




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


UpperCamelCase								=					logging.getLogger(__name__)



def 							_A       (						lowerCAmelCase_					: torch.nn.Module       ,    lowerCAmelCase_					: BnbQuantizationConfig       ,    lowerCAmelCase_					: Union[str, os.PathLike] = None       ,    lowerCAmelCase_					: Optional[Dict[str, Union[int, str, torch.device]]] = None       ,    lowerCAmelCase_					: Optional[List[str]] = None       ,    lowerCAmelCase_					: Optional[Dict[Union[int, str], Union[int, str]]] = None       ,    lowerCAmelCase_					: Optional[Union[str, os.PathLike]] = None       ,    lowerCAmelCase_					: bool = False       ,    ):
     """simple docstring"""

     lowerCAmelCase__          =			bnb_quantization_config.load_in_abit
     lowerCAmelCase__          =			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."      )

     lowerCAmelCase__          =			[]
     # custom device map
     if isinstance(lowerCAmelCase_       ,    lowerCAmelCase_      ) and len(device_map.keys()      ) > 1:
          lowerCAmelCase__          =			[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:
          lowerCAmelCase__          =			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_      )
     lowerCAmelCase__          =			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:
          lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			bnb_quantization_config.keep_in_fpaa_modules
     modules_to_not_convert.extend(lowerCAmelCase_      )

     # compatibility with peft
     lowerCAmelCase__          =			load_in_abit
     lowerCAmelCase__          =			load_in_abit

     lowerCAmelCase__          =			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."      )
          lowerCAmelCase__          =			replace_with_bnb_layers(lowerCAmelCase_       ,    lowerCAmelCase_       ,    modules_to_not_convert=lowerCAmelCase_      )
          # convert param to the right dtype
          lowerCAmelCase__          =			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:
                         lowerCAmelCase__          =			name.replace(".weight"       ,    ""      ).replace(".bias"       ,    ""      )
                         lowerCAmelCase__          =			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():
               lowerCAmelCase__          =			replace_with_bnb_layers(
                   lowerCAmelCase_       ,    lowerCAmelCase_       ,    modules_to_not_convert=lowerCAmelCase_      )

          lowerCAmelCase__          =			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():
               lowerCAmelCase__          =			True

          lowerCAmelCase__          =			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 							_A       (						lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Union[str, Any]=None       ,    lowerCAmelCase_					: Dict=None       ,    lowerCAmelCase_					: Optional[Any]=None      ):
     """simple docstring"""

     if device_map is None:
          if torch.cuda.is_available():
               lowerCAmelCase__          =			{"": 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'."      )

          lowerCAmelCase__          =			{}
          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      )
              }      )

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

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

          lowerCAmelCase__          =			max_memory
          lowerCAmelCase__          =			infer_auto_device_map(lowerCAmelCase_       ,    **lowerCAmelCase_      )

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

          lowerCAmelCase__          =			{
              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 							_A       (						lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: Dict=None       ,    lowerCAmelCase_					: Optional[int]=None      ):
     """simple docstring"""

     if modules_to_not_convert is None:
          lowerCAmelCase__          =			[]

     lowerCAmelCase__     ,		lowerCAmelCase__          =			_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 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: Optional[int]=None       ,    lowerCAmelCase_					: List[Any]=None       ,    ):
     """simple docstring"""

     lowerCAmelCase__          =			False
     for name, module in model.named_children():
          if current_key_name is None:
               lowerCAmelCase__          =			[]
          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`
               lowerCAmelCase__          =			".".join(lowerCAmelCase_      )
               lowerCAmelCase__          =			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:
                         lowerCAmelCase__          =			False
                         break
               if proceed:
                    # Load bnb module with empty weight and replace ``nn.Linear` module
                    if bnb_quantization_config.load_in_abit:
                         lowerCAmelCase__          =			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:
                         lowerCAmelCase__          =			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"      )
                    lowerCAmelCase__          =			module.weight.data
                    if module.bias is not None:
                         lowerCAmelCase__          =			module.bias.data
                    bnb_module.requires_grad_(lowerCAmelCase_      )
                    setattr(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
                    lowerCAmelCase__          =			True
          if len(list(module.children()      )      ) > 0:
               lowerCAmelCase__     ,		lowerCAmelCase__          =			_replace_with_bnb_layers(
                   lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
               lowerCAmelCase__          =			has_been_replaced | _has_been_replaced
          # Remove the last key for recursion
          current_key_name.pop(-1      )
     return model, has_been_replaced



def 							_A       (						lowerCAmelCase_					: str      ):
     """simple docstring"""

