





'''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       		=      logging.get_logger(__name__)


# General docstring
lowerCAmelCase       		=      """MobileNetV1Config"""

# Base docstring
lowerCAmelCase       		=      """google/mobilenet_v1_1.0_224"""
lowerCAmelCase       		=      [1, 10_24, 7, 7]

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


lowerCAmelCase       		=      [
    """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						(							a_						:  Tuple      ,a_						:  Optional[int]      ,a_						:  Dict=None	):
		lowerCAmelCase     :    List[str]						=   {}

		if isinstance(a_      ,a_	):
				lowerCAmelCase     :    Tuple						=   model.mobilenet_va
		else:
				lowerCAmelCase     :    Optional[Any]						=   model

		lowerCAmelCase     :    Dict						=   "MobilenetV1/Conv2d_0/"
		lowerCAmelCase     :    Optional[Any]						=   backbone.conv_stem.convolution.weight
		lowerCAmelCase     :    Union[str, Any]						=   backbone.conv_stem.normalization.bias
		lowerCAmelCase     :    Any						=   backbone.conv_stem.normalization.weight
		lowerCAmelCase     :    List[str]						=   backbone.conv_stem.normalization.running_mean
		lowerCAmelCase     :    List[str]						=   backbone.conv_stem.normalization.running_var

		for i in range(1_3	):
				lowerCAmelCase     :    List[Any]						=   i + 1
				lowerCAmelCase     :    int						=   i * 2

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

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

		if isinstance(a_      ,a_	):
				lowerCAmelCase     :    List[Any]						=   "MobilenetV1/Logits/Conv2d_1c_1x1/"
				lowerCAmelCase     :    Dict						=   model.classifier.weight
				lowerCAmelCase     :    Optional[Any]						=   model.classifier.bias

		return tf_to_pt_map






def      __A						(							a_						:  Union[str, Any]      ,a_						:  Any      ,a_						:  Optional[int]	):
		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[Any]						=   tf.train.list_variables(a_	)
		lowerCAmelCase     :    str						=   {}
		for name, shape in init_vars:
				logger.info(f'''Loading TF weight {name} with shape {shape}'''	)
				lowerCAmelCase     :    Tuple						=   tf.train.load_variable(a_      ,a_	)
				lowerCAmelCase     :    List[str]						=   array

		# Build TF to PyTorch weights loading map
		lowerCAmelCase     :    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

				lowerCAmelCase     :    str						=   tf_weights[name]

				if "depthwise_weights" in name:
						logger.info("Transposing depthwise"	)
						lowerCAmelCase     :    Union[str, Any]						=   np.transpose(a_      ,(2, 3, 0, 1)	)
				elif "weights" in name:
						logger.info("Transposing"	)
						if len(pointer.shape	) == 2:  # copying into linear layer
								lowerCAmelCase     :    Any						=   array.squeeze().transpose()
						else:
								lowerCAmelCase     :    Optional[int]						=   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}'''	)
				lowerCAmelCase     :    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      __A						(							a_						:  torch.Tensor      ,a_						:  nn.Convad	):
		lowerCAmelCase	,		lowerCAmelCase     :    List[Any]						=   features.shape[-2:]
		lowerCAmelCase	,		lowerCAmelCase     :    Any						=   conv_layer.stride
		lowerCAmelCase	,		lowerCAmelCase     :    Union[str, Any]						=   conv_layer.kernel_size

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

		if in_width % stride_width == 0:
				lowerCAmelCase     :    Tuple						=   max(kernel_width - stride_width      ,0	)
		else:
				lowerCAmelCase     :    List[str]						=   max(kernel_width - (in_width % stride_width)      ,0	)

