


'''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    :     str					=						logging.get_logger(__name__)

# General docstring
lowerCAmelCase    :     Optional[Any]					=						"""RegNetConfig"""

# Base docstring
lowerCAmelCase    :     int					=						"""facebook/regnet-y-040"""
lowerCAmelCase    :     Optional[Any]					=						[1, 10_88, 7, 7]

# Image classification docstring
lowerCAmelCase    :     Any					=						"""facebook/regnet-y-040"""
lowerCAmelCase    :     Optional[Any]					=						"""tabby, tabby cat"""

lowerCAmelCase    :     Tuple					=						[
    """facebook/regnet-y-040""",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






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

            '''simple docstring'''

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







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=	self.convolution(self.padding(snake_case__   )   )
            _lowerCAmelCase    :							Union[str, Any]   			=	self.normalization(snake_case__   )
            _lowerCAmelCase    :							int   			=	self.activation(snake_case__   )
            return hidden_state
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






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

            '''simple docstring'''

            super().__init__(**snake_case__   )
            _lowerCAmelCase    :							str   			=	config.num_channels
            _lowerCAmelCase    :							List[Any]   			=	TFRegNetConvLayer(
                out_channels=config.embedding_size      ,	kernel_size=3      ,	stride=2      ,	activation=config.hidden_act      ,	name='embedder'      ,	)







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=	shape_list(snake_case__   )[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)
            _lowerCAmelCase    :							List[Any]   			=	tf.transpose(snake_case__      ,	perm=(0, 2, 3, 1)   )
            _lowerCAmelCase    :							Tuple   			=	self.embedder(snake_case__   )
            return hidden_state
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






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

            '''simple docstring'''

            super().__init__(**snake_case__   )
            _lowerCAmelCase    :							Union[str, Any]   			=	tf.keras.layers.ConvaD(
                filters=snake_case__      ,	kernel_size=1      ,	strides=snake_case__      ,	use_bias=snake_case__      ,	name='convolution'   )
            _lowerCAmelCase    :							Tuple   			=	tf.keras.layers.BatchNormalization(epsilon=1E-5      ,	momentum=0.9      ,	name='normalization'   )







     def a	( self      ,	snake_case__      ,	snake_case__ = False   ):

            '''simple docstring'''

            return self.normalization(self.convolution(snake_case__   )      ,	training=snake_case__   )
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






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

            '''simple docstring'''

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







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=	self.pooler(snake_case__   )
            for layer_module in self.attention:
                   _lowerCAmelCase    :							Tuple   			=	layer_module(snake_case__   )
            _lowerCAmelCase    :							Optional[Any]   			=	hidden_state * pooled
            return hidden_state
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__ = 1      ,	**snake_case__   ):

            '''simple docstring'''

            super().__init__(**snake_case__   )
            _lowerCAmelCase    :							Optional[int]   			=	in_channels != out_channels or stride != 1
            _lowerCAmelCase    :							Union[str, Any]   			=	max(1      ,	out_channels // config.groups_width   )
            _lowerCAmelCase    :							Optional[Any]   			=	(
                TFRegNetShortCut(snake_case__      ,	stride=snake_case__      ,	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.
            _lowerCAmelCase    :							Any   			=	[
                TFRegNetConvLayer(snake_case__      ,	kernel_size=1      ,	activation=config.hidden_act      ,	name='layer.0'   ),
                TFRegNetConvLayer(
                    snake_case__      ,	stride=snake_case__      ,	groups=snake_case__      ,	activation=config.hidden_act      ,	name='layer.1'   ),
                TFRegNetConvLayer(snake_case__      ,	kernel_size=1      ,	activation=snake_case__      ,	name='layer.2'   ),
            ]
            _lowerCAmelCase    :							List[str]   			=	ACTaFN[config.hidden_act]







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=	hidden_state
            for layer_module in self.layers:
                   _lowerCAmelCase    :							int   			=	layer_module(snake_case__   )
            _lowerCAmelCase    :							int   			=	self.shortcut(snake_case__   )
            hidden_state += residual
            _lowerCAmelCase    :							Tuple   			=	self.activation(snake_case__   )
            return hidden_state
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__ = 1      ,	**snake_case__   ):

            '''simple docstring'''

