
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


__magic_name__            =  logging.get_logger(__name__)


# General docstring
__magic_name__            =  '''MobileNetV1Config'''

# Base docstring
__magic_name__            =  '''google/mobilenet_v1_1.0_224'''
__magic_name__            =  [1, 1_024, 7, 7]

# Image classification docstring
__magic_name__            =  '''google/mobilenet_v1_1.0_224'''
__magic_name__            =  '''tabby, tabby cat'''


__magic_name__            =  [
    '''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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase=None							):
    snake_case__						=				{}

    if isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							):
        snake_case__						=				model.mobilenet_va
    else:
        snake_case__						=				model

    snake_case__						=				"MobilenetV1/Conv2d_0/"
    snake_case__						=				backbone.conv_stem.convolution.weight
    snake_case__						=				backbone.conv_stem.normalization.bias
    snake_case__						=				backbone.conv_stem.normalization.weight
    snake_case__						=				backbone.conv_stem.normalization.running_mean
    snake_case__						=				backbone.conv_stem.normalization.running_var

    for i in range(13							):
        snake_case__						=				i + 1
        snake_case__						=				i * 2

        snake_case__						=				backbone.layer[pt_index]
        snake_case__						=				F"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
        snake_case__						=				pointer.convolution.weight
        snake_case__						=				pointer.normalization.bias
        snake_case__						=				pointer.normalization.weight
        snake_case__						=				pointer.normalization.running_mean
        snake_case__						=				pointer.normalization.running_var

        snake_case__						=				backbone.layer[pt_index + 1]
        snake_case__						=				F"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
        snake_case__						=				pointer.convolution.weight
        snake_case__						=				pointer.normalization.bias
        snake_case__						=				pointer.normalization.weight
        snake_case__						=				pointer.normalization.running_mean
        snake_case__						=				pointer.normalization.running_var

    if isinstance(__lowerCAmelCase    ,		__lowerCAmelCase							):
        snake_case__						=				"MobilenetV1/Logits/Conv2d_1c_1x1/"
        snake_case__						=				model.classifier.weight
        snake_case__						=				model.classifier.bias

    return tf_to_pt_map





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    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__						=				tf.train.list_variables(__lowerCAmelCase							)
    snake_case__						=				{}
    for name, shape in init_vars:
        logger.info(F"""Loading TF weight {name} with shape {shape}"""							)
        snake_case__						=				tf.train.load_variable(__lowerCAmelCase    ,		__lowerCAmelCase							)
        snake_case__						=				array

    # Build TF to PyTorch weights loading map
    snake_case__						=				_build_tf_to_pytorch_map(__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							)

    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__						=				tf_weights[name]

        if "depthwise_weights" in name:
            logger.info("Transposing depthwise"							)
            snake_case__						=				np.transpose(__lowerCAmelCase    ,		(2, 3, 0, 1)							)
        elif "weights" in name:
            logger.info("Transposing"							)
            if len(pointer.shape							) == 2:  # copying into linear layer
                snake_case__						=				array.squeeze().transpose()
            else:
                snake_case__						=				np.transpose(__lowerCAmelCase    ,		(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__						=				torch.from_numpy(__lowerCAmelCase							)

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

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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						,					snake_case__						=				features.shape[-2:]
    snake_case__						,					snake_case__						=				conv_layer.stride
    snake_case__						,					snake_case__						=				conv_layer.kernel_size

    if in_height % stride_height == 0:
        snake_case__						=				max(kernel_height - stride_height    ,		0							)
    else:
        snake_case__						=				max(kernel_height - (in_height % stride_height)    ,		0							)

    if in_width % stride_width == 0:
        snake_case__						=				max(kernel_width - stride_width    ,		0							)
    else:
        snake_case__						=				max(kernel_width - (in_width % stride_width)    ,		0							)

    snake_case__						=				pad_along_width // 2
    snake_case__						=				pad_along_width - pad_left
    snake_case__						=				pad_along_height // 2
    snake_case__						=				pad_along_height - pad_top

