





'''simple docstring'''






import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


A_		:  Optional[int]		 =						logging.getLogger(__name__)
class 					__snake_case  (					__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





   lowerCamelCase__    							=      '''token-classification'''

   def __init__(							self	,		__SCREAMING_SNAKE_CASE       ):
    if type(__SCREAMING_SNAKE_CASE       ) == dict:
     snake_case__     :					Optional[Any]    =  Namespace(**__SCREAMING_SNAKE_CASE       )
    snake_case__     :					int    =  import_module("""tasks"""       )
    try:
     snake_case__     :					Optional[int]    =  getattr(__SCREAMING_SNAKE_CASE	,		hparams.task_type       )
     snake_case__     :					TokenClassificationTask    =  token_classification_task_clazz()
    except AttributeError:
     raise ValueError(
         f"Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
         f"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"       )
    snake_case__     :					Optional[int]    =  self.token_classification_task.get_labels(hparams.labels       )
    snake_case__     :					Optional[int]    =  CrossEntropyLoss().ignore_index
    super().__init__(__SCREAMING_SNAKE_CASE	,		len(self.labels       )	,		self.mode       )

   def 		__UpperCamelCase						(							self	,		**__SCREAMING_SNAKE_CASE       ):
    return self.model(**__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					int    =  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
    if self.config.model_type != "distilbert":
     snake_case__     :					Any    =  (
         batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
     )  # XLM and RoBERTa don"t use token_type_ids

    snake_case__     :					Any    =  self(**__SCREAMING_SNAKE_CASE       )
    snake_case__     :					int    =  outputs[0]
    # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
    return {"loss": loss}

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Union[str, Any]    =  self.hparams
    for mode in ["train", "dev", "test"]:
     snake_case__     :					str    =  self._feature_file(__SCREAMING_SNAKE_CASE       )
     if os.path.exists(__SCREAMING_SNAKE_CASE       ) and not args.overwrite_cache:
      logger.info("""Loading features from cached file %s"""	,		__SCREAMING_SNAKE_CASE       )
      snake_case__     :					Tuple    =  torch.load(__SCREAMING_SNAKE_CASE       )
     else:
      logger.info("""Creating features from dataset file at %s"""	,		args.data_dir       )
      snake_case__     :					Optional[int]    =  self.token_classification_task.read_examples_from_file(args.data_dir	,		__SCREAMING_SNAKE_CASE       )
      snake_case__     :					Optional[int]    =  self.token_classification_task.convert_examples_to_features(
          __SCREAMING_SNAKE_CASE	,		self.labels	,		args.max_seq_length	,		self.tokenizer	,		cls_token_at_end=bool(self.config.model_type in ["""xlnet"""]       )	,		cls_token=self.tokenizer.cls_token	,		cls_token_segment_id=2 if self.config.model_type in ["""xlnet"""] else 0	,		sep_token=self.tokenizer.sep_token	,		sep_token_extra=__SCREAMING_SNAKE_CASE	,		pad_on_left=bool(self.config.model_type in ["""xlnet"""]       )	,		pad_token=self.tokenizer.pad_token_id	,		pad_token_segment_id=self.tokenizer.pad_token_type_id	,		pad_token_label_id=self.pad_token_label_id	,		)
      logger.info("""Saving features into cached file %s"""	,		__SCREAMING_SNAKE_CASE       )
      torch.save(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE = False       ):
    snake_case__     :					Optional[int]    =  self._feature_file(__SCREAMING_SNAKE_CASE       )
    logger.info("""Loading features from cached file %s"""	,		__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Tuple    =  torch.load(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Dict    =  torch.tensor([f.input_ids for f in features]	,		dtype=torch.long       )
    snake_case__     :					int    =  torch.tensor([f.attention_mask for f in features]	,		dtype=torch.long       )
    if features[0].token_type_ids is not None:
     snake_case__     :					List[str]    =  torch.tensor([f.token_type_ids for f in features]	,		dtype=torch.long       )
    else:
     snake_case__     :					Optional[Any]    =  torch.tensor([0 for f in features]	,		dtype=torch.long       )
     # HACK(we will not use this anymore soon)
    snake_case__     :					Optional[Any]    =  torch.tensor([f.label_ids for f in features]	,		dtype=torch.long       )
    return DataLoader(
        TensorDataset(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )	,		batch_size=__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
    """Compute validation""" ""
    snake_case__     :					Optional[Any]    =  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
    if self.config.model_type != "distilbert":
     snake_case__     :					Tuple    =  (
         batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
     )  # XLM and RoBERTa don"t use token_type_ids
    snake_case__     :					str    =  self(**__SCREAMING_SNAKE_CASE       )
    snake_case__  ,							snake_case__     :					Optional[Any]    =  outputs[:2]
    snake_case__     :					Union[str, Any]    =  logits.detach().cpu().numpy()
    snake_case__     :					Optional[int]    =  inputs["""labels"""].detach().cpu().numpy()
    return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					Any    =  torch.stack([x["""val_loss"""] for x in outputs]       ).mean()
    snake_case__     :					Tuple    =  np.concatenate([x["""pred"""] for x in outputs]	,		axis=0       )
    snake_case__     :					List[Any]    =  np.argmax(__SCREAMING_SNAKE_CASE	,		axis=2       )
    snake_case__     :					Optional[int]    =  np.concatenate([x["""target"""] for x in outputs]	,		axis=0       )

