


"""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


__snake_case         =					logging.get_logger(__name__)


# General docstring
__snake_case         =					"""MobileNetV1Config"""

# Base docstring
__snake_case         =					"""google/mobilenet_v1_1.0_224"""
__snake_case         =					[1, 1024, 7, 7]

# Image classification docstring
__snake_case         =					"""google/mobilenet_v1_1.0_224"""
__snake_case         =					"""tabby, tabby cat"""


__snake_case         =					[
    """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		(		lowercase						:						Optional[int]		,							lowercase						:						List[Any]		,							lowercase						:						Optional[Any]=None				)			->		Any:


    """simple docstring"""





    snake_case					:   int         =					{}

    if isinstance(lowercase		,							lowercase				):
        snake_case					:   Tuple         =					model.mobilenet_va
    else:
        snake_case					:   int         =					model

    snake_case					:   Any         =					"MobilenetV1/Conv2d_0/"
    snake_case					:   Optional[int]         =					backbone.conv_stem.convolution.weight
    snake_case					:   Optional[Any]         =					backbone.conv_stem.normalization.bias
    snake_case					:   Any         =					backbone.conv_stem.normalization.weight
    snake_case					:   Tuple         =					backbone.conv_stem.normalization.running_mean
    snake_case					:   Any         =					backbone.conv_stem.normalization.running_var

    for i in range(13				):
        snake_case					:   Any         =					i + 1
        snake_case					:   Optional[int]         =					i * 2

        snake_case					:   Optional[Any]         =					backbone.layer[pt_index]
        snake_case					:   int         =					F'MobilenetV1/Conv2d_{tf_index}_depthwise/'
        snake_case					:   Optional[Any]         =					pointer.convolution.weight
        snake_case					:   Optional[int]         =					pointer.normalization.bias
        snake_case					:   Tuple         =					pointer.normalization.weight
        snake_case					:   List[str]         =					pointer.normalization.running_mean
        snake_case					:   Optional[Any]         =					pointer.normalization.running_var

        snake_case					:   Union[str, Any]         =					backbone.layer[pt_index + 1]
        snake_case					:   int         =					F'MobilenetV1/Conv2d_{tf_index}_pointwise/'
        snake_case					:   List[str]         =					pointer.convolution.weight
        snake_case					:   Optional[Any]         =					pointer.normalization.bias
        snake_case					:   str         =					pointer.normalization.weight
        snake_case					:   Dict         =					pointer.normalization.running_mean
        snake_case					:   List[Any]         =					pointer.normalization.running_var

    if isinstance(lowercase		,							lowercase				):
        snake_case					:   int         =					"MobilenetV1/Logits/Conv2d_1c_1x1/"
        snake_case					:   List[Any]         =					model.classifier.weight
        snake_case					:   Optional[int]         =					model.classifier.bias

    return tf_to_pt_map







def 						__lowerCAmelCase		(		lowercase						:						Optional[Any]		,							lowercase						:						Tuple		,							lowercase						:						int				)			->		List[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
    snake_case					:   int         =					tf.train.list_variables(lowercase				)
    snake_case					:   Optional[int]         =					{}
    for name, shape in init_vars:
        logger.info(F'Loading TF weight {name} with shape {shape}'				)
        snake_case					:   Any         =					tf.train.load_variable(lowercase		,							lowercase				)
        snake_case					:   List[Any]         =					array

    # Build TF to PyTorch weights loading map
    snake_case					:   Optional[Any]         =					_build_tf_to_pytorch_map(lowercase		,							lowercase		,							lowercase				)

    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

        snake_case					:   List[str]         =					tf_weights[name]

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

        tf_weights.pop(lowercase		,							lowercase				)
        tf_weights.pop(name + "/RMSProp"		,							lowercase				)
        tf_weights.pop(name + "/RMSProp_1"		,							lowercase				)
        tf_weights.pop(name + "/ExponentialMovingAverage"		,							lowercase				)

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







def 						__lowerCAmelCase		(		lowercase						:						torch.Tensor		,							lowercase						:						nn.Convad				)			->		torch.Tensor:


