

import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     str       )      ->				str:
				_UpperCAmelCase				:						List[str]       						=			min(re.search("^ +"     ,					lowerCAmelCase       ).end() if t.startswith(" "       ) else 0 for t in string.splitlines()       )
				return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(lowerCAmelCase       )]       )


SCREAMING_SNAKE_CASE_						=					'\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n'

SCREAMING_SNAKE_CASE_						=					'\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n'


SCREAMING_SNAKE_CASE_						=					'\\n# Begin of markdown\n\nSome cool dataset card\n'





class  a     (		unittest.TestCase ):


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										with tempfile.TemporaryDirectory() as tmp_dir:
														_UpperCAmelCase				:						Dict       						=			Path(A_		) / "README.md"
														with open(A_  ,    "w+"		) as readme_file:
																		readme_file.write(A_		)
														_UpperCAmelCase				:						Tuple       						=			DatasetMetadata.from_readme(A_		)
														self.assertDictEqual(A_  ,    {"language": ["zh", "en"], "task_ids": ["sentiment-classification"]}		)

														with open(A_  ,    "w+"		) as readme_file:
																		readme_file.write(A_		)
														_UpperCAmelCase				:						Optional[int]       						=			DatasetMetadata.from_readme(A_		)
														self.assertDictEqual(A_  ,    {}		)

														with open(A_  ,    "w+"		) as readme_file:
																		readme_file.write(A_		)
														_UpperCAmelCase				:						int       						=			DatasetMetadata.from_readme(A_		)
														self.assertEqual(A_  ,    {}		)





						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			_dedent(
										    "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            "		)
										assert DatasetMetadata.from_yaml_string(A_		)

										_UpperCAmelCase				:						Dict       						=			_dedent(
										    "\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            "		)
										with self.assertRaises(A_		):
														DatasetMetadata.from_yaml_string(A_		)

										_UpperCAmelCase				:						Any       						=			_dedent(
										    "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            "		)
										assert DatasetMetadata.from_yaml_string(A_		)





from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


SCREAMING_SNAKE_CASE_						=					logging.get_logger(__name__)

# General docstring
SCREAMING_SNAKE_CASE_						=					'ResNetConfig'

# Base docstring
SCREAMING_SNAKE_CASE_						=					'microsoft/resnet-50'
SCREAMING_SNAKE_CASE_						=					[1, 2048, 7, 7]

# Image classification docstring
SCREAMING_SNAKE_CASE_						=					'microsoft/resnet-50'
SCREAMING_SNAKE_CASE_						=					'tiger cat'

SCREAMING_SNAKE_CASE_						=					[
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]

class  a     (		tf.keras.layers.Layer ):


						def __init__(				self  ,    A_  ,    A_ = 3  ,    A_ = 1  ,    A_ = "relu"  ,    **A_		):







										'''simple docstring'''


										super().__init__(**A_		)
										_UpperCAmelCase				:						int       						=			kernel_size // 2
										_UpperCAmelCase				:						Dict       						=			tf.keras.layers.ConvaD(
										    A_  ,    kernel_size=A_  ,    strides=A_  ,    padding="valid"  ,    use_bias=A_  ,    name="convolution"		)
										# Use same default momentum and epsilon as PyTorch equivalent
										_UpperCAmelCase				:						Tuple       						=			tf.keras.layers.BatchNormalization(epsilon=1e-5  ,    momentum=0.9  ,    name="normalization"		)
										_UpperCAmelCase				:						int       						=			ACTaFN[activation] if activation is not None else tf.keras.layers.Activation("linear"		)


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Dict       						=			(self.pad_value, self.pad_value)
										_UpperCAmelCase				:						List[Any]       						=			tf.pad(A_  ,    [(0, 0), height_pad, width_pad, (0, 0)]		)
										_UpperCAmelCase				:						int       						=			self.conv(A_		)
										return hidden_state





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = False		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[int]       						=			self.convolution(A_		)
										_UpperCAmelCase				:						Optional[Any]       						=			self.normalization(A_  ,    training=A_		)
										_UpperCAmelCase				:						Dict       						=			self.activation(A_		)
										return hidden_state







class  a     (		tf.keras.layers.Layer ):


						def __init__(				self  ,    A_  ,    **A_		):







