


'''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


__snake_case       		=logging.get_logger(__name__)

# General docstring
__snake_case       		='RegNetConfig'

# Base docstring
__snake_case       		='facebook/regnet-y-040'
__snake_case       		=[1, 1_088, 7, 7]

# Image classification docstring
__snake_case       		='facebook/regnet-y-040'
__snake_case       		='tabby, tabby cat'

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

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





     def __init__(					self			: int       , UpperCAmelCase__			: Any       , UpperCAmelCase__			: int = 3       , UpperCAmelCase__			: Optional[Any] = 1       , UpperCAmelCase__			: int = 1       , UpperCAmelCase__			: Dict = "relu"       , **UpperCAmelCase__			: Tuple       , )							->    List[Any]:
        super().__init__(**__SCREAMING_SNAKE_CASE       )
        # The padding and conv has been verified in
        # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
        lowerCAmelCase	=     tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2       )
        lowerCAmelCase	=     tf.keras.layers.ConvaD(
            filters=__SCREAMING_SNAKE_CASE       , kernel_size=__SCREAMING_SNAKE_CASE       , strides=__SCREAMING_SNAKE_CASE       , padding='VALID'       , groups=__SCREAMING_SNAKE_CASE       , use_bias=__SCREAMING_SNAKE_CASE       , name='convolution'       , )
        lowerCAmelCase	=     tf.keras.layers.BatchNormalization(epsilon=1E-5       , momentum=0.9       , name='normalization'       )
        lowerCAmelCase	=     ACTaFN[activation] if activation is not None else tf.identity





     def 				__UpperCAmelCase (					self			: List[str]       , UpperCAmelCase__			: Tuple       )							->    Tuple:
        lowerCAmelCase	=     self.convolution(self.padding(__SCREAMING_SNAKE_CASE       )       )
        lowerCAmelCase	=     self.normalization(__SCREAMING_SNAKE_CASE       )
        lowerCAmelCase	=     self.activation(__SCREAMING_SNAKE_CASE       )
        return hidden_state




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





     def __init__(					self			: Dict       , UpperCAmelCase__			: Tuple       , **UpperCAmelCase__			: Optional[int]       )							->    Dict:
        super().__init__(**__SCREAMING_SNAKE_CASE       )
        lowerCAmelCase	=     config.num_channels
        lowerCAmelCase	=     TFRegNetConvLayer(
            out_channels=config.embedding_size       , kernel_size=3       , stride=2       , activation=config.hidden_act       , name='embedder'       , )





     def 				__UpperCAmelCase (					self			: List[Any]       , UpperCAmelCase__			: Any       )							->    Optional[Any]:
        lowerCAmelCase	=     shape_list(__SCREAMING_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	=     tf.transpose(__SCREAMING_SNAKE_CASE       , perm=(0, 2, 3, 1)       )
        lowerCAmelCase	=     self.embedder(__SCREAMING_SNAKE_CASE       )
        return hidden_state




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





     def __init__(					self			: str       , UpperCAmelCase__			: Dict       , UpperCAmelCase__			: List[Any] = 2       , **UpperCAmelCase__			: List[Any]       )							->    Any:
        super().__init__(**__SCREAMING_SNAKE_CASE       )
        lowerCAmelCase	=     tf.keras.layers.ConvaD(
            filters=__SCREAMING_SNAKE_CASE       , kernel_size=1       , strides=__SCREAMING_SNAKE_CASE       , use_bias=__SCREAMING_SNAKE_CASE       , name='convolution'       )
        lowerCAmelCase	=     tf.keras.layers.BatchNormalization(epsilon=1E-5       , momentum=0.9       , name='normalization'       )





     def 				__UpperCAmelCase (					self			: int       , UpperCAmelCase__			: List[Any]       , UpperCAmelCase__			: Optional[Any] = False       )							->    tf.Tensor:
        return self.normalization(self.convolution(__SCREAMING_SNAKE_CASE       )       , training=__SCREAMING_SNAKE_CASE       )




