





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


__UpperCAmelCase							=   logging.get_logger(__name__)


# General docstring
__UpperCAmelCase							=   'MobileNetV1Config'

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

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


__UpperCAmelCase							=   [
    '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       __UpperCamelCase					(      lowercase__						:							List[str]		,     lowercase__						:							Any		,     lowercase__						:							Optional[Any]=None		)						->   Optional[int]:
    '''simple docstring'''





    lowerCAmelCase_       :						Tuple							= {}

    if isinstance(UpperCAmelCase_		,     UpperCAmelCase_		):
        lowerCAmelCase_       :						Union[str, Any]							= model.mobilenet_va
    else:
        lowerCAmelCase_       :						int							= model

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

    for i in range(13		):
        lowerCAmelCase_       :						Union[str, Any]							= i + 1
        lowerCAmelCase_       :						str							= i * 2

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

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

    if isinstance(UpperCAmelCase_		,     UpperCAmelCase_		):
        lowerCAmelCase_       :						int							= 'MobilenetV1/Logits/Conv2d_1c_1x1/'
        lowerCAmelCase_       :						Union[str, Any]							= model.classifier.weight
        lowerCAmelCase_       :						Union[str, Any]							= model.classifier.bias

    return tf_to_pt_map
def       __UpperCamelCase					(      lowercase__						:							List[str]		,     lowercase__						:							Optional[int]		,     lowercase__						:							Dict		)						->   Dict:
    '''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_       :						List[Any]							= tf.train.load_variable(UpperCAmelCase_		,     UpperCAmelCase_		)
        lowerCAmelCase_       :						int							= array

    # Build TF to PyTorch weights loading map
    lowerCAmelCase_       :						Optional[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_       :						str							= tf_weights[name]

        if "depthwise_weights" in name:
            logger.info("""Transposing depthwise"""		)
            lowerCAmelCase_       :						Optional[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_       :						str							= array.squeeze().transpose()
            else:
                lowerCAmelCase_       :						List[str]							= 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[Any]							= 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       __UpperCamelCase					(      lowercase__						:							Any		,     lowercase__						:							List[Any]		)						->   str:
    '''simple docstring'''





    lowerCAmelCase_       :						List[Any]							= features.shape[-2:]
    lowerCAmelCase_       :						Tuple							= conv_layer.stride
    lowerCAmelCase_       :						Dict							= conv_layer.kernel_size

    if in_height % stride_height == 0:
        lowerCAmelCase_       :						Optional[int]							= max(kernel_height - stride_height		,     0		)
    else:
        lowerCAmelCase_       :						Any							= max(kernel_height - (in_height % stride_height)		,     0		)

    if in_width % stride_width == 0:
        lowerCAmelCase_       :						Union[str, Any]							= max(kernel_width - stride_width		,     0		)
    else:
        lowerCAmelCase_       :						Any							= max(kernel_width - (in_width % stride_width)		,     0		)

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

    lowerCAmelCase_       :						Dict							= (pad_left, pad_right, pad_top, pad_bottom)
    return nn.functional.pad(UpperCAmelCase_		,     UpperCAmelCase_		,     """constant"""		,     0.0		)

class 		__a				(						nn.Module   ):



       def __init__(  self						: Tuple       ,      UpperCAmelCase						: MobileNetVaConfig       ,      UpperCAmelCase						: int       ,      UpperCAmelCase						: int       ,      UpperCAmelCase						: int       ,      UpperCAmelCase						: Optional[int] = 1       ,      UpperCAmelCase						: Optional[int] = 1       ,      UpperCAmelCase						: bool = False       ,      UpperCAmelCase						: Optional[bool] = True       ,      UpperCAmelCase						: Optional[bool or str] = True       ,      ):
           super().__init__()
           lowerCAmelCase_       :						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.'      )

