
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_ 				=			logging.get_logger()


def 							a__					(							snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case = True							):





  """simple docstring"""
  print(F'''Converting {name}...'''							)

  with torch.no_grad():
    if hidden_sizes == 128:
      if name[-1] == "S":
        __SCREAMING_SNAKE_CASE      :	Tuple					=		timm.create_model('''levit_128s'''       ,			pretrained=snake_case							)
      else:
        __SCREAMING_SNAKE_CASE      :	Any					=		timm.create_model('''levit_128'''       ,			pretrained=snake_case							)
    if hidden_sizes == 192:
      __SCREAMING_SNAKE_CASE      :	Dict					=		timm.create_model('''levit_192'''       ,			pretrained=snake_case							)
    if hidden_sizes == 256:
      __SCREAMING_SNAKE_CASE      :	Optional[int]					=		timm.create_model('''levit_256'''       ,			pretrained=snake_case							)
    if hidden_sizes == 384:
      __SCREAMING_SNAKE_CASE      :	Any					=		timm.create_model('''levit_384'''       ,			pretrained=snake_case							)

    from_model.eval()
    __SCREAMING_SNAKE_CASE      :	str					=		LevitForImageClassificationWithTeacher(snake_case							).eval()
    __SCREAMING_SNAKE_CASE      :	int					=		OrderedDict()

    __SCREAMING_SNAKE_CASE      :	List[Any]					=		from_model.state_dict()
    __SCREAMING_SNAKE_CASE      :	Tuple					=		list(from_model.state_dict().keys()							)
    __SCREAMING_SNAKE_CASE      :	str					=		list(our_model.state_dict().keys()							)
    print(len(snake_case							)       ,			len(snake_case							)							)
    for i in range(len(snake_case							)							):
      __SCREAMING_SNAKE_CASE      :	int					=		weights[og_keys[i]]
    our_model.load_state_dict(snake_case							)

    __SCREAMING_SNAKE_CASE      :	str					=		torch.randn((2, 3, 224, 224)							)
    __SCREAMING_SNAKE_CASE      :	Tuple					=		from_model(snake_case							)
    __SCREAMING_SNAKE_CASE      :	List[str]					=		our_model(snake_case							).logits

  assert torch.allclose(snake_case       ,			snake_case							), "The model logits don't match the original one."

  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		name
  print(snake_case							)

  if push_to_hub:
    our_model.save_pretrained(save_directory / checkpoint_name							)
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		LevitImageProcessor()
    image_processor.save_pretrained(save_directory / checkpoint_name							)

    print(F'''Pushed {checkpoint_name}'''							)


def 							a__					(							snake_case       ,			snake_case = None       ,			snake_case = True							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Dict					=		'''imagenet-1k-id2label.json'''
  __SCREAMING_SNAKE_CASE      :	int					=		1_000
  __SCREAMING_SNAKE_CASE      :	Optional[int]					=		(1, num_labels)

  __SCREAMING_SNAKE_CASE      :	Any					=		'''huggingface/label-files'''
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		num_labels
  __SCREAMING_SNAKE_CASE      :	List[Any]					=		json.load(open(hf_hub_download(snake_case       ,			snake_case       ,			repo_type='''dataset'''							)       ,			'''r'''							)							)
  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		{int(snake_case							): v for k, v in idalabel.items()}

  __SCREAMING_SNAKE_CASE      :	str					=		idalabel
  __SCREAMING_SNAKE_CASE      :	Tuple					=		{v: k for k, v in idalabel.items()}

  __SCREAMING_SNAKE_CASE      :	List[str]					=		partial(snake_case       ,			num_labels=snake_case       ,			idalabel=snake_case       ,			labelaid=snake_case							)

  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		{
      '''levit-128S''': 128,
      '''levit-128''': 128,
      '''levit-192''': 192,
      '''levit-256''': 256,
      '''levit-384''': 384,
  }

