


import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase    =					logging.get_logger(__name__)






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

      snake_case_ =   MobileNetVaConfig(layer_norm_eps=0.0_0_1      )

      if "_quant" in model_name:
            raise ValueError('''Quantized models are not supported.'''      )

      snake_case_ =   re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''      ,      SCREAMING_SNAKE_CASE      )
      if matches:
            snake_case_ =   float(matches[1]      )
            snake_case_ =   int(matches[2]      )

      # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
      # the usual 1000. The first class (index 0) is "background".
      snake_case_ =   1001
      snake_case_ =   '''imagenet-1k-id2label.json'''
      snake_case_ =   '''huggingface/label-files'''
      snake_case_ =   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      repo_type='''dataset'''      )      ,      '''r'''      )      )
      snake_case_ =   {int(SCREAMING_SNAKE_CASE      ) + 1: v for k, v in idalabel.items()}
      snake_case_ =   '''background'''
      snake_case_ =   idalabel
      snake_case_ =   {v: k for k, v in idalabel.items()}

      return config






def  __lowerCAmelCase					()->						List[str]:




      """simple docstring"""

      snake_case_ =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      snake_case_ =   Image.open(requests.get(SCREAMING_SNAKE_CASE      ,      stream=SCREAMING_SNAKE_CASE      ).raw      )
      return im






@torch.no_grad()
def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=False      )->						int:




      """simple docstring"""

      snake_case_ =   get_mobilenet_va_config(SCREAMING_SNAKE_CASE      )

      # Load 🤗 model
      snake_case_ =   MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE      ).eval()

      # Load weights from TensorFlow checkpoint
      load_tf_weights_in_mobilenet_va(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )

      # Check outputs on an image, prepared by MobileNetV1ImageProcessor
      snake_case_ =   MobileNetVaImageProcessor(
          crop_size={'''width''': config.image_size, '''height''': config.image_size}      ,      size={'''shortest_edge''': config.image_size + 32}      ,      )
      snake_case_ =   image_processor(images=prepare_img()      ,      return_tensors='''pt'''      )
      snake_case_ =   model(**SCREAMING_SNAKE_CASE      )
      snake_case_ =   outputs.logits

      assert logits.shape == (1, 1001)

      if model_name == "mobilenet_v1_1.0_224":
            snake_case_ =   torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]      )
      elif model_name == "mobilenet_v1_0.75_192":
            snake_case_ =   torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]      )
      else:
            snake_case_ =   None

      if expected_logits is not None:
            assert torch.allclose(logits[0, :3]      ,      SCREAMING_SNAKE_CASE      ,      atol=1E-4      )

      Path(SCREAMING_SNAKE_CASE      ).mkdir(exist_ok=SCREAMING_SNAKE_CASE      )
      print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''      )
      model.save_pretrained(SCREAMING_SNAKE_CASE      )
      print(f'''Saving image processor to {pytorch_dump_folder_path}'''      )
      image_processor.save_pretrained(SCREAMING_SNAKE_CASE      )

      if push_to_hub:
            print('''Pushing to the hub...'''      )
            snake_case_ =   '''google/''' + model_name
            image_processor.push_to_hub(SCREAMING_SNAKE_CASE      )
            model.push_to_hub(SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
   UpperCAmelCase    =					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model_name""",
       default="""mobilenet_v1_1.0_224""",
       type=str,
       help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
   )
   parser.add_argument(
       """--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
   )
   parser.add_argument(
       """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
   )

   UpperCAmelCase    =					parser.parse_args()
   convert_movilevit_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )



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					(SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=None      )->						Tuple:




      """simple docstring"""

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







@dataclass
class lowerCAmelCase_		:

 '''simple docstring'''

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Optional[Any]:




      """simple docstring"""

      try:
            int(SCREAMING_SNAKE_CASE      )
            return True
      except ValueError:
            return False






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      try:
            float(SCREAMING_SNAKE_CASE      )
            return True
      except ValueError:
            return False







class lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase      ):
       snake_case_ =   args
       snake_case_ =   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}      )

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





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

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

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

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

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

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

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

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

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






def  __lowerCAmelCase					()->						int:




      """simple docstring"""

      snake_case_ =   HfArgumentParser(SCREAMING_SNAKE_CASE      )
      snake_case_ =   parser.parse_args_into_dataclasses()[0]
      snake_case_ =   Plot(args=SCREAMING_SNAKE_CASE      )
      plot.plot()


if __name__ == "__main__":
   main()