										'''simple docstring'''


										super().__init__(**A_		)
										_UpperCAmelCase				:						Optional[int]       						=			TFResNetConvLayer(
										    config.embedding_size  ,    kernel_size=7  ,    stride=2  ,    activation=config.hidden_act  ,    name="embedder"  ,    )
										_UpperCAmelCase				:						List[Any]       						=			tf.keras.layers.MaxPoolaD(pool_size=3  ,    strides=2  ,    padding="valid"  ,    name="pooler"		)
										_UpperCAmelCase				:						List[Any]       						=			config.num_channels





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = False		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase				:						Dict       						=			shape_list(A_		)
										if tf.executing_eagerly() and num_channels != self.num_channels:
														raise ValueError(
														    "Make sure that the channel dimension of the pixel values match with the one set in the configuration."		)
										_UpperCAmelCase				:						Any       						=			pixel_values
										_UpperCAmelCase				:						List[str]       						=			self.embedder(A_		)
										_UpperCAmelCase				:						int       						=			tf.pad(A_  ,    [[0, 0], [1, 1], [1, 1], [0, 0]]		)
										_UpperCAmelCase				:						Dict       						=			self.pooler(A_		)
										return hidden_state







class  a     (		tf.keras.layers.Layer ):


						def __init__(				self  ,    A_  ,    A_ = 2  ,    **A_		):







										'''simple docstring'''


										super().__init__(**A_		)
										_UpperCAmelCase				:						Tuple       						=			tf.keras.layers.ConvaD(
										    A_  ,    kernel_size=1  ,    strides=A_  ,    use_bias=A_  ,    name="convolution"		)
										# Use same default momentum and epsilon as PyTorch equivalent
										_UpperCAmelCase				:						Any       						=			tf.keras.layers.BatchNormalization(epsilon=1e-5  ,    momentum=0.9  ,    name="normalization"		)





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = False		):







										'''simple docstring'''


										_UpperCAmelCase				:						str       						=			x
										_UpperCAmelCase				:						str       						=			self.convolution(A_		)
										_UpperCAmelCase				:						str       						=			self.normalization(A_  ,    training=A_		)
										return hidden_state







class  a     (		tf.keras.layers.Layer ):


						def __init__(				self  ,    A_  ,    A_  ,    A_ = 1  ,    A_ = "relu"  ,    **A_		):







										'''simple docstring'''


										super().__init__(**A_		)
										_UpperCAmelCase				:						Optional[Any]       						=			in_channels != out_channels or stride != 1
										_UpperCAmelCase				:						Optional[int]       						=			TFResNetConvLayer(A_  ,    stride=A_  ,    name="layer.0"		)
										_UpperCAmelCase				:						Optional[int]       						=			TFResNetConvLayer(A_  ,    activation=A_  ,    name="layer.1"		)
										_UpperCAmelCase				:						int       						=			(
										    TFResNetShortCut(A_  ,    stride=A_  ,    name="shortcut"		)
										    if should_apply_shortcut
										    else tf.keras.layers.Activation("linear"  ,    name="shortcut"		)
										)
										_UpperCAmelCase				:						List[str]       						=			ACTaFN[activation]





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = False		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			hidden_state
										_UpperCAmelCase				:						Optional[int]       						=			self.conva(A_  ,    training=A_		)
										_UpperCAmelCase				:						Union[str, Any]       						=			self.conva(A_  ,    training=A_		)
										_UpperCAmelCase				:						List[Any]       						=			self.shortcut(A_  ,    training=A_		)
										hidden_state += residual
										_UpperCAmelCase				:						Any       						=			self.activation(A_		)
										return hidden_state







class  a     (		tf.keras.layers.Layer ):


						def __init__(				self  ,    A_  ,    A_  ,    A_ = 1  ,    A_ = "relu"  ,    A_ = 4  ,    **A_  ,    ):







										'''simple docstring'''


										super().__init__(**A_		)
										_UpperCAmelCase				:						List[str]       						=			in_channels != out_channels or stride != 1
										_UpperCAmelCase				:						Dict       						=			out_channels // reduction
										_UpperCAmelCase				:						Optional[int]       						=			TFResNetConvLayer(A_  ,    kernel_size=1  ,    name="layer.0"		)
										_UpperCAmelCase				:						str       						=			TFResNetConvLayer(A_  ,    stride=A_  ,    name="layer.1"		)
										_UpperCAmelCase				:						Optional[Any]       						=			TFResNetConvLayer(A_  ,    kernel_size=1  ,    activation=A_  ,    name="layer.2"		)
										_UpperCAmelCase				:						List[str]       						=			(
										    TFResNetShortCut(A_  ,    stride=A_  ,    name="shortcut"		)
										    if should_apply_shortcut
										    else tf.keras.layers.Activation("linear"  ,    name="shortcut"		)
										)
										_UpperCAmelCase				:						Optional[int]       						=			ACTaFN[activation]





