from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


_A :   Union[str, Any]  			=  logging.get_logger(__name__)


# General docstring
_A :   Dict  			=  """MobileNetV1Config"""

# Base docstring
_A :   int  			=  """google/mobilenet_v1_1.0_224"""
_A :   str  			=  [1, 10_24, 7, 7]

# Image classification docstring
_A :   List[Any]  			=  """google/mobilenet_v1_1.0_224"""
_A :   str  			=  """tabby, tabby cat"""


_A :   int  			=  [
    """google/mobilenet_v1_1.0_224""",
    """google/mobilenet_v1_0.75_192""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=None					)    ->    str:







   """simple docstring"""
   lowerCamelCase__       :						str        =							{}

   if isinstance(_UpperCAmelCase      ,  _UpperCAmelCase					):
      lowerCamelCase__       :						Dict        =							model.mobilenet_va
   else:
      lowerCamelCase__       :						Any        =							model

   lowerCamelCase__       :						Any        =							'MobilenetV1/Conv2d_0/'
   lowerCamelCase__       :						List[Any]        =							backbone.conv_stem.convolution.weight
   lowerCamelCase__       :						int        =							backbone.conv_stem.normalization.bias
   lowerCamelCase__       :						List[Any]        =							backbone.conv_stem.normalization.weight
   lowerCamelCase__       :						Tuple        =							backbone.conv_stem.normalization.running_mean
   lowerCamelCase__       :						Tuple        =							backbone.conv_stem.normalization.running_var

   for i in range(13					):
      lowerCamelCase__       :						List[str]        =							i + 1
      lowerCamelCase__       :						int        =							i * 2

      lowerCamelCase__       :						Optional[int]        =							backbone.layer[pt_index]
      lowerCamelCase__       :						List[Any]        =							f"MobilenetV1/Conv2d_{tf_index}_depthwise/"
      lowerCamelCase__       :						Optional[int]        =							pointer.convolution.weight
      lowerCamelCase__       :						Any        =							pointer.normalization.bias
      lowerCamelCase__       :						Union[str, Any]        =							pointer.normalization.weight
      lowerCamelCase__       :						Dict        =							pointer.normalization.running_mean
      lowerCamelCase__       :						Optional[Any]        =							pointer.normalization.running_var

      lowerCamelCase__       :						Union[str, Any]        =							backbone.layer[pt_index + 1]
      lowerCamelCase__       :						Tuple        =							f"MobilenetV1/Conv2d_{tf_index}_pointwise/"
      lowerCamelCase__       :						Any        =							pointer.convolution.weight
      lowerCamelCase__       :						List[Any]        =							pointer.normalization.bias
      lowerCamelCase__       :						int        =							pointer.normalization.weight
      lowerCamelCase__       :						str        =							pointer.normalization.running_mean
      lowerCamelCase__       :						Optional[int]        =							pointer.normalization.running_var

   if isinstance(_UpperCAmelCase      ,  _UpperCAmelCase					):
      lowerCamelCase__       :						List[str]        =							'MobilenetV1/Logits/Conv2d_1c_1x1/'
      lowerCamelCase__       :						Any        =							model.classifier.weight
      lowerCamelCase__       :						int        =							model.classifier.bias

   return tf_to_pt_map



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Union[str, Any]:







   """simple docstring"""
   try:
      import numpy as np
      import tensorflow as tf
   except ImportError:
      logger.error(
          '''Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '''
          '''https://www.tensorflow.org/install/ for installation instructions.'''					)
      raise

   # Load weights from TF model
   lowerCamelCase__       :						List[str]        =							tf.train.list_variables(_UpperCAmelCase					)
   lowerCamelCase__       :						Tuple        =							{}
   for name, shape in init_vars:
      logger.info(f"Loading TF weight {name} with shape {shape}"					)
      lowerCamelCase__       :						Optional[int]        =							tf.train.load_variable(_UpperCAmelCase      ,  _UpperCAmelCase					)
      lowerCamelCase__       :						Optional[Any]        =							array

