




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__					:  List[Any]      							=				logging.get_logger(__name__)

# General docstring
lowerCamelCase__					:  Dict      							=				'RegNetConfig'

# Base docstring
lowerCamelCase__					:  Union[str, Any]      							=				'facebook/regnet-y-040'
lowerCamelCase__					:  Dict      							=				[1, 1_088, 7, 7]

# Image classification docstring
lowerCamelCase__					:  List[Any]      							=				'facebook/regnet-y-040'
lowerCamelCase__					:  Any      							=				'tabby, tabby cat'

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





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


 '''simple docstring'''







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




 def  lowerCAmelCase_    (				self    :						int							,							_lowerCAmelCase    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.convolution(self.padding(_lowerCAmelCase		)		)
       SCREAMING_SNAKE_CASE_      		=     self.normalization(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.activation(_lowerCAmelCase		)
       return hidden_state





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


 '''simple docstring'''







 def __init__(				self    :						Optional[int]							,							_lowerCAmelCase    :						RegNetConfig							,							**_lowerCAmelCase    :						List[Any]		):
       super().__init__(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     config.num_channels
       SCREAMING_SNAKE_CASE_      		=     TFRegNetConvLayer(
           out_channels=config.embedding_size							,							kernel_size=3							,							stride=2							,							activation=config.hidden_act							,							name='embedder'							,							)




 def  lowerCAmelCase_    (				self    :						List[str]							,							_lowerCAmelCase    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     shape_list(_lowerCAmelCase		)[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)
       SCREAMING_SNAKE_CASE_      		=     tf.transpose(_lowerCAmelCase							,							perm=(0, 2, 3, 1)		)
       SCREAMING_SNAKE_CASE_      		=     self.embedder(_lowerCAmelCase		)
       return hidden_state





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


 '''simple docstring'''







 def __init__(				self    :						int							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						int = 2							,							**_lowerCAmelCase    :						Dict		):
       super().__init__(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     tf.keras.layers.ConvaD(
           filters=_lowerCAmelCase							,							kernel_size=1							,							strides=_lowerCAmelCase							,							use_bias=_lowerCAmelCase							,							name='convolution'		)
       SCREAMING_SNAKE_CASE_      		=     tf.keras.layers.BatchNormalization(epsilon=1E-5							,							momentum=0.9							,							name='normalization'		)




 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						tf.Tensor							,							_lowerCAmelCase    :						bool = False		):
       return self.normalization(self.convolution(_lowerCAmelCase		)							,							training=_lowerCAmelCase		)





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


 '''simple docstring'''







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




 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						int		):
       # [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
       SCREAMING_SNAKE_CASE_      		=     self.pooler(_lowerCAmelCase		)
       for layer_module in self.attention:
             SCREAMING_SNAKE_CASE_      		=     layer_module(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     hidden_state * pooled
       return hidden_state





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


 '''simple docstring'''







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




 def  lowerCAmelCase_    (				self    :						Optional[int]							,							_lowerCAmelCase    :						Dict		):
       SCREAMING_SNAKE_CASE_      		=     hidden_state
       for layer_module in self.layers:
             SCREAMING_SNAKE_CASE_      		=     layer_module(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.shortcut(_lowerCAmelCase		)
       hidden_state += residual
       SCREAMING_SNAKE_CASE_      		=     self.activation(_lowerCAmelCase		)
       return hidden_state





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


 '''simple docstring'''







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




 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Dict		):
       SCREAMING_SNAKE_CASE_      		=     hidden_state
       for layer_module in self.layers:
             SCREAMING_SNAKE_CASE_      		=     layer_module(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.shortcut(_lowerCAmelCase		)
       hidden_state += residual
       SCREAMING_SNAKE_CASE_      		=     self.activation(_lowerCAmelCase		)
       return hidden_state





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


 '''simple docstring'''







 def __init__(				self    :						str							,							_lowerCAmelCase    :						RegNetConfig							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						int = 2							,							_lowerCAmelCase    :						int = 2							,							**_lowerCAmelCase    :						Optional[Any]		):
       super().__init__(**_lowerCAmelCase		)

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




 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						Optional[int]		):
       for layer_module in self.layers:
             SCREAMING_SNAKE_CASE_      		=     layer_module(_lowerCAmelCase		)
       return hidden_state





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


 '''simple docstring'''