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

           lowerCAmelCase_       :						Any							= 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:
               lowerCAmelCase_       :						Optional[int]							= nn.BatchNormad(
                   num_features=lowerCamelCase__       ,      eps=config.layer_norm_eps       ,      momentum=0.9997       ,      affine=lowerCamelCase__       ,      track_running_stats=lowerCamelCase__       ,      )
           else:
               lowerCAmelCase_       :						Dict							= None

           if use_activation:
               if isinstance(lowerCamelCase__       ,      lowerCamelCase__      ):
                   lowerCAmelCase_       :						Any							= ACTaFN[use_activation]
               elif isinstance(config.hidden_act       ,      lowerCamelCase__      ):
                   lowerCAmelCase_       :						List[str]							= ACTaFN[config.hidden_act]
               else:
                   lowerCAmelCase_       :						List[Any]							= config.hidden_act
           else:
               lowerCAmelCase_       :						Optional[int]							= None



       def 			A				(  self						: str       ,      UpperCAmelCase						: torch.Tensor      ):
           if self.config.tf_padding:
               lowerCAmelCase_       :						str							= apply_tf_padding(lowerCamelCase__       ,      self.convolution      )
           lowerCAmelCase_       :						int							= self.convolution(lowerCamelCase__      )
           if self.normalization is not None:
               lowerCAmelCase_       :						str							= self.normalization(lowerCamelCase__      )
           if self.activation is not None:
               lowerCAmelCase_       :						int							= self.activation(lowerCamelCase__      )
           return features






class 		__a				(						__UpperCamelCase   ):
       __snake_case    : str   =		MobileNetVaConfig
       __snake_case    : Dict   =		load_tf_weights_in_mobilenet_va
       __snake_case    : Union[str, Any]   =		"""mobilenet_v1"""
       __snake_case    : Union[str, Any]   =		"""pixel_values"""
       __snake_case    : Optional[Any]   =		False



       def 			A				(  self						: Tuple       ,      UpperCAmelCase						: Union[nn.Linear, nn.Convad]      ):
           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      )






__UpperCAmelCase							=   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'

__UpperCAmelCase							=   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."""      ,__UpperCamelCase      ,)
class 		__a				(						__UpperCamelCase   ):



       def __init__(  self						: str       ,      UpperCAmelCase						: MobileNetVaConfig       ,      UpperCAmelCase						: bool = True      ):
           super().__init__(lowerCamelCase__      )
           lowerCAmelCase_       :						str							= config

           lowerCAmelCase_       :						Any							= 32
           lowerCAmelCase_       :						Optional[Any]							= max(int(depth * config.depth_multiplier      )       ,      config.min_depth      )

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

           lowerCAmelCase_       :						Dict							= [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

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

               if strides[i] == 2 or i == 0:
                   depth *= 2
                   lowerCAmelCase_       :						List[str]							= 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       ,      )      )

           lowerCAmelCase_       :						int							= nn.AdaptiveAvgPoolad((1, 1)      ) if add_pooling_layer else None

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



       def 			A				(  self						: List[str]       ,      UpperCAmelCase						: str      ):
           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						: Optional[int]       ,      UpperCAmelCase						: Optional[torch.Tensor] = None       ,      UpperCAmelCase						: Optional[bool] = None       ,      UpperCAmelCase						: Optional[bool] = None       ,      ):
           lowerCAmelCase_       :						Optional[int]							= (
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           lowerCAmelCase_       :						Dict							= 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_       :						Dict							= self.conv_stem(lowerCamelCase__      )

           lowerCAmelCase_       :						Tuple							= () if output_hidden_states else None

           for i, layer_module in enumerate(self.layer      ):
               lowerCAmelCase_       :						Union[str, Any]							= layer_module(lowerCamelCase__      )

               if output_hidden_states:
                   lowerCAmelCase_       :						Optional[Any]							= all_hidden_states + (hidden_states,)

           lowerCAmelCase_       :						List[str]							= hidden_states

           if self.pooler is not None:
               lowerCAmelCase_       :						List[Any]							= torch.flatten(self.pooler(lowerCamelCase__      )       ,      start_dim=1      )
           else:
               lowerCAmelCase_       :						Optional[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=lowerCamelCase__       ,      pooler_output=lowerCamelCase__       ,      hidden_states=lowerCamelCase__       ,      )






@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.
    """      ,__UpperCamelCase      ,)
class 		__a				(						__UpperCamelCase   ):



       def __init__(  self						: Union[str, Any]       ,      UpperCAmelCase						: MobileNetVaConfig      ):
           super().__init__(lowerCamelCase__      )

           lowerCAmelCase_       :						List[Any]							= config.num_labels
           lowerCAmelCase_       :						int							= MobileNetVaModel(lowerCamelCase__      )

