






import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
	import jax
	import jax.numpy as jnp



@slow
@require_flax
class 				SCREAMING_SNAKE_CASE__		(							unittest.TestCase		):





					"""simple docstring"""






					def 			_lowercase	(   self	:    List[Any]				,						__A	:    Tuple				,						__A	:    Union[str, Any]			):
						return f'''gaussian_noise_s={seed}_shape={'_'.join([str(A_			) for s in shape]			)}.npy'''



					def 			_lowercase	(   self	:    List[Any]			):
						super().tearDown()
						gc.collect()



					def 			_lowercase	(   self	:    int				,						__A	:    Dict=0				,						__A	:    List[str]=(4, 4, 6_4, 6_4)				,						__A	:    List[str]=False			):
						snake_case__					: List[Any]  =    jnp.bfloataa if fpaa else jnp.floataa
						snake_case__					: str  =    jnp.array(load_hf_numpy(self.get_file_format(A_				,						A_			)			)				,						dtype=A_			)
						return image



					def 			_lowercase	(   self	:    Union[str, Any]				,						__A	:    str=False				,						__A	:    Dict="CompVis/stable-diffusion-v1-4"			):
						snake_case__					: Any  =    jnp.bfloataa if fpaa else jnp.floataa
						snake_case__					: int  =    "bf16" if fpaa else None

						snake_case__,		snake_case__					: Optional[int]  =    FlaxUNetaDConditionModel.from_pretrained(
						    A_				,						subfolder="unet"				,						dtype=A_				,						revision=A_			)
						return model, params



					def 			_lowercase	(   self	:    str				,						__A	:    str=0				,						__A	:    Optional[int]=(4, 7_7, 7_6_8)				,						__A	:    Any=False			):
						snake_case__					: List[str]  =    jnp.bfloataa if fpaa else jnp.floataa
						snake_case__					: List[str]  =    jnp.array(load_hf_numpy(self.get_file_format(A_				,						A_			)			)				,						dtype=A_			)
						return hidden_states



					@parameterized.expand(
					    [
					        # fmt: off
					        [8_3, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
					        [1_7, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
					        [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
					        [3, 1_0_0_0, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
					        # fmt: on
					    ]			)
					def 			_lowercase	(   self	:    Tuple				,						__A	:    Union[str, Any]				,						__A	:    Dict				,						__A	:    List[str]			):
						snake_case__,		snake_case__					: str  =    self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"				,						fpaa=A_			)
						snake_case__					: Optional[int]  =    self.get_latents(A_				,						fpaa=A_			)
						snake_case__					: int  =    self.get_encoder_hidden_states(A_				,						fpaa=A_			)

						snake_case__					: List[Any]  =    model.apply(
						    {"params": params}				,						A_				,						jnp.array(A_				,						dtype=jnp.intaa			)				,						encoder_hidden_states=A_				,						).sample

						assert sample.shape == latents.shape

						snake_case__					: Any  =    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())			)				,						dtype=jnp.floataa			)
						snake_case__					: List[Any]  =    jnp.array(A_				,						dtype=jnp.floataa			)

						# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
						assert jnp.allclose(A_				,						A_				,						atol=1e-2			)






					@parameterized.expand(
					    [
					        # fmt: off
					        [8_3, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
					        [1_7, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
					        [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
					        [3, 1_0_0_0, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
					        # fmt: on
					    ]			)
					def 			_lowercase	(   self	:    Optional[Any]				,						__A	:    List[str]				,						__A	:    str				,						__A	:    Any			):
						snake_case__,		snake_case__					: int  =    self.get_unet_model(model_id="stabilityai/stable-diffusion-2"				,						fpaa=A_			)
						snake_case__					: List[Any]  =    self.get_latents(A_				,						shape=(4, 4, 9_6, 9_6)				,						fpaa=A_			)
						snake_case__					: str  =    self.get_encoder_hidden_states(A_				,						shape=(4, 7_7, 1_0_2_4)				,						fpaa=A_			)

						snake_case__					: Optional[int]  =    model.apply(
						    {"params": params}				,						A_				,						jnp.array(A_				,						dtype=jnp.intaa			)				,						encoder_hidden_states=A_				,						).sample

						assert sample.shape == latents.shape

						snake_case__					: Tuple  =    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())			)				,						dtype=jnp.floataa			)
						snake_case__					: List[Any]  =    jnp.array(A_				,						dtype=jnp.floataa			)

						# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
						assert jnp.allclose(A_				,						A_				,						atol=1e-2			)





'''simple docstring'''
from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


lowerCAmelCase					:    Dict           = logging.get_logger(__name__)

