





'''simple docstring'''

from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
      import PIL

# soft dependency
if is_pytesseract_available():
      import pytesseract

lowercase_					= logging.get_logger(__name__)





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Tuple					,       __lowerCamelCase :    int    )						->Optional[int]:
    return [
        int(1000 * (box[0] / width)    ),
        int(1000 * (box[1] / height)    ),
        int(1000 * (box[2] / width)    ),
        int(1000 * (box[3] / height)    ),
    ]





def lowerCamelCase						(     __lowerCamelCase :    np.ndarray					,       __lowerCamelCase :    Optional[str]					,       __lowerCamelCase :    Optional[str] = None    )						->Dict:
    _SCREAMING_SNAKE_CASE									=			tesseract_config if tesseract_config is not None else """"""

    # apply OCR
    _SCREAMING_SNAKE_CASE									=			to_pil_image(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			pil_image.size
    _SCREAMING_SNAKE_CASE									=			pytesseract.image_to_data(__lowerCamelCase					,       lang=__lowerCamelCase					,       output_type="""dict"""					,       config=__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

    # filter empty words and corresponding coordinates
    _SCREAMING_SNAKE_CASE									=			[idx for idx, word in enumerate(__lowerCamelCase    ) if not word.strip()]
    _SCREAMING_SNAKE_CASE									=			[word for idx, word in enumerate(__lowerCamelCase    ) if idx not in irrelevant_indices]
    _SCREAMING_SNAKE_CASE									=			[coord for idx, coord in enumerate(__lowerCamelCase    ) if idx not in irrelevant_indices]
    _SCREAMING_SNAKE_CASE									=			[coord for idx, coord in enumerate(__lowerCamelCase    ) if idx not in irrelevant_indices]
    _SCREAMING_SNAKE_CASE									=			[coord for idx, coord in enumerate(__lowerCamelCase    ) if idx not in irrelevant_indices]
    _SCREAMING_SNAKE_CASE									=			[coord for idx, coord in enumerate(__lowerCamelCase    ) if idx not in irrelevant_indices]

    # turn coordinates into (left, top, left+width, top+height) format
    _SCREAMING_SNAKE_CASE									=			[]
    for x, y, w, h in zip(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    ):
        _SCREAMING_SNAKE_CASE									=			[x, y, x + w, y + h]
        actual_boxes.append(__lowerCamelCase    )

    # finally, normalize the bounding boxes
    _SCREAMING_SNAKE_CASE									=			[]
    for box in actual_boxes:
        normalized_boxes.append(normalize_box(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )    )

    assert len(__lowerCamelCase    ) == len(__lowerCamelCase    ), "Not as many words as there are bounding boxes"

    return words, normalized_boxes






class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = ['''pixel_values''']


    def __init__(		self ,		A = True ,		A = None ,		A = PILImageResampling.BILINEAR ,		A = True ,		A = None ,		A = "" ,		**A ,		)		->   None:
        super().__init__(**A       )
        _SCREAMING_SNAKE_CASE									=			size if size is not None else {"""height""": 224, """width""": 224}
        _SCREAMING_SNAKE_CASE									=			get_size_dict(A       )

        _SCREAMING_SNAKE_CASE									=			do_resize
        _SCREAMING_SNAKE_CASE									=			size
        _SCREAMING_SNAKE_CASE									=			resample
        _SCREAMING_SNAKE_CASE									=			apply_ocr
        _SCREAMING_SNAKE_CASE									=			ocr_lang
        _SCREAMING_SNAKE_CASE									=			tesseract_config


    def 			snake_case_(		self ,		A ,		A ,		A = PILImageResampling.BILINEAR ,		A = None ,		**A ,		)		->   np.ndarray:
        _SCREAMING_SNAKE_CASE									=			get_size_dict(A       )
        if "height" not in size or "width" not in size:
            raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'       )
        _SCREAMING_SNAKE_CASE									=			(size["""height"""], size["""width"""])
        return resize(A ,		size=A ,		resample=A ,		data_format=A ,		**A       )





    def 			snake_case_(		self ,		A ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = ChannelDimension.FIRST ,		**A ,		)		->   PIL.Image.Image:
        _SCREAMING_SNAKE_CASE									=			do_resize if do_resize is not None else self.do_resize
        _SCREAMING_SNAKE_CASE									=			size if size is not None else self.size
        _SCREAMING_SNAKE_CASE									=			get_size_dict(A       )
        _SCREAMING_SNAKE_CASE									=			resample if resample is not None else self.resample
        _SCREAMING_SNAKE_CASE									=			apply_ocr if apply_ocr is not None else self.apply_ocr
        _SCREAMING_SNAKE_CASE									=			ocr_lang if ocr_lang is not None else self.ocr_lang
        _SCREAMING_SNAKE_CASE									=			tesseract_config if tesseract_config is not None else self.tesseract_config

        _SCREAMING_SNAKE_CASE									=			make_list_of_images(A       )

        if not valid_images(A       ):
            raise ValueError(
                """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                """torch.Tensor, tf.Tensor or jax.ndarray."""       )

        if do_resize and size is None:
            raise ValueError("""Size must be specified if do_resize is True."""       )

