

'''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


_a					:			int							=    logging.get_logger(__name__)

# General docstring
_a					:			Any							=    'RegNetConfig'

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

# Image classification docstring
_a					:			Optional[int]							=    'facebook/regnet-y-040'
_a					:			Any							=    'tabby, tabby cat'

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



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

  '''simple docstring'''





  def __init__(  self ,			a_ ,			a_ = 3 ,			a_ = 1 ,			a_ = 1 ,			a_ = "relu" ,			**a_ ,			)				->					Optional[Any]:


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





  def     snake_case_      (  self ,			a_  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						self.convolution(self.padding(a_  )  )
    UpperCAmelCase    				=						self.normalization(a_  )
    UpperCAmelCase    				=						self.activation(a_  )
    return hidden_state




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

  '''simple docstring'''





  def __init__(  self ,			a_ ,			**a_  )				->					List[str]:


    """simple docstring"""
    super().__init__(**a_  )
    UpperCAmelCase    				=						config.num_channels
    UpperCAmelCase    				=						TFRegNetConvLayer(
        out_channels=config.embedding_size ,			kernel_size=3 ,			stride=2 ,			activation=config.hidden_act ,			name='embedder' ,			)





  def     snake_case_      (  self ,			a_  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						shape_list(a_  )[1]
    if tf.executing_eagerly() and num_channels != self.num_channels:
      raise ValueError(
          'Make sure that the channel dimension of the pixel values match with the one set in the configuration.'  )

    # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
    # So change the input format from `NCHW` to `NHWC`.
    # shape = (batch_size, in_height, in_width, in_channels=num_channels)
    UpperCAmelCase    				=						tf.transpose(a_ ,			perm=(0, 2, 3, 1)  )
    UpperCAmelCase    				=						self.embedder(a_  )
    return hidden_state




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

  '''simple docstring'''





  def __init__(  self ,			a_ ,			a_ = 2 ,			**a_  )				->					int:


    """simple docstring"""
    super().__init__(**a_  )
    UpperCAmelCase    				=						tf.keras.layers.ConvaD(
        filters=a_ ,			kernel_size=1 ,			strides=a_ ,			use_bias=a_ ,			name='convolution'  )
    UpperCAmelCase    				=						tf.keras.layers.BatchNormalization(epsilon=1E-5 ,			momentum=0.9 ,			name='normalization'  )





  def     snake_case_      (  self ,			a_ ,			a_ = False  )				->					tf.Tensor:


    """simple docstring"""
    return self.normalization(self.convolution(a_  ) ,			training=a_  )




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

  '''simple docstring'''





  def __init__(  self ,			a_ ,			a_ ,			**a_  )				->					Optional[Any]:


    """simple docstring"""
    super().__init__(**a_  )
    UpperCAmelCase    				=						tf.keras.layers.GlobalAveragePoolingaD(keepdims=a_ ,			name='pooler'  )
    UpperCAmelCase    				=						[
        tf.keras.layers.ConvaD(filters=a_ ,			kernel_size=1 ,			activation='relu' ,			name='attention.0'  ),
        tf.keras.layers.ConvaD(filters=a_ ,			kernel_size=1 ,			activation='sigmoid' ,			name='attention.2'  ),
    ]





  def     snake_case_      (  self ,			a_  )				->					List[str]:


    """simple docstring"""
    # [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
    UpperCAmelCase    				=						self.pooler(a_  )
    for layer_module in self.attention:
      UpperCAmelCase    				=						layer_module(a_  )
    UpperCAmelCase    				=						hidden_state * pooled
    return hidden_state




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

  '''simple docstring'''





  def __init__(  self ,			a_ ,			a_ ,			a_ ,			a_ = 1 ,			**a_  )				->					Dict:


    """simple docstring"""
    super().__init__(**a_  )
    UpperCAmelCase    				=						in_channels != out_channels or stride != 1
    UpperCAmelCase    				=						max(1 ,			out_channels // config.groups_width  )
    UpperCAmelCase    				=						(
        TFRegNetShortCut(a_ ,			stride=a_ ,			name='shortcut'  )
        if should_apply_shortcut
        else tf.keras.layers.Activation('linear' ,			name='shortcut'  )
    )
    # `self.layers` instead of `self.layer` because that is a reserved argument.
    UpperCAmelCase    				=						[
        TFRegNetConvLayer(a_ ,			kernel_size=1 ,			activation=config.hidden_act ,			name='layer.0'  ),
        TFRegNetConvLayer(
            a_ ,			stride=a_ ,			groups=a_ ,			activation=config.hidden_act ,			name='layer.1'  ),
        TFRegNetConvLayer(a_ ,			kernel_size=1 ,			activation=a_ ,			name='layer.2'  ),
    ]
    UpperCAmelCase    				=						ACTaFN[config.hidden_act]





  def     snake_case_      (  self ,			a_  )				->					Optional[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						hidden_state
    for layer_module in self.layers:
      UpperCAmelCase    				=						layer_module(a_  )
    UpperCAmelCase    				=						self.shortcut(a_  )
    hidden_state += residual
    UpperCAmelCase    				=						self.activation(a_  )
    return hidden_state