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







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=	hidden_state
            for layer_module in self.layers:
                   _lowerCAmelCase    :							List[Any]   			=	layer_module(snake_case__   )
            _lowerCAmelCase    :							Tuple   			=	self.shortcut(snake_case__   )
            hidden_state += residual
            _lowerCAmelCase    :							str   			=	self.activation(snake_case__   )
            return hidden_state
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






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

            '''simple docstring'''

            super().__init__(**snake_case__   )

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







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            for layer_module in self.layers:
                   _lowerCAmelCase    :							int   			=	layer_module(snake_case__   )
            return hidden_state
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






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

            '''simple docstring'''

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







     def a	( self      ,	snake_case__      ,	snake_case__ = False      ,	snake_case__ = True   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=	() if output_hidden_states else None

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

                   _lowerCAmelCase    :							List[str]   			=	stage_module(snake_case__   )

            if output_hidden_states:
                   _lowerCAmelCase    :							Dict   			=	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=snake_case__      ,	hidden_states=snake_case__   )
@keras_serializable
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






     __magic_name__    			=    RegNetConfig







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

            '''simple docstring'''

            super().__init__(**snake_case__   )
            _lowerCAmelCase    :							Union[str, Any]   			=	config
            _lowerCAmelCase    :							Union[str, Any]   			=	TFRegNetEmbeddings(snake_case__      ,	name='embedder'   )
            _lowerCAmelCase    :							Optional[int]   			=	TFRegNetEncoder(snake_case__      ,	name='encoder'   )
            _lowerCAmelCase    :							Dict   			=	tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case__      ,	name='pooler'   )







     @unpack_inputs
     def a	( self      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = False      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=	(
                output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
            )
            _lowerCAmelCase    :							Optional[Any]   			=	return_dict if return_dict is not None else self.config.use_return_dict

            _lowerCAmelCase    :							int   			=	self.embedder(snake_case__      ,	training=snake_case__   )

            _lowerCAmelCase    :							List[str]   			=	self.encoder(
                snake_case__      ,	output_hidden_states=snake_case__      ,	return_dict=snake_case__      ,	training=snake_case__   )

            _lowerCAmelCase    :							List[Any]   			=	encoder_outputs[0]
            _lowerCAmelCase    :							Tuple   			=	self.pooler(snake_case__   )

            # Change to NCHW output format have uniformity in the modules
            _lowerCAmelCase    :							Optional[int]   			=	tf.transpose(snake_case__      ,	perm=(0, 3, 1, 2)   )
            _lowerCAmelCase    :							Optional[Any]   			=	tf.transpose(snake_case__      ,	perm=(0, 3, 1, 2)   )

            # Change the other hidden state outputs to NCHW as well
            if output_hidden_states:
                   _lowerCAmelCase    :							Union[str, Any]   			=	tuple([tf.transpose(snake_case__      ,	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=snake_case__      ,	pooler_output=snake_case__      ,	hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states      ,	)
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    RegNetConfig
     __magic_name__    			=    "regnet"
     __magic_name__    			=    "pixel_values"







     @property
     def a	( self   ):

            '''simple docstring'''

            return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)      ,	dtype=tf.floataa   )}
lowerCAmelCase    :     List[Any]					=						r"""
    Parameters:
    This model is a Tensorflow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
    behavior.
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""

lowerCAmelCase    :     Dict					=						r"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConveNextImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top."    ,					SCREAMING_SNAKE_CASE_    ,					)
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






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

            '''simple docstring'''

            super().__init__(snake_case__      ,	*snake_case__      ,	**snake_case__   )
            _lowerCAmelCase    :							List[str]   			=	TFRegNetMainLayer(snake_case__      ,	name='regnet'   )







     @unpack_inputs
     @add_start_docstrings_to_model_forward(snake_case__   )
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC      ,	output_type=snake_case__      ,	config_class=_CONFIG_FOR_DOC      ,	modality='vision'      ,	expected_output=_EXPECTED_OUTPUT_SHAPE      ,	)
     def a	( self      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__=False      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=	(
                output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
            )
            _lowerCAmelCase    :							Any   			=	return_dict if return_dict is not None else self.config.use_return_dict

            _lowerCAmelCase    :							str   			=	self.regnet(
                pixel_values=snake_case__      ,	output_hidden_states=snake_case__      ,	return_dict=snake_case__      ,	training=snake_case__      ,	)
            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(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "    ,					SCREAMING_SNAKE_CASE_    ,					)
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_    ,					SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