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

           # Classifier head
           lowerCAmelCase_       :						List[Any]							= nn.Dropout(config.classifier_dropout_prob       ,      inplace=lowerCamelCase__      )
           lowerCAmelCase_       :						Dict							= 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						: List[Any]       ,      UpperCAmelCase						: Optional[torch.Tensor] = None       ,      UpperCAmelCase						: Optional[bool] = None       ,      UpperCAmelCase						: Optional[torch.Tensor] = None       ,      UpperCAmelCase						: Optional[bool] = None       ,      ):
           lowerCAmelCase_       :						Dict							= return_dict if return_dict is not None else self.config.use_return_dict

           lowerCAmelCase_       :						Dict							= self.mobilenet_va(lowerCamelCase__       ,      output_hidden_states=lowerCamelCase__       ,      return_dict=lowerCamelCase__      )

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

           lowerCAmelCase_       :						Optional[Any]							= self.classifier(self.dropout(lowerCamelCase__      )      )

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

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

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

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







'''simple docstring'''
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def A__       (	UpperCAmelCase_				):
						if "model" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('model.'    ,    ''				)
						if "norm1" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('norm1'    ,    'attention.output.LayerNorm'				)
						if "norm2" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.replace('norm2'    ,    'output.LayerNorm'				)
						if "norm" in orig_key:
												_UpperCamelCase   :						str												=  orig_key.replace('norm'    ,    'LayerNorm'				)
						if "transformer" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.split('.'				)[0].split('_'				)[-1]
												_UpperCamelCase   :						Tuple												=  orig_key.replace(f'transformer_{layer_num}'    ,    f'encoder.layer.{layer_num}'				)
						if "mha.attn" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('mha.attn'    ,    'attention.self'				)
						if "mha" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('mha'    ,    'attention'				)
						if "W_q" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('W_q'    ,    'self.query'				)
						if "W_k" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('W_k'    ,    'self.key'				)
						if "W_v" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('W_v'    ,    'self.value'				)
						if "ff1" in orig_key:
												_UpperCamelCase   :						Optional[Any]												=  orig_key.replace('ff1'    ,    'intermediate.dense'				)
						if "ff2" in orig_key:
												_UpperCamelCase   :						List[str]												=  orig_key.replace('ff2'    ,    'output.dense'				)
						if "ff" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('ff'    ,    'output.dense'				)
						if "mlm_class" in orig_key:
												_UpperCamelCase   :						Tuple												=  orig_key.replace('mlm.mlm_class'    ,    'cls.predictions.decoder'				)
						if "mlm" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.replace('mlm'    ,    'cls.predictions.transform'				)
						if "cls" not in orig_key:
												_UpperCamelCase   :						Optional[int]												=  'yoso.' + orig_key

						return orig_key






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						for key in orig_state_dict.copy().keys():
												_UpperCamelCase   :						Any												=  orig_state_dict.pop(UpperCAmelCase_				)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		_UpperCamelCase   :						Union[str, Any]												=  val

						_UpperCamelCase   :						Any												=  orig_state_dict['cls.predictions.decoder.bias']
						_UpperCamelCase   :						Union[str, Any]												=  torch.arange(UpperCAmelCase_				).expand((1, -1)				) + 2

						return orig_state_dict






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						List[Any]												=  torch.load(UpperCAmelCase_    ,    map_location='cpu'				)['model_state_dict']
						_UpperCamelCase   :						List[str]												=  YosoConfig.from_json_file(UpperCAmelCase_				)
						_UpperCamelCase   :						Union[str, Any]												=  YosoForMaskedLM(UpperCAmelCase_				)

						_UpperCamelCase   :						Tuple												=  convert_checkpoint_helper(config.max_position_embeddings    ,    UpperCAmelCase_				)

						print(model.load_state_dict(UpperCAmelCase_				)				)
						model.eval()
						model.save_pretrained(UpperCAmelCase_				)

						print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'				)


if __name__ == "__main__":
					snake_case_					:      int    			=    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					snake_case_					:      List[Any]    			=    parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


