



'''simple docstring'''






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


lowerCAmelCase:      Any		 =		logging.get_logger(__name__)


# General docstring
lowerCAmelCase:      Any		 =		'MobileNetV1Config'

# Base docstring
lowerCAmelCase:      Union[str, Any]		 =		'google/mobilenet_v1_1.0_224'
lowerCAmelCase:      Dict		 =		[1, 1_0_2_4, 7, 7]

# Image classification docstring
lowerCAmelCase:      Dict		 =		'google/mobilenet_v1_1.0_224'
lowerCAmelCase:      List[Any]		 =		'tabby, tabby cat'


lowerCAmelCase:      Tuple		 =		[
    '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    lowerCamelCase__ (		_A				,    _A				,    _A=None       ):
  a		:      str       =  {}

  if isinstance(_A				,    _A       ):
    a		:      Optional[int]       =  model.mobilenet_va
  else:
    a		:      List[Any]       =  model

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

  for i in range(13       ):
    a		:      Any       =  i + 1
    a		:      Tuple       =  i * 2

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

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

  if isinstance(_A				,    _A       ):
    a		:      List[str]       =  'MobilenetV1/Logits/Conv2d_1c_1x1/'
    a		:      List[str]       =  model.classifier.weight
    a		:      Tuple       =  model.classifier.bias

  return tf_to_pt_map
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  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
  a		:      Dict       =  tf.train.list_variables(_A       )
  a		:      Any       =  {}
  for name, shape in init_vars:
    logger.info(f"""Loading TF weight {name} with shape {shape}"""       )
    a		:      int       =  tf.train.load_variable(_A				,    _A       )
    a		:      Dict       =  array

  # Build TF to PyTorch weights loading map
  a		:      Union[str, Any]       =  _build_tf_to_pytorch_map(_A				,    _A				,    _A       )

  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

    a		:      Dict       =  tf_weights[name]

    if "depthwise_weights" in name:
      logger.info('Transposing depthwise'       )
      a		:      Any       =  np.transpose(_A				,    (2, 3, 0, 1)       )
    elif "weights" in name:
      logger.info('Transposing'       )
      if len(pointer.shape       ) == 2:  # copying into linear layer
        a		:      Optional[Any]       =  array.squeeze().transpose()
      else:
        a		:      Union[str, Any]       =  np.transpose(_A				,    (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}"""       )
    a		:      Optional[Any]       =  torch.from_numpy(_A       )

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

  logger.info(f"""Weights not copied to PyTorch model: {", ".join(tf_weights.keys()       )}"""       )
  return model
def    lowerCamelCase__ (		_A				,    _A       ):
  a     ,			a		:      Optional[Any]       =  features.shape[-2:]
  a     ,			a		:      Any       =  conv_layer.stride
  a     ,			a		:      List[str]       =  conv_layer.kernel_size

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

  if in_width % stride_width == 0:
    a		:      int       =  max(kernel_width - stride_width				,    0       )
  else:
    a		:      str       =  max(kernel_width - (in_width % stride_width)				,    0       )

  a		:      Dict       =  pad_along_width // 2
  a		:      str       =  pad_along_width - pad_left
  a		:      Union[str, Any]       =  pad_along_height // 2
  a		:      Optional[int]       =  pad_along_height - pad_top

  a		:      List[str]       =  (pad_left, pad_right, pad_top, pad_bottom)
  return nn.functional.pad(_A				,    _A				,    'constant'				,    0.0       )

class 							a__(      nn.Module			):







    def __init__(   self				:						Union[str, Any]		,    __snake_case				:						MobileNetVaConfig		,    __snake_case				:						int		,    __snake_case				:						int		,    __snake_case				:						int		,    __snake_case				:						Optional[int] = 1		,    __snake_case				:						Optional[int] = 1		,    __snake_case				:						bool = False		,    __snake_case				:						Optional[bool] = True		,    __snake_case				:						Optional[bool or str] = True		,    ):
      super().__init__()
      a		:      Any       =  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."""			)

      a		:      Dict       =  0 if config.tf_padding else int((kernel_size - 1) / 2			)

      a		:      Dict       =  nn.Convad(
          in_channels=__snake_case		,    out_channels=__snake_case		,    kernel_size=__snake_case		,    stride=__snake_case		,    padding=__snake_case		,    groups=__snake_case		,    bias=__snake_case		,    padding_mode='zeros'		,    )

      if use_normalization:
        a		:      int       =  nn.BatchNormad(
            num_features=__snake_case		,    eps=config.layer_norm_eps		,    momentum=0.9997		,    affine=__snake_case		,    track_running_stats=__snake_case		,    )
      else:
        a		:      Any       =  None

      if use_activation:
        if isinstance(__snake_case		,    __snake_case			):
          a		:      Dict       =  ACTaFN[use_activation]
        elif isinstance(config.hidden_act		,    __snake_case			):
          a		:      Dict       =  ACTaFN[config.hidden_act]
        else:
          a		:      List[str]       =  config.hidden_act
      else:
        a		:      int       =  None







    def     lowercase_ (   self				:						str		,    __snake_case				:						torch.Tensor			):
      if self.config.tf_padding:
        a		:      str       =  apply_tf_padding(__snake_case		,    self.convolution			)
      a		:      Any       =  self.convolution(__snake_case			)
      if self.normalization is not None:
        a		:      int       =  self.normalization(__snake_case			)
      if self.activation is not None:
        a		:      Union[str, Any]       =  self.activation(__snake_case			)
      return features

class 							a__(      lowerCamelCase__			):
    lowercase__				=					MobileNetVaConfig
    lowercase__				=					load_tf_weights_in_mobilenet_va
    lowercase__				=					"""mobilenet_v1"""
    lowercase__				=					"""pixel_values"""
    lowercase__				=					False