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

            '''simple docstring'''

            super().__init__(snake_case__      ,	*snake_case__      ,	**snake_case__   )
            _lowerCAmelCase    :							Optional[Any]   			=	config.num_labels
            _lowerCAmelCase    :							Optional[Any]   			=	TFRegNetMainLayer(snake_case__      ,	name='regnet'   )
            # classification head
            _lowerCAmelCase    :							Optional[int]   			=	[
                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(snake_case__   )
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT      ,	output_type=snake_case__      ,	config_class=_CONFIG_FOR_DOC      ,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,	)
     def a	( self      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__=False      ,	):

            '''simple docstring'''

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

            _lowerCAmelCase    :							Dict   			=	self.regnet(
                snake_case__      ,	output_hidden_states=snake_case__      ,	return_dict=snake_case__      ,	training=snake_case__   )

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

            _lowerCAmelCase    :							List[Any]   			=	self.classifier[0](snake_case__   )
            _lowerCAmelCase    :							Tuple   			=	self.classifier[1](snake_case__   )

            _lowerCAmelCase    :							int   			=	None if labels is None else self.hf_compute_loss(labels=snake_case__      ,	logits=snake_case__   )

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

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





'''simple docstring'''







import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
       from transformers.models.mobilevitva.modeling_mobilevitva import (
           MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
           make_divisible,
       )


if is_vision_available():
       from PIL import Image

       from transformers import MobileViTImageProcessor
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.config_class(**self.inputs_dict   )
            self.parent.assertTrue(hasattr(snake_case__      ,	'width_multiplier'   )   )
class 	UpperCamelCase__					:



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__=13      ,	snake_case__=64      ,	snake_case__=2      ,	snake_case__=3      ,	snake_case__="swish"      ,	snake_case__=3      ,	snake_case__=32      ,	snake_case__=0.1      ,	snake_case__=0.02      ,	snake_case__=True      ,	snake_case__=True      ,	snake_case__=10      ,	snake_case__=None      ,	snake_case__=0.25      ,	snake_case__=0.0      ,	snake_case__=0.0      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       parent
            _lowerCAmelCase    :							Optional[int]   			=       batch_size
            _lowerCAmelCase    :							List[Any]   			=       image_size
            _lowerCAmelCase    :							List[Any]   			=       patch_size
            _lowerCAmelCase    :							List[str]   			=       num_channels
            _lowerCAmelCase    :							Optional[Any]   			=       make_divisible(512 * width_multiplier      ,	divisor=8   )
            _lowerCAmelCase    :							Optional[Any]   			=       hidden_act
            _lowerCAmelCase    :							List[Any]   			=       conv_kernel_size
            _lowerCAmelCase    :							Optional[Any]   			=       output_stride
            _lowerCAmelCase    :							List[Any]   			=       classifier_dropout_prob
            _lowerCAmelCase    :							str   			=       use_labels
            _lowerCAmelCase    :							List[str]   			=       is_training
            _lowerCAmelCase    :							Optional[int]   			=       num_labels
            _lowerCAmelCase    :							List[str]   			=       initializer_range
            _lowerCAmelCase    :							str   			=       scope
            _lowerCAmelCase    :							Any   			=       width_multiplier
            _lowerCAmelCase    :							Union[str, Any]   			=       ffn_dropout
            _lowerCAmelCase    :							Optional[int]   			=       attn_dropout







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

            _lowerCAmelCase    :							Optional[Any]   			=       None
            _lowerCAmelCase    :							Dict   			=       None
            if self.use_labels:
                   _lowerCAmelCase    :							Optional[Any]   			=       ids_tensor([self.batch_size]      ,	self.num_labels   )
                   _lowerCAmelCase    :							Optional[Any]   			=       ids_tensor([self.batch_size, self.image_size, self.image_size]      ,	self.num_labels   )

            _lowerCAmelCase    :							Optional[int]   			=       self.get_config()

            return config, pixel_values, labels, pixel_labels







     def a	( self   ):

            '''simple docstring'''

            return MobileViTVaConfig(
                image_size=self.image_size      ,	patch_size=self.patch_size      ,	num_channels=self.num_channels      ,	hidden_act=self.hidden_act      ,	conv_kernel_size=self.conv_kernel_size      ,	output_stride=self.output_stride      ,	classifier_dropout_prob=self.classifier_dropout_prob      ,	initializer_range=self.initializer_range      ,	width_multiplier=self.width_multiplier      ,	ffn_dropout=self.ffn_dropout_prob      ,	attn_dropout=self.attn_dropout_prob      ,	)







