
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


lowercase_											=	logging.get_logger(__name__)

# General docstring
lowercase_											=	"ResNetConfig"

# Base docstring
lowercase_											=	"microsoft/resnet-50"
lowercase_											=	[1, 2_048, 7, 7]

# Image classification docstring
lowercase_											=	"microsoft/resnet-50"
lowercase_											=	"tiger cat"

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





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






     """simple docstring"""







     def __init__(							self    :    Dict      ,		_A    :    int      ,		_A    :    int = 3      ,		_A    :    int = 1      ,		_A    :    str = "relu"      ,		**_A    :    Dict				):







       """simple docstring"""




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



     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    tf.Tensor				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						(self.pad_value, self.pad_value)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						tf.pad(UpperCamelCase_      ,		[(0, 0), height_pad, width_pad, (0, 0)]				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.conv(UpperCamelCase_				)
       return hidden_state




     def 				UpperCAmelCase__    (							self    :    Optional[int]      ,		_A    :    tf.Tensor      ,		_A    :    bool = False				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						self.convolution(UpperCamelCase_				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.normalization(UpperCamelCase_      ,		training=UpperCamelCase_				)
       __SCREAMING_SNAKE_CASE     :					Tuple			=						self.activation(UpperCamelCase_				)
       return hidden_state






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






     """simple docstring"""







     def __init__(							self    :    int      ,		_A    :    ResNetConfig      ,		**_A    :    Tuple				):







       """simple docstring"""




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




     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    tf.Tensor      ,		_A    :    bool = False				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					List[str]			=						shape_list(UpperCamelCase_				)
       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.'''				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						pixel_values
       __SCREAMING_SNAKE_CASE     :					Any			=						self.embedder(UpperCamelCase_				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						tf.pad(UpperCamelCase_      ,		[[0, 0], [1, 1], [1, 1], [0, 0]]				)
       __SCREAMING_SNAKE_CASE     :					int			=						self.pooler(UpperCamelCase_				)
       return hidden_state






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






     """simple docstring"""







     def __init__(							self    :    str      ,		_A    :    int      ,		_A    :    int = 2      ,		**_A    :    Optional[Any]				):







       """simple docstring"""




       super().__init__(**UpperCamelCase_				)
       __SCREAMING_SNAKE_CASE     :					Tuple			=						tf.keras.layers.ConvaD(
           UpperCamelCase_      ,		kernel_size=1      ,		strides=UpperCamelCase_      ,		use_bias=UpperCamelCase_      ,		name='''convolution'''				)
       # Use same default momentum and epsilon as PyTorch equivalent
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						tf.keras.layers.BatchNormalization(epsilon=1e-5      ,		momentum=0.9      ,		name='''normalization'''				)




     def 				UpperCAmelCase__    (							self    :    str      ,		_A    :    tf.Tensor      ,		_A    :    bool = False				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						x
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.convolution(UpperCamelCase_				)
       __SCREAMING_SNAKE_CASE     :					Any			=						self.normalization(UpperCamelCase_      ,		training=UpperCamelCase_				)
       return hidden_state






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






     """simple docstring"""







     def __init__(							self    :    List[str]      ,		_A    :    int      ,		_A    :    int      ,		_A    :    int = 1      ,		_A    :    str = "relu"      ,		**_A    :    Any				):







       """simple docstring"""




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




     def 				UpperCAmelCase__    (							self    :    Union[str, Any]      ,		_A    :    tf.Tensor      ,		_A    :    bool = False				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						hidden_state
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.conva(UpperCamelCase_      ,		training=UpperCamelCase_				)
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.conva(UpperCamelCase_      ,		training=UpperCamelCase_				)
       __SCREAMING_SNAKE_CASE     :					Tuple			=						self.shortcut(UpperCamelCase_      ,		training=UpperCamelCase_				)
       hidden_state += residual
       __SCREAMING_SNAKE_CASE     :					Any			=						self.activation(UpperCamelCase_				)
       return hidden_state






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






     """simple docstring"""







     def __init__(							self    :    Optional[Any]      ,		_A    :    int      ,		_A    :    int      ,		_A    :    int = 1      ,		_A    :    str = "relu"      ,		_A    :    int = 4      ,		**_A    :    str      ,		):







       """simple docstring"""