# General docstring
lowerCAmelCase					:    str           = 'RegNetConfig'

# Base docstring
lowerCAmelCase					:    str           = 'facebook/regnet-y-040'
lowerCAmelCase					:    Dict           = [1, 10_88, 7, 7]

# Image classification docstring
lowerCAmelCase					:    Dict           = 'facebook/regnet-y-040'
lowerCAmelCase					:    int           = 'tabby, tabby cat'

lowerCAmelCase					:    int           = [
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]


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







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







											'''simple docstring'''






											super().__init__(**A_     )
											# The padding and conv has been verified in
											# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
											UpperCamelCase   			=     tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2     )
											UpperCamelCase   			=     tf.keras.layers.ConvaD(
											    filters=A_						,				kernel_size=A_						,				strides=A_						,				padding='VALID'						,				groups=A_						,				use_bias=A_						,				name='convolution'						,				)
											UpperCamelCase   			=     tf.keras.layers.BatchNormalization(epsilon=1e-5						,				momentum=0.9						,				name='normalization'     )
											UpperCamelCase   			=     ACTaFN[activation] if activation is not None else tf.identity







				def 						UpperCAmelCase_    (       self						,				A_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     self.convolution(self.padding(A_     )     )
											UpperCamelCase   			=     self.normalization(A_     )
											UpperCamelCase   			=     self.activation(A_     )
											return hidden_state


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







				def __init__(       self						,				A_						,				**A_     )->		Optional[Any]:







											'''simple docstring'''






											super().__init__(**A_     )
											UpperCamelCase   			=     config.num_channels
											UpperCamelCase   			=     TFRegNetConvLayer(
											    out_channels=config.embedding_size						,				kernel_size=3						,				stride=2						,				activation=config.hidden_act						,				name='embedder'						,				)







				def 						UpperCAmelCase_    (       self						,				A_     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     shape_list(A_     )[1]
											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.'     )

											# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
											# So change the input format from `NCHW` to `NHWC`.
											# shape = (batch_size, in_height, in_width, in_channels=num_channels)
											UpperCamelCase   			=     tf.transpose(A_						,				perm=(0, 2, 3, 1)     )
											UpperCamelCase   			=     self.embedder(A_     )
											return hidden_state


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







				def __init__(       self						,				A_						,				A_ = 2						,				**A_     )->		List[Any]:







											'''simple docstring'''






											super().__init__(**A_     )
											UpperCamelCase   			=     tf.keras.layers.ConvaD(
											    filters=A_						,				kernel_size=1						,				strides=A_						,				use_bias=A_						,				name='convolution'     )
											UpperCamelCase   			=     tf.keras.layers.BatchNormalization(epsilon=1e-5						,				momentum=0.9						,				name='normalization'     )







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = False     )->		tf.Tensor:







											'''simple docstring'''






											return self.normalization(self.convolution(A_     )						,				training=A_     )


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







				def __init__(       self						,				A_						,				A_						,				**A_     )->		Optional[Any]:







											'''simple docstring'''






											super().__init__(**A_     )
											UpperCamelCase   			=     tf.keras.layers.GlobalAveragePoolingaD(keepdims=A_						,				name='pooler'     )
											UpperCamelCase   			=     [
											    tf.keras.layers.ConvaD(filters=A_						,				kernel_size=1						,				activation='relu'						,				name='attention.0'     ),
											    tf.keras.layers.ConvaD(filters=A_						,				kernel_size=1						,				activation='sigmoid'						,				name='attention.2'     ),
											]







				def 						UpperCAmelCase_    (       self						,				A_     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.pooler(A_     )
											for layer_module in self.attention:
																		UpperCamelCase   			=     layer_module(A_     )
											UpperCamelCase   			=     hidden_state * pooled
											return hidden_state


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







				def __init__(       self						,				A_						,				A_						,				A_						,				A_ = 1						,				**A_     )->		Dict:







											'''simple docstring'''






											super().__init__(**A_     )
											UpperCamelCase   			=     in_channels != out_channels or stride != 1
											UpperCamelCase   			=     max(1						,				out_channels // config.groups_width     )
											UpperCamelCase   			=     (
											    TFRegNetShortCut(A_						,				stride=A_						,				name='shortcut'     )
											    if should_apply_shortcut
											    else tf.keras.layers.Activation('linear'						,				name='shortcut'     )
											)
											# `self.layers` instead of `self.layer` because that is a reserved argument.
											UpperCamelCase   			=     [
											    TFRegNetConvLayer(A_						,				kernel_size=1						,				activation=config.hidden_act						,				name='layer.0'     ),
											    TFRegNetConvLayer(
											        A_						,				stride=A_						,				groups=A_						,				activation=config.hidden_act						,				name='layer.1'     ),
											    TFRegNetConvLayer(A_						,				kernel_size=1						,				activation=A_						,				name='layer.2'     ),
											]
											UpperCamelCase   			=     ACTaFN[config.hidden_act]