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

		lowerCAmelCase     :    Dict						=   (pad_left, pad_right, pad_top, pad_bottom)
		return nn.functional.pad(a_      ,a_      ,"constant"      ,0.0	)
class 		lowerCamelCase  (      nn.Module       ):



	def __init__(   self		,      a_		,      a_		,      a_		,      a_		,      a_ = 1		,      a_ = 1		,      a_ = False		,      a_ = True		,      a_ = True		,      ):
			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     :    str						=   0 if config.tf_padding else int((kernel_size - 1) / 2	)

			lowerCAmelCase     :    str						=   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=0.9997		,      affine=a_		,      track_running_stats=a_		,      )
			else:
					lowerCAmelCase     :    List[Any]						=   None

			if use_activation:
					if isinstance(a_		,      a_	):
							lowerCAmelCase     :    Dict						=   ACTaFN[use_activation]
					elif isinstance(config.hidden_act		,      a_	):
							lowerCAmelCase     :    List[str]						=   ACTaFN[config.hidden_act]
					else:
							lowerCAmelCase     :    Dict						=   config.hidden_act
			else:
					lowerCAmelCase     :    Union[str, Any]						=   None





	def   _lowerCamelCase    (   self		,      a_	):
			if self.config.tf_padding:
					lowerCAmelCase     :    List[str]						=   apply_tf_padding(a_		,      self.convolution	)
			lowerCAmelCase     :    Optional[int]						=   self.convolution(a_	)
			if self.normalization is not None:
					lowerCAmelCase     :    Any						=   self.normalization(a_	)
			if self.activation is not None:
					lowerCAmelCase     :    Any						=   self.activation(a_	)
			return features






class 		lowerCamelCase  (      _A       ):
	snake_case_      =   MobileNetVaConfig
	snake_case_      =   load_tf_weights_in_mobilenet_va
	snake_case_      =   "mobilenet_v1"
	snake_case_      =   "pixel_values"
	snake_case_      =   False



	def   _lowerCamelCase    (   self		,      a_	):
			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	)






lowerCAmelCase       		=      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.
"""

lowerCAmelCase       		=      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."      ,   _A      ,   )
class 		lowerCamelCase  (      _A       ):



	def __init__(   self		,      a_		,      a_ = True	):
			super().__init__(a_	)
			lowerCAmelCase     :    str						=   config

			lowerCAmelCase     :    Union[str, Any]						=   32
			lowerCAmelCase     :    int						=   max(int(depth * config.depth_multiplier	)		,      config.min_depth	)

			lowerCAmelCase     :    Any						=   MobileNetVaConvLayer(
			    a_		,      in_channels=config.num_channels		,      out_channels=a_		,      kernel_size=3		,      stride=2		,      )

			lowerCAmelCase     :    Optional[int]						=   [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

			lowerCAmelCase     :    Any						=   nn.ModuleList()
			for i in range(13	):
					lowerCAmelCase     :    Union[str, Any]						=   out_channels

					if strides[i] == 2 or i == 0:
							depth *= 2
							lowerCAmelCase     :    str						=   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		,      a_	):
			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		,      a_ = None		,      a_ = None		,      a_ = None		,      ):
			lowerCAmelCase     :    Dict						=   (
			    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
			)
			lowerCAmelCase     :    List[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"	)

			lowerCAmelCase     :    Optional[Any]						=   self.conv_stem(a_	)

			lowerCAmelCase     :    str						=   () if output_hidden_states else None

			for i, layer_module in enumerate(self.layer	):
					lowerCAmelCase     :    Tuple						=   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     :    Any						=   torch.flatten(self.pooler(a_	)		,      start_dim=1	)
			else:
					lowerCAmelCase     :    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=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    "      ,   _A      ,   )
class 		lowerCamelCase  (      _A       ):



	def __init__(   self		,      a_	):
			super().__init__(a_	)

			lowerCAmelCase     :    Union[str, Any]						=   config.num_labels
			lowerCAmelCase     :    int						=   MobileNetVaModel(a_	)

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

			# Classifier head
			lowerCAmelCase     :    Optional[int]						=   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		,      a_ = None		,      a_ = None		,      a_ = None		,      a_ = None		,      ):
			lowerCAmelCase     :    Any						=   return_dict if return_dict is not None else self.config.use_return_dict

			lowerCAmelCase     :    Dict						=   self.mobilenet_va(a_		,      output_hidden_states=a_		,      return_dict=a_	)

			lowerCAmelCase     :    Dict						=   outputs.pooler_output if return_dict else outputs[1]

			lowerCAmelCase     :    Any						=   self.classifier(self.dropout(a_	)	)

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

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

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

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










'''simple docstring'''






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
					lowerCAmelCase       		=      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=5_12,
					    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      __A						(							a_						:  List[str]	):
							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)

					lowerCAmelCase       		=      parser.parse_args()

					lowerCAmelCase       		=      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)