  __SCREAMING_SNAKE_CASE      :	Optional[int]					=		{
      '''levit-128S''': ImageNetPreTrainedConfig(
          hidden_sizes=[128, 256, 384]       ,			num_attention_heads=[4, 6, 8]       ,			depths=[2, 3, 4]       ,			key_dim=[16, 16, 16]       ,			drop_path_rate=0       ,			),
      '''levit-128''': ImageNetPreTrainedConfig(
          hidden_sizes=[128, 256, 384]       ,			num_attention_heads=[4, 8, 12]       ,			depths=[4, 4, 4]       ,			key_dim=[16, 16, 16]       ,			drop_path_rate=0       ,			),
      '''levit-192''': ImageNetPreTrainedConfig(
          hidden_sizes=[192, 288, 384]       ,			num_attention_heads=[3, 5, 6]       ,			depths=[4, 4, 4]       ,			key_dim=[32, 32, 32]       ,			drop_path_rate=0       ,			),
      '''levit-256''': ImageNetPreTrainedConfig(
          hidden_sizes=[256, 384, 512]       ,			num_attention_heads=[4, 6, 8]       ,			depths=[4, 4, 4]       ,			key_dim=[32, 32, 32]       ,			drop_path_rate=0       ,			),
      '''levit-384''': ImageNetPreTrainedConfig(
          hidden_sizes=[384, 512, 768]       ,			num_attention_heads=[6, 9, 12]       ,			depths=[4, 4, 4]       ,			key_dim=[32, 32, 32]       ,			drop_path_rate=0.1       ,			),
  }

  if model_name:
    convert_weight_and_push(
        names_to_hidden_sizes[model_name]       ,			snake_case       ,			names_to_config[model_name]       ,			snake_case       ,			snake_case							)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(names_to_hidden_sizes[model_name]       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case							)
  return config, expected_shape


if __name__ == "__main__":
   lowercase_ 				=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model_name""",
       default=None,
       type=str,
       help="""The name of the model you wish to convert, it must be one of the supported Levit* architecture,""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default="""levit-dump-folder/""",
       type=Path,
       required=False,
       help="""Path to the output PyTorch model directory.""",
   )
   parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
   parser.add_argument(
       """--no-push_to_hub""",
       dest="""push_to_hub""",
       action="""store_false""",
       help="""Do not push model and image processor to the hub""",
   )

   lowercase_ 				=			parser.parse_args()
   lowercase_ 				=			args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


a_    :  Optional[int]						  =		logging.get_logger(__name__)

# General docstring
a_    :  List[str]						  =		'RegNetConfig'

# Base docstring
a_    :  Union[str, Any]						  =		'facebook/regnet-y-040'
a_    :  Optional[Any]						  =		[1, 1_0_8_8, 7, 7]

# Image classification docstring
a_    :  Dict						  =		'facebook/regnet-y-040'
a_    :  List[Any]						  =		'tabby, tabby cat'

a_    :  Union[str, Any]						  =		[
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]





class  lowerCamelCase__	(    nn.Module):
     """simple docstring"""



     def __init__(self					,      __a					,      __a					,      __a = 3					,      __a = 1					,      __a = 1					,      __a = "relu"					,      ):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase             =		nn.Convad(
               __a					,      __a					,      kernel_size=__a					,      stride=__a					,      padding=kernel_size // 2					,      groups=__a					,      bias=__a					,      )
           lowerCamelCase             =		nn.BatchNormad(__a     )
           lowerCamelCase             =		ACTaFN[activation] if activation is not None else nn.Identity()







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.convolution(__a     )
           lowerCamelCase             =		self.normalization(__a     )
           lowerCamelCase             =		self.activation(__a     )
           return hidden_state





class  lowerCamelCase__	(    nn.Module):
     """simple docstring"""



     def __init__(self					,      __a     ):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase             =		RegNetConvLayer(
               config.num_channels					,      config.embedding_size					,      kernel_size=3					,      stride=2					,      activation=config.hidden_act     )
           lowerCamelCase             =		config.num_channels







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		pixel_values.shape[1]
           if num_channels != self.num_channels:
                 raise ValueError(
                     "Make sure that the channel dimension of the pixel values match with the one set in the configuration."     )
           lowerCamelCase             =		self.embedder(__a     )
           return hidden_state





class  lowerCamelCase__	(    nn.Module):
     """simple docstring"""



     def __init__(self					,      __a					,      __a					,      __a = 2     ):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase             =		nn.Convad(__a					,      __a					,      kernel_size=1					,      stride=__a					,      bias=__a     )
           lowerCamelCase             =		nn.BatchNormad(__a     )







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.convolution(__a     )
           lowerCamelCase             =		self.normalization(__a     )
           return hidden_state





class  lowerCamelCase__	(    nn.Module):
     """simple docstring"""



     def __init__(self					,      __a					,      __a     ):






           '''simple docstring'''



           super().__init__()

           lowerCamelCase             =		nn.AdaptiveAvgPoolad((1, 1)     )
           lowerCamelCase             =		nn.Sequential(
               nn.Convad(__a					,      __a					,      kernel_size=1     )					,      nn.ReLU()					,      nn.Convad(__a					,      __a					,      kernel_size=1     )					,      nn.Sigmoid()					,      )







