

"""simple docstring"""





import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list







@require_torchaudio
@require_sentencepiece
class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						lowerCAmelCase__      :		Dict    							=      '''laion/clap-htsat-unfused'''
						lowerCAmelCase__      :		str    							=      tempfile.mkdtemp()







					def 			__lowerCAmelCase					(			self     :							Optional[Any]  ,**lowercase_     :							Dict						):
						return RobertaTokenizer.from_pretrained(self.checkpoint  ,**lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Tuple  ,**lowercase_     :							int						):
						return ClapFeatureExtractor.from_pretrained(self.checkpoint  ,**lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						shutil.rmtree(self.tmpdirname						)







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						lowerCAmelCase__      :		Tuple    							=      self.get_tokenizer()
						lowerCAmelCase__      :		Optional[Any]    							=      self.get_feature_extractor()

						lowerCAmelCase__      :		Optional[int]    							=      ClapProcessor(tokenizer=lowercase_  ,feature_extractor=lowercase_						)

						processor.save_pretrained(self.tmpdirname						)
						lowerCAmelCase__      :		Tuple    							=      ClapProcessor.from_pretrained(self.tmpdirname						)

						self.assertEqual(processor.tokenizer.get_vocab()  ,tokenizer.get_vocab()						)
						self.assertIsInstance(processor.tokenizer  ,lowercase_						)

						self.assertEqual(processor.feature_extractor.to_json_string()  ,feature_extractor.to_json_string()						)
						self.assertIsInstance(processor.feature_extractor  ,lowercase_						)







					def 			__lowerCAmelCase					(			self     :							int						):
						lowerCAmelCase__      :		Any    							=      ClapProcessor(tokenizer=self.get_tokenizer()  ,feature_extractor=self.get_feature_extractor()						)
						processor.save_pretrained(self.tmpdirname						)

						lowerCAmelCase__      :		str    							=      self.get_tokenizer(bos_token='''(BOS)'''  ,eos_token='''(EOS)'''						)
						lowerCAmelCase__      :		Dict    							=      self.get_feature_extractor(do_normalize=lowercase_  ,padding_value=1.0						)

						lowerCAmelCase__      :		int    							=      ClapProcessor.from_pretrained(
						    self.tmpdirname  ,bos_token='''(BOS)'''  ,eos_token='''(EOS)'''  ,do_normalize=lowercase_  ,padding_value=1.0						)

						self.assertEqual(processor.tokenizer.get_vocab()  ,tokenizer_add_kwargs.get_vocab()						)
						self.assertIsInstance(processor.tokenizer  ,lowercase_						)

						self.assertEqual(processor.feature_extractor.to_json_string()  ,feature_extractor_add_kwargs.to_json_string()						)
						self.assertIsInstance(processor.feature_extractor  ,lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		Any    							=      self.get_feature_extractor()
						lowerCAmelCase__      :		str    							=      self.get_tokenizer()

						lowerCAmelCase__      :		List[str]    							=      ClapProcessor(tokenizer=lowercase_  ,feature_extractor=lowercase_						)

						lowerCAmelCase__      :		List[Any]    							=      floats_list((3, 1_0_0_0)						)

						lowerCAmelCase__      :		Optional[Any]    							=      feature_extractor(lowercase_  ,return_tensors='''np'''						)
						lowerCAmelCase__      :		Union[str, Any]    							=      processor(audios=lowercase_  ,return_tensors='''np'''						)

						for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum()  ,input_processor[key].sum()  ,delta=1E-2						)







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						lowerCAmelCase__      :		int    							=      self.get_feature_extractor()
						lowerCAmelCase__      :		Any    							=      self.get_tokenizer()

						lowerCAmelCase__      :		str    							=      ClapProcessor(tokenizer=lowercase_  ,feature_extractor=lowercase_						)

						lowerCAmelCase__      :		Any    							=      '''This is a test string'''

						lowerCAmelCase__      :		Dict    							=      processor(text=lowercase_						)

						lowerCAmelCase__      :		Tuple    							=      tokenizer(lowercase_						)

						for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]  ,encoded_processor[key]						)