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

  '''simple docstring'''





  def __init__(  self ,			a_ ,			a_ ,			a_ ,			a_ = 1 ,			**a_  )				->					Optional[Any]:


    """simple docstring"""
    super().__init__(**a_  )
    UpperCAmelCase    				=						in_channels != out_channels or stride != 1
    UpperCAmelCase    				=						max(1 ,			out_channels // config.groups_width  )
    UpperCAmelCase    				=						(
        TFRegNetShortCut(a_ ,			stride=a_ ,			name='shortcut'  )
        if should_apply_shortcut
        else tf.keras.layers.Activation('linear' ,			name='shortcut'  )
    )
    UpperCAmelCase    				=						[
        TFRegNetConvLayer(a_ ,			kernel_size=1 ,			activation=config.hidden_act ,			name='layer.0'  ),
        TFRegNetConvLayer(
            a_ ,			stride=a_ ,			groups=a_ ,			activation=config.hidden_act ,			name='layer.1'  ),
        TFRegNetSELayer(a_ ,			reduced_channels=int(round(in_channels / 4  )  ) ,			name='layer.2'  ),
        TFRegNetConvLayer(a_ ,			kernel_size=1 ,			activation=a_ ,			name='layer.3'  ),
    ]
    UpperCAmelCase    				=						ACTaFN[config.hidden_act]





  def     snake_case_      (  self ,			a_  )				->					Tuple:


    """simple docstring"""
    UpperCAmelCase    				=						hidden_state
    for layer_module in self.layers:
      UpperCAmelCase    				=						layer_module(a_  )
    UpperCAmelCase    				=						self.shortcut(a_  )
    hidden_state += residual
    UpperCAmelCase    				=						self.activation(a_  )
    return hidden_state




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

  '''simple docstring'''





  def __init__(  self ,			a_ ,			a_ ,			a_ ,			a_ = 2 ,			a_ = 2 ,			**a_  )				->					int:


    """simple docstring"""
    super().__init__(**a_  )

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





  def     snake_case_      (  self ,			a_  )				->					Tuple:


    """simple docstring"""
    for layer_module in self.layers:
      UpperCAmelCase    				=						layer_module(a_  )
    return hidden_state




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

  '''simple docstring'''





  def __init__(  self ,			a_ ,			**a_  )				->					str:


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





  def     snake_case_      (  self ,			a_ ,			a_ = False ,			a_ = True  )				->					TFBaseModelOutputWithNoAttention:


    """simple docstring"""
    UpperCAmelCase    				=						() if output_hidden_states else None

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

      UpperCAmelCase    				=						stage_module(a_  )

    if output_hidden_states:
      UpperCAmelCase    				=						hidden_states + (hidden_state,)

    if not return_dict:
      return tuple(v for v in [hidden_state, hidden_states] if v is not None  )

    return TFBaseModelOutputWithNoAttention(last_hidden_state=a_ ,			hidden_states=a_  )




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

  '''simple docstring'''





  __lowerCAmelCase       :			str     	= RegNetConfig
  def __init__(  self ,			a_ ,			**a_  )				->					Tuple:


    """simple docstring"""
    super().__init__(**a_  )
    UpperCAmelCase    				=						config
    UpperCAmelCase    				=						TFRegNetEmbeddings(a_ ,			name='embedder'  )
    UpperCAmelCase    				=						TFRegNetEncoder(a_ ,			name='encoder'  )
    UpperCAmelCase    				=						tf.keras.layers.GlobalAveragePoolingaD(keepdims=a_ ,			name='pooler'  )





  @unpack_inputs
  def     snake_case_      (  self ,			a_ ,			a_ = None ,			a_ = None ,			a_ = False ,			)				->					TFBaseModelOutputWithPoolingAndNoAttention:


    """simple docstring"""
    UpperCAmelCase    				=						(
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    UpperCAmelCase    				=						return_dict if return_dict is not None else self.config.use_return_dict

    UpperCAmelCase    				=						self.embedder(a_ ,			training=a_  )

    UpperCAmelCase    				=						self.encoder(
        a_ ,			output_hidden_states=a_ ,			return_dict=a_ ,			training=a_  )