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						Union[nn.Linear, nn.Convad]			):
      if isinstance(__snake_case		,    (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(__snake_case		,    nn.BatchNormad			):
        module.bias.data.zero_()
        module.weight.data.fill_(1.0			)
lowerCAmelCase:      Optional[Any]		 =		r'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`MobileNetV1Config`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

lowerCAmelCase:      Any		 =		r'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`MobileNetV1ImageProcessor.__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 MobileNetV1 model outputting raw hidden-states without any specific head on top."""	,				lowerCamelCase__	,				)
class 							a__(      lowerCamelCase__			):







    def __init__(   self				:						Union[str, Any]		,    __snake_case				:						MobileNetVaConfig		,    __snake_case				:						bool = True			):
      super().__init__(__snake_case			)
      a		:      Any       =  config

      a		:      Any       =  32
      a		:      Dict       =  max(int(depth * config.depth_multiplier			)		,    config.min_depth			)

      a		:      List[Any]       =  MobileNetVaConvLayer(
          __snake_case		,    in_channels=config.num_channels		,    out_channels=__snake_case		,    kernel_size=3		,    stride=2		,    )

      a		:      int       =  [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

      a		:      Optional[int]       =  nn.ModuleList()
      for i in range(13			):
        a		:      Any       =  out_channels

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

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

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

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

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







    def     lowercase_ (   self				:						Optional[int]		,    __snake_case				:						int			):
      raise NotImplementedError







    @add_start_docstrings_to_model_forward(__snake_case			)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC		,    output_type=__snake_case		,    config_class=_CONFIG_FOR_DOC		,    modality='vision'		,    expected_output=_EXPECTED_OUTPUT_SHAPE		,    )
    def     lowercase_ (   self				:						Any		,    __snake_case				:						Optional[torch.Tensor] = None		,    __snake_case				:						Optional[bool] = None		,    __snake_case				:						Optional[bool] = None		,    ):
      a		:      int       =  (
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      a		:      Union[str, Any]       =  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'			)

      a		:      int       =  self.conv_stem(__snake_case			)

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

      for i, layer_module in enumerate(self.layer			):
        a		:      Tuple       =  layer_module(__snake_case			)

        if output_hidden_states:
          a		:      int       =  all_hidden_states + (hidden_states,)

      a		:      List[Any]       =  hidden_states

      if self.pooler is not None:
        a		:      Optional[int]       =  torch.flatten(self.pooler(__snake_case			)		,    start_dim=1			)
      else:
        a		:      Tuple       =  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=__snake_case		,    pooler_output=__snake_case		,    hidden_states=__snake_case		,    )

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







    def __init__(   self				:						str		,    __snake_case				:						MobileNetVaConfig			):
      super().__init__(__snake_case			)

      a		:      List[str]       =  config.num_labels
      a		:      str       =  MobileNetVaModel(__snake_case			)

      a		:      Any       =  self.mobilenet_va.layer[-1].convolution.out_channels

      # Classifier head
      a		:      List[str]       =  nn.Dropout(config.classifier_dropout_prob		,    inplace=__snake_case			)
      a		:      Optional[Any]       =  nn.Linear(__snake_case		,    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(__snake_case			)
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT		,    output_type=__snake_case		,    config_class=_CONFIG_FOR_DOC		,    expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		,    )
    def     lowercase_ (   self				:						int		,    __snake_case				:						Optional[torch.Tensor] = None		,    __snake_case				:						Optional[bool] = None		,    __snake_case				:						Optional[torch.Tensor] = None		,    __snake_case				:						Optional[bool] = None		,    ):
      a		:      List[Any]       =  return_dict if return_dict is not None else self.config.use_return_dict

      a		:      List[str]       =  self.mobilenet_va(__snake_case		,    output_hidden_states=__snake_case		,    return_dict=__snake_case			)

      a		:      Union[str, Any]       =  outputs.pooler_output if return_dict else outputs[1]

      a		:      Any       =  self.classifier(self.dropout(__snake_case			)			)

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

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

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

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




'''simple docstring'''






from pathlib import Path

import fire
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[Any]       =  Path(_A       )
  a		:      Tuple       =  Path(_A       )
  dest_dir.mkdir(exist_ok=_A       )
  for path in src_dir.iterdir():
    a		:      Tuple       =  [x.rstrip() for x in list(path.open().readlines()       )][:n]
    a		:      List[str]       =  dest_dir.joinpath(path.name       )
    print(_A       )
    dest_path.open('w'       ).write('\n'.join(_A       )       )


if __name__ == "__main__":
     fire.Fire(minify)