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





     def __init__(					self			: str       , UpperCAmelCase__			: Any       , UpperCAmelCase__			: Union[str, Any]       , **UpperCAmelCase__			: Optional[int]       )							->    List[Any]:
        super().__init__(**__SCREAMING_SNAKE_CASE       )
        lowerCAmelCase	=     tf.keras.layers.GlobalAveragePoolingaD(keepdims=__SCREAMING_SNAKE_CASE       , name='pooler'       )
        lowerCAmelCase	=     [
            tf.keras.layers.ConvaD(filters=__SCREAMING_SNAKE_CASE       , kernel_size=1       , activation='relu'       , name='attention.0'       ),
            tf.keras.layers.ConvaD(filters=__SCREAMING_SNAKE_CASE       , kernel_size=1       , activation='sigmoid'       , name='attention.2'       ),
        ]





     def 				__UpperCAmelCase (					self			: str       , UpperCAmelCase__			: Any       )							->    Optional[int]:
        lowerCAmelCase	=     self.pooler(__SCREAMING_SNAKE_CASE       )
        for layer_module in self.attention:
           lowerCAmelCase	=     layer_module(__SCREAMING_SNAKE_CASE       )
        lowerCAmelCase	=     hidden_state * pooled
        return hidden_state




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





     def __init__(					self			: str       , UpperCAmelCase__			: Union[str, Any]       , UpperCAmelCase__			: List[str]       , UpperCAmelCase__			: Union[str, Any]       , UpperCAmelCase__			: Optional[int] = 1       , **UpperCAmelCase__			: int       )							->    Dict:
        super().__init__(**__SCREAMING_SNAKE_CASE       )
        lowerCAmelCase	=     in_channels != out_channels or stride != 1
        lowerCAmelCase	=     max(1       , out_channels // config.groups_width       )
        lowerCAmelCase	=     (
            TFRegNetShortCut(__SCREAMING_SNAKE_CASE       , stride=__SCREAMING_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	=     [
            TFRegNetConvLayer(__SCREAMING_SNAKE_CASE       , kernel_size=1       , activation=config.hidden_act       , name='layer.0'       ),
            TFRegNetConvLayer(
                __SCREAMING_SNAKE_CASE       , stride=__SCREAMING_SNAKE_CASE       , groups=__SCREAMING_SNAKE_CASE       , activation=config.hidden_act       , name='layer.1'       ),
            TFRegNetConvLayer(__SCREAMING_SNAKE_CASE       , kernel_size=1       , activation=__SCREAMING_SNAKE_CASE       , name='layer.2'       ),
        ]
        lowerCAmelCase	=     ACTaFN[config.hidden_act]





     def 				__UpperCAmelCase (					self			: str       , UpperCAmelCase__			: Union[str, Any]       )							->    Tuple:
        lowerCAmelCase	=     hidden_state
        for layer_module in self.layers:
           lowerCAmelCase	=     layer_module(__SCREAMING_SNAKE_CASE       )
        lowerCAmelCase	=     self.shortcut(__SCREAMING_SNAKE_CASE       )
        hidden_state += residual
        lowerCAmelCase	=     self.activation(__SCREAMING_SNAKE_CASE       )
        return hidden_state




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





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





     def 				__UpperCAmelCase (					self			: Tuple       , UpperCAmelCase__			: List[str]       )							->    str:
        lowerCAmelCase	=     hidden_state
        for layer_module in self.layers:
           lowerCAmelCase	=     layer_module(__SCREAMING_SNAKE_CASE       )
        lowerCAmelCase	=     self.shortcut(__SCREAMING_SNAKE_CASE       )
        hidden_state += residual
        lowerCAmelCase	=     self.activation(__SCREAMING_SNAKE_CASE       )
        return hidden_state




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





     def __init__(					self			: str       , UpperCAmelCase__			: Optional[int]       , UpperCAmelCase__			: Tuple       , UpperCAmelCase__			: List[Any]       , UpperCAmelCase__			: int = 2       , UpperCAmelCase__			: Optional[int] = 2       , **UpperCAmelCase__			: Any       )							->    Tuple:
        super().__init__(**__SCREAMING_SNAKE_CASE       )

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





     def 				__UpperCAmelCase (					self			: Any       , UpperCAmelCase__			: Tuple       )							->    Tuple:



        for layer_module in self.layers:
           lowerCAmelCase	=     layer_module(__SCREAMING_SNAKE_CASE       )
        return hidden_state