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = False		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			hidden_state
										_UpperCAmelCase				:						Union[str, Any]       						=			self.conva(A_  ,    training=A_		)
										_UpperCAmelCase				:						Tuple       						=			self.conva(A_  ,    training=A_		)
										_UpperCAmelCase				:						Dict       						=			self.conva(A_  ,    training=A_		)
										_UpperCAmelCase				:						Optional[Any]       						=			self.shortcut(A_  ,    training=A_		)
										hidden_state += residual
										_UpperCAmelCase				:						Any       						=			self.activation(A_		)
										return hidden_state







class  a     (		tf.keras.layers.Layer ):


						def __init__(				self  ,    A_  ,    A_  ,    A_  ,    A_ = 2  ,    A_ = 2  ,    **A_		):







										'''simple docstring'''


										super().__init__(**A_		)

										_UpperCAmelCase				:						str       						=			TFResNetBottleNeckLayer if config.layer_type == "bottleneck" else TFResNetBasicLayer

										_UpperCAmelCase				:						List[str]       						=			[layer(A_  ,    A_  ,    stride=A_  ,    activation=config.hidden_act  ,    name="layers.0"		)]
										layers += [
										    layer(A_  ,    A_  ,    activation=config.hidden_act  ,    name=f'layers.{i + 1}'		)
										    for i in range(depth - 1		)
										]
										_UpperCAmelCase				:						Optional[Any]       						=			layers





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = False		):







										'''simple docstring'''


										for layer in self.stage_layers:
														_UpperCAmelCase				:						Union[str, Any]       						=			layer(A_  ,    training=A_		)
										return hidden_state







class  a     (		tf.keras.layers.Layer ):


						def __init__(				self  ,    A_  ,    **A_		):







										'''simple docstring'''


										super().__init__(**A_		)
										# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
										_UpperCAmelCase				:						List[str]       						=			[
										    TFResNetStage(
										        A_  ,    config.embedding_size  ,    config.hidden_sizes[0]  ,    stride=2 if config.downsample_in_first_stage else 1  ,    depth=config.depths[0]  ,    name="stages.0"  ,    )
										]
										for i, (in_channels, out_channels, depth) in enumerate(
										    zip(config.hidden_sizes  ,    config.hidden_sizes[1:]  ,    config.depths[1:]		)		):
														self.stages.append(TFResNetStage(A_  ,    A_  ,    A_  ,    depth=A_  ,    name=f'stages.{i + 1}'		)		)





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = False  ,    A_ = True  ,    A_ = False  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						str       						=			() if output_hidden_states else None

										for stage_module in self.stages:
														if output_hidden_states:
																		_UpperCAmelCase				:						str       						=			hidden_states + (hidden_state,)

														_UpperCAmelCase				:						Union[str, Any]       						=			stage_module(A_  ,    training=A_		)

										if output_hidden_states:
														_UpperCAmelCase				:						Union[str, Any]       						=			hidden_states + (hidden_state,)

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

										return TFBaseModelOutputWithNoAttention(last_hidden_state=A_  ,    hidden_states=A_		)







class  a     (		UpperCAmelCase ):
						_lowercase			  =	ResNetConfig
						_lowercase			  =	"resnet"
						_lowercase			  =	"pixel_values"


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)  ,    dtype=tf.floataa		)}







SCREAMING_SNAKE_CASE_						=					R'\n    This model is a TensorFlow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): 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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'


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

@keras_serializable
class  a     (		tf.keras.layers.Layer ):
						_lowercase			  =	ResNetConfig


						def __init__(				self  ,    A_  ,    **A_		):







										'''simple docstring'''


										super().__init__(**A_		)
										_UpperCAmelCase				:						List[Any]       						=			config
										_UpperCAmelCase				:						Optional[Any]       						=			TFResNetEmbeddings(A_  ,    name="embedder"		)
										_UpperCAmelCase				:						Tuple       						=			TFResNetEncoder(A_  ,    name="encoder"		)
										_UpperCAmelCase				:						Tuple       						=			tf.keras.layers.GlobalAveragePoolingaD(keepdims=A_		)