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

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

     # Check if it is a base model
     lowerCAmelCase__          =			False
     if hasattr(lowerCAmelCase_       ,    "base_model_prefix"      ):
          lowerCAmelCase__          =			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
     lowerCAmelCase__          =			list(model.named_children()      )
     lowerCAmelCase__          =			[list_modules[-1][0]]

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

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

     return filtered_module_names



def 							_A       (						lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

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



def 							_A       (						lowerCAmelCase_					: nn.Module      ):
     """simple docstring"""

     return next(parameter.parameters()      ).device






def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Any       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Union[str, Any]      ):
     """simple docstring"""

     if fpaa_statistics is None:
          set_module_tensor_to_device(lowerCAmelCase_       ,    lowerCAmelCase_       ,    0       ,    dtype=lowerCAmelCase_       ,    value=lowerCAmelCase_      )
          lowerCAmelCase__          =			param_name
          lowerCAmelCase__          =			model
          if "." in tensor_name:
               lowerCAmelCase__          =			tensor_name.split("."      )
               for split in splits[:-1]:
                    lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    lowerCAmelCase_      )
                    if new_module is None:
                         raise ValueError(F'{module} has no attribute {split}.'      )
                    lowerCAmelCase__          =			new_module
               lowerCAmelCase__          =			splits[-1]
          # offload weights
          lowerCAmelCase__          =			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()      )      )






import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


UpperCamelCase								=					logging.get_logger(__name__)

# General docstring
UpperCamelCase								=					'PoolFormerConfig'

# Base docstring
UpperCamelCase								=					'sail/poolformer_s12'
UpperCamelCase								=					[1, 512, 7, 7]

# Image classification docstring
UpperCamelCase								=					'sail/poolformer_s12'
UpperCamelCase								=					'tabby, tabby cat'

UpperCamelCase								=					[
    'sail/poolformer_s12',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]



def 							_A       (						lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: float = 0.0       ,    lowerCAmelCase_					: bool = False      ):
     """simple docstring"""

     if drop_prob == 0.0 or not training:
          return input
     lowerCAmelCase__          =			1 - drop_prob
     lowerCAmelCase__          =			(input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
     lowerCAmelCase__          =			keep_prob + torch.rand(lowerCAmelCase_       ,    dtype=input.dtype       ,    device=input.device      )
     random_tensor.floor_()  # binarize
     lowerCAmelCase__          =			input.div(lowerCAmelCase_      ) * random_tensor
     return output





class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[float] = None       )				->	None:
         super().__init__()
         lowerCAmelCase__          =			drop_prob


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					torch.Tensor       )				->	torch.Tensor:
         return drop_path(SCREAMING_SNAKE_CASE__       ,     self.drop_prob       ,     self.training       )


    def        a				(	self					:					Optional[Any]       )				->	str:
         return "p={}".format(self.drop_prob       )






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					str=None       )				->	Optional[Any]:
         super().__init__()
         lowerCAmelCase__          =			patch_size if isinstance(SCREAMING_SNAKE_CASE__       ,     collections.abc.Iterable       ) else (patch_size, patch_size)
         lowerCAmelCase__          =			stride if isinstance(SCREAMING_SNAKE_CASE__       ,     collections.abc.Iterable       ) else (stride, stride)
         lowerCAmelCase__          =			padding if isinstance(SCREAMING_SNAKE_CASE__       ,     collections.abc.Iterable       ) else (padding, padding)

         lowerCAmelCase__          =			nn.Convad(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     kernel_size=SCREAMING_SNAKE_CASE__       ,     stride=SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			norm_layer(SCREAMING_SNAKE_CASE__       ) if norm_layer else nn.Identity()


