
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser






def 						lowerCamelCase__							(      a=None	,	a=None   )   ->     Any:
   return field(default_factory=lambda: default	,	metadata=__lowerCAmelCase   )






@dataclass
class UpperCAmelCase     :





   '''simple docstring'''
   __UpperCamelCase	:					str				       =					field(
       metadata={'''help''': '''The csv file to plot.'''}							,							)
   __UpperCamelCase	:					bool				       =					field(
       default=A__							,							metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}							,							)
   __UpperCamelCase	:					bool				       =					field(
       default=A__							,							metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}							,							)
   __UpperCamelCase	:					bool				       =					field(
       default=A__							,							metadata={'''help''': '''Disable logarithmic scale when plotting'''}							,							)
   __UpperCamelCase	:					bool				       =					field(
       default=A__							,							metadata={
           '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
       }							,							)
   __UpperCamelCase	:					Optional[str]				       =					field(
       default=A__							,							metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}							,							)
   __UpperCamelCase	:					Optional[List[str]]				       =					list_field(
       default=A__							,							metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}     )






def 						lowerCamelCase__							(      a   )   ->     List[str]:
   try:
      int(__lowerCAmelCase   )
      return True
   except ValueError:
      return False






def 						lowerCamelCase__							(      a   )   ->     Tuple:
   try:
      float(__lowerCAmelCase   )
      return True
   except ValueError:
      return False






class UpperCAmelCase     :





   '''simple docstring'''


   def __init__(      self  :							Tuple    ,    lowerCAmelCase_  :							str  ):

      """simple docstring"""
      _A:						Optional[Any]	    = args
      _A:						Dict	    = defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}  )

      with open(self.args.csv_file    ,    newline=''''''  ) as csv_file:
         _A:						Union[str, Any]	    = csv.DictReader(lowerCAmelCase_  )
         for row in reader:
            _A:						str	    = row["model"]
            self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']  )  )
            self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']  )  )
            if can_convert_to_int(row['''result''']  ):
               # value is not None
               _A:						List[str]	    = int(row['''result''']  )
            elif can_convert_to_float(row['''result''']  ):
               # value is not None
               _A:						Optional[int]	    = float(row['''result''']  )


   def 				__magic_name__       (      self  :							Optional[int]  ):

      """simple docstring"""
      _A:						Optional[Any]	    = plt.subplots()
      _A:						Optional[Any]	    = "Time usage" if self.args.is_time else "Memory usage"
      _A:						Dict	    = title_str + " for training" if self.args.is_train else title_str + " for inference"

      if not self.args.no_log_scale:
         # set logarithm scales
         ax.set_xscale('''log'''  )
         ax.set_yscale('''log'''  )

      for axis in [ax.xaxis, ax.yaxis]:
         axis.set_major_formatter(ScalarFormatter()  )

      for model_name_idx, model_name in enumerate(self.result_dict.keys()  ):
         _A:						Dict	    = sorted(set(self.result_dict[model_name]['''bsz''']  )  )
         _A:						str	    = sorted(set(self.result_dict[model_name]['''seq_len''']  )  )
         _A:						List[Any]	    = self.result_dict[model_name]["result"]

         (_A):						str	    = (
             (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
         )

         _A:						Optional[Any]	    = (
             model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
         )

         for inner_loop_value in inner_loop_array:
            if self.args.plot_along_batch:
               _A:						List[str]	    = np.asarray(
                   [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]    ,    dtype=lowerCAmelCase_    ,    )
            else:
               _A:						Dict	    = np.asarray(
                   [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]    ,    dtype=np.floataa    ,    )

            (_A):						Union[str, Any]	    = (
                ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
            )

            _A:						Union[str, Any]	    = np.asarray(lowerCAmelCase_    ,    lowerCAmelCase_  )[: len(lowerCAmelCase_  )]
            plt.scatter(
                lowerCAmelCase_    ,    lowerCAmelCase_    ,    label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""  )
            plt.plot(lowerCAmelCase_    ,    lowerCAmelCase_    ,    '''--'''  )

         title_str += F""" {label_model_name} vs."""