    snake_case__     :					List[str]    =  dict(enumerate(self.labels       )       )
    snake_case__     :					Any    =  [[] for _ in range(out_label_ids.shape[0]       )]
    snake_case__     :					Optional[int]    =  [[] for _ in range(out_label_ids.shape[0]       )]

    for i in range(out_label_ids.shape[0]       ):
     for j in range(out_label_ids.shape[1]       ):
      if out_label_ids[i, j] != self.pad_token_label_id:
       out_label_list[i].append(label_map[out_label_ids[i][j]]       )
       preds_list[i].append(label_map[preds[i][j]]       )

    snake_case__     :					int    =  {
        """val_loss""": val_loss_mean,
        """accuracy_score""": accuracy_score(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ),
        """precision""": precision_score(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ),
        """recall""": recall_score(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ),
        """f1""": fa_score(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ),
    }

    snake_case__     :					Any    =  dict(results.items()       )
    snake_case__     :					Dict    =  results
    return ret, preds_list, out_label_list

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    # when stable
    snake_case__  ,							snake_case__  ,							snake_case__     :					Any    =  self._eval_end(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Any    =  ret["""log"""]
    return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    # updating to test_epoch_end instead of deprecated test_end
    snake_case__  ,							snake_case__  ,							snake_case__     :					Union[str, Any]    =  self._eval_end(__SCREAMING_SNAKE_CASE       )

    # Converting to the dict required by pl
    # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
    # pytorch_lightning/trainer/logging.py#L139
    snake_case__     :					Dict    =  ret["""log"""]
    # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
    return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


   @staticmethod
   def 		__UpperCamelCase						(							__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
    # Add NER specific options
    BaseTransformer.add_model_specific_args(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )
    parser.add_argument(
        """--task_type"""	,		default="""NER"""	,		type=__SCREAMING_SNAKE_CASE	,		help="""Task type to fine tune in training (e.g. NER, POS, etc)"""       )
    parser.add_argument(
        """--max_seq_length"""	,		default=1_2_8	,		type=__SCREAMING_SNAKE_CASE	,		help=(
            """The maximum total input sequence length after tokenization. Sequences longer """
            """than this will be truncated, sequences shorter will be padded."""
        )	,		)

    parser.add_argument(
        """--labels"""	,		default=""""""	,		type=__SCREAMING_SNAKE_CASE	,		help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""	,		)
    parser.add_argument(
        """--gpus"""	,		default=0	,		type=__SCREAMING_SNAKE_CASE	,		help="""The number of GPUs allocated for this, it is by default 0 meaning none"""	,		)

    parser.add_argument(
        """--overwrite_cache"""	,		action="""store_true"""	,		help="""Overwrite the cached training and evaluation sets"""       )

    return parser


if __name__ == "__main__":
    A_		:  Dict		 =						argparse.ArgumentParser()
    add_generic_args(parser, os.getcwd())
    A_		:  int		 =						NERTransformer.add_model_specific_args(parser, os.getcwd())
    A_		:  List[Any]		 =						parser.parse_args()
    A_		:  Union[str, Any]		 =						NERTransformer(args)
    A_		:  str		 =						generic_train(model, args)

    if args.do_predict:
        # See https://github.com/huggingface/transformers/issues/3159
        # pl use this default format to create a checkpoint:
        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
        # /pytorch_lightning/callbacks/model_checkpoint.py#L322
        A_		:  Union[str, Any]		 =						sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
        A_		:  List[str]		 =						model.load_from_checkpoint(checkpoints[-1])
        trainer.test(model)












from typing import Optional, Union

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

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, 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_mobilenet_va import MobileNetVaConfig


