

import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast



@dataclass
class   __lowercase				( datasets.BuilderConfig	):




			"""simple docstring"""







			_UpperCAmelCase   :							Optional[datasets.Features]										=     None







class   __lowercase				( datasets.ArrowBasedBuilder	):




			"""simple docstring"""







			_UpperCAmelCase   :							Dict										=     PandasConfig



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									return datasets.DatasetInfo(features=self.config.features)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     Union[str, Any]):
									if not self.config.data_files:
															raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}")
									SCREAMING_SNAKE_CASE_: List[Any]  		=       dl_manager.download_and_extract(self.config.data_files)
									if isinstance(lowerCAmelCase__   ,						(str, list, tuple)):
															SCREAMING_SNAKE_CASE_: List[str]  		=       data_files
															if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__):
																					SCREAMING_SNAKE_CASE_: int  		=       [files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															SCREAMING_SNAKE_CASE_: int  		=       [dl_manager.iter_files(lowerCAmelCase__) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,						gen_kwargs={"files": files})]
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       []
									for split_name, files in data_files.items():
															if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__):
																					SCREAMING_SNAKE_CASE_: List[Any]  		=       [files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															SCREAMING_SNAKE_CASE_: str  		=       [dl_manager.iter_files(lowerCAmelCase__) for file in files]
															splits.append(datasets.SplitGenerator(name=lowerCAmelCase__   ,						gen_kwargs={"files": files}))
									return splits



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     pa.Table):
									if self.config.features is not None:
															# more expensive cast to support nested features with keys in a different order
															# allows str <-> int/float or str to Audio for example
															SCREAMING_SNAKE_CASE_: List[str]  		=       table_cast(lowerCAmelCase__   ,						self.config.features.arrow_schema)
									return pa_table




			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Any):
									for i, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase__)):
															with open(lowerCAmelCase__   ,						"rb") as f:
																					SCREAMING_SNAKE_CASE_: Tuple  		=       pa.Table.from_pandas(pd.read_pickle(lowerCAmelCase__))
																					yield i, self._cast_table(lowerCAmelCase__)









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


lowerCAmelCase							:  int   	=						logging.get_logger(__name__)


# General docstring
lowerCAmelCase							:  int   	=						"""MobileNetV1Config"""

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

# Image classification docstring
lowerCAmelCase							:  Union[str, Any]   	=						"""google/mobilenet_v1_1.0_224"""
lowerCAmelCase							:  Any   	=						"""tabby, tabby cat"""


lowerCAmelCase							:  List[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 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=None    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       {}

						if isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: Any  		=       model.mobilenet_va
						else:
												SCREAMING_SNAKE_CASE_: int  		=       model

						SCREAMING_SNAKE_CASE_: Dict  		=       "MobilenetV1/Conv2d_0/"
						SCREAMING_SNAKE_CASE_: str  		=       backbone.conv_stem.convolution.weight
						SCREAMING_SNAKE_CASE_: List[str]  		=       backbone.conv_stem.normalization.bias
						SCREAMING_SNAKE_CASE_: int  		=       backbone.conv_stem.normalization.weight
						SCREAMING_SNAKE_CASE_: List[str]  		=       backbone.conv_stem.normalization.running_mean
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       backbone.conv_stem.normalization.running_var

						for i in range(13    ):
												SCREAMING_SNAKE_CASE_: List[str]  		=       i + 1
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       i * 2

												SCREAMING_SNAKE_CASE_: Any  		=       backbone.layer[pt_index]
												SCREAMING_SNAKE_CASE_: Any  		=       f"MobilenetV1/Conv2d_{tf_index}_depthwise/"
												SCREAMING_SNAKE_CASE_: Any  		=       pointer.convolution.weight
												SCREAMING_SNAKE_CASE_: Any  		=       pointer.normalization.bias
												SCREAMING_SNAKE_CASE_: str  		=       pointer.normalization.weight
												SCREAMING_SNAKE_CASE_: Dict  		=       pointer.normalization.running_mean
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       pointer.normalization.running_var

												SCREAMING_SNAKE_CASE_: Tuple  		=       backbone.layer[pt_index + 1]
												SCREAMING_SNAKE_CASE_: List[str]  		=       f"MobilenetV1/Conv2d_{tf_index}_pointwise/"
												SCREAMING_SNAKE_CASE_: int  		=       pointer.convolution.weight
												SCREAMING_SNAKE_CASE_: Any  		=       pointer.normalization.bias
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       pointer.normalization.weight
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       pointer.normalization.running_mean
												SCREAMING_SNAKE_CASE_: Dict  		=       pointer.normalization.running_var

						if isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: List[str]  		=       "MobilenetV1/Logits/Conv2d_1c_1x1/"
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       model.classifier.weight
												SCREAMING_SNAKE_CASE_: Tuple  		=       model.classifier.bias

						return tf_to_pt_map







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						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
						SCREAMING_SNAKE_CASE_: int  		=       tf.train.list_variables(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: int  		=       {}
						for name, shape in init_vars:
												logger.info(f"Loading TF weight {name} with shape {shape}"    )
												SCREAMING_SNAKE_CASE_: Any  		=       tf.train.load_variable(_UpperCAmelCase				,	_UpperCAmelCase    )
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       array