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.pooler(__a     )
           lowerCamelCase             =		self.attention(__a     )
           lowerCamelCase             =		hidden_state * attention
           return hidden_state





class  lowerCamelCase__	(    nn.Module):
     """simple docstring"""



     def __init__(self					,      __a					,      __a					,      __a					,      __a = 1     ):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase             =		in_channels != out_channels or stride != 1
           lowerCamelCase             =		max(1					,      out_channels // config.groups_width     )
           lowerCamelCase             =		(
               RegNetShortCut(__a					,      __a					,      stride=__a     ) if should_apply_shortcut else nn.Identity()
           )
           lowerCamelCase             =		nn.Sequential(
               RegNetConvLayer(__a					,      __a					,      kernel_size=1					,      activation=config.hidden_act     )					,      RegNetConvLayer(__a					,      __a					,      stride=__a					,      groups=__a					,      activation=config.hidden_act     )					,      RegNetConvLayer(__a					,      __a					,      kernel_size=1					,      activation=__a     )					,      )
           lowerCamelCase             =		ACTaFN[config.hidden_act]







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		hidden_state
           lowerCamelCase             =		self.layer(__a     )
           lowerCamelCase             =		self.shortcut(__a     )
           hidden_state += residual
           lowerCamelCase             =		self.activation(__a     )
           return hidden_state





class  lowerCamelCase__	(    nn.Module):
     """simple docstring"""



     def __init__(self					,      __a					,      __a					,      __a					,      __a = 1     ):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase             =		in_channels != out_channels or stride != 1
           lowerCamelCase             =		max(1					,      out_channels // config.groups_width     )
           lowerCamelCase             =		(
               RegNetShortCut(__a					,      __a					,      stride=__a     ) if should_apply_shortcut else nn.Identity()
           )
           lowerCamelCase             =		nn.Sequential(
               RegNetConvLayer(__a					,      __a					,      kernel_size=1					,      activation=config.hidden_act     )					,      RegNetConvLayer(__a					,      __a					,      stride=__a					,      groups=__a					,      activation=config.hidden_act     )					,      RegNetSELayer(__a					,      reduced_channels=int(round(in_channels / 4     )     )     )					,      RegNetConvLayer(__a					,      __a					,      kernel_size=1					,      activation=__a     )					,      )
           lowerCamelCase             =		ACTaFN[config.hidden_act]







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		hidden_state
           lowerCamelCase             =		self.layer(__a     )
           lowerCamelCase             =		self.shortcut(__a     )
           hidden_state += residual
           lowerCamelCase             =		self.activation(__a     )
           return hidden_state





class  lowerCamelCase__	(    nn.Module):
     """simple docstring"""



     def __init__(self					,      __a					,      __a					,      __a					,      __a = 2					,      __a = 2					,      ):






           '''simple docstring'''



           super().__init__()

           lowerCamelCase             =		RegNetXLayer if config.layer_type == "x" else RegNetYLayer

           lowerCamelCase             =		nn.Sequential(
               # downsampling is done in the first layer with stride of 2
               layer(
                   __a					,      __a					,      __a					,      stride=__a					,      )					,      *[layer(__a					,      __a					,      __a     ) for _ in range(depth - 1     )]					,      )







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.layers(__a     )
           return hidden_state





class  lowerCamelCase__	(    nn.Module):
     """simple docstring"""



     def __init__(self					,      __a     ):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase             =		nn.ModuleList([]     )
           # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
           self.stages.append(
               RegNetStage(
                   __a					,      config.embedding_size					,      config.hidden_sizes[0]					,      stride=2 if config.downsample_in_first_stage else 1					,      depth=config.depths[0]					,      )     )
           lowerCamelCase             =		zip(config.hidden_sizes					,      config.hidden_sizes[1:]     )
           for (in_channels, out_channels), depth in zip(__a					,      config.depths[1:]     ):
                 self.stages.append(RegNetStage(__a					,      __a					,      __a					,      depth=__a     )     )







     def     _a   (self					,      __a					,      __a = False					,      __a = True     ):






