

'''simple docstring'''

import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
A_      =							logging.getLogger(__name__)





def 				A_					(	):
  SCREAMING_SNAKE_CASE:Any   =   argparse.ArgumentParser(description="PyTorch Transformer Language Model"	)
  parser.add_argument("--model_name" ,    type=snake_case ,    default="transfo-xl-wt103" ,    help="pretrained model name"	)
  parser.add_argument(
      "--split" ,    type=snake_case ,    default="test" ,    choices=["all", "valid", "test"] ,    help="which split to evaluate"	)
  parser.add_argument("--batch_size" ,    type=snake_case ,    default=10 ,    help="batch size"	)
  parser.add_argument("--tgt_len" ,    type=snake_case ,    default=128 ,    help="number of tokens to predict"	)
  parser.add_argument("--ext_len" ,    type=snake_case ,    default=0 ,    help="length of the extended context"	)
  parser.add_argument("--mem_len" ,    type=snake_case ,    default=1600 ,    help="length of the retained previous heads"	)
  parser.add_argument("--clamp_len" ,    type=snake_case ,    default=1000 ,    help="max positional embedding index"	)
  parser.add_argument("--no_cuda" ,    action="store_true" ,    help="Do not use CUDA even though CUA is available"	)
  parser.add_argument("--work_dir" ,    type=snake_case ,    required=snake_case ,    help="path to the work_dir"	)
  parser.add_argument("--no_log" ,    action="store_true" ,    help="do not log the eval result"	)
  parser.add_argument("--same_length" ,    action="store_true" ,    help="set same length attention with masking"	)
  parser.add_argument("--server_ip" ,    type=snake_case ,    default="" ,    help="Can be used for distant debugging."	)
  parser.add_argument("--server_port" ,    type=snake_case ,    default="" ,    help="Can be used for distant debugging."	)
  SCREAMING_SNAKE_CASE:Optional[int]   =   parser.parse_args()
  assert args.ext_len >= 0, "extended context length must be non-negative"

  if args.server_ip and args.server_port:
    # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
    import ptvsd

    print("Waiting for debugger attach"	)
    ptvsd.enable_attach(address=(args.server_ip, args.server_port) ,    redirect_output=snake_case	)
    ptvsd.wait_for_attach()

  SCREAMING_SNAKE_CASE:Optional[int]   =   torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"	)
  logger.info("device: {}".format(snake_case	)	)

  # Load a pre-processed dataset
  # You can also build the corpus yourself using TransfoXLCorpus methods
  # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
  # and tokenizing the dataset
  # The pre-processed corpus is a convertion (using the conversion script )
  SCREAMING_SNAKE_CASE:Optional[int]   =   TransfoXLCorpus.from_pretrained(args.model_name	)

  SCREAMING_SNAKE_CASE:str   =   corpus.get_iterator("valid" ,    args.batch_size ,    args.tgt_len ,    device=snake_case ,    ext_len=args.ext_len	)
  SCREAMING_SNAKE_CASE:Any   =   corpus.get_iterator("test" ,    args.batch_size ,    args.tgt_len ,    device=snake_case ,    ext_len=args.ext_len	)

  # Load a pre-trained model
  SCREAMING_SNAKE_CASE:List[Any]   =   TransfoXLLMHeadModel.from_pretrained(args.model_name	)
  model.to(snake_case	)

  logger.info(
      "Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}".format(
          args.batch_size ,    args.tgt_len ,    args.ext_len ,    args.mem_len ,    args.clamp_len	)	)

  model.reset_memory_length(args.mem_len	)
  if args.clamp_len > 0:
    SCREAMING_SNAKE_CASE:Any   =   args.clamp_len
  if args.same_length:
    SCREAMING_SNAKE_CASE:int   =   True

  ###############################################################################
  # Evaluation code
  ###############################################################################
  def evaluate(snake_case	):
    # Turn on evaluation mode which disables dropout.
    model.eval()
    SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE:int   =   0, 0.0
    SCREAMING_SNAKE_CASE:Tuple   =   time.time()
    with torch.no_grad():
      SCREAMING_SNAKE_CASE:Any   =   None
      for idx, (data, target, seq_len) in enumerate(snake_case	):
        SCREAMING_SNAKE_CASE:Optional[Any]   =   model(snake_case ,    lm_labels=snake_case ,    mems=snake_case	)
        SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE:Optional[int]   =   ret
        SCREAMING_SNAKE_CASE:int   =   loss.mean()
        total_loss += seq_len * loss.item()
        total_len += seq_len
      SCREAMING_SNAKE_CASE:Optional[int]   =   time.time() - start_time
    logger.info("Time : {:.2f}s, {:.2f}ms/segment".format(snake_case ,    1000 * total_time / (idx + 1)	)	)
    return total_loss / total_len

  # Run on test data.
  if args.split == "all":
    SCREAMING_SNAKE_CASE:List[Any]   =   evaluate(snake_case	)
    SCREAMING_SNAKE_CASE:str   =   evaluate(snake_case	)
  elif args.split == "valid":
    SCREAMING_SNAKE_CASE:Dict   =   evaluate(snake_case	)
    SCREAMING_SNAKE_CASE:str   =   None
  elif args.split == "test":
    SCREAMING_SNAKE_CASE:List[Any]   =   evaluate(snake_case	)
    SCREAMING_SNAKE_CASE:Any   =   None

  def format_log(snake_case ,    snake_case	):
    SCREAMING_SNAKE_CASE:str   =   "| {0} loss {1:5.2f} | {0} ppl {2:9.3f} ".format(snake_case ,    snake_case ,    math.exp(snake_case	)	)
    return log_str