    """simple docstring"""





    snake_case      ,snake_case					:   int         =					features.shape[-2:]
    snake_case      ,snake_case					:   Tuple         =					conv_layer.stride
    snake_case      ,snake_case					:   int         =					conv_layer.kernel_size

    if in_height % stride_height == 0:
        snake_case					:   Union[str, Any]         =					max(kernel_height - stride_height		,							0				)
    else:
        snake_case					:   List[Any]         =					max(kernel_height - (in_height % stride_height)		,							0				)

    if in_width % stride_width == 0:
        snake_case					:   Dict         =					max(kernel_width - stride_width		,							0				)
    else:
        snake_case					:   Optional[int]         =					max(kernel_width - (in_width % stride_width)		,							0				)

    snake_case					:   List[Any]         =					pad_along_width // 2
    snake_case					:   List[str]         =					pad_along_width - pad_left
    snake_case					:   List[str]         =					pad_along_height // 2
    snake_case					:   int         =					pad_along_height - pad_top

    snake_case					:   str         =					(pad_left, pad_right, pad_top, pad_bottom)
    return nn.functional.pad(lowercase		,							lowercase		,							"constant"		,							0.0				)







class _lowerCAmelCase       (   nn.Module			):







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__ = 1		,			UpperCamelCase__ = 1		,			UpperCamelCase__ = False		,			UpperCamelCase__ = True		,			UpperCamelCase__ = True		,			)			->	None:

       '''simple docstring'''




       super().__init__()
       snake_case					:   List[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.'		)

       snake_case					:   Union[str, Any]         =					0 if config.tf_padding else int((kernel_size - 1) / 2		)

       snake_case					:   List[Any]         =					nn.Convad(
           in_channels=UpperCamelCase__		,			out_channels=UpperCamelCase__		,			kernel_size=UpperCamelCase__		,			stride=UpperCamelCase__		,			padding=UpperCamelCase__		,			groups=UpperCamelCase__		,			bias=UpperCamelCase__		,			padding_mode="zeros"		,			)

       if use_normalization:
           snake_case					:   Tuple         =					nn.BatchNormad(
               num_features=UpperCamelCase__		,			eps=config.layer_norm_eps		,			momentum=0.9997		,			affine=UpperCamelCase__		,			track_running_stats=UpperCamelCase__		,			)
       else:
           snake_case					:   int         =					None

       if use_activation:
           if isinstance(UpperCamelCase__		,			UpperCamelCase__		):
               snake_case					:   Any         =					ACTaFN[use_activation]
           elif isinstance(config.hidden_act		,			UpperCamelCase__		):
               snake_case					:   List[str]         =					ACTaFN[config.hidden_act]
           else:
               snake_case					:   List[Any]         =					config.hidden_act
       else:
           snake_case					:   List[str]         =					None







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	torch.Tensor:

       '''simple docstring'''




       if self.config.tf_padding:
           snake_case					:   Any         =					apply_tf_padding(UpperCamelCase__		,			self.convolution		)
       snake_case					:   Dict         =					self.convolution(UpperCamelCase__		)
       if self.normalization is not None:
           snake_case					:   Optional[Any]         =					self.normalization(UpperCamelCase__		)
       if self.activation is not None:
           snake_case					:   Optional[int]         =					self.activation(UpperCamelCase__		)
       return features







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: List[Any]													=						MobileNetVaConfig
   __UpperCAmelCase				: Tuple													=						load_tf_weights_in_mobilenet_va
   __UpperCAmelCase				: Any													=						'''mobilenet_v1'''
   __UpperCAmelCase				: Optional[int]													=						'''pixel_values'''
   __UpperCAmelCase				: Any													=						False







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	None:

       '''simple docstring'''




       if isinstance(UpperCamelCase__		,			(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(UpperCamelCase__		,			nn.BatchNormad		):
           module.bias.data.zero_()
           module.weight.data.fill_(1.0		)



__snake_case         =					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.
"""

__snake_case         =					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.'''					,							snake_case_					,							)
class _lowerCAmelCase       (   snake_case_			):







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__ = True		)			->	Dict:

       '''simple docstring'''




       super().__init__(UpperCamelCase__		)
       snake_case					:   List[str]         =					config

       snake_case					:   int         =					32
       snake_case					:   int         =					max(int(depth * config.depth_multiplier		)		,			config.min_depth		)

       snake_case					:   Tuple         =					MobileNetVaConvLayer(
           UpperCamelCase__		,			in_channels=config.num_channels		,			out_channels=UpperCamelCase__		,			kernel_size=3		,			stride=2		,			)

       snake_case					:   Any         =					[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

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

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

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

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

       snake_case					:   List[str]         =					nn.AdaptiveAvgPoolad((1, 1)		) if add_pooling_layer else None

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







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	List[str]:

       '''simple docstring'''




       raise NotImplementedError







   @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 				lowerCamelCase	(			self		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			)			->	Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:

       '''simple docstring'''




       snake_case					:   List[str]         =					(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       snake_case					:   str         =					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"		)

       snake_case					:   int         =					self.conv_stem(UpperCamelCase__		)

       snake_case					:   Any         =					() if output_hidden_states else None

       for i, layer_module in enumerate(self.layer		):
           snake_case					:   Optional[Any]         =					layer_module(UpperCamelCase__		)

           if output_hidden_states:
               snake_case					:   Tuple         =					all_hidden_states + (hidden_states,)

       snake_case					:   Union[str, Any]         =					hidden_states

       if self.pooler is not None:
           snake_case					:   Dict         =					torch.flatten(self.pooler(UpperCamelCase__		)		,			start_dim=1		)
       else:
           snake_case					:   Optional[int]         =					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=UpperCamelCase__		,			pooler_output=UpperCamelCase__		,			hidden_states=UpperCamelCase__		,			)







@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.
    '''					,							snake_case_					,							)
class _lowerCAmelCase       (   snake_case_			):







   def __init__(			self		,			UpperCamelCase__		)			->	None:

       '''simple docstring'''




       super().__init__(UpperCamelCase__		)

       snake_case					:   Union[str, Any]         =					config.num_labels
       snake_case					:   str         =					MobileNetVaModel(UpperCamelCase__		)

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

       # Classifier head
       snake_case					:   Any         =					nn.Dropout(config.classifier_dropout_prob		,			inplace=UpperCamelCase__		)
       snake_case					:   Any         =					nn.Linear(UpperCamelCase__		,			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(UpperCamelCase__		)
   @add_code_sample_docstrings(
       checkpoint=_IMAGE_CLASS_CHECKPOINT		,			output_type=UpperCamelCase__		,			config_class=_CONFIG_FOR_DOC		,			expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		,			)
   def 				lowerCamelCase	(			self		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			)			->	Union[tuple, ImageClassifierOutputWithNoAttention]:

       '''simple docstring'''




       snake_case					:   Tuple         =					return_dict if return_dict is not None else self.config.use_return_dict

       snake_case					:   Union[str, Any]         =					self.mobilenet_va(UpperCamelCase__		,			output_hidden_states=UpperCamelCase__		,			return_dict=UpperCamelCase__		)

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

       snake_case					:   Tuple         =					self.classifier(self.dropout(UpperCamelCase__		)		)

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

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

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

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









"""simple docstring"""



import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__snake_case         =					"""src/diffusers"""

# Matches is_xxx_available()
__snake_case         =					re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
__snake_case         =					re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


__snake_case         =					"""
{0} = None
"""

__snake_case         =					"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


__snake_case         =					"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""







def 						__lowerCAmelCase		(		lowercase						:						Tuple				)			->		str:


    """simple docstring"""





    snake_case					:   Optional[Any]         =					_re_backend.findall(lowercase				)
    if len(lowercase				) == 0:
        return None

    return "_and_".join(lowercase				)







def 						__lowerCAmelCase		(		)			->		Tuple:


    """simple docstring"""





    with open(os.path.join(lowercase		,							"__init__.py"				)		,							"r"		,							encoding="utf-8"		,							newline="\n"				) as f:
        snake_case					:   Tuple         =					f.readlines()