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       MobileViTVaModel(config=snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							str   			=       model(snake_case__   )
            self.parent.assertEqual(
                result.last_hidden_state.shape      ,	(
                    self.batch_size,
                    self.last_hidden_size,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                )      ,	)







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.num_labels
            _lowerCAmelCase    :							List[Any]   			=       MobileViTVaForImageClassification(snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							int   			=       model(snake_case__      ,	labels=snake_case__   )
            self.parent.assertEqual(result.logits.shape      ,	(self.batch_size, self.num_labels)   )







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.num_labels
            _lowerCAmelCase    :							Optional[int]   			=       MobileViTVaForSemanticSegmentation(snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							Dict   			=       model(snake_case__   )
            self.parent.assertEqual(
                result.logits.shape      ,	(
                    self.batch_size,
                    self.num_labels,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                )      ,	)
            _lowerCAmelCase    :							Any   			=       model(snake_case__      ,	labels=snake_case__   )
            self.parent.assertEqual(
                result.logits.shape      ,	(
                    self.batch_size,
                    self.num_labels,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                )      ,	)







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.prepare_config_and_inputs()
            _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[int]   			=       config_and_inputs
            _lowerCAmelCase    :							Tuple   			=       {'pixel_values': pixel_values}
            return config, inputs_dict
@require_torch
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_    ,					SCREAMING_SNAKE_CASE_    ,					unittest.TestCase	):



     """simple docstring"""






     __magic_name__    			=    (
         (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
         if is_torch_available()
         else ()
     )

     __magic_name__    			=    (
         {
             "feature-extraction": MobileViTVaModel,
             "image-classification": MobileViTVaForImageClassification,
             "image-segmentation": MobileViTVaForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     __magic_name__    			=    False
     __magic_name__    			=    False
     __magic_name__    			=    False
     __magic_name__    			=    False







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       MobileViTVaModelTester(self   )
            _lowerCAmelCase    :							Dict   			=       MobileViTVaConfigTester(self      ,	config_class=snake_case__      ,	has_text_modality=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            self.config_tester.run_common_tests()







     @unittest.skip(reason='MobileViTV2 does not use inputs_embeds'   )
     def a	( self   ):

            '''simple docstring'''

            pass







     @unittest.skip(reason='MobileViTV2 does not support input and output embeddings'   )
     def a	( self   ):

            '''simple docstring'''

            pass







     @unittest.skip(reason='MobileViTV2 does not output attentions'   )
     def a	( self   ):

            '''simple docstring'''

            pass







     @require_torch_multi_gpu
     @unittest.skip(reason='Got `CUDA error: misaligned address` for tests after this one being run.'   )
     def a	( self   ):

            '''simple docstring'''

            pass







     @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'   )
     def a	( self   ):

            '''simple docstring'''

            pass







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[int]   			=       self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _lowerCAmelCase    :							str   			=       model_class(snake_case__   )
                   _lowerCAmelCase    :							List[str]   			=       inspect.signature(model.forward   )
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   _lowerCAmelCase    :							int   			=       [*signature.parameters.keys()]

                   _lowerCAmelCase    :							Any   			=       ['pixel_values']
                   self.assertListEqual(arg_names[:1]      ,	snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            def check_hidden_states_output(snake_case__      ,	snake_case__      ,	snake_case__   ):
                   _lowerCAmelCase    :							Dict   			=       model_class(snake_case__   )
                   model.to(snake_case__   )
                   model.eval()

                   with torch.no_grad():
                          _lowerCAmelCase    :							Optional[Any]   			=       model(**self._prepare_for_class(snake_case__      ,	snake_case__   )   )

                   _lowerCAmelCase    :							List[str]   			=       outputs.hidden_states

                   _lowerCAmelCase    :							List[str]   			=       5
                   self.assertEqual(len(snake_case__   )      ,	snake_case__   )

                   # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
                   # with the width and height being successively divided by 2.
                   _lowerCAmelCase    :							List[Any]   			=       2
                   for i in range(len(snake_case__   )   ):
                          self.assertListEqual(
                              list(hidden_states[i].shape[-2:]   )      ,	[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]      ,	)
                          divisor *= 2

                   self.assertEqual(self.model_tester.output_stride      ,	divisor // 2   )

