

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a__												=						logging.get_logger(__name__)

a__												=						{
    '''microsoft/beit-base-patch16-224-pt22k''': (
        '''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		str             =       "beit"


							def __init__(     self			,				_a=8_1_9_2			,				_a=7_6_8			,				_a=1_2			,				_a=1_2			,				_a=3_0_7_2			,				_a="gelu"			,				_a=0.0			,				_a=0.0			,				_a=0.02			,				_a=1e-1_2			,				_a=2_2_4			,				_a=1_6			,				_a=3			,				_a=False			,				_a=False			,				_a=False			,				_a=False			,				_a=0.1			,				_a=0.1			,				_a=True			,				_a=[3, 5, 7, 1_1]			,				_a=[1, 2, 3, 6]			,				_a=True			,				_a=0.4			,				_a=2_5_6			,				_a=1			,				_a=False			,				_a=2_5_5			,				**_a			,				)       ->       Tuple:
												super().__init__(**_a			)

												_a      :       Dict   =						vocab_size
												_a      :       Tuple   =						hidden_size
												_a      :       List[Any]   =						num_hidden_layers
												_a      :       int   =						num_attention_heads
												_a      :       List[Any]   =						intermediate_size
												_a      :       Any   =						hidden_act
												_a      :       Optional[int]   =						hidden_dropout_prob
												_a      :       Union[str, Any]   =						attention_probs_dropout_prob
												_a      :       Any   =						initializer_range
												_a      :       List[str]   =						layer_norm_eps

												_a      :       Dict   =						image_size
												_a      :       int   =						patch_size
												_a      :       Tuple   =						num_channels
												_a      :       Any   =						use_mask_token
												_a      :       int   =						use_absolute_position_embeddings
												_a      :       Optional[int]   =						use_relative_position_bias
												_a      :       List[Any]   =						use_shared_relative_position_bias
												_a      :       str   =						layer_scale_init_value
												_a      :       List[str]   =						drop_path_rate
												_a      :       List[Any]   =						use_mean_pooling
												# decode head attributes (semantic segmentation)
												_a      :       str   =						out_indices
												_a      :       Tuple   =						pool_scales
												# auxiliary head attributes (semantic segmentation)
												_a      :       Any   =						use_auxiliary_head
												_a      :       List[Any]   =						auxiliary_loss_weight
												_a      :       Optional[int]   =						auxiliary_channels
												_a      :       Tuple   =						auxiliary_num_convs
												_a      :       List[Any]   =						auxiliary_concat_input
												_a      :       Tuple   =						semantic_loss_ignore_index



class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		str             =       version.parse("1.11"  )


							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												return OrderedDict(
												    [
												        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
												    ]			)




							@property
							def 							__lowercase							(     self			)       ->       float:
												return 1e-4





'''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     )