    UpperCAmelCase    				=						encoder_outputs[0]
    UpperCAmelCase    				=						self.pooler(a_  )

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

    # Change the other hidden state outputs to NCHW as well
    if output_hidden_states:
      UpperCAmelCase    				=						tuple([tf.transpose(a_ ,			perm=(0, 3, 1, 2)  ) for h in encoder_outputs[1]]  )

    if not return_dict:
      return (last_hidden_state, pooled_output) + encoder_outputs[1:]

    return TFBaseModelOutputWithPoolingAndNoAttention(
        last_hidden_state=a_ ,			pooler_output=a_ ,			hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states ,			)




class 							lowercase_						(		a			):

  '''simple docstring'''





  __lowerCAmelCase       :			Optional[int]     	= RegNetConfig
  __lowerCAmelCase       :			str     	= "regnet"
  __lowerCAmelCase       :			Dict     	= "pixel_values"
  @property
  def     snake_case_      (  self  )				->					Any:


    """simple docstring"""
    return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4) ,			dtype=tf.floataa  )}




_a					:			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'

_a					:			int							=    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."			,							a			,							)
class 							lowercase_						(		a			):

  '''simple docstring'''





  def __init__(  self ,			a_ ,			*a_ ,			**a_  )				->					str:


    """simple docstring"""
    super().__init__(a_ ,			*a_ ,			**a_  )
    UpperCAmelCase    				=						TFRegNetMainLayer(a_ ,			name='regnet'  )





  @unpack_inputs
  @add_start_docstrings_to_model_forward(a_  )
  @add_code_sample_docstrings(
      checkpoint=_CHECKPOINT_FOR_DOC ,			output_type=a_ ,			config_class=_CONFIG_FOR_DOC ,			modality='vision' ,			expected_output=_EXPECTED_OUTPUT_SHAPE ,			)
  def     snake_case_      (  self ,			a_ ,			a_ = None ,			a_ = None ,			a_=False ,			)				->					Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:


    """simple docstring"""
    UpperCAmelCase    				=						(
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    UpperCAmelCase    				=						return_dict if return_dict is not None else self.config.use_return_dict

    UpperCAmelCase    				=						self.regnet(
        pixel_values=a_ ,			output_hidden_states=a_ ,			return_dict=a_ ,			training=a_ ,			)
    if not return_dict:
      return (outputs[0],) + outputs[1:]

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




@add_start_docstrings(
    "\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    "			,							a			,							)
class 							lowercase_						(		a			,							a			):

  '''simple docstring'''





  def __init__(  self ,			a_ ,			*a_ ,			**a_  )				->					Optional[Any]:


    """simple docstring"""
    super().__init__(a_ ,			*a_ ,			**a_  )
    UpperCAmelCase    				=						config.num_labels
    UpperCAmelCase    				=						TFRegNetMainLayer(a_ ,			name='regnet'  )
    # classification head
    UpperCAmelCase    				=						[
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(config.num_labels ,			name='classifier.1'  ) if config.num_labels > 0 else tf.identity,
    ]





  @unpack_inputs
  @add_start_docstrings_to_model_forward(a_  )
  @add_code_sample_docstrings(
      checkpoint=_IMAGE_CLASS_CHECKPOINT ,			output_type=a_ ,			config_class=_CONFIG_FOR_DOC ,			expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,			)
  def     snake_case_      (  self ,			a_ = None ,			a_ = None ,			a_ = None ,			a_ = None ,			a_=False ,			)				->					Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:


    """simple docstring"""
    UpperCAmelCase    				=						(
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    UpperCAmelCase    				=						return_dict if return_dict is not None else self.config.use_return_dict

    UpperCAmelCase    				=						self.regnet(
        a_ ,			output_hidden_states=a_ ,			return_dict=a_ ,			training=a_  )

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

    UpperCAmelCase    				=						self.classifier[0](a_  )
    UpperCAmelCase    				=						self.classifier[1](a_  )

    UpperCAmelCase    				=						None if labels is None else self.hf_compute_loss(labels=a_ ,			logits=a_  )

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

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





'''simple docstring'''




import qiskit


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int							, SCREAMING_SNAKE_CASE   : int    ):
  UpperCAmelCase    				=						qiskit.Aer.get_backend('aer_simulator'    )

  # Create a Quantum Circuit acting on the q register
  UpperCAmelCase    				=						qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

  # Map the quantum measurement to the classical bits
  circuit.measure([0]							, [0]    )

  # Execute the circuit on the simulator
  UpperCAmelCase    				=						qiskit.execute(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, shots=1000    )

  # Return the histogram data of the results of the experiment.
  return job.result().get_counts(SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
  print(F'''Total count for various states are: {single_qubit_measure(1, 1)}''')


