



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						    =  logging.get_logger(__name__)

# General docstring
_lowerCamelCase						    =  """RegNetConfig"""

# Base docstring
_lowerCamelCase						    =  """facebook/regnet-y-040"""
_lowerCamelCase						    =  [1, 1088, 7, 7]

# Image classification docstring
_lowerCamelCase						    =  """facebook/regnet-y-040"""
_lowerCamelCase						    =  """tabby, tabby cat"""

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

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





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






     def 	__snake_case    (       self :							Optional[Any]     ,  UpperCamelCase :							Optional[Any]				)->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    int       =	self.convolution(self.padding(UpperCamelCase				)				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.normalization(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.activation(UpperCamelCase				)
      return hidden_state






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





     def __init__(       self :							Optional[int]     ,  UpperCamelCase :							RegNetConfig     ,  **UpperCamelCase :							int				)->List[Any]:
      super().__init__(**UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	config.num_channels
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	TFRegNetConvLayer(
          out_channels=config.embedding_size     ,  kernel_size=3     ,  stride=2     ,  activation=config.hidden_act     ,  name="embedder"     ,  )






     def 	__snake_case    (       self :							Tuple     ,  UpperCamelCase :							Optional[Any]				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	shape_list(UpperCamelCase				)[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       :    Dict       =	tf.transpose(UpperCamelCase     ,  perm=(0, 2, 3, 1)				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.embedder(UpperCamelCase				)
      return hidden_state






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





     def __init__(       self :							Optional[Any]     ,  UpperCamelCase :							int     ,  UpperCamelCase :							int = 2     ,  **UpperCamelCase :							Optional[int]				)->Union[str, Any]:
      super().__init__(**UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	tf.keras.layers.ConvaD(
          filters=UpperCamelCase     ,  kernel_size=1     ,  strides=UpperCamelCase     ,  use_bias=UpperCamelCase     ,  name="convolution"				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	tf.keras.layers.BatchNormalization(epsilon=1E-5     ,  momentum=0.9     ,  name="normalization"				)






     def 	__snake_case    (       self :							Optional[int]     ,  UpperCamelCase :							tf.Tensor     ,  UpperCamelCase :							bool = False				)->tf.Tensor:
      return self.normalization(self.convolution(UpperCamelCase				)     ,  training=UpperCamelCase				)






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





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






     def 	__snake_case    (       self :							List[str]     ,  UpperCamelCase :							Tuple				)->Any:
      # [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.pooler(UpperCamelCase				)
      for layer_module in self.attention:
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	layer_module(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Any       =	hidden_state * pooled
      return hidden_state






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





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






     def 	__snake_case    (       self :							str     ,  UpperCamelCase :							str				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Dict       =	hidden_state
      for layer_module in self.layers:
       __SCREAMING_SNAKE_CASE       :    Tuple       =	layer_module(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	self.shortcut(UpperCamelCase				)
      hidden_state += residual
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.activation(UpperCamelCase				)
      return hidden_state






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





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






     def 	__snake_case    (       self :							int     ,  UpperCamelCase :							str				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    str       =	hidden_state
      for layer_module in self.layers:
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	layer_module(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.shortcut(UpperCamelCase				)
      hidden_state += residual
      __SCREAMING_SNAKE_CASE       :    int       =	self.activation(UpperCamelCase				)
      return hidden_state






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





     def __init__(       self :							str     ,  UpperCamelCase :							RegNetConfig     ,  UpperCamelCase :							int     ,  UpperCamelCase :							int     ,  UpperCamelCase :							int = 2     ,  UpperCamelCase :							int = 2     ,  **UpperCamelCase :							List[Any]				)->List[str]:
      super().__init__(**UpperCamelCase				)

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






     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							Any				)->List[str]:
      for layer_module in self.layers:
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	layer_module(UpperCamelCase				)
      return hidden_state






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





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






     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							tf.Tensor     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							bool = True				)->TFBaseModelOutputWithNoAttention:
      __SCREAMING_SNAKE_CASE       :    int       =	() if output_hidden_states else None

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

       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	stage_module(UpperCamelCase				)

      if output_hidden_states:
       __SCREAMING_SNAKE_CASE       :    str       =	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=UpperCamelCase     ,  hidden_states=UpperCamelCase				)






@keras_serializable
class       _SCREAMING_SNAKE_CASE	(tf.keras.layers.Layer ):
     lowerCAmelCase					=			RegNetConfig





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






     @unpack_inputs
     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							tf.Tensor     ,  UpperCamelCase :							Optional[bool] = None     ,  UpperCamelCase :							Optional[bool] = None     ,  UpperCamelCase :							bool = False     ,  )->TFBaseModelOutputWithPoolingAndNoAttention:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	(
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      __SCREAMING_SNAKE_CASE       :    int       =	return_dict if return_dict is not None else self.config.use_return_dict