   # Build TF to PyTorch weights loading map
   lowerCamelCase__       :						int        =							_build_tf_to_pytorch_map(_UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase					)

   for name, pointer in tf_to_pt_map.items():
      logger.info(f"Importing {name}"					)
      if name not in tf_weights:
         logger.info(f"{name} not in tf pre-trained weights, skipping"					)
         continue

      lowerCamelCase__       :						Optional[Any]        =							tf_weights[name]

      if "depthwise_weights" in name:
         logger.info('''Transposing depthwise'''					)
         lowerCamelCase__       :						Any        =							np.transpose(_UpperCAmelCase      ,  (2, 3, 0, 1)					)
      elif "weights" in name:
         logger.info('''Transposing'''					)
         if len(pointer.shape					) == 2:  # copying into linear layer
            lowerCamelCase__       :						Optional[int]        =							array.squeeze().transpose()
         else:
            lowerCamelCase__       :						Tuple        =							np.transpose(_UpperCAmelCase      ,  (3, 2, 0, 1)					)

      if pointer.shape != array.shape:
         raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"					)

      logger.info(f"Initialize PyTorch weight {name} {array.shape}"					)
      lowerCamelCase__       :						List[str]        =							torch.from_numpy(_UpperCAmelCase					)

      tf_weights.pop(_UpperCAmelCase      ,  _UpperCAmelCase					)
      tf_weights.pop(name + '''/RMSProp'''      ,  _UpperCAmelCase					)
      tf_weights.pop(name + '''/RMSProp_1'''      ,  _UpperCAmelCase					)
      tf_weights.pop(name + '''/ExponentialMovingAverage'''      ,  _UpperCAmelCase					)

   logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys()					)}"					)
   return model



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    torch.Tensor:







   """simple docstring"""
   lowerCamelCase__       :						Dict        =							features.shape[-2:]
   lowerCamelCase__       :						str        =							conv_layer.stride
   lowerCamelCase__       :						Any        =							conv_layer.kernel_size

   if in_height % stride_height == 0:
      lowerCamelCase__       :						str        =							max(kernel_height - stride_height      ,  0					)
   else:
      lowerCamelCase__       :						Dict        =							max(kernel_height - (in_height % stride_height)      ,  0					)

   if in_width % stride_width == 0:
      lowerCamelCase__       :						int        =							max(kernel_width - stride_width      ,  0					)
   else:
      lowerCamelCase__       :						Tuple        =							max(kernel_width - (in_width % stride_width)      ,  0					)

   lowerCamelCase__       :						Any        =							pad_along_width // 2
   lowerCamelCase__       :						Optional[Any]        =							pad_along_width - pad_left
   lowerCamelCase__       :						Any        =							pad_along_height // 2
   lowerCamelCase__       :						Optional[int]        =							pad_along_height - pad_top

   lowerCamelCase__       :						str        =							(pad_left, pad_right, pad_top, pad_bottom)
   return nn.functional.pad(_UpperCAmelCase      ,  _UpperCAmelCase      ,  '''constant'''      ,  0.0					)


class        __SCREAMING_SNAKE_CASE      (			nn.Module ):







       def __init__(				self					:     Dict		, A					:     MobileNetVaConfig		, A					:     int		, A					:     int		, A					:     int		, A					:     Optional[int] = 1		, A					:     Optional[int] = 1		, A					:     bool = False		, A					:     Optional[bool] = True		, A					:     Optional[bool or str] = True		, )       ->None:
          super().__init__()
          lowerCamelCase__       :						Dict        =							config

          if in_channels % groups != 0:
             raise ValueError(F"Input channels ({in_channels}) are not divisible by {groups} groups."      )
          if out_channels % groups != 0:
             raise ValueError(F"Output channels ({out_channels}) are not divisible by {groups} groups."      )

          lowerCamelCase__       :						List[str]        =							0 if config.tf_padding else int((kernel_size - 1) / 2      )

          lowerCamelCase__       :						Any        =							nn.Convad(
              in_channels=A		, out_channels=A		, kernel_size=A		, stride=A		, padding=A		, groups=A		, bias=A		, padding_mode='''zeros'''		, )

          if use_normalization:
             lowerCamelCase__       :						Optional[int]        =							nn.BatchNormad(
                 num_features=A		, eps=config.layer_norm_eps		, momentum=0.99_97		, affine=A		, track_running_stats=A		, )
          else:
             lowerCamelCase__       :						Tuple        =							None

          if use_activation:
             if isinstance(A		, A      ):
                lowerCamelCase__       :						Tuple        =							ACTaFN[use_activation]
             elif isinstance(config.hidden_act		, A      ):
                lowerCamelCase__       :						Any        =							ACTaFN[config.hidden_act]
             else:
                lowerCamelCase__       :						Dict        =							config.hidden_act
          else:
             lowerCamelCase__       :						Optional[int]        =							None