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		Dict    							=      self.get_feature_extractor()
						lowerCAmelCase__      :		Optional[Any]    							=      self.get_tokenizer()

						lowerCAmelCase__      :		Optional[int]    							=      ClapProcessor(tokenizer=lowercase_  ,feature_extractor=lowercase_						)

						lowerCAmelCase__      :		List[str]    							=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						lowerCAmelCase__      :		List[Any]    							=      processor.batch_decode(lowercase_						)
						lowerCAmelCase__      :		str    							=      tokenizer.batch_decode(lowercase_						)

						self.assertListEqual(lowercase_  ,lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						lowerCAmelCase__      :		Any    							=      self.get_feature_extractor()
						lowerCAmelCase__      :		Union[str, Any]    							=      self.get_tokenizer()

						lowerCAmelCase__      :		int    							=      ClapProcessor(tokenizer=lowercase_  ,feature_extractor=lowercase_						)

						self.assertListEqual(
						    processor.model_input_names[2:]  ,feature_extractor.model_input_names  ,msg='''`processor` and `feature_extractor` model input names do not match'''  ,)







"""simple docstring"""





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


__UpperCamelCase    : Dict				 =						logging.get_logger(__name__)


# General docstring
__UpperCamelCase    : List[str]				 =						'''MobileNetV1Config'''

# Base docstring
__UpperCamelCase    : Dict				 =						'''google/mobilenet_v1_1.0_224'''
__UpperCamelCase    : Dict				 =						[1, 1_0_2_4, 7, 7]

# Image classification docstring
__UpperCamelCase    : Tuple				 =						'''google/mobilenet_v1_1.0_224'''
__UpperCamelCase    : List[str]				 =						'''tabby, tabby cat'''


__UpperCamelCase    : Optional[int]				 =						[
    '''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    ( A_		,	A_		,	A_=None					):
	lowerCAmelCase__      :		Union[str, Any]    							=      {}

	if isinstance(A_		,	A_					):
		lowerCAmelCase__      :		List[Any]    							=      model.mobilenet_va
	else:
		lowerCAmelCase__      :		Optional[Any]    							=      model

	lowerCAmelCase__      :		Dict    							=      '''MobilenetV1/Conv2d_0/'''
	lowerCAmelCase__      :		Optional[Any]    							=      backbone.conv_stem.convolution.weight
	lowerCAmelCase__      :		Optional[int]    							=      backbone.conv_stem.normalization.bias
	lowerCAmelCase__      :		Union[str, Any]    							=      backbone.conv_stem.normalization.weight
	lowerCAmelCase__      :		List[str]    							=      backbone.conv_stem.normalization.running_mean
	lowerCAmelCase__      :		List[Any]    							=      backbone.conv_stem.normalization.running_var

	for i in range(13					):
		lowerCAmelCase__      :		str    							=      i + 1
		lowerCAmelCase__      :		Any    							=      i * 2

		lowerCAmelCase__      :		Dict    							=      backbone.layer[pt_index]
		lowerCAmelCase__      :		int    							=      f'MobilenetV1/Conv2d_{tf_index}_depthwise/'
		lowerCAmelCase__      :		Optional[int]    							=      pointer.convolution.weight
		lowerCAmelCase__      :		Dict    							=      pointer.normalization.bias
		lowerCAmelCase__      :		Tuple    							=      pointer.normalization.weight
		lowerCAmelCase__      :		Optional[int]    							=      pointer.normalization.running_mean
		lowerCAmelCase__      :		Optional[int]    							=      pointer.normalization.running_var