        # All transformations expect numpy arrays.
        _SCREAMING_SNAKE_CASE									=			[to_numpy_array(A       ) for image in images]

        if apply_ocr:
            requires_backends(self ,		"""pytesseract"""       )
            _SCREAMING_SNAKE_CASE									=			[]
            _SCREAMING_SNAKE_CASE									=			[]
            for image in images:
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			apply_tesseract(A ,		A ,		A       )
                words_batch.append(A       )
                boxes_batch.append(A       )

        if do_resize:
            _SCREAMING_SNAKE_CASE									=			[self.resize(image=A ,		size=A ,		resample=A       ) for image in images]

        # flip color channels from RGB to BGR (as Detectron2 requires this)
        _SCREAMING_SNAKE_CASE									=			[flip_channel_order(A       ) for image in images]
        _SCREAMING_SNAKE_CASE									=			[to_channel_dimension_format(A ,		A       ) for image in images]

        _SCREAMING_SNAKE_CASE									=			BatchFeature(data={"""pixel_values""": images} ,		tensor_type=A       )

        if apply_ocr:
            _SCREAMING_SNAKE_CASE									=			words_batch
            _SCREAMING_SNAKE_CASE									=			boxes_batch
        return data










'''simple docstring'''

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


lowercase_					= logging.get_logger(__name__)

# General docstring
lowercase_					= """PoolFormerConfig"""

# Base docstring
lowercase_					= """sail/poolformer_s12"""
lowercase_					= [1, 512, 7, 7]

# Image classification docstring
lowercase_					= """sail/poolformer_s12"""
lowercase_					= """tabby, tabby cat"""

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





def lowerCamelCase						(     __lowerCamelCase :    List[Any]					,       __lowerCamelCase :    float = 0.0					,       __lowerCamelCase :    bool = False    )						->int:
    if drop_prob == 0.0 or not training:
        return input
    _SCREAMING_SNAKE_CASE									=			1 - drop_prob
    _SCREAMING_SNAKE_CASE									=			(input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
    _SCREAMING_SNAKE_CASE									=			keep_prob + torch.rand(__lowerCamelCase					,       dtype=input.dtype					,       device=input.device    )
    random_tensor.floor_()  # binarize
    _SCREAMING_SNAKE_CASE									=			input.div(__lowerCamelCase    ) * random_tensor
    return output


class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A = None       )		->   None:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			drop_prob


    def 			snake_case_(		self ,		A       )		->   torch.Tensor:
        return drop_path(A ,		self.drop_prob ,		self.training       )





    def 			snake_case_(		self       )		->   str:
        return "p={}".format(self.drop_prob       )




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A ,		A ,		A ,		A ,		A=None       )		->   Union[str, Any]:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			patch_size if isinstance(A ,		collections.abc.Iterable       ) else (patch_size, patch_size)
        _SCREAMING_SNAKE_CASE									=			stride if isinstance(A ,		collections.abc.Iterable       ) else (stride, stride)
        _SCREAMING_SNAKE_CASE									=			padding if isinstance(A ,		collections.abc.Iterable       ) else (padding, padding)

        _SCREAMING_SNAKE_CASE									=			nn.Convad(A ,		A ,		kernel_size=A ,		stride=A ,		padding=A       )
        _SCREAMING_SNAKE_CASE									=			norm_layer(A       ) if norm_layer else nn.Identity()





    def 			snake_case_(		self ,		A       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			self.projection(A       )
        _SCREAMING_SNAKE_CASE									=			self.norm(A       )
        return embeddings




class 					a_     (				nn.GroupNorm       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		**A       )		->   Union[str, Any]:
        super().__init__(1 ,		A ,		**A       )




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A       )		->   Union[str, Any]:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			nn.AvgPoolad(A ,		stride=1 ,		padding=pool_size // 2 ,		count_include_pad=A       )





    def 			snake_case_(		self ,		A       )		->   Union[str, Any]:
        return self.pool(A       ) - hidden_states




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A ,		A ,		A       )		->   List[Any]:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			nn.Convad(A ,		A ,		1       )
        _SCREAMING_SNAKE_CASE									=			nn.Convad(A ,		A ,		1       )
        _SCREAMING_SNAKE_CASE									=			PoolFormerDropPath(A       )
        if isinstance(config.hidden_act ,		A       ):
            _SCREAMING_SNAKE_CASE									=			ACTaFN[config.hidden_act]
        else:
            _SCREAMING_SNAKE_CASE									=			config.hidden_act





    def 			snake_case_(		self ,		A       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			self.conva(A       )
        _SCREAMING_SNAKE_CASE									=			self.act_fn(A       )
        _SCREAMING_SNAKE_CASE									=			self.drop(A       )
        _SCREAMING_SNAKE_CASE									=			self.conva(A       )
        _SCREAMING_SNAKE_CASE									=			self.drop(A       )

        return hidden_states




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A ,		A ,		A ,		A ,		A       )		->   Union[str, Any]:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			PoolFormerPooling(A       )
        _SCREAMING_SNAKE_CASE									=			PoolFormerOutput(A ,		A ,		A ,		A       )
        _SCREAMING_SNAKE_CASE									=			PoolFormerGroupNorm(A       )
        _SCREAMING_SNAKE_CASE									=			PoolFormerGroupNorm(A       )

