






"""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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
a  :						int					       =							logging.get_logger(__name__)




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->Optional[Any]:
 UpperCAmelCase__ 						=				MobileNetVaConfig(layer_norm_eps=0.001			)

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

 UpperCAmelCase__ 						=				re.match(r"""^.*mobilenet_v2_([^_]*)_([^_]*)$"""       ,	_SCREAMING_SNAKE_CASE			)
 if matches:
  UpperCAmelCase__ 						=				float(matches[1]			)
  UpperCAmelCase__ 						=				int(matches[2]			)

 if model_name.startswith("""deeplabv3_"""			):
  UpperCAmelCase__ 						=				8
  UpperCAmelCase__ 						=				2_1
  UpperCAmelCase__ 						=				"""pascal-voc-id2label.json"""
 else:
  # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
  # of the usual 1000. The first class (index 0) is "background".
  UpperCAmelCase__ 						=				1_0_0_1
  UpperCAmelCase__ 						=				"""imagenet-1k-id2label.json"""

 UpperCAmelCase__ 						=				"""huggingface/label-files"""
 UpperCAmelCase__ 						=				json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	repo_type="""dataset"""			)       ,	"""r"""			)			)

 if config.num_labels == 1_0_0_1:
  UpperCAmelCase__ 						=				{int(_SCREAMING_SNAKE_CASE			) + 1: v for k, v in idalabel.items()}
  UpperCAmelCase__ 						=				"""background"""
 else:
  UpperCAmelCase__ 						=				{int(_SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}

 UpperCAmelCase__ 						=				idalabel
 UpperCAmelCase__ 						=				{v: k for k, v in idalabel.items()}

 return config




def       snake_case__		(   )    ->Tuple:
 UpperCAmelCase__ 						=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCAmelCase__ 						=				Image.open(requests.get(_SCREAMING_SNAKE_CASE       ,	stream=_SCREAMING_SNAKE_CASE			).raw			)
 return im




@torch.no_grad()
def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=False			)    ->List[Any]:
 UpperCAmelCase__ 						=				get_mobilenet_va_config(_SCREAMING_SNAKE_CASE			)

 # Load 🤗 model
 if model_name.startswith("""deeplabv3_"""			):
  UpperCAmelCase__ 						=				MobileNetVaForSemanticSegmentation(_SCREAMING_SNAKE_CASE			).eval()
 else:
  UpperCAmelCase__ 						=				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 MobileNetV2ImageProcessor
 UpperCAmelCase__ 						=				MobileNetVaImageProcessor(
     crop_size={"""width""": config.image_size, """height""": config.image_size}       ,	size={"""shortest_edge""": config.image_size + 3_2}       ,	)
 UpperCAmelCase__ 						=				image_processor(images=prepare_img()       ,	return_tensors="""pt"""			)
 UpperCAmelCase__ 						=				model(**_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				outputs.logits

 if model_name.startswith("""deeplabv3_"""			):
  assert logits.shape == (1, 2_1, 6_5, 6_5)

  if model_name == "deeplabv3_mobilenet_v2_1.0_513":
   UpperCAmelCase__ 						=				torch.tensor(
       [
           [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
           [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
           [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
       ]			)

  else:
   raise ValueError(F'''Unknown model name: {model_name}'''			)

  assert torch.allclose(logits[0, :3, :3, :3]       ,	_SCREAMING_SNAKE_CASE       ,	atol=1E-4			)
 else:
  assert logits.shape == (1, 1_0_0_1)

  if model_name == "mobilenet_v2_1.4_224":
   UpperCAmelCase__ 						=				torch.tensor([0.0181, -1.0015, 0.4688]			)
  elif model_name == "mobilenet_v2_1.0_224":
   UpperCAmelCase__ 						=				torch.tensor([0.2445, -1.1993, 0.1905]			)
  elif model_name == "mobilenet_v2_0.75_160":
   UpperCAmelCase__ 						=				torch.tensor([0.2482, 0.4136, 0.6669]			)
  elif model_name == "mobilenet_v2_0.35_96":
   UpperCAmelCase__ 						=				torch.tensor([0.1451, -0.4624, 0.7192]			)
  else:
   UpperCAmelCase__ 						=				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..."""			)
  UpperCAmelCase__ 						=				"""google/""" + model_name
  image_processor.push_to_hub(_SCREAMING_SNAKE_CASE			)
  model.push_to_hub(_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
 a  :						Optional[Any]					       =							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--model_name''',
     default='''mobilenet_v2_1.0_224''',
     type=str,
     help='''Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<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  :						Optional[Any]					       =							parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )











"""simple docstring"""



import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
 import torch

 from transformers import GPTaLMHeadModel


@require_torch
@require_sentencepiece
@require_tokenizers
class 	_UpperCamelCase ( unittest.TestCase							):



    '''simple docstring'''


    @slow
    @unittest.skip("""Model is not available."""			)
    def 		A__      (      self			):
     UpperCAmelCase__ 						=				"""nvidia/megatron-gpt2-345m/"""
     if "MYDIR" in os.environ:
      UpperCAmelCase__ 						=				os.path.join(os.environ["""MYDIR"""]   ,			__lowercase			)
     UpperCAmelCase__ 						=				GPTaLMHeadModel.from_pretrained(__lowercase			)
     model.to(__lowercase			)
     model.half()

     UpperCAmelCase__ 						=				torch.tensor(
         [[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]   ,			device=__lowercase   ,			dtype=torch.long   ,			)

     with torch.no_grad():
      UpperCAmelCase__ 						=				model(__lowercase			).logits

     UpperCAmelCase__ 						=				torch.Size((1, 9, 50257)			)
     self.assertEqual(output.shape   ,			__lowercase			)

     UpperCAmelCase__ 						=				torch.tensor(
         [
             4.9_414,
             -0.2_920,
             -1.2_148,
             -4.0_273,
             -0.5_161,
             -5.2_109,
             -1.2_412,
             -1.8_301,
             -1.7_734,
             -4.7_148,
             -0.2_317,
             -1.0_811,
             -2.1_777,
             0.4_141,
             -3.7_969,
             -4.0_586,
             -2.5_332,
             -3.3_809,
             4.3_867,
         ]   ,			device=__lowercase   ,			dtype=torch.half   ,			)

     for i in range(19			):
      UpperCAmelCase__						,	UpperCAmelCase__ 						=				8 * i // 17, 2792 * i  # along the diagonal
      UpperCAmelCase__						,	UpperCAmelCase__ 						=				output[0, r, c], expected_diag[i]
      UpperCAmelCase__ 						=				F'''row={r} col={c} computed={computed} expected={expected}'''
      self.assertAlmostEqual(__lowercase   ,			__lowercase   ,			delta=1e-4   ,			msg=__lowercase			)