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





     def __init__(					self			: Union[str, Any]       , UpperCAmelCase__			: List[Any]       , **UpperCAmelCase__			: str       )							->    str:
        super().__init__(**__SCREAMING_SNAKE_CASE       )
        lowerCAmelCase	=     []
        # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
        self.stages.append(
            TFRegNetStage(
                __SCREAMING_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	=     zip(config.hidden_sizes       , config.hidden_sizes[1:]       )
        for i, ((in_channels, out_channels), depth) in enumerate(zip(__SCREAMING_SNAKE_CASE       , config.depths[1:]       )       ):
           self.stages.append(TFRegNetStage(__SCREAMING_SNAKE_CASE       , __SCREAMING_SNAKE_CASE       , __SCREAMING_SNAKE_CASE       , depth=__SCREAMING_SNAKE_CASE       , name=F'''stages.{i+1}'''       )       )





     def 				__UpperCAmelCase (					self			: Union[str, Any]       , UpperCAmelCase__			: str       , UpperCAmelCase__			: int = False       , UpperCAmelCase__			: Tuple = True       )							->    TFBaseModelOutputWithNoAttention:
        lowerCAmelCase	=     () if output_hidden_states else None

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

           lowerCAmelCase	=     stage_module(__SCREAMING_SNAKE_CASE       )

        if output_hidden_states:
           lowerCAmelCase	=     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=__SCREAMING_SNAKE_CASE       , hidden_states=__SCREAMING_SNAKE_CASE       )




@keras_serializable
class 						UpperCAmelCase_ (    tf.keras.layers.Layer						):
     lowerCamelCase							:      List[str]            =	RegNetConfig





     def __init__(					self			: List[str]       , UpperCAmelCase__			: Optional[int]       , **UpperCAmelCase__			: Union[str, Any]       )							->    Dict:
        super().__init__(**__SCREAMING_SNAKE_CASE       )
        lowerCAmelCase	=     config
        lowerCAmelCase	=     TFRegNetEmbeddings(__SCREAMING_SNAKE_CASE       , name='embedder'       )
        lowerCAmelCase	=     TFRegNetEncoder(__SCREAMING_SNAKE_CASE       , name='encoder'       )
        lowerCAmelCase	=     tf.keras.layers.GlobalAveragePoolingaD(keepdims=__SCREAMING_SNAKE_CASE       , name='pooler'       )





     @unpack_inputs
     def 				__UpperCAmelCase (					self			: int       , UpperCAmelCase__			: str       , UpperCAmelCase__			: List[Any] = None       , UpperCAmelCase__			: Union[str, Any] = None       , UpperCAmelCase__			: Dict = False       , )							->    TFBaseModelOutputWithPoolingAndNoAttention:
        lowerCAmelCase	=     (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        lowerCAmelCase	=     return_dict if return_dict is not None else self.config.use_return_dict

        lowerCAmelCase	=     self.embedder(__SCREAMING_SNAKE_CASE       , training=__SCREAMING_SNAKE_CASE       )

        lowerCAmelCase	=     self.encoder(
            __SCREAMING_SNAKE_CASE       , output_hidden_states=__SCREAMING_SNAKE_CASE       , return_dict=__SCREAMING_SNAKE_CASE       , training=__SCREAMING_SNAKE_CASE       )

        lowerCAmelCase	=     encoder_outputs[0]
        lowerCAmelCase	=     self.pooler(__SCREAMING_SNAKE_CASE       )

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

        # Change the other hidden state outputs to NCHW as well
        if output_hidden_states:
           lowerCAmelCase	=     tuple([tf.transpose(__SCREAMING_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=__SCREAMING_SNAKE_CASE       , pooler_output=__SCREAMING_SNAKE_CASE       , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states       , )




class 						UpperCAmelCase_ (    _lowercase						):
     lowerCamelCase							:      List[Any]            =	RegNetConfig
     lowerCamelCase							:      Optional[Any]            =	'regnet'
     lowerCamelCase							:      int            =	'pixel_values'





     @property
     def 				__UpperCAmelCase (					self			: List[Any]       )							->    Dict:
        return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4)       , dtype=tf.floataa       )}




__snake_case       		=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'

__snake_case       		=r'\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`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.'''			,      _lowercase			,      )
class 						UpperCAmelCase_ (    _lowercase						):





     def __init__(					self			: Any       , UpperCAmelCase__			: List[str]       , *UpperCAmelCase__			: int       , **UpperCAmelCase__			: Union[str, Any]       )							->    List[Any]:
        super().__init__(__SCREAMING_SNAKE_CASE       , *__SCREAMING_SNAKE_CASE       , **__SCREAMING_SNAKE_CASE       )
        lowerCAmelCase	=     TFRegNetMainLayer(__SCREAMING_SNAKE_CASE       , name='regnet'       )