    snake_case__						=				(pad_left, pad_right, pad_top, pad_bottom)
    return nn.functional.pad(__lowerCAmelCase    ,		__lowerCAmelCase    ,		"constant"    ,		0.0							)




class 					_SCREAMING_SNAKE_CASE  (      nn.Module ):
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase = 1		,	lowerCamelCase = 1		,	lowerCamelCase = False		,	lowerCamelCase = True		,	lowerCamelCase = True		,	):
          super().__init__()
          snake_case__						=				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__						=				0 if config.tf_padding else int((kernel_size - 1) / 2 )

          snake_case__						=				nn.Convad(
              in_channels=lowerCamelCase		,	out_channels=lowerCamelCase		,	kernel_size=lowerCamelCase		,	stride=lowerCamelCase		,	padding=lowerCamelCase		,	groups=lowerCamelCase		,	bias=lowerCamelCase		,	padding_mode="zeros"		,	)

          if use_normalization:
              snake_case__						=				nn.BatchNormad(
                  num_features=lowerCamelCase		,	eps=config.layer_norm_eps		,	momentum=0.9_9_9_7		,	affine=lowerCamelCase		,	track_running_stats=lowerCamelCase		,	)
          else:
              snake_case__						=				None

          if use_activation:
              if isinstance(lowerCamelCase		,	lowerCamelCase ):
                  snake_case__						=				ACTaFN[use_activation]
              elif isinstance(config.hidden_act		,	lowerCamelCase ):
                  snake_case__						=				ACTaFN[config.hidden_act]
              else:
                  snake_case__						=				config.hidden_act
          else:
              snake_case__						=				None



      def A_			(			self		,	lowerCamelCase ):
          if self.config.tf_padding:
              snake_case__						=				apply_tf_padding(lowerCamelCase		,	self.convolution )
          snake_case__						=				self.convolution(lowerCamelCase )
          if self.normalization is not None:
              snake_case__						=				self.normalization(lowerCamelCase )
          if self.activation is not None:
              snake_case__						=				self.activation(lowerCamelCase )
          return features





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		List[str]    	=     MobileNetVaConfig
      _A						:		int    	=     load_tf_weights_in_mobilenet_va
      _A						:		Tuple    	=     'mobilenet_v1'
      _A						:		Optional[int]    	=     'pixel_values'
      _A						:		Union[str, Any]    	=     False
      def A_			(			self		,	lowerCamelCase ):
          if isinstance(lowerCamelCase		,	(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(lowerCamelCase		,	nn.BatchNormad ):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0 )





__magic_name__            =  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.
'''

__magic_name__            =  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		,	lowerCamelCase		,	lowerCamelCase = True ):
          super().__init__(lowerCamelCase )
          snake_case__						=				config

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

          snake_case__						=				MobileNetVaConvLayer(
              lowerCamelCase		,	in_channels=config.num_channels		,	out_channels=lowerCamelCase		,	kernel_size=3		,	stride=2		,	)

          snake_case__						=				[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

          snake_case__						=				nn.ModuleList()
          for i in range(13 ):
              snake_case__						=				out_channels

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

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

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

          snake_case__						=				nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None

          # Initialize weights and apply final processing
          self.post_init()
      def A_			(			self		,	lowerCamelCase ):
          raise NotImplementedError



      @add_start_docstrings_to_model_forward(lowerCamelCase )
      @add_code_sample_docstrings(
          checkpoint=_CHECKPOINT_FOR_DOC		,	output_type=lowerCamelCase		,	config_class=_CONFIG_FOR_DOC		,	modality="vision"		,	expected_output=_EXPECTED_OUTPUT_SHAPE		,	)
      def A_			(			self		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = None		,	):
          snake_case__						=				(
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          snake_case__						=				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__						=				self.conv_stem(lowerCamelCase )

          snake_case__						=				() if output_hidden_states else None

          for i, layer_module in enumerate(self.layer ):
              snake_case__						=				layer_module(lowerCamelCase )

              if output_hidden_states:
                  snake_case__						=				all_hidden_states + (hidden_states,)

          snake_case__						=				hidden_states

          if self.pooler is not None:
              snake_case__						=				torch.flatten(self.pooler(lowerCamelCase )		,	start_dim=1 )
          else:
              snake_case__						=				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=lowerCamelCase		,	pooler_output=lowerCamelCase		,	hidden_states=lowerCamelCase		,	)