      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.embedder(UpperCamelCase     ,  training=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    str       =	self.encoder(
          UpperCamelCase     ,  output_hidden_states=UpperCamelCase     ,  return_dict=UpperCamelCase     ,  training=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	encoder_outputs[0]
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.pooler(UpperCamelCase				)

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

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






class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			RegNetConfig
     lowerCAmelCase					=			"""regnet"""
     lowerCAmelCase					=			"""pixel_values"""





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






_lowerCamelCase						    =  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						    =  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."""			,    UpperCamelCase			,    )
class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							Dict     ,  UpperCamelCase :							RegNetConfig     ,  *UpperCamelCase :							Optional[Any]     ,  **UpperCamelCase :							Optional[Any]				)->Optional[Any]:
      super().__init__(UpperCamelCase     ,  *UpperCamelCase     ,  **UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	TFRegNetMainLayer(UpperCamelCase     ,  name="regnet"				)






     @unpack_inputs
     @add_start_docstrings_to_model_forward(UpperCamelCase				)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC     ,  output_type=UpperCamelCase     ,  config_class=_CONFIG_FOR_DOC     ,  modality="vision"     ,  expected_output=_EXPECTED_OUTPUT_SHAPE     ,  )
     def 	__snake_case    (       self :							int     ,  UpperCamelCase :							tf.Tensor     ,  UpperCamelCase :							Optional[bool] = None     ,  UpperCamelCase :							Optional[bool] = None     ,  UpperCamelCase :							int=False     ,  )->Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
      __SCREAMING_SNAKE_CASE       :    Dict       =	(
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	return_dict if return_dict is not None else self.config.use_return_dict

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

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






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





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

      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.regnet(
          UpperCamelCase     ,  output_hidden_states=UpperCamelCase     ,  return_dict=UpperCamelCase     ,  training=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    str       =	outputs.pooler_output if return_dict else outputs[1]

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.classifier[0](UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.classifier[1](UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Any       =	None if labels is None else self.hf_compute_loss(labels=UpperCamelCase     ,  logits=UpperCamelCase				)

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

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











from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Tuple       =	analyze_text(__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	list(" " + ascii_lowercase     )
 # what is our total sum of probabilities.
 __SCREAMING_SNAKE_CASE       :    int       =	sum(single_char_strings.values()     )

 # one length string
 __SCREAMING_SNAKE_CASE       :    str       =	0
 # for each alpha we go in our dict and if it is in it we calculate entropy
 for ch in my_alphas:
  if ch in single_char_strings:
   __SCREAMING_SNAKE_CASE       :    List[Any]       =	single_char_strings[ch]
   __SCREAMING_SNAKE_CASE       :    str       =	my_str / all_sum
   my_fir_sum += prob * math.loga(__lowerCamelCase     )  # entropy formula.

    # print entropy
 print(F"""{round(-1 * my_fir_sum     ):.1f}"""     )

 # two len string
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	sum(two_char_strings.values()     )
 __SCREAMING_SNAKE_CASE       :    int       =	0
 # for each alpha (two in size) calculate entropy.
 for cha in my_alphas:
  for cha in my_alphas:
   __SCREAMING_SNAKE_CASE       :    int       =	cha + cha
   if sequence in two_char_strings:
    __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	two_char_strings[sequence]
    __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	int(__lowerCamelCase     ) / all_sum
    my_sec_sum += prob * math.loga(__lowerCamelCase     )

    # print second entropy
 print(F"""{round(-1 * my_sec_sum     ):.1f}"""     )

 # print the difference between them
 print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)     ):.1f}"""     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Any       =	Counter()  # type: ignore
 __SCREAMING_SNAKE_CASE       :    int       =	Counter()  # type: ignore
 single_char_strings[text[-1]] += 1

 # first case when we have space at start.
 two_char_strings[" " + text[0]] += 1
 for i in range(0		,	len(__lowerCamelCase     ) - 1     ):
  single_char_strings[text[i]] += 1
  two_char_strings[text[i : i + 2]] += 1
 return single_char_strings, two_char_strings






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 import doctest

 doctest.testmod()
 # text = (
 #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
 #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
 #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
 #     "beloved evident through no service elderly is. Blind there if every no so "
 #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
 #     "of message cottage windows do besides against uncivil.  Delightful "
 #     "unreserved impossible few estimating men favourable see entreaties. She "
 #     "propriety immediate was improving. He or entrance humoured likewise "
 #     "moderate. Much nor game son say feel. Fat make met can must form into "
 #     "gate. Me we offending prevailed discovery. "
 # )

 # calculate_prob(text)


if __name__ == "__main__":
      main()






