]=True							) ->			List[str]:
      SCREAMING_SNAKE_CASE_     						=      []
      for root, _, files in os.walk(__UpperCAmelCase							):
            if "__init__.py" in files:
                  SCREAMING_SNAKE_CASE_     						=      sort_imports(os.path.join(__UpperCAmelCase      ,      '__init__.py'							)      ,      check_only=__UpperCAmelCase							)
                  if result:
                        SCREAMING_SNAKE_CASE_     						=      [os.path.join(__UpperCAmelCase      ,      '__init__.py'							)]
      if len(__UpperCAmelCase							) > 0:
            raise ValueError(f"Would overwrite {len(__UpperCAmelCase							)} files, run `make style`."							)


if __name__ == "__main__":
   lowerCamelCase__			:       int         =							argparse.ArgumentParser()
   parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
   lowerCamelCase__			:       Optional[Any]         =							parser.parse_args()

   sort_imports_in_all_inits(check_only=args.check_only)