


"""simple docstring"""
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()
UpperCAmelCase				=						logging.get_logger()

def 				_snake_case      (   __snake_case							:      int						, __snake_case							:      str						, __snake_case							:      LevitConfig						, __snake_case							:      Path						, __snake_case							:      bool = True   ):




   """simple docstring"""







   print(F'Converting {name}...'   )

   with torch.no_grad():
      if hidden_sizes == 128:
         if name[-1] == "S":
            _lowerCamelCase	:					List[str]   =		timm.create_model("""levit_128s"""						, pretrained=__snake_case   )
         else:
            _lowerCamelCase	:					Any   =		timm.create_model("""levit_128"""						, pretrained=__snake_case   )
      if hidden_sizes == 192:
         _lowerCamelCase	:					str   =		timm.create_model("""levit_192"""						, pretrained=__snake_case   )
      if hidden_sizes == 256:
         _lowerCamelCase	:					Tuple   =		timm.create_model("""levit_256"""						, pretrained=__snake_case   )
      if hidden_sizes == 384:
         _lowerCamelCase	:					List[Any]   =		timm.create_model("""levit_384"""						, pretrained=__snake_case   )

      from_model.eval()
      _lowerCamelCase	:					List[Any]   =		LevitForImageClassificationWithTeacher(__snake_case   ).eval()
      _lowerCamelCase	:					Union[str, Any]   =		OrderedDict()

      _lowerCamelCase	:					Union[str, Any]   =		from_model.state_dict()
      _lowerCamelCase	:					List[Any]   =		list(from_model.state_dict().keys()   )
      _lowerCamelCase	:					Any   =		list(our_model.state_dict().keys()   )
      print(len(__snake_case   )						, len(__snake_case   )   )
      for i in range(len(__snake_case   )   ):
         _lowerCamelCase	:					Optional[int]   =		weights[og_keys[i]]
      our_model.load_state_dict(__snake_case   )

      _lowerCamelCase	:					Dict   =		torch.randn((2, 3, 224, 224)   )
      _lowerCamelCase	:					Optional[int]   =		from_model(__snake_case   )
      _lowerCamelCase	:					Optional[int]   =		our_model(__snake_case   ).logits

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

   _lowerCamelCase	:					str   =		name
   print(__snake_case   )

   if push_to_hub:
      our_model.save_pretrained(save_directory / checkpoint_name   )
      _lowerCamelCase	:					Optional[int]   =		LevitImageProcessor()
      image_processor.save_pretrained(save_directory / checkpoint_name   )

      print(F'Pushed {checkpoint_name}'   )

def 				_snake_case      (   __snake_case							:      Path						, __snake_case							:      str = None						, __snake_case							:      bool = True   ):




   """simple docstring"""







   _lowerCamelCase	:					str   =		"""imagenet-1k-id2label.json"""
   _lowerCamelCase	:					Optional[Any]   =		1000
   _lowerCamelCase	:					Any   =		(1, num_labels)

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

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

   _lowerCamelCase	:					int   =		partial(__snake_case						, num_labels=__snake_case						, idalabel=__snake_case						, labelaid=__snake_case   )

   _lowerCamelCase	:					Optional[Any]   =		{
       """levit-128S""": 128,
       """levit-128""": 128,
       """levit-192""": 192,
       """levit-256""": 256,
       """levit-384""": 384,
   }

   _lowerCamelCase	:					Optional[Any]   =		{
       """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__":
 UpperCAmelCase				=						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""",
 )

 UpperCAmelCase				=						parser.parse_args()
 UpperCAmelCase				=						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   A_      (       SCREAMING_SNAKE_CASE_=None ,	SCREAMING_SNAKE_CASE_=None						)			->str:
  return field(default_factory=lambda: default ,	metadata=SCREAMING_SNAKE_CASE_						)





@dataclass
class       _a    :


  """simple docstring"""

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






def   A_      (       SCREAMING_SNAKE_CASE_						)			->Dict:
  try:
    int(SCREAMING_SNAKE_CASE_						)
    return True
  except ValueError:
    return False

def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:
  try:
    float(SCREAMING_SNAKE_CASE_						)
    return True
  except ValueError:
    return False





class       _a    :


  """simple docstring"""

  def __init__(		self      :       str   ,				lowercase_      :       List[str]      ):







    '''simple docstring'''


    lowercase_     =       args
    lowercase_     =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}      )

    with open(self.args.csv_file   ,				newline=""""""      ) as csv_file:
      lowercase_     =       csv.DictReader(lowercase_      )
      for row in reader:
        lowercase_     =       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
          lowercase_     =       int(row["""result"""]      )
        elif can_convert_to_float(row["""result"""]      ):
          # value is not None
          lowercase_     =       float(row["""result"""]      )





  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    lowercase_     ,	lowercase_     =       plt.subplots()
    lowercase_     =       """Time usage""" if self.args.is_time else """Memory usage"""
    lowercase_     =       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()      ):
      lowercase_     =       sorted(set(self.result_dict[model_name]["""bsz"""]      )      )
      lowercase_     =       sorted(set(self.result_dict[model_name]["""seq_len"""]      )      )
      lowercase_     =       self.result_dict[model_name]["""result"""]

      ((lowercase_)     ,	(lowercase_))     =       (
          (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
      )

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

        ((lowercase_)     ,	(lowercase_))     =       (
            ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
        )

        lowercase_     =       np.asarray(lowercase_   ,				lowercase_      )[: len(lowercase_      )]
        plt.scatter(
            lowercase_   ,				lowercase_   ,				label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""      )
        plt.plot(lowercase_   ,				lowercase_   ,				"""--"""      )

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

    lowercase_     =       title_str[:-4]
    lowercase_     =       """Time in s""" if self.args.is_time else """Memory in MB"""

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

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






def   A_      (       )			->Tuple:
  lowercase_     =       HfArgumentParser(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       parser.parse_args_into_dataclasses()[0]
  lowercase_     =       Plot(args=SCREAMING_SNAKE_CASE_						)
  plot.plot()


if __name__ == "__main__":
       main()