            _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[int]   			=       self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _lowerCAmelCase    :							Optional[int]   			=       True
                   check_hidden_states_output(snake_case__      ,	snake_case__      ,	snake_case__   )

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   _lowerCAmelCase    :							Any   			=       True

                   check_hidden_states_output(snake_case__      ,	snake_case__      ,	snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_semantic_segmentation(*snake_case__   )







     @slow
     def a	( self   ):

            '''simple docstring'''

            for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _lowerCAmelCase    :							Dict   			=       MobileViTVaModel.from_pretrained(snake_case__   )
                   self.assertIsNotNone(snake_case__   )






def 			lowercase				():







       """simple docstring"""







       _lowerCAmelCase    :							Any   			=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
       return image
@require_torch
@require_vision
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     @cached_property
     def a	( self   ):

            '''simple docstring'''

            return (
                MobileViTImageProcessor.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'   )
                if is_vision_available()
                else None
            )







     @slow
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       MobileViTVaForImageClassification.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'   ).to(
                snake_case__   )

            _lowerCAmelCase    :							str   			=       self.default_image_processor
            _lowerCAmelCase    :							Any   			=       prepare_img()
            _lowerCAmelCase    :							Optional[int]   			=       image_processor(images=snake_case__      ,	return_tensors='pt'   ).to(snake_case__   )

            # forward pass
            with torch.no_grad():
                   _lowerCAmelCase    :							Tuple   			=       model(**snake_case__   )

            # verify the logits
            _lowerCAmelCase    :							Optional[Any]   			=       torch.Size((1, 1000)   )
            self.assertEqual(outputs.logits.shape      ,	snake_case__   )

            _lowerCAmelCase    :							Tuple   			=       torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01]   ).to(snake_case__   )

            self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,	snake_case__      ,	atol=1E-4   )   )







     @slow
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )
            _lowerCAmelCase    :							Any   			=       model.to(snake_case__   )

            _lowerCAmelCase    :							int   			=       MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )

            _lowerCAmelCase    :							Optional[int]   			=       prepare_img()
            _lowerCAmelCase    :							Dict   			=       image_processor(images=snake_case__      ,	return_tensors='pt'   ).to(snake_case__   )

            # forward pass
            with torch.no_grad():
                   _lowerCAmelCase    :							int   			=       model(**snake_case__   )
            _lowerCAmelCase    :							Dict   			=       outputs.logits

            # verify the logits
            _lowerCAmelCase    :							str   			=       torch.Size((1, 21, 32, 32)   )
            self.assertEqual(logits.shape      ,	snake_case__   )

            _lowerCAmelCase    :							Any   			=       torch.tensor(
                [
                    [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
                    [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
                    [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
                ]      ,	device=snake_case__      ,	)

            self.assertTrue(torch.allclose(logits[0, :3, :3, :3]      ,	snake_case__      ,	atol=1E-4   )   )







     @slow
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )
            _lowerCAmelCase    :							List[Any]   			=       model.to(snake_case__   )

            _lowerCAmelCase    :							str   			=       MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )

            _lowerCAmelCase    :							Tuple   			=       prepare_img()
            _lowerCAmelCase    :							List[str]   			=       image_processor(images=snake_case__      ,	return_tensors='pt'   ).to(snake_case__   )

            # forward pass
            with torch.no_grad():
                   _lowerCAmelCase    :							Any   			=       model(**snake_case__   )

            _lowerCAmelCase    :							Optional[Any]   			=       outputs.logits.detach().cpu()

            _lowerCAmelCase    :							Any   			=       image_processor.post_process_semantic_segmentation(outputs=snake_case__      ,	target_sizes=[(50, 60)]   )
            _lowerCAmelCase    :							List[Any]   			=       torch.Size((50, 60)   )
            self.assertEqual(segmentation[0].shape      ,	snake_case__   )

            _lowerCAmelCase    :							List[str]   			=       image_processor.post_process_semantic_segmentation(outputs=snake_case__   )
            _lowerCAmelCase    :							Tuple   			=       torch.Size((32, 32)   )
            self.assertEqual(segmentation[0].shape      ,	snake_case__   )