           '''simple docstring'''



           lowerCamelCase             =		() if output_hidden_states else None

           for stage_module in self.stages:
                 if output_hidden_states:
                       lowerCamelCase             =		hidden_states + (hidden_state,)

                 lowerCamelCase             =		stage_module(__a     )

           if output_hidden_states:
                 lowerCamelCase             =		hidden_states + (hidden_state,)

           if not return_dict:
                 return tuple(v for v in [hidden_state, hidden_states] if v is not None     )

           return BaseModelOutputWithNoAttention(last_hidden_state=__a					,      hidden_states=__a     )





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     _A      =      RegNetConfig
     _A      =      'regnet'
     _A      =      'pixel_values'
     _A      =      True







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           if isinstance(__a					,      nn.Convad     ):
                 nn.init.kaiming_normal_(module.weight					,      mode="fan_out"					,      nonlinearity="relu"     )
           elif isinstance(__a					,      (nn.BatchNormad, nn.GroupNorm)     ):
                 nn.init.constant_(module.weight					,      1     )
                 nn.init.constant_(module.bias					,      0     )







     def     _a   (self					,      __a					,      __a=False     ):






           '''simple docstring'''



           if isinstance(__a					,      __a     ):
                 lowerCamelCase             =		value



a_    :  Dict						  =		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 ([`RegNetConfig`]): 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'

a_    :  Optional[Any]						  =		R'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\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 [`~file_utils.ModelOutput`] instead of a plain tuple.\n'





@add_start_docstrings(
    'The bare RegNet model outputting raw features without any specific head on top.'							,     UpperCAmelCase_							,     )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     def __init__(self					,      __a     ):






           '''simple docstring'''



           super().__init__(__a     )
           lowerCamelCase             =		config
           lowerCamelCase             =		RegNetEmbeddings(__a     )
           lowerCamelCase             =		RegNetEncoder(__a     )
           lowerCamelCase             =		nn.AdaptiveAvgPoolad((1, 1)     )
           # Initialize weights and apply final processing
           self.post_init()







     @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     _a   (self					,      __a					,      __a = None					,      __a = None     ):






           '''simple docstring'''



           lowerCamelCase             =		(
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           lowerCamelCase             =		return_dict if return_dict is not None else self.config.use_return_dict

           lowerCamelCase             =		self.embedder(__a     )

           lowerCamelCase             =		self.encoder(
               __a					,      output_hidden_states=__a					,      return_dict=__a     )

           lowerCamelCase             =		encoder_outputs[0]

           lowerCamelCase             =		self.pooler(__a     )

           if not return_dict:
                 return (last_hidden_state, pooled_output) + encoder_outputs[1:]

           return BaseModelOutputWithPoolingAndNoAttention(
               last_hidden_state=__a					,      pooler_output=__a					,      hidden_states=encoder_outputs.hidden_states					,      )





@add_start_docstrings(
    '\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '							,     UpperCAmelCase_							,     )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     def __init__(self					,      __a     ):






           '''simple docstring'''



           super().__init__(__a     )
           lowerCamelCase             =		config.num_labels
           lowerCamelCase             =		RegNetModel(__a     )
           # classification head
           lowerCamelCase             =		nn.Sequential(
               nn.Flatten()					,      nn.Linear(config.hidden_sizes[-1]					,      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     _a   (self					,      __a = None					,      __a = None					,      __a = None					,      __a = None					,      ):






           '''simple docstring'''



           lowerCamelCase             =		return_dict if return_dict is not None else self.config.use_return_dict

           lowerCamelCase             =		self.regnet(__a					,      output_hidden_states=__a					,      return_dict=__a     )

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

           lowerCamelCase             =		self.classifier(__a     )

           lowerCamelCase             =		None

           if labels is not None:
                 if self.config.problem_type is None:
                       if self.num_labels == 1:
                             lowerCamelCase             =		"regression"
                       elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                             lowerCamelCase             =		"single_label_classification"
                       else:
                             lowerCamelCase             =		"multi_label_classification"
                 if self.config.problem_type == "regression":
                       lowerCamelCase             =		MSELoss()
                       if self.num_labels == 1:
                             lowerCamelCase             =		loss_fct(logits.squeeze()					,      labels.squeeze()     )
                       else:
                             lowerCamelCase             =		loss_fct(__a					,      __a     )
                 elif self.config.problem_type == "single_label_classification":
                       lowerCamelCase             =		CrossEntropyLoss()
                       lowerCamelCase             =		loss_fct(logits.view(-1					,      self.num_labels     )					,      labels.view(-1     )     )
                 elif self.config.problem_type == "multi_label_classification":
                       lowerCamelCase             =		BCEWithLogitsLoss()
                       lowerCamelCase             =		loss_fct(__a					,      __a     )

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

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