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




 def  lowerCAmelCase_    (				self    :						List[str]							,							_lowerCAmelCase    :						tf.Tensor							,							_lowerCAmelCase    :						bool = False							,							_lowerCAmelCase    :						bool = True		):
       SCREAMING_SNAKE_CASE_      		=     () if output_hidden_states else None

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

             SCREAMING_SNAKE_CASE_      		=     stage_module(_lowerCAmelCase		)

       if output_hidden_states:
             SCREAMING_SNAKE_CASE_      		=     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=_lowerCAmelCase							,							hidden_states=_lowerCAmelCase		)





@keras_serializable
class 			lowerCamelCase_				( tf.keras.layers.Layer   ):


 '''simple docstring'''







 lowercase_							       =       RegNetConfig



 def __init__(				self    :						int							,							_lowerCAmelCase    :						Optional[int]							,							**_lowerCAmelCase    :						Optional[int]		):
       super().__init__(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     config
       SCREAMING_SNAKE_CASE_      		=     TFRegNetEmbeddings(_lowerCAmelCase							,							name='embedder'		)
       SCREAMING_SNAKE_CASE_      		=     TFRegNetEncoder(_lowerCAmelCase							,							name='encoder'		)
       SCREAMING_SNAKE_CASE_      		=     tf.keras.layers.GlobalAveragePoolingaD(keepdims=_lowerCAmelCase							,							name='pooler'		)




 @unpack_inputs
 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						tf.Tensor							,							_lowerCAmelCase    :						Optional[bool] = None							,							_lowerCAmelCase    :						Optional[bool] = None							,							_lowerCAmelCase    :						bool = False							,							):
       SCREAMING_SNAKE_CASE_      		=     (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       SCREAMING_SNAKE_CASE_      		=     return_dict if return_dict is not None else self.config.use_return_dict

       SCREAMING_SNAKE_CASE_      		=     self.embedder(_lowerCAmelCase							,							training=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     self.encoder(
           _lowerCAmelCase							,							output_hidden_states=_lowerCAmelCase							,							return_dict=_lowerCAmelCase							,							training=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     encoder_outputs[0]
       SCREAMING_SNAKE_CASE_      		=     self.pooler(_lowerCAmelCase		)

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

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





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       RegNetConfig
 lowercase_							       =       "regnet"
 lowercase_							       =       "pixel_values"



 @property
 def  lowerCAmelCase_    (				self    :						List[str]		):
       return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)							,							dtype=tf.floataa		)}


lowerCamelCase__					:  int      							=				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__					:  Any      							=				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."			,  _SCREAMING_SNAKE_CASE			,  )
class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def __init__(				self    :						Dict							,							_lowerCAmelCase    :						RegNetConfig							,							*_lowerCAmelCase    :						Optional[int]							,							**_lowerCAmelCase    :						Tuple		):
       super().__init__(_lowerCAmelCase							,							*_lowerCAmelCase							,							**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     TFRegNetMainLayer(_lowerCAmelCase							,							name='regnet'		)




 @unpack_inputs
 @add_start_docstrings_to_model_forward(_lowerCAmelCase		)
 @add_code_sample_docstrings(
     checkpoint=_CHECKPOINT_FOR_DOC							,							output_type=_lowerCAmelCase							,							config_class=_CONFIG_FOR_DOC							,							modality='vision'							,							expected_output=_EXPECTED_OUTPUT_SHAPE							,							)
 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						tf.Tensor							,							_lowerCAmelCase    :						Optional[bool] = None							,							_lowerCAmelCase    :						Optional[bool] = None							,							_lowerCAmelCase    :						str=False							,							):
       SCREAMING_SNAKE_CASE_      		=     (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       SCREAMING_SNAKE_CASE_      		=     return_dict if return_dict is not None else self.config.use_return_dict