      _A:						Optional[Any]	    = title_str[:-4]
      _A:						int	    = "Time in s" if self.args.is_time else "Memory in MB"

      # plot
      plt.title(lowerCAmelCase_  )
      plt.xlabel(lowerCAmelCase_  )
      plt.ylabel(lowerCAmelCase_  )
      plt.legend()

      if self.args.figure_png_file is not None:
         plt.savefig(self.args.figure_png_file  )
      else:
         plt.show()






def 						lowerCamelCase__							(      )   ->     int:
   _A:						List[str]	    = HfArgumentParser(__lowerCAmelCase   )
   _A:						Union[str, Any]	    = parser.parse_args_into_dataclasses()[0]
   _A:						Union[str, Any]	    = Plot(args=__lowerCAmelCase   )
   plot.plot()


if __name__ == "__main__":
   main()





import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__	:  Optional[Any]      =   logging.get_logger(__name__)






@dataclass
class UpperCAmelCase     :





   '''simple docstring'''
   __UpperCamelCase	:					nn.Module
   __UpperCamelCase	:					List[nn.Module]				       =					field(default_factory=SCREAMING_SNAKE_CASE__     )
   __UpperCamelCase	:					list				       =					field(default_factory=SCREAMING_SNAKE_CASE__     )


   def 				__magic_name__       (      self  :							str    ,    lowerCAmelCase_  :							Any    ,    lowerCAmelCase_  :							Tensor    ,    lowerCAmelCase_  :							Tensor  ):

      """simple docstring"""
      _A:						Optional[Any]	    = len(list(m.modules()  )  ) == 1 or isinstance(lowerCAmelCase_    ,    nn.Convad  ) or isinstance(lowerCAmelCase_    ,    nn.BatchNormad  )
      if has_not_submodules:
         if not isinstance(lowerCAmelCase_    ,    lowerCAmelCase_  ):
            self.traced.append(lowerCAmelCase_  )


   def __call__(      self  :							str    ,    lowerCAmelCase_  :							Tensor  ):

      """simple docstring"""
      for m in self.module.modules():
         self.handles.append(m.register_forward_hook(self._forward_hook  )  )
      self.module(lowerCAmelCase_  )
      [x.remove() for x in self.handles]
      return self


   @property
   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""
      # check the len of the state_dict keys to see if we have learnable params
      return list(filter(lambda lowerCAmelCase_  : len(list(x.state_dict().keys()  )  ) > 0    ,    self.traced  )  )






@dataclass
class UpperCAmelCase     :





   '''simple docstring'''
   __UpperCamelCase	:					nn.Module
   __UpperCamelCase	:					nn.Module
   __UpperCamelCase	:					int				       =					0
   __UpperCamelCase	:					List				       =					field(default_factory=SCREAMING_SNAKE_CASE__     )
   __UpperCamelCase	:					List				       =					field(default_factory=SCREAMING_SNAKE_CASE__     )


   def __call__(      self  :							Any    ,    lowerCAmelCase_  :							Tensor  ):

      """simple docstring"""
      _A:						int	    = Tracker(self.dest  )(lowerCAmelCase_  ).parametrized
      _A:						Union[str, Any]	    = Tracker(self.src  )(lowerCAmelCase_  ).parametrized

      _A:						Any	    = list(filter(lambda lowerCAmelCase_  : type(lowerCAmelCase_  ) not in self.src_skip    ,    lowerCAmelCase_  )  )
      _A:						int	    = list(filter(lambda lowerCAmelCase_  : type(lowerCAmelCase_  ) not in self.dest_skip    ,    lowerCAmelCase_  )  )

      if len(lowerCAmelCase_  ) != len(lowerCAmelCase_  ):
         raise Exception(
             F"""Numbers of operations are different. Source module has {len(lowerCAmelCase_  )} operations while"""
             F""" destination module has {len(lowerCAmelCase_  )}."""  )

      for dest_m, src_m in zip(lowerCAmelCase_    ,    lowerCAmelCase_  ):
         dest_m.load_state_dict(src_m.state_dict()  )
         if self.verbose == 1:
            print(F"""Transfered from={src_m} to={dest_m}"""  )






def 						lowerCamelCase__							(      a	,	a   )   ->     nn.Module:
   # nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
   _A:						Tuple	    = from_model.state_dict()
   _A:						Dict	    = our_model.state_dict()
   _A:						str	    = our_model.config
   _A:						List[str]	    = []
   for stage_idx in range(len(config.hidden_sizes   )   ):
      for block_id in range(config.depths[stage_idx]   ):
         _A:						Tuple	    = f"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
         _A:						List[str]	    = f"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

         all_keys.append((from_key, to_key)   )
         _A:						str	    = f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
         _A:						Union[str, Any]	    = f"""van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"""

         all_keys.append((from_key, to_key)   )

   for from_key, to_key in all_keys:
      _A:						Union[str, Any]	    = from_state_dict.pop(a   )

