



'''simple docstring'''






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

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase: List[Any]							=						logging.get_logger()

@dataclass
class 							a__:
    lowercase__			=   42
    lowercase__			=   field(default_factory=lowerCamelCase__     )
    lowercase__			=   field(default_factory=lowerCamelCase__     )







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  List[str]						,      __snake_case			:  Tensor						,      __snake_case			:  Tensor     ):
      a						: Union[str, Any]					       =					len(list(m.modules()     )     ) == 1 or isinstance(__snake_case						,      nn.Convad     ) or isinstance(__snake_case						,      nn.BatchNormad     )
      if has_not_submodules:
        self.traced.append(__snake_case     )







    def __call__(				self			:  int						,      __snake_case			:  Tensor     ):
      for m in self.module.modules():
        self.handles.append(m.register_forward_hook(self._forward_hook     )     )
      self.module(__snake_case     )
      [x.remove() for x in self.handles]
      return self







    @property
    def       lowercase_	(				self			:  List[Any]     ):
      # check the len of the state_dict keys to see if we have learnable params
      return list(filter(lambda __snake_case     : len(list(x.state_dict().keys()     )     ) > 0						,      self.traced     )     )

@dataclass
class 							a__:
    lowercase__			=   42
    lowercase__			=   42
    lowercase__			=   0
    lowercase__			=   field(default_factory=lowerCamelCase__     )
    lowercase__			=   field(default_factory=lowerCamelCase__     )







    def __call__(				self			:  List[str]						,      __snake_case			:  Tensor     ):
      a						: List[Any]					       =					Tracker(self.dest     )(__snake_case     ).parametrized
      a						: Tuple					       =					Tracker(self.src     )(__snake_case     ).parametrized

      a						: Tuple					       =					list(filter(lambda __snake_case     : type(__snake_case     ) not in self.src_skip						,      __snake_case     )     )
      a						: Optional[int]					       =					list(filter(lambda __snake_case     : type(__snake_case     ) not in self.dest_skip						,      __snake_case     )     )

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

      for dest_m, src_m in zip(__snake_case						,      __snake_case     ):
        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			,      _A			,      _A = True    ):
  print(f"""Converting {name}..."""    )
  with torch.no_grad():
    a						: Optional[int]					       =					timm.create_model(_A			,      pretrained=_A    ).eval()
    a						: List[Any]					       =					ResNetForImageClassification(_A    ).eval()
    a						: Any					       =					ModuleTransfer(src=_A			,      dest=_A    )
    a						: Tuple					       =					torch.randn((1, 3, 224, 224)    )
    module_transfer(_A    )

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

  a						: Optional[int]					       =					f"""resnet{"-".join(name.split("resnet"    )    )}"""
  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						: int					       =					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    ):
  a						: Optional[int]					       =					'imagenet-1k-id2label.json'
  a						: Optional[int]					       =					1000
  a						: str					       =					(1, num_labels)

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

  a						: Optional[int]					       =					idalabel
  a						: List[str]					       =					{v: k for k, v in idalabel.items()}

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

  a						: Tuple					       =					{
      'resnet18': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]			,      hidden_sizes=[64, 128, 256, 512]			,      layer_type='basic'    ),
      'resnet26': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]			,      hidden_sizes=[256, 512, 1024, 2048]			,      layer_type='bottleneck'    ),
      'resnet34': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]			,      hidden_sizes=[64, 128, 256, 512]			,      layer_type='basic'    ),
      'resnet50': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]			,      hidden_sizes=[256, 512, 1024, 2048]			,      layer_type='bottleneck'    ),
      'resnet101': ImageNetPreTrainedConfig(
          depths=[3, 4, 23, 3]			,      hidden_sizes=[256, 512, 1024, 2048]			,      layer_type='bottleneck'    ),
      'resnet152': ImageNetPreTrainedConfig(
          depths=[3, 8, 36, 3]			,      hidden_sizes=[256, 512, 1024, 2048]			,      layer_type='bottleneck'    ),
  }

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


if __name__ == "__main__":
     lowerCAmelCase: 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: resnet18,26,34,50,101,152. 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(
         '--push_to_hub',
         default=True,
         type=bool,
         required=False,
         help='If True, push model and image processor to the hub.',
     )

     lowerCAmelCase: int							=						parser.parse_args()
     lowerCAmelCase: Path							=						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)




'''simple docstring'''






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    ):
  return field(default_factory=lambda: default			,      metadata=_A    )

@dataclass
class 							a__:
    lowercase__			=   field(
        metadata={"""help""": """The csv file to plot."""}		,			)
    lowercase__			=   field(
        default=lowerCamelCase__		,			metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}		,			)
    lowercase__			=   field(
        default=lowerCamelCase__		,			metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}		,			)
    lowercase__			=   field(
        default=lowerCamelCase__		,			metadata={"""help""": """Disable logarithmic scale when plotting"""}		,			)
    lowercase__			=   field(
        default=lowerCamelCase__		,			metadata={
            """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
        }		,			)
    lowercase__			=   field(
        default=lowerCamelCase__		,			metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}		,			)
    lowercase__			=   list_field(
        default=lowerCamelCase__		,			metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}     )
def 				lowerCamelCase__	(						_A    ):
  try:
    int(_A    )
    return True
  except ValueError:
    return False
def 				lowerCamelCase__	(						_A    ):
  try:
    float(_A    )
    return True
  except ValueError:
    return False

class 							a__:







    def __init__(				self			:  Union[str, Any]						,      __snake_case			:  Optional[int]     ):
      a						: int					       =					args
      a						: Dict					       =					defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}     )

      with open(self.args.csv_file						,      newline=''     ) as csv_file:
        a						: List[Any]					       =					csv.DictReader(__snake_case     )
        for row in reader:
          a						: Dict					       =					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						: Dict					       =					int(row['result']     )
          elif can_convert_to_float(row['result']     ):
            # value is not None
            a						: int					       =					float(row['result']     )







    def       lowercase_	(				self			:  int     ):
      a       ,     a						: Dict					       =					plt.subplots()
      a						: int					       =					'Time usage' if self.args.is_time else 'Memory usage'
      a						: int					       =					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						: Any					       =					sorted(set(self.result_dict[model_name]['seq_len']     )     )
        a						: Any					       =					self.result_dict[model_name]['result']

        ((a)       ,     (a))						: Optional[Any]					       =					(
            (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
        )

        a						: int					       =					(
            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						: Dict					       =					np.asarray(
                [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]						,      dtype=__snake_case						,      )
          else:
            a						: int					       =					np.asarray(
                [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]						,      dtype=np.floataa						,      )

          ((a)       ,     (a))						: Optional[int]					       =					(
              ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
          )

          a						: List[str]					       =					np.asarray(__snake_case						,      __snake_case     )[: len(__snake_case     )]
          plt.scatter(
              __snake_case						,      __snake_case						,      label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""     )
          plt.plot(__snake_case						,      __snake_case						,      '--'     )

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

      a						: List[Any]					       =					title_str[:-4]
      a						: Optional[Any]					       =					'Time in s' if self.args.is_time else 'Memory in MB'

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

      if self.args.figure_png_file is not None:
        plt.savefig(self.args.figure_png_file     )
      else:
        plt.show()
def 				lowerCamelCase__	(						):
  a						: Dict					       =					HfArgumentParser(_A    )
  a						: List[str]					       =					parser.parse_args_into_dataclasses()[0]
  a						: Dict					       =					Plot(args=_A    )
  plot.plot()


if __name__ == "__main__":
     main()