@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		,	lowerCamelCase ):
          super().__init__(lowerCamelCase )

          snake_case__						=				config.num_labels
          snake_case__						=				MobileNetVaModel(lowerCamelCase )

          snake_case__						=				self.mobilenet_va.layer[-1].convolution.out_channels

          # Classifier head
          snake_case__						=				nn.Dropout(config.classifier_dropout_prob		,	inplace=lowerCamelCase )
          snake_case__						=				nn.Linear(lowerCamelCase		,	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(lowerCamelCase )
      @add_code_sample_docstrings(
          checkpoint=_IMAGE_CLASS_CHECKPOINT		,	output_type=lowerCamelCase		,	config_class=_CONFIG_FOR_DOC		,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		,	)
      def A_			(			self		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = None		,	):
          snake_case__						=				return_dict if return_dict is not None else self.config.use_return_dict

          snake_case__						=				self.mobilenet_va(lowerCamelCase		,	output_hidden_states=lowerCamelCase		,	return_dict=lowerCamelCase )

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

          snake_case__						=				self.classifier(self.dropout(lowerCamelCase ) )

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

              if self.config.problem_type == "regression":
                  snake_case__						=				MSELoss()
                  if self.num_labels == 1:
                      snake_case__						=				loss_fct(logits.squeeze()		,	labels.squeeze() )
                  else:
                      snake_case__						=				loss_fct(lowerCamelCase		,	lowerCamelCase )
              elif self.config.problem_type == "single_label_classification":
                  snake_case__						=				CrossEntropyLoss()
                  snake_case__						=				loss_fct(logits.view(-1		,	self.num_labels )		,	labels.view(-1 ) )
              elif self.config.problem_type == "multi_label_classification":
                  snake_case__						=				BCEWithLogitsLoss()
                  snake_case__						=				loss_fct(lowerCamelCase		,	lowerCamelCase )

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

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





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
     __magic_name__            =  argparse.ArgumentParser()

     parser.add_argument(
         '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
     )
     parser.add_argument(
         '''--original_config_file''',
         type=str,
         required=True,
         help='''The YAML config file corresponding to the original architecture.''',
     )
     parser.add_argument(
         '''--num_in_channels''',
         default=None,
         type=int,
         help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
     )
     parser.add_argument(
         '''--image_size''',
         default=512,
         type=int,
         help=(
             '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
             ''' Base. Use 768 for Stable Diffusion v2.'''
         ),
     )
     parser.add_argument(
         '''--extract_ema''',
         action='''store_true''',
         help=(
             '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
             ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
             ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
         ),
     )
     parser.add_argument(
         '''--upcast_attention''',
         action='''store_true''',
         help=(
             '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
             ''' diffusion 2.1.'''
         ),
     )
     parser.add_argument(
         '''--from_safetensors''',
         action='''store_true''',
         help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
     )
     parser.add_argument(
         '''--to_safetensors''',
         action='''store_true''',
         help='''Whether to store pipeline in safetensors format or not.''',
     )
     parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
     parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')





     def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
         if string == "True":
             return True
         elif string == "False":
             return False
         else:
             raise ValueError(F"""could not parse string as bool {string}"""							)

     parser.add_argument(
         '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
     )

     parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

     __magic_name__            =  parser.parse_args()

     __magic_name__            =  download_controlnet_from_original_ckpt(
         checkpoint_path=args.checkpoint_path,
         original_config_file=args.original_config_file,
         image_size=args.image_size,
         extract_ema=args.extract_ema,
         num_in_channels=args.num_in_channels,
         upcast_attention=args.upcast_attention,
         from_safetensors=args.from_safetensors,
         device=args.device,
         use_linear_projection=args.use_linear_projection,
         cross_attention_dim=args.cross_attention_dim,
     )





     controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