						# Build TF to PyTorch weights loading map
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       _build_tf_to_pytorch_map(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )

						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

												SCREAMING_SNAKE_CASE_: int  		=       tf_weights[name]

												if "depthwise_weights" in name:
																		logger.info("Transposing depthwise"    )
																		SCREAMING_SNAKE_CASE_: int  		=       np.transpose(_UpperCAmelCase				,	(2, 3, 0, 1)    )
												elif "weights" in name:
																		logger.info("Transposing"    )
																		if len(pointer.shape    ) == 2:  # copying into linear layer
																								SCREAMING_SNAKE_CASE_: List[str]  		=       array.squeeze().transpose()
																		else:
																								SCREAMING_SNAKE_CASE_: Any  		=       np.transpose(_UpperCAmelCase				,	(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}"    )
												SCREAMING_SNAKE_CASE_: int  		=       torch.from_numpy(_UpperCAmelCase    )

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

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







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       features.shape[-2:]
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       conv_layer.stride
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       conv_layer.kernel_size

						if in_height % stride_height == 0:
												SCREAMING_SNAKE_CASE_: int  		=       max(kernel_height - stride_height				,	0    )
						else:
												SCREAMING_SNAKE_CASE_: Tuple  		=       max(kernel_height - (in_height % stride_height)				,	0    )

						if in_width % stride_width == 0:
												SCREAMING_SNAKE_CASE_: str  		=       max(kernel_width - stride_width				,	0    )
						else:
												SCREAMING_SNAKE_CASE_: Dict  		=       max(kernel_width - (in_width % stride_width)				,	0    )

						SCREAMING_SNAKE_CASE_: str  		=       pad_along_width // 2
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       pad_along_width - pad_left
						SCREAMING_SNAKE_CASE_: int  		=       pad_along_height // 2
						SCREAMING_SNAKE_CASE_: Tuple  		=       pad_along_height - pad_top

						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       (pad_left, pad_right, pad_top, pad_bottom)
						return nn.functional.pad(_UpperCAmelCase				,	_UpperCAmelCase				,	"constant"				,	0.0    )



class   __lowercase				( nn.Module	):




			"""simple docstring"""







			def __init__( self  :     Optional[int]   ,						lowerCAmelCase__  :     MobileNetVaConfig   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Optional[int] = 1   ,						lowerCAmelCase__  :     Optional[int] = 1   ,						lowerCAmelCase__  :     bool = False   ,						lowerCAmelCase__  :     Optional[bool] = True   ,						lowerCAmelCase__  :     Optional[bool or str] = True   ,						):
									super().__init__()
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       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.")

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

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       nn.Convad(
									    in_channels=lowerCAmelCase__   ,						out_channels=lowerCAmelCase__   ,						kernel_size=lowerCAmelCase__   ,						stride=lowerCAmelCase__   ,						padding=lowerCAmelCase__   ,						groups=lowerCAmelCase__   ,						bias=lowerCAmelCase__   ,						padding_mode="zeros"   ,						)

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

									if use_activation:
															if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__):
																					SCREAMING_SNAKE_CASE_: Dict  		=       ACTaFN[use_activation]
															elif isinstance(config.hidden_act   ,						lowerCAmelCase__):
																					SCREAMING_SNAKE_CASE_: Dict  		=       ACTaFN[config.hidden_act]
															else:
																					SCREAMING_SNAKE_CASE_: Any  		=       config.hidden_act
									else:
															SCREAMING_SNAKE_CASE_: int  		=       None




			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     torch.Tensor):
									if self.config.tf_padding:
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       apply_tf_padding(lowerCAmelCase__   ,						self.convolution)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.convolution(lowerCAmelCase__)
									if self.normalization is not None:
															SCREAMING_SNAKE_CASE_: int  		=       self.normalization(lowerCAmelCase__)
									if self.activation is not None:
															SCREAMING_SNAKE_CASE_: List[Any]  		=       self.activation(lowerCAmelCase__)
									return features







class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[str]										=     MobileNetVaConfig
			_UpperCAmelCase   :							List[Any]										=     load_tf_weights_in_mobilenet_va
			_UpperCAmelCase   :							List[Any]										=     '''mobilenet_v1'''
			_UpperCAmelCase   :							int										=     '''pixel_values'''
			_UpperCAmelCase   :							List[Any]										=     False