				def 						UpperCAmelCase_    (       self						,				A_     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     hidden_state
											for layer_module in self.layers:
																		UpperCamelCase   			=     layer_module(A_     )
											UpperCamelCase   			=     self.shortcut(A_     )
											hidden_state += residual
											UpperCamelCase   			=     self.activation(A_     )
											return hidden_state


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







				def __init__(       self						,				A_						,				A_						,				A_						,				A_ = 1						,				**A_     )->		Any:







											'''simple docstring'''






											super().__init__(**A_     )
											UpperCamelCase   			=     in_channels != out_channels or stride != 1
											UpperCamelCase   			=     max(1						,				out_channels // config.groups_width     )
											UpperCamelCase   			=     (
											    TFRegNetShortCut(A_						,				stride=A_						,				name='shortcut'     )
											    if should_apply_shortcut
											    else tf.keras.layers.Activation('linear'						,				name='shortcut'     )
											)
											UpperCamelCase   			=     [
											    TFRegNetConvLayer(A_						,				kernel_size=1						,				activation=config.hidden_act						,				name='layer.0'     ),
											    TFRegNetConvLayer(
											        A_						,				stride=A_						,				groups=A_						,				activation=config.hidden_act						,				name='layer.1'     ),
											    TFRegNetSELayer(A_						,				reduced_channels=int(round(in_channels / 4     )     )						,				name='layer.2'     ),
											    TFRegNetConvLayer(A_						,				kernel_size=1						,				activation=A_						,				name='layer.3'     ),
											]
											UpperCamelCase   			=     ACTaFN[config.hidden_act]







				def 						UpperCAmelCase_    (       self						,				A_     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     hidden_state
											for layer_module in self.layers:
																		UpperCamelCase   			=     layer_module(A_     )
											UpperCamelCase   			=     self.shortcut(A_     )
											hidden_state += residual
											UpperCamelCase   			=     self.activation(A_     )
											return hidden_state


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







				def __init__(       self						,				A_						,				A_						,				A_						,				A_ = 2						,				A_ = 2						,				**A_     )->		Dict:







											'''simple docstring'''






											super().__init__(**A_     )

											UpperCamelCase   			=     TFRegNetXLayer if config.layer_type == 'x' else TFRegNetYLayer
											UpperCamelCase   			=     [
											    # downsampling is done in the first layer with stride of 2
											    layer(A_						,				A_						,				A_						,				stride=A_						,				name='layers.0'     ),
											    *[layer(A_						,				A_						,				A_						,				name=F'''layers.{i+1}'''     ) for i in range(depth - 1     )],
											]







				def 						UpperCAmelCase_    (       self						,				A_     )->		List[Any]:







											'''simple docstring'''






											for layer_module in self.layers:
																		UpperCamelCase   			=     layer_module(A_     )
											return hidden_state


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







				def __init__(       self						,				A_						,				**A_     )->		str:







											'''simple docstring'''






											super().__init__(**A_     )
											UpperCamelCase   			=     []
											# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
											self.stages.append(
											    TFRegNetStage(
											        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'						,				)     )
											UpperCamelCase   			=     zip(config.hidden_sizes						,				config.hidden_sizes[1:]     )
											for i, ((in_channels, out_channels), depth) in enumerate(zip(A_						,				config.depths[1:]     )     ):
																		self.stages.append(TFRegNetStage(A_						,				A_						,				A_						,				depth=A_						,				name=F'''stages.{i+1}'''     )     )







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = False						,				A_ = True     )->		TFBaseModelOutputWithNoAttention:







											'''simple docstring'''






											UpperCamelCase   			=     () if output_hidden_states else None

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

																		UpperCamelCase   			=     stage_module(A_     )

											if output_hidden_states:
																		UpperCamelCase   			=     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_     )


@keras_serializable
class 			SCREAMING_SNAKE_CASE__      (     tf.keras.layers.Layer):
				lowerCAmelCase_     			=							RegNetConfig







				def __init__(       self						,				A_						,				**A_     )->		Union[str, Any]:







											'''simple docstring'''






											super().__init__(**A_     )
											UpperCamelCase   			=     config
											UpperCamelCase   			=     TFRegNetEmbeddings(A_						,				name='embedder'     )
											UpperCamelCase   			=     TFRegNetEncoder(A_						,				name='encoder'     )
											UpperCamelCase   			=     tf.keras.layers.GlobalAveragePoolingaD(keepdims=A_						,				name='pooler'     )







				@unpack_inputs
				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None						,				A_ = None						,				A_ = False						,				)->		TFBaseModelOutputWithPoolingAndNoAttention:







											'''simple docstring'''






											UpperCamelCase   			=     (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											UpperCamelCase   			=     return_dict if return_dict is not None else self.config.use_return_dict

											UpperCamelCase   			=     self.embedder(A_						,				training=A_     )

											UpperCamelCase   			=     self.encoder(
											    A_						,				output_hidden_states=A_						,				return_dict=A_						,				training=A_     )

											UpperCamelCase   			=     encoder_outputs[0]
											UpperCamelCase   			=     self.pooler(A_     )

											# Change to NCHW output format have uniformity in the modules
											UpperCamelCase   			=     tf.transpose(A_						,				perm=(0, 3, 1, 2)     )
											UpperCamelCase   			=     tf.transpose(A_						,				perm=(0, 3, 1, 2)     )

											# Change the other hidden state outputs to NCHW as well
											if output_hidden_states:
																		UpperCamelCase   			=     tuple([tf.transpose(A_						,				perm=(0, 3, 1, 2)     ) for h in encoder_outputs[1]]     )

											if not return_dict:
																		return (last_hidden_state, pooled_output) + encoder_outputs[1:]

											return TFBaseModelOutputWithPoolingAndNoAttention(
											    last_hidden_state=A_						,				pooler_output=A_						,				hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states						,				)


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							RegNetConfig
				lowerCAmelCase_     			=							"""regnet"""
				lowerCAmelCase_     			=							"""pixel_values"""







				@property
				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






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


lowerCAmelCase					:    str           = r'\n    Parameters:\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        config ([`RegNetConfig`]): 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'

lowerCAmelCase					:    List[str]           = 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            [`ConveNextImageProcessor.__call__`] for details.\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'


@add_start_docstrings(
    """The bare RegNet model outputting raw features without any specific head on top."""							,       snake_case_							,       )
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_						,				*A_						,				**A_     )->		List[Any]:







											'''simple docstring'''






											super().__init__(A_						,				*A_						,				**A_     )
											UpperCamelCase   			=     TFRegNetMainLayer(A_						,				name='regnet'     )







				@unpack_inputs
				@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 						UpperCAmelCase_    (       self						,				A_						,				A_ = None						,				A_ = None						,				A_=False						,				)->		Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:







											'''simple docstring'''






											UpperCamelCase   			=     (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											UpperCamelCase   			=     return_dict if return_dict is not None else self.config.use_return_dict

											UpperCamelCase   			=     self.regnet(
											    pixel_values=A_						,				output_hidden_states=A_						,				return_dict=A_						,				training=A_						,				)
											if not return_dict:
																		return (outputs[0],) + outputs[1:]

											return TFBaseModelOutputWithPoolingAndNoAttention(
											    last_hidden_state=outputs.last_hidden_state						,				pooler_output=outputs.pooler_output						,				hidden_states=outputs.hidden_states						,				)


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







				def __init__(       self						,				A_						,				*A_						,				**A_     )->		str:







											'''simple docstring'''






											super().__init__(A_						,				*A_						,				**A_     )
											UpperCamelCase   			=     config.num_labels
											UpperCamelCase   			=     TFRegNetMainLayer(A_						,				name='regnet'     )
											# classification head
											UpperCamelCase   			=     [
											    tf.keras.layers.Flatten(),
											    tf.keras.layers.Dense(config.num_labels						,				name='classifier.1'     ) if config.num_labels > 0 else tf.identity,
											]







				@unpack_inputs
				@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 						UpperCAmelCase_    (       self						,				A_ = None						,				A_ = None						,				A_ = None						,				A_ = None						,				A_=False						,				)->		Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:







											'''simple docstring'''






											UpperCamelCase   			=     (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											UpperCamelCase   			=     return_dict if return_dict is not None else self.config.use_return_dict

											UpperCamelCase   			=     self.regnet(
											    A_						,				output_hidden_states=A_						,				return_dict=A_						,				training=A_     )

											UpperCamelCase   			=     outputs.pooler_output if return_dict else outputs[1]

											UpperCamelCase   			=     self.classifier[0](A_     )
											UpperCamelCase   			=     self.classifier[1](A_     )

											UpperCamelCase   			=     None if labels is None else self.hf_compute_loss(labels=A_						,				logits=A_     )

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

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