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			self.projection(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.norm(SCREAMING_SNAKE_CASE__       )
         return embeddings






class 			__lowerCamelCase	(   nn.GroupNorm   ):




    """simple docstring"""

    def __init__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     **SCREAMING_SNAKE_CASE__					:					Any       )				->	Dict:
         super().__init__(1       ,     SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	List[Any]:
         super().__init__()
         lowerCAmelCase__          =			nn.AvgPoolad(SCREAMING_SNAKE_CASE__       ,     stride=1       ,     padding=pool_size // 2       ,     count_include_pad=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	Optional[Any]:
         return self.pool(SCREAMING_SNAKE_CASE__       ) - hidden_states






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Dict:
         super().__init__()
         lowerCAmelCase__          =			nn.Convad(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     1       )
         lowerCAmelCase__          =			nn.Convad(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     1       )
         lowerCAmelCase__          =			PoolFormerDropPath(SCREAMING_SNAKE_CASE__       )
         if isinstance(config.hidden_act       ,     SCREAMING_SNAKE_CASE__       ):
              lowerCAmelCase__          =			ACTaFN[config.hidden_act]
         else:
              lowerCAmelCase__          =			config.hidden_act


    def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	List[Any]:
         lowerCAmelCase__          =			self.conva(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.act_fn(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.drop(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.conva(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.drop(SCREAMING_SNAKE_CASE__       )

         return hidden_states






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Optional[Any]:
         super().__init__()
         lowerCAmelCase__          =			PoolFormerPooling(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PoolFormerOutput(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PoolFormerGroupNorm(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PoolFormerGroupNorm(SCREAMING_SNAKE_CASE__       )

         # Useful for training neural nets
         lowerCAmelCase__          =			PoolFormerDropPath(SCREAMING_SNAKE_CASE__       ) if drop_path > 0.0 else nn.Identity()
         lowerCAmelCase__          =			config.use_layer_scale
         if config.use_layer_scale:
              lowerCAmelCase__          =			nn.Parameter(
                  config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE__)       )       ,     requires_grad=SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			nn.Parameter(
                  config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE__)       )       ,     requires_grad=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	int:
         if self.use_layer_scale:
              lowerCAmelCase__          =			self.pooling(self.before_norm(SCREAMING_SNAKE_CASE__       )       )
              lowerCAmelCase__          =			self.layer_scale_a.unsqueeze(-1       ).unsqueeze(-1       ) * pooling_output
              # First residual connection
              lowerCAmelCase__          =			hidden_states + self.drop_path(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			()

              lowerCAmelCase__          =			self.output(self.after_norm(SCREAMING_SNAKE_CASE__       )       )
              lowerCAmelCase__          =			self.layer_scale_a.unsqueeze(-1       ).unsqueeze(-1       ) * layer_output
              # Second residual connection
              lowerCAmelCase__          =			hidden_states + self.drop_path(SCREAMING_SNAKE_CASE__       )

              lowerCAmelCase__          =			(output,) + outputs
              return outputs

         else:
              lowerCAmelCase__          =			self.drop_path(self.pooling(self.before_norm(SCREAMING_SNAKE_CASE__       )       )       )
              # First residual connection
              lowerCAmelCase__          =			pooling_output + hidden_states
              lowerCAmelCase__          =			()

              # Second residual connection inside the PoolFormerOutput block
              lowerCAmelCase__          =			self.drop_path(self.output(self.after_norm(SCREAMING_SNAKE_CASE__       )       )       )
              lowerCAmelCase__          =			hidden_states + layer_output

              lowerCAmelCase__          =			(output,) + outputs
              return outputs






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       )				->	Any:
         super().__init__()
         lowerCAmelCase__          =			config
         # stochastic depth decay rule
         lowerCAmelCase__          =			[x.item() for x in torch.linspace(0       ,     config.drop_path_rate       ,     sum(config.depths       )       )]