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




     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    tf.Tensor      ,		_A    :    bool = False				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						hidden_state
       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.conva(UpperCamelCase_      ,		training=UpperCamelCase_				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.conva(UpperCamelCase_      ,		training=UpperCamelCase_				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.conva(UpperCamelCase_      ,		training=UpperCamelCase_				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.shortcut(UpperCamelCase_      ,		training=UpperCamelCase_				)
       hidden_state += residual
       __SCREAMING_SNAKE_CASE     :					Dict			=						self.activation(UpperCamelCase_				)
       return hidden_state






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






     """simple docstring"""







     def __init__(							self    :    str      ,		_A    :    ResNetConfig      ,		_A    :    int      ,		_A    :    int      ,		_A    :    int = 2      ,		_A    :    int = 2      ,		**_A    :    Optional[int]				):







       """simple docstring"""




       super().__init__(**UpperCamelCase_				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						TFResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else TFResNetBasicLayer

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						[layer(UpperCamelCase_      ,		UpperCamelCase_      ,		stride=UpperCamelCase_      ,		activation=config.hidden_act      ,		name='''layers.0'''				)]
       layers += [
           layer(UpperCamelCase_      ,		UpperCamelCase_      ,		activation=config.hidden_act      ,		name=F'''layers.{i + 1}'''				)
           for i in range(depth - 1				)
       ]
       __SCREAMING_SNAKE_CASE     :					Any			=						layers




     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    tf.Tensor      ,		_A    :    bool = False				):







       """simple docstring"""




       for layer in self.stage_layers:
         __SCREAMING_SNAKE_CASE     :					Optional[int]			=						layer(UpperCamelCase_      ,		training=UpperCamelCase_				)
       return hidden_state






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






     """simple docstring"""







     def __init__(							self    :    int      ,		_A    :    ResNetConfig      ,		**_A    :    Dict				):







       """simple docstring"""




       super().__init__(**UpperCamelCase_				)
       # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
       __SCREAMING_SNAKE_CASE     :					Any			=						[
           TFResNetStage(
               UpperCamelCase_      ,		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(UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		depth=UpperCamelCase_      ,		name=F'''stages.{i + 1}'''				)				)




     def 				UpperCAmelCase__    (							self    :    Optional[Any]      ,		_A    :    tf.Tensor      ,		_A    :    bool = False      ,		_A    :    bool = True      ,		_A    :    bool = False      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						() if output_hidden_states else None

       for stage_module in self.stages:
         if output_hidden_states:
           __SCREAMING_SNAKE_CASE     :					List[Any]			=						hidden_states + (hidden_state,)

         __SCREAMING_SNAKE_CASE     :					List[Any]			=						stage_module(UpperCamelCase_      ,		training=UpperCamelCase_				)

       if output_hidden_states:
         __SCREAMING_SNAKE_CASE     :					Tuple			=						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=UpperCamelCase_      ,		hidden_states=UpperCamelCase_				)






class   __UpperCamelCase				(    lowercase_   ):






     """simple docstring"""







     lowerCAmelCase_     =	ResNetConfig
     lowerCAmelCase_     =	'''resnet'''
     lowerCAmelCase_     =	'''pixel_values'''



     @property
     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




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






lowercase_											=	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"


lowercase_											=	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   __UpperCamelCase				(    tf.keras.layers.Layer   ):






     """simple docstring"""







     lowerCAmelCase_     =	ResNetConfig



     def __init__(							self    :    str      ,		_A    :    ResNetConfig      ,		**_A    :    str				):







       """simple docstring"""




       super().__init__(**UpperCamelCase_				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						config
       __SCREAMING_SNAKE_CASE     :					str			=						TFResNetEmbeddings(UpperCamelCase_      ,		name='''embedder'''				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						TFResNetEncoder(UpperCamelCase_      ,		name='''encoder'''				)
       __SCREAMING_SNAKE_CASE     :					int			=						tf.keras.layers.GlobalAveragePoolingaD(keepdims=UpperCamelCase_				)




     @unpack_inputs
     def 				UpperCAmelCase__    (							self    :    str      ,		_A    :    tf.Tensor      ,		_A    :    Optional[bool] = None      ,		_A    :    Optional[bool] = None      ,		_A    :    bool = False      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						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)
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						tf.transpose(UpperCamelCase_      ,		perm=[0, 2, 3, 1]				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.embedder(UpperCamelCase_      ,		training=UpperCamelCase_				)