       SCREAMING_SNAKE_CASE_      		=     self.regnet(
           pixel_values=_lowerCAmelCase							,							output_hidden_states=_lowerCAmelCase							,							return_dict=_lowerCAmelCase							,							training=_lowerCAmelCase							,							)
       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 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						RegNetConfig							,							*_lowerCAmelCase    :						List[Any]							,							**_lowerCAmelCase    :						Optional[int]		):
       super().__init__(_lowerCAmelCase							,							*_lowerCAmelCase							,							**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     config.num_labels
       SCREAMING_SNAKE_CASE_      		=     TFRegNetMainLayer(_lowerCAmelCase							,							name='regnet'		)
       # classification head
       SCREAMING_SNAKE_CASE_      		=     [
           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(_lowerCAmelCase		)
 @add_code_sample_docstrings(
     checkpoint=_IMAGE_CLASS_CHECKPOINT							,							output_type=_lowerCAmelCase							,							config_class=_CONFIG_FOR_DOC							,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT							,							)
 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						tf.Tensor = None							,							_lowerCAmelCase    :						tf.Tensor = None							,							_lowerCAmelCase    :						bool = None							,							_lowerCAmelCase    :						bool = None							,							_lowerCAmelCase    :						Dict=False							,							):
       SCREAMING_SNAKE_CASE_      		=     (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       SCREAMING_SNAKE_CASE_      		=     return_dict if return_dict is not None else self.config.use_return_dict

       SCREAMING_SNAKE_CASE_      		=     self.regnet(
           _lowerCAmelCase							,							output_hidden_states=_lowerCAmelCase							,							return_dict=_lowerCAmelCase							,							training=_lowerCAmelCase		)

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

       SCREAMING_SNAKE_CASE_      		=     self.classifier[0](_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.classifier[1](_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     None if labels is None else self.hf_compute_loss(labels=_lowerCAmelCase							,							logits=_lowerCAmelCase		)

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

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





from __future__ import annotations

from typing import TypedDict





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       42
 lowercase_							       =       42


def    UpperCAmelCase_				( __UpperCAmelCase		:      str				)   ->					list[str]:
      if not isinstance(__UpperCAmelCase     ,     __UpperCAmelCase				):
            raise TypeError('The parameter s type must be str.'				)

      return [s[i:] + s[:i] for i in range(len(__UpperCAmelCase				)				)]

def    UpperCAmelCase_				( __UpperCAmelCase		:      str				)   ->					BWTTransformDict:
      if not isinstance(__UpperCAmelCase     ,     __UpperCAmelCase				):
            raise TypeError('The parameter s type must be str.'				)
      if not s:
            raise ValueError('The parameter s must not be empty.'				)

      SCREAMING_SNAKE_CASE_      		=     all_rotations(__UpperCAmelCase				)
      rotations.sort()  # sort the list of rotations in alphabetically order
      # make a string composed of the last char of each rotation
      SCREAMING_SNAKE_CASE_      		=     {
          "bwt_string": "".join([word[-1] for word in rotations]				),
          "idx_original_string": rotations.index(__UpperCAmelCase				),
      }
      return response

def    UpperCAmelCase_				( __UpperCAmelCase		:      str     ,     __UpperCAmelCase		:      int				)   ->					str:
      if not isinstance(__UpperCAmelCase     ,     __UpperCAmelCase				):
            raise TypeError('The parameter bwt_string type must be str.'				)
      if not bwt_string:
            raise ValueError('The parameter bwt_string must not be empty.'				)
      try:
            SCREAMING_SNAKE_CASE_      		=     int(__UpperCAmelCase				)
      except ValueError:
            raise TypeError(
                'The parameter idx_original_string type must be int or passive'
                ' of cast to int.'				)
      if idx_original_string < 0:
            raise ValueError('The parameter idx_original_string must not be lower than 0.'				)
      if idx_original_string >= len(__UpperCAmelCase				):
            raise ValueError(
                'The parameter idx_original_string must be lower than' ' len(bwt_string).'				)

      SCREAMING_SNAKE_CASE_      		=     [''] * len(__UpperCAmelCase				)
      for _ in range(len(__UpperCAmelCase				)				):
            for i in range(len(__UpperCAmelCase				)				):
                  SCREAMING_SNAKE_CASE_      		=     bwt_string[i] + ordered_rotations[i]
            ordered_rotations.sort()
      return ordered_rotations[idx_original_string]


if __name__ == "__main__":
   lowerCamelCase__					:  Optional[int]      							=				'Provide a string that I will generate its BWT transform: '
   lowerCamelCase__					:  List[str]      							=				input(entry_msg).strip()
   lowerCamelCase__					:  int      							=				bwt_transform(s)
   print(
       f'''Burrows Wheeler transform for string \'{s}\' results '''
       f'''in \'{result['bwt_string']}\''''
   )
   lowerCamelCase__					:  Dict      							=				reverse_bwt(result['bwt_string'], result['idx_original_string'])
   print(
       f'''Reversing Burrows Wheeler transform for entry \'{result['bwt_string']}\' '''
       f'''we get original string \'{original_string}\''''
   )