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

	if isinstance(A_		,	A_					):
		lowerCAmelCase__      :		Dict    							=      '''MobilenetV1/Logits/Conv2d_1c_1x1/'''
		lowerCAmelCase__      :		str    							=      model.classifier.weight
		lowerCAmelCase__      :		Union[str, Any]    							=      model.classifier.bias

	return tf_to_pt_map

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_		,	A_					):
	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
	lowerCAmelCase__      :		Optional[Any]    							=      tf.train.list_variables(A_					)
	lowerCAmelCase__      :		List[Any]    							=      {}
	for name, shape in init_vars:
		logger.info(f'Loading TF weight {name} with shape {shape}'					)
		lowerCAmelCase__      :		Tuple    							=      tf.train.load_variable(A_		,	A_					)
		lowerCAmelCase__      :		Optional[int]    							=      array

	# Build TF to PyTorch weights loading map
	lowerCAmelCase__      :		Optional[int]    							=      _build_tf_to_pytorch_map(A_		,	A_		,	A_					)

	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

		lowerCAmelCase__      :		Optional[Any]    							=      tf_weights[name]

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

		tf_weights.pop(A_		,	A_					)
		tf_weights.pop(name + '''/RMSProp'''		,	A_					)
		tf_weights.pop(name + '''/RMSProp_1'''		,	A_					)
		tf_weights.pop(name + '''/ExponentialMovingAverage'''		,	A_					)

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

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	lowerCAmelCase__							,lowerCAmelCase__      :		Any    							=      features.shape[-2:]
	lowerCAmelCase__							,lowerCAmelCase__      :		Any    							=      conv_layer.stride
	lowerCAmelCase__							,lowerCAmelCase__      :		str    							=      conv_layer.kernel_size

	if in_height % stride_height == 0:
		lowerCAmelCase__      :		Dict    							=      max(kernel_height - stride_height		,	0					)
	else:
		lowerCAmelCase__      :		List[Any]    							=      max(kernel_height - (in_height % stride_height)		,	0					)

	if in_width % stride_width == 0:
		lowerCAmelCase__      :		Optional[int]    							=      max(kernel_width - stride_width		,	0					)
	else:
		lowerCAmelCase__      :		Dict    							=      max(kernel_width - (in_width % stride_width)		,	0					)

	lowerCAmelCase__      :		Optional[Any]    							=      pad_along_width // 2
	lowerCAmelCase__      :		Dict    							=      pad_along_width - pad_left
	lowerCAmelCase__      :		Optional[Any]    							=      pad_along_height // 2
	lowerCAmelCase__      :		Union[str, Any]    							=      pad_along_height - pad_top

	lowerCAmelCase__      :		Any    							=      (pad_left, pad_right, pad_top, pad_bottom)
	return nn.functional.pad(A_		,	A_		,	'''constant'''		,	0.0					)







class 							SCREAMING_SNAKE_CASE   (						nn.Module			):






					"""simple docstring"""



					def __init__(			self     :							List[str]  ,lowercase_     :							MobileNetVaConfig  ,lowercase_     :							int  ,lowercase_     :							int  ,lowercase_     :							int  ,lowercase_     :							Optional[int] = 1  ,lowercase_     :							Optional[int] = 1  ,lowercase_     :							bool = False  ,lowercase_     :							Optional[bool] = True  ,lowercase_     :							Optional[bool or str] = True  ,):
						super().__init__()
						lowerCAmelCase__      :		Optional[Any]    							=      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.'						)

						lowerCAmelCase__      :		List[str]    							=      0 if config.tf_padding else int((kernel_size - 1) / 2						)

						lowerCAmelCase__      :		str    							=      nn.Convad(
						    in_channels=lowercase_  ,out_channels=lowercase_  ,kernel_size=lowercase_  ,stride=lowercase_  ,padding=lowercase_  ,groups=lowercase_  ,bias=lowercase_  ,padding_mode='''zeros'''  ,)

						if use_normalization:
							lowerCAmelCase__      :		Optional[Any]    							=      nn.BatchNormad(
							    num_features=lowercase_  ,eps=config.layer_norm_eps  ,momentum=0.9997  ,affine=lowercase_  ,track_running_stats=lowercase_  ,)
						else:
							lowerCAmelCase__      :		int    							=      None