__a   :							Union[str, Any]						= logging.get_logger(__name__)


# General docstring
__a   :							List[str]						= "MobileNetV1Config"

# Base docstring
__a   :							int						= "google/mobilenet_v1_1.0_224"
__a   :							List[Any]						= [1, 1024, 7, 7]

# Image classification docstring
__a   :							Optional[int]						= "google/mobilenet_v1_1.0_224"
__a   :							List[Any]						= "tabby, tabby cat"


__a   :							Union[str, Any]						= [
    "google/mobilenet_v1_1.0_224",
    "google/mobilenet_v1_0.75_192",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[str]      ,					__lowercase :      str      ,					__lowercase :      Union[str, Any]=None     )    ->   Any:





    """simple docstring"""

    __A       =						{}

    if isinstance(__lowercase      ,					__lowercase     ):
        __A       =						model.mobilenet_va
    else:
        __A       =						model

    __A       =						"""MobilenetV1/Conv2d_0/"""
    __A       =						backbone.conv_stem.convolution.weight
    __A       =						backbone.conv_stem.normalization.bias
    __A       =						backbone.conv_stem.normalization.weight
    __A       =						backbone.conv_stem.normalization.running_mean
    __A       =						backbone.conv_stem.normalization.running_var

    for i in range(1_3     ):
        __A       =						i + 1
        __A       =						i * 2

        __A       =						backbone.layer[pt_index]
        __A       =						f"MobilenetV1/Conv2d_{tf_index}_depthwise/"
        __A       =						pointer.convolution.weight
        __A       =						pointer.normalization.bias
        __A       =						pointer.normalization.weight
        __A       =						pointer.normalization.running_mean
        __A       =						pointer.normalization.running_var

        __A       =						backbone.layer[pt_index + 1]
        __A       =						f"MobilenetV1/Conv2d_{tf_index}_pointwise/"
        __A       =						pointer.convolution.weight
        __A       =						pointer.normalization.bias
        __A       =						pointer.normalization.weight
        __A       =						pointer.normalization.running_mean
        __A       =						pointer.normalization.running_var

    if isinstance(__lowercase      ,					__lowercase     ):
        __A       =						"""MobilenetV1/Logits/Conv2d_1c_1x1/"""
        __A       =						model.classifier.weight
        __A       =						model.classifier.bias

    return tf_to_pt_map



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Optional[Any]      ,					__lowercase :      int      ,					__lowercase :      int     )    ->   str:





    """simple docstring"""

    try:
        import numpy as np
        import tensorflow as tf
    except ImportError:
        logger.error(
            """Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see """
            """https://www.tensorflow.org/install/ for installation instructions."""     )
        raise

    # Load weights from TF model
    __A       =						tf.train.list_variables(__lowercase     )
    __A       =						{}
    for name, shape in init_vars:
        logger.info(f"Loading TF weight {name} with shape {shape}"     )
        __A       =						tf.train.load_variable(__lowercase      ,					__lowercase     )
        __A       =						array

    # Build TF to PyTorch weights loading map
    __A       =						_build_tf_to_pytorch_map(__lowercase      ,					__lowercase      ,					__lowercase     )

    for name, pointer in tf_to_pt_map.items():
        logger.info(f"Importing {name}"     )
        if name not in tf_weights:
            logger.info(f"{name} not in tf pre-trained weights, skipping"     )
            continue

        __A       =						tf_weights[name]

        if "depthwise_weights" in name:
            logger.info("""Transposing depthwise"""     )
            __A       =						np.transpose(__lowercase      ,					(2, 3, 0, 1)     )
        elif "weights" in name:
            logger.info("""Transposing"""     )
            if len(pointer.shape     ) == 2:  # copying into linear layer
                __A       =						array.squeeze().transpose()
            else:
                __A       =						np.transpose(__lowercase      ,					(3, 2, 0, 1)     )

        if pointer.shape != array.shape:
            raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"     )

        logger.info(f"Initialize PyTorch weight {name} {array.shape}"     )
        __A       =						torch.from_numpy(__lowercase     )

        tf_weights.pop(__lowercase      ,					__lowercase     )
        tf_weights.pop(name + """/RMSProp"""      ,					__lowercase     )
        tf_weights.pop(name + """/RMSProp_1"""      ,					__lowercase     )
        tf_weights.pop(name + """/ExponentialMovingAverage"""      ,					__lowercase     )

    logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys()     )}"     )
    return model