    # Get to the point we do the actual imports for type checking
    snake_case					:   Tuple         =					0
    snake_case					:   Optional[Any]         =					{}
    # Go through the end of the file
    while line_index < len(lowercase				):
        # If the line contains is_backend_available, we grab all objects associated with the `else` block
        snake_case					:   List[Any]         =					find_backend(lines[line_index]				)
        if backend is not None:
            while not lines[line_index].startswith("else:"				):
                line_index += 1
            line_index += 1
            snake_case					:   str         =					[]
            # Until we unindent, add backend objects to the list
            while line_index < len(lowercase				) and len(lines[line_index]				) > 1:
                snake_case					:   List[str]         =					lines[line_index]
                snake_case					:   Any         =					_re_single_line_import.search(lowercase				)
                if single_line_import_search is not None:
                    objects.extend(single_line_import_search.groups()[0].split(", "				)				)
                elif line.startswith(" " * 8				):
                    objects.append(line[8:-2]				)
                line_index += 1

            if len(lowercase				) > 0:
                snake_case					:   Any         =					objects
        else:
            line_index += 1

    return backend_specific_objects







def 						__lowerCAmelCase		(		lowercase						:						Union[str, Any]		,							lowercase						:						Tuple				)			->		int:


    """simple docstring"""





    if name.isupper():
        return DUMMY_CONSTANT.format(lowercase				)
    elif name.islower():
        return DUMMY_FUNCTION.format(lowercase		,							lowercase				)
    else:
        return DUMMY_CLASS.format(lowercase		,							lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Union[str, Any]=None				)			->		Optional[int]:


    """simple docstring"""





    if backend_specific_objects is None:
        snake_case					:   List[str]         =					read_init()
    # For special correspondence backend to module name as used in the function requires_modulename
    snake_case					:   Dict         =					{}

    for backend, objects in backend_specific_objects.items():
        snake_case					:   Union[str, Any]         =					"[" + ", ".join(F'"{b}"' for b in backend.split("_and_"				)				) + "]"
        snake_case					:   Union[str, Any]         =					"# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
        dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
        dummy_file += "\n".join([create_dummy_object(lowercase		,							lowercase				) for o in objects]				)
        snake_case					:   Optional[Any]         =					dummy_file

    return dummy_files







def 						__lowerCAmelCase		(		lowercase						:						Optional[Any]=False				)			->		Any:


    """simple docstring"""





    snake_case					:   str         =					create_dummy_files()
    # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
    snake_case					:   Dict         =					{"torch": "pt"}

    # Locate actual dummy modules and read their content.
    snake_case					:   Optional[Any]         =					os.path.join(lowercase		,							"utils"				)
    snake_case					:   Union[str, Any]         =					{
        backend: os.path.join(lowercase		,							F'dummy_{short_names.get(lowercase		,							lowercase				)}_objects.py'				)
        for backend in dummy_files.keys()
    }

    snake_case					:   Optional[Any]         =					{}
    for backend, file_path in dummy_file_paths.items():
        if os.path.isfile(lowercase				):
            with open(lowercase		,							"r"		,							encoding="utf-8"		,							newline="\n"				) as f:
                snake_case					:   int         =					f.read()
        else:
            snake_case					:   Optional[Any]         =					""

    for backend in dummy_files.keys():
        if dummy_files[backend] != actual_dummies[backend]:
            if overwrite:
                print(
                    F'Updating diffusers.utils.dummy_{short_names.get(lowercase		,							lowercase				)}_objects.py as the main '
                    "__init__ has new objects."				)
                with open(dummy_file_paths[backend]		,							"w"		,							encoding="utf-8"		,							newline="\n"				) as f:
                    f.write(dummy_files[backend]				)
            else:
                raise ValueError(
                    "The main __init__ has objects that are not present in "
                    F'diffusers.utils.dummy_{short_names.get(lowercase		,							lowercase				)}_objects.py. Run `make fix-copies` '
                    "to fix this."				)


if __name__ == "__main__":
  __snake_case         =					argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  __snake_case         =					parser.parse_args()

  check_dummies(args.fix_and_overwrite)