   our_model.load_state_dict(a   )
   return our_model






def 						lowerCamelCase__							(      a	,	a	,	a	,	a	,	a	,	a = True	,	)   ->     Dict:
   print(f"""Downloading weights for {name}..."""   )
   _A:						Dict	    = cached_download(a   )
   print(f"""Converting {name}..."""   )
   _A:						int	    = torch.load(a   )['''state_dict''']
   from_model.load_state_dict(a   )
   from_model.eval()
   with torch.no_grad():
      _A:						Any	    = VanForImageClassification(a   ).eval()
      _A:						Tuple	    = ModuleTransfer(src=a	,	dest=a   )
      _A:						List[str]	    = torch.randn((1, 3, 2_24, 2_24)   )
      module_transfer(a   )
      _A:						Tuple	    = copy_parameters(a	,	a   )

   if not torch.allclose(from_model(a   )	,	our_model(a   ).logits   ):
      raise ValueError('''The model logits don\'t match the original one.'''   )

   _A:						str	    = name
   print(a   )

   if push_to_hub:
      our_model.push_to_hub(
          repo_path_or_name=save_directory / checkpoint_name	,	commit_message='''Add model'''	,	use_temp_dir=a	,	)

      # we can use the convnext one
      _A:						Tuple	    = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''   )
      image_processor.push_to_hub(
          repo_path_or_name=save_directory / checkpoint_name	,	commit_message='''Add image processor'''	,	use_temp_dir=a	,	)

      print(f"""Pushed {checkpoint_name}"""   )






def 						lowerCamelCase__							(      a	,	a = None	,	a = True   )   ->     int:
   _A:						str	    = '''imagenet-1k-id2label.json'''
   _A:						Optional[int]	    = 10_00

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

   _A:						Any	    = idalabel
   _A:						Union[str, Any]	    = {v: k for k, v in idalabel.items()}

   _A:						Optional[int]	    = partial(a	,	num_labels=a	,	idalabel=a	,	labelaid=a   )

   _A:						Optional[int]	    = {
       '''van-tiny''': ImageNetPreTrainedConfig(
           hidden_sizes=[32, 64, 1_60, 2_56]	,	depths=[3, 3, 5, 2]	,	mlp_ratios=[8, 8, 4, 4]	,	),
       '''van-small''': ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]	,	depths=[2, 2, 4, 2]	,	mlp_ratios=[8, 8, 4, 4]	,	),
       '''van-base''': ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]	,	depths=[3, 3, 12, 3]	,	mlp_ratios=[8, 8, 4, 4]	,	),
       '''van-large''': ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]	,	depths=[3, 5, 27, 3]	,	mlp_ratios=[8, 8, 4, 4]	,	),
   }

   _A:						str	    = {
       '''van-tiny''': van_tiny,
       '''van-small''': van_small,
       '''van-base''': van_base,
       '''van-large''': van_large,
   }

   _A:						Union[str, Any]	    = {
       '''van-tiny''': (
           '''https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'''
       ),
       '''van-small''': (
           '''https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'''
       ),
       '''van-base''': (
           '''https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'''
       ),
       '''van-large''': (
           '''https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'''
       ),
   }

   if model_name:
      convert_weight_and_push(
          a	,	names_to_config[model_name]	,	checkpoint=names_to_original_checkpoints[model_name]	,	from_model=names_to_original_models[model_name]()	,	save_directory=a	,	push_to_hub=a	,	)
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(
             a	,	a	,	checkpoint=names_to_original_checkpoints[model_name]	,	from_model=names_to_original_models[model_name]()	,	save_directory=a	,	push_to_hub=a	,	)


if __name__ == "__main__":
   UpperCAmelCase__	:  Tuple      =   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 resnet* architecture,'
           ' currently: van-tiny/small/base/large. If `None`, all of them will the converted.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_folder_path',
       default=None,
       type=Path,
       required=True,
       help='Path to the output PyTorch model directory.',
   )
   parser.add_argument(
       '--van_dir',
       required=True,
       type=Path,
       help=(
           'A path to VAN\'s original implementation directory. You can download from here:'
           ' https://github.com/Visual-Attention-Network/VAN-Classification'
       ),
   )
   parser.add_argument(
       '--push_to_hub',
       default=True,
       type=bool,
       required=False,
       help='If True, push model and image processor to the hub.',
   )

   UpperCAmelCase__	:  Dict      =   parser.parse_args()
   UpperCAmelCase__	:  Path      =   args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   UpperCAmelCase__	:  Optional[int]      =   args.van_dir
   # append the path to the parents to maskformer dir
   sys.path.append(str(van_dir.parent))
   from van.models.van import van_base, van_large, van_small, van_tiny

   convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