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.encoder(
           UpperCamelCase_      ,		output_hidden_states=UpperCamelCase_      ,		return_dict=UpperCamelCase_      ,		training=UpperCamelCase_				)

       __SCREAMING_SNAKE_CASE     :					Dict			=						encoder_outputs[0]

       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.pooler(UpperCamelCase_				)

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

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

       __SCREAMING_SNAKE_CASE     :					Tuple			=						hidden_states if output_hidden_states else None

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






@add_start_docstrings(
    '''The bare ResNet model outputting raw features without any specific head on top.'''				,		lowercase_				,		)
class   __UpperCamelCase				(    lowercase_   ):






     """simple docstring"""







     def __init__(							self    :    int      ,		_A    :    ResNetConfig      ,		**_A    :    Dict				):







       """simple docstring"""




       super().__init__(UpperCamelCase_      ,		**UpperCamelCase_				)
       __SCREAMING_SNAKE_CASE     :					Any			=						TFResNetMainLayer(config=UpperCamelCase_      ,		name='''resnet'''				)




     @add_start_docstrings_to_model_forward(UpperCamelCase_				)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC      ,		output_type=UpperCamelCase_      ,		config_class=_CONFIG_FOR_DOC      ,		modality='''vision'''      ,		expected_output=_EXPECTED_OUTPUT_SHAPE      ,		)
     @unpack_inputs
     def 				UpperCAmelCase__    (							self    :    Dict      ,		_A    :    tf.Tensor      ,		_A    :    Optional[bool] = None      ,		_A    :    Optional[bool] = None      ,		_A    :    bool = False      ,		):







       """simple docstring"""




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

       __SCREAMING_SNAKE_CASE     :					str			=						self.resnet(
           pixel_values=UpperCamelCase_      ,		output_hidden_states=UpperCamelCase_      ,		return_dict=UpperCamelCase_      ,		training=UpperCamelCase_      ,		)
       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    '''				,		lowercase_				,		)
class   __UpperCamelCase				(    lowercase_				,		lowercase_   ):






     """simple docstring"""







     def __init__(							self    :    int      ,		_A    :    ResNetConfig      ,		**_A    :    Any				):







       """simple docstring"""




       super().__init__(UpperCamelCase_      ,		**UpperCamelCase_				)
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						config.num_labels
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						TFResNetMainLayer(UpperCamelCase_      ,		name='''resnet'''				)
       # classification head
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						(
           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    :    Union[str, Any]      ,		_A    :    tf.Tensor				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						tf.keras.layers.Flatten()(UpperCamelCase_				)
       __SCREAMING_SNAKE_CASE     :					Any			=						self.classifier_layer(UpperCamelCase_				)
       return logits




     @add_start_docstrings_to_model_forward(UpperCamelCase_				)
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT      ,		output_type=UpperCamelCase_      ,		config_class=_CONFIG_FOR_DOC      ,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,		)
     @unpack_inputs
     def 				UpperCAmelCase__    (							self    :    str      ,		_A    :    tf.Tensor = None      ,		_A    :    tf.Tensor = None      ,		_A    :    bool = None      ,		_A    :    bool = None      ,		_A    :    bool = False      ,		):







       """simple docstring"""




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

       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.resnet(
           UpperCamelCase_      ,		output_hidden_states=UpperCamelCase_      ,		return_dict=UpperCamelCase_      ,		training=UpperCamelCase_				)

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

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.classifier(UpperCamelCase_				)

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						None if labels is None else self.hf_compute_loss(UpperCamelCase_      ,		UpperCamelCase_				)

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

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


from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


lowercase_											=	[
    """AutomaticSpeechRecognition""",
    """AudioClassification""",
    """ImageClassification""",
    """LanguageModeling""",
    """QuestionAnsweringExtractive""",
    """Summarization""",
    """TaskTemplate""",
    """TextClassification""",
]

lowercase_											=	get_logger(__name__)


lowercase_											=	{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}


def     a__			(    snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					str			=						task_template_dict.get('''task'''    )
  if task_name is None:
    logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(snake_case    )}'''    )
    return None
  __SCREAMING_SNAKE_CASE     :					Any			=						NAMEaTEMPLATE.get(snake_case    )
  return template.from_dict(snake_case    )