       def 						__lowerCamelCase   (				self					:     Union[str, Any]		, A					:     torch.Tensor      )       ->torch.Tensor:
          if self.config.tf_padding:
             lowerCamelCase__       :						Dict        =							apply_tf_padding(A		, self.convolution      )
          lowerCamelCase__       :						Union[str, Any]        =							self.convolution(A      )
          if self.normalization is not None:
             lowerCamelCase__       :						Union[str, Any]        =							self.normalization(A      )
          if self.activation is not None:
             lowerCamelCase__       :						List[str]        =							self.activation(A      )
          return features


class        __SCREAMING_SNAKE_CASE      (			__UpperCamelCase ):
       _UpperCAmelCase				:  str			   =   MobileNetVaConfig
       _UpperCAmelCase				:  List[str]			   =   load_tf_weights_in_mobilenet_va
       _UpperCAmelCase				:  str			   =   "mobilenet_v1"
       _UpperCAmelCase				:  Dict			   =   "pixel_values"
       _UpperCAmelCase				:  Tuple			   =   False







       def 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     Union[nn.Linear, nn.Convad]      )       ->None:
          if isinstance(A		, (nn.Linear, nn.Convad)      ):
             module.weight.data.normal_(mean=0.0		, std=self.config.initializer_range      )
             if module.bias is not None:
                module.bias.data.zero_()
          elif isinstance(A		, nn.BatchNormad      ):
             module.bias.data.zero_()
             module.weight.data.fill_(1.0      )


_A :   str  			=  R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`MobileNetV1Config`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

_A :   Union[str, Any]  			=  R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileNetV1ImageProcessor.__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 MobileNetV1 model outputting raw hidden-states without any specific head on top."					,__UpperCamelCase					,)
class        __SCREAMING_SNAKE_CASE      (			__UpperCamelCase ):







       def __init__(				self					:     Dict		, A					:     MobileNetVaConfig		, A					:     bool = True      )       ->Dict:
          super().__init__(A      )
          lowerCamelCase__       :						Optional[Any]        =							config

          lowerCamelCase__       :						Dict        =							3_2
          lowerCamelCase__       :						List[str]        =							max(int(depth * config.depth_multiplier      )		, config.min_depth      )

          lowerCamelCase__       :						str        =							MobileNetVaConvLayer(
              A		, in_channels=config.num_channels		, out_channels=A		, kernel_size=3		, stride=2		, )

          lowerCamelCase__       :						List[str]        =							[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

          lowerCamelCase__       :						Any        =							nn.ModuleList()
          for i in range(1_3      ):
             lowerCamelCase__       :						Tuple        =							out_channels

             if strides[i] == 2 or i == 0:
                depth *= 2
                lowerCamelCase__       :						List[Any]        =							max(int(depth * config.depth_multiplier      )		, config.min_depth      )

             self.layer.append(
                 MobileNetVaConvLayer(
                     A		, in_channels=A		, out_channels=A		, kernel_size=3		, stride=strides[i]		, groups=A		, )      )

             self.layer.append(
                 MobileNetVaConvLayer(
                     A		, in_channels=A		, out_channels=A		, kernel_size=1		, )      )

          lowerCamelCase__       :						Optional[int]        =							nn.AdaptiveAvgPoolad((1, 1)      ) if add_pooling_layer else None

          # Initialize weights and apply final processing
          self.post_init()







       def 						__lowerCamelCase   (				self					:     str		, A					:     Union[str, Any]      )       ->List[str]:
          raise NotImplementedError







       @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 						__lowerCamelCase   (				self					:     Dict		, A					:     Optional[torch.Tensor] = None		, A					:     Optional[bool] = None		, A					:     Optional[bool] = None		, )       ->Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
          lowerCamelCase__       :						Optional[Any]        =							(
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          lowerCamelCase__       :						int        =							return_dict if return_dict is not None else self.config.use_return_dict

          if pixel_values is None:
             raise ValueError('''You have to specify pixel_values'''      )

          lowerCamelCase__       :						Tuple        =							self.conv_stem(A      )

          lowerCamelCase__       :						Optional[Any]        =							() if output_hidden_states else None

          for i, layer_module in enumerate(self.layer      ):
             lowerCamelCase__       :						Any        =							layer_module(A      )

             if output_hidden_states:
                lowerCamelCase__       :						Union[str, Any]        =							all_hidden_states + (hidden_states,)

          lowerCamelCase__       :						List[Any]        =							hidden_states

          if self.pooler is not None:
             lowerCamelCase__       :						Tuple        =							torch.flatten(self.pooler(A      )		, start_dim=1      )
          else:
             lowerCamelCase__       :						Any        =							None

          if not return_dict:
             return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None      )

          return BaseModelOutputWithPoolingAndNoAttention(
              last_hidden_state=A		, pooler_output=A		, hidden_states=A		, )