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





    def 			snake_case_(		self ,		A       )		->   Optional[Any]:
        if self.use_layer_scale:
            _SCREAMING_SNAKE_CASE									=			self.pooling(self.before_norm(A       )       )
            _SCREAMING_SNAKE_CASE									=			self.layer_scale_a.unsqueeze(-1       ).unsqueeze(-1       ) * pooling_output
            # First residual connection
            _SCREAMING_SNAKE_CASE									=			hidden_states + self.drop_path(A       )
            _SCREAMING_SNAKE_CASE									=			()

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

            _SCREAMING_SNAKE_CASE									=			(output,) + outputs
            return outputs

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

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

            _SCREAMING_SNAKE_CASE									=			(output,) + outputs
            return outputs




class 					a_     (				nn.Module       ):

    '''simple docstring'''


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

        # patch embeddings
        _SCREAMING_SNAKE_CASE									=			[]
        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] ,		)       )
        _SCREAMING_SNAKE_CASE									=			nn.ModuleList(A       )

        # Transformer blocks
        _SCREAMING_SNAKE_CASE									=			[]
        _SCREAMING_SNAKE_CASE									=			0
        for i in range(config.num_encoder_blocks       ):
            # each block consists of layers
            _SCREAMING_SNAKE_CASE									=			[]
            if i != 0:
                cur += config.depths[i - 1]
            for j in range(config.depths[i]       ):
                layers.append(
                    PoolFormerLayer(
                        A ,		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(A       )       )

        _SCREAMING_SNAKE_CASE									=			nn.ModuleList(A       )





    def 			snake_case_(		self ,		A ,		A=False ,		A=True       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			() if output_hidden_states else None

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

            if output_hidden_states:
                _SCREAMING_SNAKE_CASE									=			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=A ,		hidden_states=A       )




class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = PoolFormerConfig
    UpperCamelCase     = '''poolformer'''
    UpperCamelCase     = '''pixel_values'''
    UpperCamelCase     = True


    def 			snake_case_(		self ,		A       )		->   int:
        if isinstance(A ,		(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(A ,		nn.LayerNorm       ):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0       )





    def 			snake_case_(		self ,		A ,		A=False       )		->   Dict:
        if isinstance(A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			value




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

    Parameters:
        config ([`PoolFormerConfig`]): 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.
"""

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


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

    '''simple docstring'''


    def __init__(		self ,		A       )		->   int:
        super().__init__(A       )
        _SCREAMING_SNAKE_CASE									=			config

        _SCREAMING_SNAKE_CASE									=			PoolFormerEncoder(A       )

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


    def 			snake_case_(		self       )		->   Any:
        return self.embeddings.patch_embeddings





    @add_start_docstrings_to_model_forward(A       )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC ,		output_type=A ,		config_class=_CONFIG_FOR_DOC ,		modality="""vision""" ,		expected_output=_EXPECTED_OUTPUT_SHAPE ,		)
    def 			snake_case_(		self ,		A = None ,		A = None ,		A = None ,		)		->   Union[Tuple, BaseModelOutputWithNoAttention]:
        _SCREAMING_SNAKE_CASE									=			(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        _SCREAMING_SNAKE_CASE									=			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									=			self.encoder(
            A ,		output_hidden_states=A ,		return_dict=A ,		)
        _SCREAMING_SNAKE_CASE									=			encoder_outputs[0]

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

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




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A       )		->   Dict:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			nn.Linear(config.hidden_size ,		config.hidden_size       )





    def 			snake_case_(		self ,		A       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self.dense(A       )
        return output




@add_start_docstrings(
    '''
    PoolFormer Model transformer with an image classification head on top
    '''      ,    snake_case_      ,    )
class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		A       )		->   Optional[Any]:
        super().__init__(A       )
        _SCREAMING_SNAKE_CASE									=			config.num_labels
        _SCREAMING_SNAKE_CASE									=			PoolFormerModel(A       )

        # Final norm
        _SCREAMING_SNAKE_CASE									=			PoolFormerGroupNorm(config.hidden_sizes[-1]       )
        # Classifier head
        _SCREAMING_SNAKE_CASE									=			(
            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(A       )
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT ,		output_type=A ,		config_class=_CONFIG_FOR_DOC ,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,		)
    def 			snake_case_(		self ,		A = None ,		A = None ,		A = None ,		A = None ,		)		->   Union[Tuple, ImageClassifierOutputWithNoAttention]:
        _SCREAMING_SNAKE_CASE									=			return_dict if return_dict is not None else self.config.use_return_dict

        _SCREAMING_SNAKE_CASE									=			self.poolformer(
            A ,		output_hidden_states=A ,		return_dict=A ,		)

        _SCREAMING_SNAKE_CASE									=			outputs[0]

        _SCREAMING_SNAKE_CASE									=			self.classifier(self.norm(A       ).mean([-2, -1]       )       )

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

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

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

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