         # patch embeddings
         lowerCAmelCase__          =			[]
         for i in range(config.num_encoder_blocks       ):
              embeddings.append(
                  PoolFormerEmbeddings(
                      patch_size=config.patch_sizes[i]       ,     stride=config.strides[i]       ,     padding=config.padding[i]       ,     num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]       ,     hidden_size=config.hidden_sizes[i]       ,     )       )
         lowerCAmelCase__          =			nn.ModuleList(SCREAMING_SNAKE_CASE__       )

         # Transformer blocks
         lowerCAmelCase__          =			[]
         lowerCAmelCase__          =			0
         for i in range(config.num_encoder_blocks       ):
              # each block consists of layers
              lowerCAmelCase__          =			[]
              if i != 0:
                   cur += config.depths[i - 1]
              for j in range(config.depths[i]       ):
                   layers.append(
                       PoolFormerLayer(
                           SCREAMING_SNAKE_CASE__       ,     num_channels=config.hidden_sizes[i]       ,     pool_size=config.pool_size       ,     hidden_size=config.hidden_sizes[i]       ,     intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio       )       ,     drop_path=dpr[cur + j]       ,     )       )
              blocks.append(nn.ModuleList(SCREAMING_SNAKE_CASE__       )       )

         lowerCAmelCase__          =			nn.ModuleList(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					int=False       ,     SCREAMING_SNAKE_CASE__					:					List[str]=True       )				->	Dict:
         lowerCAmelCase__          =			() if output_hidden_states else None

         lowerCAmelCase__          =			pixel_values
         for idx, layers in enumerate(zip(self.patch_embeddings       ,     self.block       )       ):
              lowerCAmelCase__     ,		lowerCAmelCase__          =			layers
              # Get patch embeddings from hidden_states
              lowerCAmelCase__          =			embedding_layer(SCREAMING_SNAKE_CASE__       )
              # Send the embeddings through the blocks
              for _, blk in enumerate(SCREAMING_SNAKE_CASE__       ):
                   lowerCAmelCase__          =			blk(SCREAMING_SNAKE_CASE__       )
                   lowerCAmelCase__          =			layer_outputs[0]

              if output_hidden_states:
                   lowerCAmelCase__          =			all_hidden_states + (hidden_states,)

         if not return_dict:
              return tuple(v for v in [hidden_states, all_hidden_states] if v is not None       )

         return BaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE__       ,     hidden_states=SCREAMING_SNAKE_CASE__       )






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				PoolFormerConfig
    snake_case__ =				"poolformer"
    snake_case__ =				"pixel_values"
    snake_case__ =				True


    def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	List[Any]:
         if isinstance(SCREAMING_SNAKE_CASE__       ,     (nn.Linear, nn.Convad)       ):
              module.weight.data.normal_(mean=0.0       ,     std=self.config.initializer_range       )
              if module.bias is not None:
                   module.bias.data.zero_()
         elif isinstance(SCREAMING_SNAKE_CASE__       ,     nn.LayerNorm       ):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0       )


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=False       )				->	Tuple:
         if isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ):
              lowerCAmelCase__          =			value






UpperCamelCase								=					R'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

UpperCamelCase								=					R'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`PoolFormerImageProcessor.__call__`] for details.\n'





@add_start_docstrings(
    "The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top."     ,    UpperCamelCase__     ,    )
class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Optional[Any]:
         super().__init__(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			config

         lowerCAmelCase__          =			PoolFormerEncoder(SCREAMING_SNAKE_CASE__       )

         # Initialize weights and apply final processing
         self.post_init()


    def        a				(	self					:					Optional[int]       )				->	Optional[Any]:
         return self.embeddings.patch_embeddings