@add_start_docstrings(
    "\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "					,__UpperCamelCase					,)
class        __SCREAMING_SNAKE_CASE      (			__UpperCamelCase ):







       def __init__(				self					:     Optional[int]		, A					:     MobileNetVaConfig      )       ->None:
          super().__init__(A      )

          lowerCamelCase__       :						List[str]        =							config.num_labels
          lowerCamelCase__       :						Dict        =							MobileNetVaModel(A      )

          lowerCamelCase__       :						Optional[int]        =							self.mobilenet_va.layer[-1].convolution.out_channels

          # Classifier head
          lowerCamelCase__       :						str        =							nn.Dropout(config.classifier_dropout_prob		, inplace=A      )
          lowerCamelCase__       :						str        =							nn.Linear(A		, config.num_labels      ) if config.num_labels > 0 else nn.Identity()

          # Initialize weights and apply final processing
          self.post_init()







       @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 						__lowerCamelCase   (				self					:     Dict		, A					:     Optional[torch.Tensor] = None		, A					:     Optional[bool] = None		, A					:     Optional[torch.Tensor] = None		, A					:     Optional[bool] = None		, )       ->Union[tuple, ImageClassifierOutputWithNoAttention]:
          lowerCamelCase__       :						List[Any]        =							return_dict if return_dict is not None else self.config.use_return_dict

          lowerCamelCase__       :						List[str]        =							self.mobilenet_va(A		, output_hidden_states=A		, return_dict=A      )

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

          lowerCamelCase__       :						List[Any]        =							self.classifier(self.dropout(A      )      )

          lowerCamelCase__       :						Tuple        =							None
          if labels is not None:
             if self.config.problem_type is None:
                if self.num_labels == 1:
                   lowerCamelCase__       :						Dict        =							'regression'
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                   lowerCamelCase__       :						Tuple        =							'single_label_classification'
                else:
                   lowerCamelCase__       :						Dict        =							'multi_label_classification'

             if self.config.problem_type == "regression":
                lowerCamelCase__       :						Union[str, Any]        =							MSELoss()
                if self.num_labels == 1:
                   lowerCamelCase__       :						int        =							loss_fct(logits.squeeze()		, labels.squeeze()      )
                else:
                   lowerCamelCase__       :						Dict        =							loss_fct(A		, A      )
             elif self.config.problem_type == "single_label_classification":
                lowerCamelCase__       :						Optional[int]        =							CrossEntropyLoss()
                lowerCamelCase__       :						List[str]        =							loss_fct(logits.view(-1		, self.num_labels      )		, labels.view(-1      )      )
             elif self.config.problem_type == "multi_label_classification":
                lowerCamelCase__       :						Union[str, Any]        =							BCEWithLogitsLoss()
                lowerCamelCase__       :						int        =							loss_fct(A		, A      )

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

          return ImageClassifierOutputWithNoAttention(
              loss=A		, logits=A		, hidden_states=outputs.hidden_states		, )










from __future__ import annotations

_A :   List[str]  			=  '#'


class        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     List[Any]      )       ->None:
          lowerCamelCase__       :						dict        =							{}







       def 						__lowerCamelCase   (				self					:     Union[str, Any]		, A					:     str      )       ->None:
          lowerCamelCase__       :						Any        =							self._trie
          for char in text:
             if char not in trie:
                lowerCamelCase__       :						Any        =							{}
             lowerCamelCase__       :						Any        =							trie[char]
          lowerCamelCase__       :						List[str]        =							True







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     str      )       ->tuple | list:
          lowerCamelCase__       :						Dict        =							self._trie
          for char in prefix:
             if char in trie:
                lowerCamelCase__       :						List[Any]        =							trie[char]
             else:
                return []
          return self._elements(A      )







       def 						__lowerCamelCase   (				self					:     Dict		, A					:     dict      )       ->tuple:
          lowerCamelCase__       :						Optional[Any]        =							[]
          for c, v in d.items():
             lowerCamelCase__       :						Any        =							[''' '''] if c == END else [(c + s) for s in self._elements(A      )]
             result.extend(A      )
          return tuple(A      )


_A :   str  			=  Trie()
_A :   List[Any]  			=  ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
       trie.insert_word(word)



def 	_a						(   UpperCAmelCase					)    ->    tuple:







   """simple docstring"""
   lowerCamelCase__       :						Optional[int]        =							trie.find_word(UpperCAmelCase					)
   return tuple(string + word for word in suffixes					)



def 	_a						(   )    ->    None:







   """simple docstring"""
   print(autocomplete_using_trie('''de'''					)					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()