     @unpack_inputs
     @add_start_docstrings_to_model_forward(__SCREAMING_SNAKE_CASE       )
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC       , output_type=__SCREAMING_SNAKE_CASE       , config_class=_CONFIG_FOR_DOC       , modality='vision'       , expected_output=_EXPECTED_OUTPUT_SHAPE       , )
     def 				__UpperCAmelCase (					self			: List[str]       , UpperCAmelCase__			: Dict       , UpperCAmelCase__			: Optional[Any] = None       , UpperCAmelCase__			: List[Any] = None       , UpperCAmelCase__			: Optional[Any]=False       , )							->    Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
        lowerCAmelCase	=     (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        lowerCAmelCase	=     return_dict if return_dict is not None else self.config.use_return_dict

        lowerCAmelCase	=     self.regnet(
            pixel_values=__SCREAMING_SNAKE_CASE       , output_hidden_states=__SCREAMING_SNAKE_CASE       , return_dict=__SCREAMING_SNAKE_CASE       , training=__SCREAMING_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    '''			,      _lowercase			,      )
class 						UpperCAmelCase_ (    _lowercase			,      _lowercase						):





     def __init__(					self			: str       , UpperCAmelCase__			: List[Any]       , *UpperCAmelCase__			: Union[str, Any]       , **UpperCAmelCase__			: int       )							->    str:
        super().__init__(__SCREAMING_SNAKE_CASE       , *__SCREAMING_SNAKE_CASE       , **__SCREAMING_SNAKE_CASE       )
        lowerCAmelCase	=     config.num_labels
        lowerCAmelCase	=     TFRegNetMainLayer(__SCREAMING_SNAKE_CASE       , name='regnet'       )
        # classification head
        lowerCAmelCase	=     [
            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(__SCREAMING_SNAKE_CASE       )
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT       , output_type=__SCREAMING_SNAKE_CASE       , config_class=_CONFIG_FOR_DOC       , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       , )
     def 				__UpperCAmelCase (					self			: Optional[int]       , UpperCAmelCase__			: List[str] = None       , UpperCAmelCase__			: Any = None       , UpperCAmelCase__			: Dict = None       , UpperCAmelCase__			: List[str] = None       , UpperCAmelCase__			: str=False       , )							->    Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
        lowerCAmelCase	=     (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        lowerCAmelCase	=     return_dict if return_dict is not None else self.config.use_return_dict

        lowerCAmelCase	=     self.regnet(
            __SCREAMING_SNAKE_CASE       , output_hidden_states=__SCREAMING_SNAKE_CASE       , return_dict=__SCREAMING_SNAKE_CASE       , training=__SCREAMING_SNAKE_CASE       )

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

        lowerCAmelCase	=     self.classifier[0](__SCREAMING_SNAKE_CASE       )
        lowerCAmelCase	=     self.classifier[1](__SCREAMING_SNAKE_CASE       )

        lowerCAmelCase	=     None if labels is None else self.hf_compute_loss(labels=__SCREAMING_SNAKE_CASE       , logits=__SCREAMING_SNAKE_CASE       )

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

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







"""simple docstring"""




from __future__ import annotations

a		:       str     						=       {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}







class     lowercase:



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     None:







         """simple docstring"""



         a__		=			graph
         # mapping node to its parent in resulting breadth first tree
         a__		=			{}
         a__		=			source_vertex



     def 			lowercase__  (							self   )		->     None:







         """simple docstring"""



         a__		=			{self.source_vertex}
         a__		=			None
         a__		=			[self.source_vertex]  # first in first out queue

         while queue:
             a__		=			queue.pop(0   )
             for adjacent_vertex in self.graph[vertex]:
                 if adjacent_vertex not in visited:
                     visited.add(__SCREAMING_SNAKE_CASE   )
                     a__		=			vertex
                     queue.append(__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     str:







         """simple docstring"""



         if target_vertex == self.source_vertex:
             return self.source_vertex

         a__		=			self.parent.get(__SCREAMING_SNAKE_CASE   )
         if target_vertex_parent is None:
             a__		=			(
                 f'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
             )
             raise ValueError(__SCREAMING_SNAKE_CASE   )

         return self.shortest_path(__SCREAMING_SNAKE_CASE   ) + f'->{target_vertex}'


if __name__ == "__main__":
    a		:       Optional[Any]     						=       Graph(graph, 'G')
    g.breath_first_search()
    print(g.shortest_path('D'))
    print(g.shortest_path('G'))
    print(g.shortest_path('Foo'))


