






'''simple docstring'''





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()
a_  			=			logging.get_logger(__name__)



def       __UpperCAmelCase					(lowercase__					)		->					Optional[Any]:

						'''simple docstring'''


						a_					  = MobileNetVaConfig(layer_norm_eps=0.001					)

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

						a_					  = re.match(r"^mobilenet_v1_([^_]*)_([^_]*)$"						,_UpperCamelCase					)
						if matches:
												a_					  = float(matches[1]					)
												a_					  = int(matches[2]					)

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

						return config



def       __UpperCAmelCase					()		->					Any:

						'''simple docstring'''


						a_					  = "http://images.cocodataset.org/val2017/000000039769.jpg"
						a_					  = Image.open(requests.get(_UpperCamelCase						,stream=_UpperCamelCase					).raw					)
						return im



@torch.no_grad()
def       __UpperCAmelCase					(lowercase__						,lowercase__						,lowercase__						,lowercase__=False					)		->					Dict:

						'''simple docstring'''


						a_					  = get_mobilenet_va_config(_UpperCamelCase					)

						# Load 🤗 model
						a_					  = MobileNetVaForImageClassification(_UpperCamelCase					).eval()

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

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

						assert logits.shape == (1, 1001)

						if model_name == "mobilenet_v1_1.0_224":
												a_					  = torch.tensor([-4.1739, -1.1233, 3.1205]					)
						elif model_name == "mobilenet_v1_0.75_192":
												a_					  = torch.tensor([-3.9440, -2.3141, -0.3333]					)
						else:
												a_					  = None

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

						Path(_UpperCamelCase					).mkdir(exist_ok=_UpperCamelCase					)
						print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""					)
						model.save_pretrained(_UpperCamelCase					)
						print(F"""Saving image processor to {pytorch_dump_folder_path}"""					)
						image_processor.save_pretrained(_UpperCamelCase					)

						if push_to_hub:
												print("Pushing to the hub..."					)
												a_					  = "google/" + model_name
												image_processor.push_to_hub(_UpperCamelCase					)
												model.push_to_hub(_UpperCamelCase					)


if __name__ == "__main__":
							a_  			=			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.'
							)

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











'''simple docstring'''
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


UpperCamelCase__			=      ['small', 'medium', 'large']

UpperCamelCase__			=      'lm_head.decoder.weight'
UpperCamelCase__			=      'lm_head.weight'




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  torch.load(_UpperCamelCase				)
   lowercase_						:     List[Any]							     =  d.pop(_UpperCamelCase				)
   os.makedirs(_UpperCamelCase    ,		exist_ok=_UpperCamelCase				)
   torch.save(_UpperCamelCase    ,		os.path.join(_UpperCamelCase    ,		_UpperCamelCase				)				)


if __name__ == "__main__":
 UpperCamelCase__			=      argparse.ArgumentParser()
 parser.add_argument('--dialogpt_path', default='.', type=str)
 UpperCamelCase__			=      parser.parse_args()
 for MODEL in DIALOGPT_MODELS:
  UpperCamelCase__			=      os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
  UpperCamelCase__			=      f"""./DialoGPT-{MODEL}"""
  convert_dialogpt_checkpoint(
      checkpoint_path,
      pytorch_dump_folder_path,
  )