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      torch.Tensor      ,					__lowercase :      nn.Convad     )    ->   torch.Tensor:





    """simple docstring"""

    __A     ,						__A       =						features.shape[-2:]
    __A     ,						__A       =						conv_layer.stride
    __A     ,						__A       =						conv_layer.kernel_size

    if in_height % stride_height == 0:
        __A       =						max(kernel_height - stride_height      ,					0     )
    else:
        __A       =						max(kernel_height - (in_height % stride_height)      ,					0     )

    if in_width % stride_width == 0:
        __A       =						max(kernel_width - stride_width      ,					0     )
    else:
        __A       =						max(kernel_width - (in_width % stride_width)      ,					0     )

    __A       =						pad_along_width // 2
    __A       =						pad_along_width - pad_left
    __A       =						pad_along_height // 2
    __A       =						pad_along_height - pad_top

    __A       =						(pad_left, pad_right, pad_top, pad_bottom)
    return nn.functional.pad(__lowercase      ,					__lowercase      ,					"""constant"""      ,					0.0     )


class    __lowercase   (						nn.Module	):







     '''simple docstring'''




     def __init__(					self							:      int			,						UpperCamelCase_							:      MobileNetVaConfig			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Optional[int] = 1			,						UpperCamelCase_							:      Optional[int] = 1			,						UpperCamelCase_							:      bool = False			,						UpperCamelCase_							:      Optional[bool] = True			,						UpperCamelCase_							:      Optional[bool or str] = True			,						):


         """simple docstring"""



         super().__init__()
         __A       =						config

         if in_channels % groups != 0:
             raise ValueError(F"Input channels ({in_channels}) are not divisible by {groups} groups."					)
         if out_channels % groups != 0:
             raise ValueError(F"Output channels ({out_channels}) are not divisible by {groups} groups."					)

         __A       =						0 if config.tf_padding else int((kernel_size - 1) / 2					)

         __A       =						nn.Convad(
             in_channels=UpperCamelCase_			,						out_channels=UpperCamelCase_			,						kernel_size=UpperCamelCase_			,						stride=UpperCamelCase_			,						padding=UpperCamelCase_			,						groups=UpperCamelCase_			,						bias=UpperCamelCase_			,						padding_mode="""zeros"""			,						)

         if use_normalization:
             __A       =						nn.BatchNormad(
                 num_features=UpperCamelCase_			,						eps=config.layer_norm_eps			,						momentum=0.9997			,						affine=UpperCamelCase_			,						track_running_stats=UpperCamelCase_			,						)
         else:
             __A       =						None

         if use_activation:
             if isinstance(UpperCamelCase_			,						UpperCamelCase_					):
                 __A       =						ACTaFN[use_activation]
             elif isinstance(config.hidden_act			,						UpperCamelCase_					):
                 __A       =						ACTaFN[config.hidden_act]
             else:
                 __A       =						config.hidden_act
         else:
             __A       =						None





     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      torch.Tensor					):


         """simple docstring"""



         if self.config.tf_padding:
             __A       =						apply_tf_padding(UpperCamelCase_			,						self.convolution					)
         __A       =						self.convolution(UpperCamelCase_					)
         if self.normalization is not None:
             __A       =						self.normalization(UpperCamelCase_					)
         if self.activation is not None:
             __A       =						self.activation(UpperCamelCase_					)
         return features


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					MobileNetVaConfig
     SCREAMING_SNAKE_CASE     =					load_tf_weights_in_mobilenet_va
     SCREAMING_SNAKE_CASE     =					"mobilenet_v1"
     SCREAMING_SNAKE_CASE     =					"pixel_values"
     SCREAMING_SNAKE_CASE     =					False




     def 		lowerCAmelCase_  (					self							:      Optional[Any]			,						UpperCamelCase_							:      Union[nn.Linear, nn.Convad]					):


         """simple docstring"""



         if isinstance(UpperCamelCase_			,						(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(UpperCamelCase_			,						nn.BatchNormad					):
             module.bias.data.zero_()
             module.weight.data.fill_(1.0					)
__a   :							Tuple						= R"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    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 ([`MobileNetV1Config`]): 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"

__a   :							int						= 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            [`MobileNetV1ImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"