    @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__       )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC       ,     output_type=SCREAMING_SNAKE_CASE__       ,     config_class=_CONFIG_FOR_DOC       ,     modality="vision"       ,     expected_output=_EXPECTED_OUTPUT_SHAPE       ,     )
    def        a				(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.FloatTensor] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     )				->	Union[Tuple, BaseModelOutputWithNoAttention]:
         lowerCAmelCase__          =			(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         lowerCAmelCase__          =			return_dict if return_dict is not None else self.config.use_return_dict

         if pixel_values is None:
              raise ValueError("You have to specify pixel_values"       )

         lowerCAmelCase__          =			self.encoder(
             SCREAMING_SNAKE_CASE__       ,     output_hidden_states=SCREAMING_SNAKE_CASE__       ,     return_dict=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			encoder_outputs[0]

         if not return_dict:
              return (sequence_output, None) + encoder_outputs[1:]

         return BaseModelOutputWithNoAttention(
             last_hidden_state=SCREAMING_SNAKE_CASE__       ,     hidden_states=encoder_outputs.hidden_states       ,     )






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	List[str]:
         super().__init__()
         lowerCAmelCase__          =			nn.Linear(config.hidden_size       ,     config.hidden_size       )


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       )				->	int:
         lowerCAmelCase__          =			self.dense(SCREAMING_SNAKE_CASE__       )
         return output






@add_start_docstrings(
    "\n    PoolFormer Model transformer with an image classification head on top\n    "     ,    UpperCamelCase__     ,    )
class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	Optional[int]:
         super().__init__(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			config.num_labels
         lowerCAmelCase__          =			PoolFormerModel(SCREAMING_SNAKE_CASE__       )

         # Final norm
         lowerCAmelCase__          =			PoolFormerGroupNorm(config.hidden_sizes[-1]       )
         # Classifier head
         lowerCAmelCase__          =			(
             nn.Linear(config.hidden_sizes[-1]       ,     config.num_labels       ) if config.num_labels > 0 else nn.Identity()
         )

         # Initialize weights and apply final processing
         self.post_init()


    @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__       )
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT       ,     output_type=SCREAMING_SNAKE_CASE__       ,     config_class=_CONFIG_FOR_DOC       ,     expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,     )
    def        a				(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.FloatTensor] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.LongTensor] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[bool] = None       ,     )				->	Union[Tuple, ImageClassifierOutputWithNoAttention]:
         lowerCAmelCase__          =			return_dict if return_dict is not None else self.config.use_return_dict

         lowerCAmelCase__          =			self.poolformer(
             SCREAMING_SNAKE_CASE__       ,     output_hidden_states=SCREAMING_SNAKE_CASE__       ,     return_dict=SCREAMING_SNAKE_CASE__       ,     )

         lowerCAmelCase__          =			outputs[0]

         lowerCAmelCase__          =			self.classifier(self.norm(SCREAMING_SNAKE_CASE__       ).mean([-2, -1]       )       )

         lowerCAmelCase__          =			None
         if labels is not None:
              if self.config.problem_type is None:
                   if self.num_labels == 1:
                        lowerCAmelCase__          =			"regression"
                   elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                        lowerCAmelCase__          =			"single_label_classification"
                   else:
                        lowerCAmelCase__          =			"multi_label_classification"

              if self.config.problem_type == "regression":
                   lowerCAmelCase__          =			MSELoss()
                   if self.num_labels == 1:
                        lowerCAmelCase__          =			loss_fct(logits.squeeze()       ,     labels.squeeze()       )
                   else:
                        lowerCAmelCase__          =			loss_fct(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
              elif self.config.problem_type == "single_label_classification":
                   lowerCAmelCase__          =			CrossEntropyLoss()
                   lowerCAmelCase__          =			loss_fct(logits.view(-1       ,     self.num_labels       )       ,     labels.view(-1       )       )
              elif self.config.problem_type == "multi_label_classification":
                   lowerCAmelCase__          =			BCEWithLogitsLoss()
                   lowerCAmelCase__          =			loss_fct(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         if not return_dict:
              lowerCAmelCase__          =			(logits,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output

         return ImageClassifierOutputWithNoAttention(loss=SCREAMING_SNAKE_CASE__       ,     logits=SCREAMING_SNAKE_CASE__       ,     hidden_states=outputs.hidden_states       )