  SCREAMING_SNAKE_CASE:Dict   =   ""
  if valid_loss is not None:
    log_str += format_log(snake_case ,    "valid"	)
  if test_loss is not None:
    log_str += format_log(snake_case ,    "test"	)

  logger.info("=" * 100	)
  logger.info(snake_case	)
  logger.info("=" * 100	)


if __name__ == "__main__":
    main()









'''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_      =							logging.get_logger(__name__)





def 				A_					(	snake_case	):
  SCREAMING_SNAKE_CASE:Optional[int]   =   MobileNetVaConfig(layer_norm_eps=0.001	)

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

  SCREAMING_SNAKE_CASE:Any   =   re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$" ,    snake_case	)
  if matches:
    SCREAMING_SNAKE_CASE:str   =   float(matches[1]	)
    SCREAMING_SNAKE_CASE:Optional[int]   =   int(matches[2]	)

  if model_name.startswith("deeplabv3_"	):
    SCREAMING_SNAKE_CASE:str   =   8
    SCREAMING_SNAKE_CASE:Optional[int]   =   21
    SCREAMING_SNAKE_CASE:Dict   =   "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".
    SCREAMING_SNAKE_CASE:Optional[int]   =   1001
    SCREAMING_SNAKE_CASE:Tuple   =   "imagenet-1k-id2label.json"

  SCREAMING_SNAKE_CASE:List[str]   =   "huggingface/label-files"
  SCREAMING_SNAKE_CASE:List[Any]   =   json.load(open(hf_hub_download(snake_case ,    snake_case ,    repo_type="dataset"	) ,    "r"	)	)

  if config.num_labels == 1001:
    SCREAMING_SNAKE_CASE:Optional[Any]   =   {int(snake_case	) + 1: v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE:Dict   =   "background"
  else:
    SCREAMING_SNAKE_CASE:Union[str, Any]   =   {int(snake_case	): v for k, v in idalabel.items()}

  SCREAMING_SNAKE_CASE:str   =   idalabel
  SCREAMING_SNAKE_CASE:int   =   {v: k for k, v in idalabel.items()}

  return config





def 				A_					(	):
  SCREAMING_SNAKE_CASE:Optional[Any]   =   "http://images.cocodataset.org/val2017/000000039769.jpg"
  SCREAMING_SNAKE_CASE:str   =   Image.open(requests.get(snake_case ,    stream=snake_case	).raw	)
  return im





@torch.no_grad()
def 				A_					(	snake_case ,    snake_case ,    snake_case ,    snake_case=False	):
  SCREAMING_SNAKE_CASE:Any   =   get_mobilenet_va_config(snake_case	)

  # Load 🤗 model
  if model_name.startswith("deeplabv3_"	):
    SCREAMING_SNAKE_CASE:Tuple   =   MobileNetVaForSemanticSegmentation(snake_case	).eval()
  else:
    SCREAMING_SNAKE_CASE:List[str]   =   MobileNetVaForImageClassification(snake_case	).eval()

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

  # Check outputs on an image, prepared by MobileNetV2ImageProcessor
  SCREAMING_SNAKE_CASE:List[str]   =   MobileNetVaImageProcessor(
      crop_size={"width": config.image_size, "height": config.image_size} ,    size={"shortest_edge": config.image_size + 32} ,    )
  SCREAMING_SNAKE_CASE:List[Any]   =   image_processor(images=prepare_img() ,    return_tensors="pt"	)
  SCREAMING_SNAKE_CASE:Union[str, Any]   =   model(**snake_case	)
  SCREAMING_SNAKE_CASE:str   =   outputs.logits

  if model_name.startswith("deeplabv3_"	):
    assert logits.shape == (1, 21, 65, 65)

    if model_name == "deeplabv3_mobilenet_v2_1.0_513":
      SCREAMING_SNAKE_CASE:int   =   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] ,    snake_case ,    atol=1e-4	)
  else:
    assert logits.shape == (1, 1001)

    if model_name == "mobilenet_v2_1.4_224":
      SCREAMING_SNAKE_CASE:List[str]   =   torch.tensor([0.0181, -1.0015, 0.4688]	)
    elif model_name == "mobilenet_v2_1.0_224":
      SCREAMING_SNAKE_CASE:Optional[int]   =   torch.tensor([0.2445, -1.1993, 0.1905]	)
    elif model_name == "mobilenet_v2_0.75_160":
      SCREAMING_SNAKE_CASE:Optional[Any]   =   torch.tensor([0.2482, 0.4136, 0.6669]	)
    elif model_name == "mobilenet_v2_0.35_96":
      SCREAMING_SNAKE_CASE:int   =   torch.tensor([0.1451, -0.4624, 0.7192]	)
    else:
      SCREAMING_SNAKE_CASE:Optional[Any]   =   None

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

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

  if push_to_hub:
    print("Pushing to the hub..."	)
    SCREAMING_SNAKE_CASE:Tuple   =   "google/" + model_name
    image_processor.push_to_hub(snake_case	)
    model.push_to_hub(snake_case	)


if __name__ == "__main__":
    A_      =							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_      =							parser.parse_args()
    convert_movilevit_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )