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







					def 			__lowerCAmelCase					(			self     :							Dict  ,lowercase_     :							torch.Tensor						):
						if self.config.tf_padding:
							lowerCAmelCase__      :		Optional[Any]    							=      apply_tf_padding(lowercase_  ,self.convolution						)
						lowerCAmelCase__      :		Optional[Any]    							=      self.convolution(lowercase_						)
						if self.normalization is not None:
							lowerCAmelCase__      :		Optional[Any]    							=      self.normalization(lowercase_						)
						if self.activation is not None:
							lowerCAmelCase__      :		Any    							=      self.activation(lowercase_						)
						return features







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			MobileNetVaConfig
					lowercase__												=			load_tf_weights_in_mobilenet_va
					lowercase__												=			"mobilenet_v1"
					lowercase__												=			"pixel_values"
					lowercase__												=			False







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

__UpperCamelCase    : str				 =						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.
'''

__UpperCamelCase    : 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."       ,				a_       ,				)
class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					def __init__(			self     :							int  ,lowercase_     :							MobileNetVaConfig  ,lowercase_     :							bool = True						):
						super().__init__(lowercase_						)
						lowerCAmelCase__      :		Union[str, Any]    							=      config

						lowerCAmelCase__      :		int    							=      3_2
						lowerCAmelCase__      :		List[Any]    							=      max(int(depth * config.depth_multiplier						)  ,config.min_depth						)

						lowerCAmelCase__      :		List[str]    							=      MobileNetVaConvLayer(
						    lowercase_  ,in_channels=config.num_channels  ,out_channels=lowercase_  ,kernel_size=3  ,stride=2  ,)

						lowerCAmelCase__      :		List[str]    							=      [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

						lowerCAmelCase__      :		str    							=      nn.ModuleList()
						for i in range(1_3						):
							lowerCAmelCase__      :		Dict    							=      out_channels

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

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

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

						lowerCAmelCase__      :		Any    							=      nn.AdaptiveAvgPoolad((1, 1)						) if add_pooling_layer else None

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







					def 			__lowerCAmelCase					(			self     :							List[str]  ,lowercase_     :							str						):
						raise NotImplementedError







					@add_start_docstrings_to_model_forward(lowercase_						)
					@add_code_sample_docstrings(
					    checkpoint=_CHECKPOINT_FOR_DOC  ,output_type=lowercase_  ,config_class=_CONFIG_FOR_DOC  ,modality='''vision'''  ,expected_output=_EXPECTED_OUTPUT_SHAPE  ,)
					def 			__lowerCAmelCase					(			self     :							Tuple  ,lowercase_     :							Optional[torch.Tensor] = None  ,lowercase_     :							Optional[bool] = None  ,lowercase_     :							Optional[bool] = None  ,):
						lowerCAmelCase__      :		Dict    							=      (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						lowerCAmelCase__      :		Optional[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'''						)

						lowerCAmelCase__      :		Dict    							=      self.conv_stem(lowercase_						)

						lowerCAmelCase__      :		Union[str, Any]    							=      () if output_hidden_states else None

						for i, layer_module in enumerate(self.layer						):
							lowerCAmelCase__      :		Union[str, Any]    							=      layer_module(lowercase_						)

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

						lowerCAmelCase__      :		Any    							=      hidden_states

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







@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    "       ,				a_       ,				)
class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					def __init__(			self     :							List[str]  ,lowercase_     :							MobileNetVaConfig						):
						super().__init__(lowercase_						)

						lowerCAmelCase__      :		Dict    							=      config.num_labels
						lowerCAmelCase__      :		Optional[int]    							=      MobileNetVaModel(lowercase_						)

						lowerCAmelCase__      :		Union[str, Any]    							=      self.mobilenet_va.layer[-1].convolution.out_channels

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

						lowerCAmelCase__      :		Optional[Any]    							=      self.mobilenet_va(lowercase_  ,output_hidden_states=lowercase_  ,return_dict=lowercase_						)

						lowerCAmelCase__      :		Dict    							=      outputs.pooler_output if return_dict else outputs[1]

						lowerCAmelCase__      :		int    							=      self.classifier(self.dropout(lowercase_						)						)

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

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

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

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