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     Union[nn.Linear, nn.Convad]):
									if isinstance(lowerCAmelCase__   ,						(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(lowerCAmelCase__   ,						nn.BatchNormad):
															module.bias.data.zero_()
															module.weight.data.fill_(1.0)







lowerCAmelCase							:  Any   	=						R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

lowerCAmelCase							:  List[str]   	=						R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileNetV1ImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""



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




			"""simple docstring"""







			def __init__( self  :     Dict   ,						lowerCAmelCase__  :     MobileNetVaConfig   ,						lowerCAmelCase__  :     bool = True):
									super().__init__(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Dict  		=       config

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       32
									SCREAMING_SNAKE_CASE_: Dict  		=       max(int(depth * config.depth_multiplier)   ,						config.min_depth)

									SCREAMING_SNAKE_CASE_: Tuple  		=       MobileNetVaConvLayer(
									    lowerCAmelCase__   ,						in_channels=config.num_channels   ,						out_channels=lowerCAmelCase__   ,						kernel_size=3   ,						stride=2   ,						)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

									SCREAMING_SNAKE_CASE_: str  		=       nn.ModuleList()
									for i in range(13):
															SCREAMING_SNAKE_CASE_: List[Any]  		=       out_channels

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

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

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

									SCREAMING_SNAKE_CASE_: List[str]  		=       nn.AdaptiveAvgPoolad((1, 1)) if add_pooling_layer else None

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



			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     str):
									raise NotImplementedError




			@add_start_docstrings_to_model_forward(lowerCAmelCase__)
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC   ,						output_type=lowerCAmelCase__   ,						config_class=_CONFIG_FOR_DOC   ,						modality="vision"   ,						expected_output=_EXPECTED_OUTPUT_SHAPE   ,						)
			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     Optional[torch.Tensor] = None   ,						lowerCAmelCase__  :     Optional[bool] = None   ,						lowerCAmelCase__  :     Optional[bool] = None   ,						):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       (
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									SCREAMING_SNAKE_CASE_: Any  		=       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")

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.conv_stem(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       () if output_hidden_states else None

									for i, layer_module in enumerate(self.layer):
															SCREAMING_SNAKE_CASE_: Tuple  		=       layer_module(lowerCAmelCase__)

															if output_hidden_states:
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       all_hidden_states + (hidden_states,)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       hidden_states

									if self.pooler is not None:
															SCREAMING_SNAKE_CASE_: int  		=       torch.flatten(self.pooler(lowerCAmelCase__)   ,						start_dim=1)
									else:
															SCREAMING_SNAKE_CASE_: List[str]  		=       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=lowerCAmelCase__   ,						pooler_output=lowerCAmelCase__   ,						hidden_states=lowerCAmelCase__   ,						)







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




			"""simple docstring"""







			def __init__( self  :     Any   ,						lowerCAmelCase__  :     MobileNetVaConfig):
									super().__init__(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       config.num_labels
									SCREAMING_SNAKE_CASE_: Dict  		=       MobileNetVaModel(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Tuple  		=       self.mobilenet_va.layer[-1].convolution.out_channels

									# Classifier head
									SCREAMING_SNAKE_CASE_: str  		=       nn.Dropout(config.classifier_dropout_prob   ,						inplace=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       nn.Linear(lowerCAmelCase__   ,						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(lowerCAmelCase__)
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT   ,						output_type=lowerCAmelCase__   ,						config_class=_CONFIG_FOR_DOC   ,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT   ,						)
			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     Optional[torch.Tensor] = None   ,						lowerCAmelCase__  :     Optional[bool] = None   ,						lowerCAmelCase__  :     Optional[torch.Tensor] = None   ,						lowerCAmelCase__  :     Optional[bool] = None   ,						):
									SCREAMING_SNAKE_CASE_: List[str]  		=       return_dict if return_dict is not None else self.config.use_return_dict

									SCREAMING_SNAKE_CASE_: List[str]  		=       self.mobilenet_va(lowerCAmelCase__   ,						output_hidden_states=lowerCAmelCase__   ,						return_dict=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       outputs.pooler_output if return_dict else outputs[1]

									SCREAMING_SNAKE_CASE_: Tuple  		=       self.classifier(self.dropout(lowerCAmelCase__))

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       None
									if labels is not None:
															if self.config.problem_type is None:
																					if self.num_labels == 1:
																											SCREAMING_SNAKE_CASE_: List[Any]  		=       "regression"
																					elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																											SCREAMING_SNAKE_CASE_: int  		=       "single_label_classification"
																					else:
																											SCREAMING_SNAKE_CASE_: str  		=       "multi_label_classification"

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

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

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