						@unpack_inputs
						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = None  ,    A_ = None  ,    A_ = False  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						Dict       						=			(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										_UpperCAmelCase				:						int       						=			return_dict if return_dict is not None else self.config.use_return_dict

										# TF 2.0 image layers can't use NCHW format when running on CPU.
										# We transpose to NHWC format and then transpose back after the full forward pass.
										# (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
										_UpperCAmelCase				:						List[Any]       						=			tf.transpose(A_  ,    perm=[0, 2, 3, 1]		)
										_UpperCAmelCase				:						Dict       						=			self.embedder(A_  ,    training=A_		)

										_UpperCAmelCase				:						Any       						=			self.encoder(
										    A_  ,    output_hidden_states=A_  ,    return_dict=A_  ,    training=A_		)

										_UpperCAmelCase				:						Optional[int]       						=			encoder_outputs[0]

										_UpperCAmelCase				:						Tuple       						=			self.pooler(A_		)

										# Transpose all the outputs to the NCHW format
										# (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
										_UpperCAmelCase				:						Union[str, Any]       						=			tf.transpose(A_  ,    (0, 3, 1, 2)		)
										_UpperCAmelCase				:						Any       						=			tf.transpose(A_  ,    (0, 3, 1, 2)		)
										_UpperCAmelCase				:						Tuple       						=			()
										for hidden_state in encoder_outputs[1:]:
														_UpperCAmelCase				:						Optional[int]       						=			hidden_states + tuple(tf.transpose(A_  ,    (0, 3, 1, 2)		) for h in hidden_state		)

										if not return_dict:
														return (last_hidden_state, pooled_output) + hidden_states

										_UpperCAmelCase				:						Any       						=			hidden_states if output_hidden_states else None

										return TFBaseModelOutputWithPoolingAndNoAttention(
										    last_hidden_state=A_  ,    pooler_output=A_  ,    hidden_states=A_  ,    )







@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top."							,       UpperCAmelCase							,       )
class  a     (		UpperCAmelCase ):


						def __init__(				self  ,    A_  ,    **A_		):







										'''simple docstring'''


										super().__init__(A_  ,    **A_		)
										_UpperCAmelCase				:						int       						=			TFResNetMainLayer(config=A_  ,    name="resnet"		)





						@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  ,    )
						@unpack_inputs
						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = None  ,    A_ = None  ,    A_ = False  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										_UpperCAmelCase				:						str       						=			return_dict if return_dict is not None else self.config.use_return_dict

										_UpperCAmelCase				:						List[Any]       						=			self.resnet(
										    pixel_values=A_  ,    output_hidden_states=A_  ,    return_dict=A_  ,    training=A_  ,    )
										return resnet_outputs







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


						def __init__(				self  ,    A_  ,    **A_		):







										'''simple docstring'''


										super().__init__(A_  ,    **A_		)
										_UpperCAmelCase				:						Dict       						=			config.num_labels
										_UpperCAmelCase				:						List[Any]       						=			TFResNetMainLayer(A_  ,    name="resnet"		)
										# classification head
										_UpperCAmelCase				:						Union[str, Any]       						=			(
										    tf.keras.layers.Dense(config.num_labels  ,    name="classifier.1"		)
										    if config.num_labels > 0
										    else tf.keras.layers.Activation("linear"  ,    name="classifier.1"		)
										)


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Tuple       						=			tf.keras.layers.Flatten()(A_		)
										_UpperCAmelCase				:						Dict       						=			self.classifier_layer(A_		)
										return logits





						@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  ,    )
						@unpack_inputs
						def 				_UpperCAmelCase (				self  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = False  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						int       						=			return_dict if return_dict is not None else self.config.use_return_dict

										_UpperCAmelCase				:						Optional[Any]       						=			self.resnet(
										    A_  ,    output_hidden_states=A_  ,    return_dict=A_  ,    training=A_		)

										_UpperCAmelCase				:						Tuple       						=			outputs.pooler_output if return_dict else outputs[1]

										_UpperCAmelCase				:						int       						=			self.classifier(A_		)

										_UpperCAmelCase				:						str       						=			None if labels is None else self.hf_compute_loss(A_  ,    A_		)

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

										return TFImageClassifierOutputWithNoAttention(loss=A_  ,    logits=A_  ,    hidden_states=outputs.hidden_states		)