@add_start_docstrings(
    "The bare MobileNetV1 model outputting raw hidden-states without any specific head on top."      ,   lowercase_      ,   )
class    __lowercase   (						lowercase_	):







     '''simple docstring'''




     def __init__(					self							:      int			,						UpperCamelCase_							:      MobileNetVaConfig			,						UpperCamelCase_							:      bool = True					):


         """simple docstring"""



         super().__init__(UpperCamelCase_					)
         __A       =						config

         __A       =						32
         __A       =						max(int(depth * config.depth_multiplier					)			,						config.min_depth					)

         __A       =						MobileNetVaConvLayer(
             UpperCamelCase_			,						in_channels=config.num_channels			,						out_channels=UpperCamelCase_			,						kernel_size=3			,						stride=2			,						)

         __A       =						[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

         __A       =						nn.ModuleList()
         for i in range(13					):
             __A       =						out_channels

             if strides[i] == 2 or i == 0:
                 depth *= 2
                 __A       =						max(int(depth * config.depth_multiplier					)			,						config.min_depth					)

             self.layer.append(
                 MobileNetVaConvLayer(
                     UpperCamelCase_			,						in_channels=UpperCamelCase_			,						out_channels=UpperCamelCase_			,						kernel_size=3			,						stride=strides[i]			,						groups=UpperCamelCase_			,						)					)

             self.layer.append(
                 MobileNetVaConvLayer(
                     UpperCamelCase_			,						in_channels=UpperCamelCase_			,						out_channels=UpperCamelCase_			,						kernel_size=1			,						)					)

         __A       =						nn.AdaptiveAvgPoolad((1, 1)					) if add_pooling_layer else None

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




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      Optional[int]					):


         """simple docstring"""



         raise NotImplementedError





     @add_start_docstrings_to_model_forward(UpperCamelCase_					)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC			,						output_type=UpperCamelCase_			,						config_class=_CONFIG_FOR_DOC			,						modality="""vision"""			,						expected_output=_EXPECTED_OUTPUT_SHAPE			,						)
     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      Optional[torch.Tensor] = None			,						UpperCamelCase_							:      Optional[bool] = None			,						UpperCamelCase_							:      Optional[bool] = None			,						):


         """simple docstring"""



         __A       =						(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         __A       =						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"""					)

         __A       =						self.conv_stem(UpperCamelCase_					)

         __A       =						() if output_hidden_states else None

         for i, layer_module in enumerate(self.layer					):
             __A       =						layer_module(UpperCamelCase_					)

             if output_hidden_states:
                 __A       =						all_hidden_states + (hidden_states,)

         __A       =						hidden_states

         if self.pooler is not None:
             __A       =						torch.flatten(self.pooler(UpperCamelCase_					)			,						start_dim=1					)
         else:
             __A       =						None

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

         return BaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=UpperCamelCase_			,						pooler_output=UpperCamelCase_			,						hidden_states=UpperCamelCase_			,						)


@add_start_docstrings(
    "\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "      ,   lowercase_      ,   )
class    __lowercase   (						lowercase_	):







     '''simple docstring'''




     def __init__(					self							:      Optional[int]			,						UpperCamelCase_							:      MobileNetVaConfig					):


         """simple docstring"""



         super().__init__(UpperCamelCase_					)

         __A       =						config.num_labels
         __A       =						MobileNetVaModel(UpperCamelCase_					)

         __A       =						self.mobilenet_va.layer[-1].convolution.out_channels

         # Classifier head
         __A       =						nn.Dropout(config.classifier_dropout_prob			,						inplace=UpperCamelCase_					)
         __A       =						nn.Linear(UpperCamelCase_			,						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(UpperCamelCase_					)
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT			,						output_type=UpperCamelCase_			,						config_class=_CONFIG_FOR_DOC			,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT			,						)
     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      Optional[torch.Tensor] = None			,						UpperCamelCase_							:      Optional[bool] = None			,						UpperCamelCase_							:      Optional[torch.Tensor] = None			,						UpperCamelCase_							:      Optional[bool] = None			,						):


         """simple docstring"""



         __A       =						return_dict if return_dict is not None else self.config.use_return_dict

         __A       =						self.mobilenet_va(UpperCamelCase_			,						output_hidden_states=UpperCamelCase_			,						return_dict=UpperCamelCase_					)

         __A       =						outputs.pooler_output if return_dict else outputs[1]

         __A       =						self.classifier(self.dropout(UpperCamelCase_					)					)

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

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

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

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




