



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__     (						_UpperCamelCase     :	Any							):
			__lowerCamelCase  							=      MobileNetVaConfig(layer_norm_eps=0.001							)

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

			__lowerCamelCase  							=      re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''	,_UpperCamelCase							)
			if matches:
						__lowerCamelCase  							=      float(matches[1]							)
						__lowerCamelCase  							=      int(matches[2]							)

			if model_name.startswith('''deeplabv3_'''							):
						__lowerCamelCase  							=      8
						__lowerCamelCase  							=      21
						__lowerCamelCase  							=      '''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".
						__lowerCamelCase  							=      10_01
						__lowerCamelCase  							=      '''imagenet-1k-id2label.json'''

			__lowerCamelCase  							=      '''huggingface/label-files'''
			__lowerCamelCase  							=      json.load(open(hf_hub_download(_UpperCamelCase	,_UpperCamelCase	,repo_type='''dataset'''							)	,'''r'''							)							)

			if config.num_labels == 10_01:
						__lowerCamelCase  							=      {int(_UpperCamelCase							) + 1: v for k, v in idalabel.items()}
						__lowerCamelCase  							=      '''background'''
			else:
						__lowerCamelCase  							=      {int(_UpperCamelCase							): v for k, v in idalabel.items()}

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

			return config





def 						a__     (						):
			__lowerCamelCase  							=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			__lowerCamelCase  							=      Image.open(requests.get(_UpperCamelCase	,stream=_UpperCamelCase							).raw							)
			return im





@torch.no_grad()
def 						a__     (						_UpperCamelCase     :	Optional[Any]	,_UpperCamelCase     :	Optional[int]	,_UpperCamelCase     :	List[Any]	,_UpperCamelCase     :	Dict=False							):
			__lowerCamelCase  							=      get_mobilenet_va_config(_UpperCamelCase							)

			# Load 🤗 model
			if model_name.startswith('''deeplabv3_'''							):
						__lowerCamelCase  							=      MobileNetVaForSemanticSegmentation(_UpperCamelCase							).eval()
			else:
						__lowerCamelCase  							=      MobileNetVaForImageClassification(_UpperCamelCase							).eval()

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

			# Check outputs on an image, prepared by MobileNetV2ImageProcessor
			__lowerCamelCase  							=      MobileNetVaImageProcessor(
			    crop_size={'''width''': config.image_size, '''height''': config.image_size}	,size={'''shortest_edge''': config.image_size + 32}	,)
			__lowerCamelCase  							=      image_processor(images=prepare_img()	,return_tensors='''pt'''							)
			__lowerCamelCase  							=      model(**_UpperCamelCase							)
			__lowerCamelCase  							=      outputs.logits

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

						if model_name == "deeplabv3_mobilenet_v2_1.0_513":
									__lowerCamelCase  							=      torch.tensor(
									    [
									        [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
									        [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
									        [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
									    ]							)

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

						assert torch.allclose(logits[0, :3, :3, :3]	,_UpperCamelCase	,atol=1e-4							)
			else:
						assert logits.shape == (1, 10_01)

						if model_name == "mobilenet_v2_1.4_224":
									__lowerCamelCase  							=      torch.tensor([0.0_181, -1.0_015, 0.4_688]							)
						elif model_name == "mobilenet_v2_1.0_224":
									__lowerCamelCase  							=      torch.tensor([0.2_445, -1.1_993, 0.1_905]							)
						elif model_name == "mobilenet_v2_0.75_160":
									__lowerCamelCase  							=      torch.tensor([0.2_482, 0.4_136, 0.6_669]							)
						elif model_name == "mobilenet_v2_0.35_96":
									__lowerCamelCase  							=      torch.tensor([0.1_451, -0.4_624, 0.7_192]							)
						else:
									__lowerCamelCase  							=      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...'''							)
						__lowerCamelCase  							=      '''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_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
		)











import argparse
import json
import re
import string
import sys
from collections import Counter





def 						a__     (						_UpperCamelCase     :	Tuple							):

   def remove_articles(_UpperCamelCase     :	int							):
      return re.sub(R'''\b(a|an|the)\b'''	,''' '''	,_UpperCamelCase							)

   def white_space_fix(_UpperCamelCase     :	int							):
      return " ".join(text.split()							)

   def remove_punc(_UpperCamelCase     :	int							):
      __lowerCamelCase  							=      set(string.punctuation							)
      return "".join(ch for ch in text if ch not in exclude							)

   def lower(_UpperCamelCase     :	str							):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(_UpperCamelCase							)							)							)							)





def 						a__     (						_UpperCamelCase     :	Union[str, Any]	,_UpperCamelCase     :	List[str]							):
   __lowerCamelCase  							=      normalize_answer(_UpperCamelCase							).split()
   __lowerCamelCase  							=      normalize_answer(_UpperCamelCase							).split()
   __lowerCamelCase  							=      Counter(_UpperCamelCase							) & Counter(_UpperCamelCase							)
   __lowerCamelCase  							=      sum(common.values()							)
   if num_same == 0:
      return 0
   __lowerCamelCase  							=      1.0 * num_same / len(_UpperCamelCase							)
   __lowerCamelCase  							=      1.0 * num_same / len(_UpperCamelCase							)
   __lowerCamelCase  							=      (2 * precision * recall) / (precision + recall)
   return fa





def 						a__     (						_UpperCamelCase     :	Optional[Any]	,_UpperCamelCase     :	str							):
   return normalize_answer(_UpperCamelCase							) == normalize_answer(_UpperCamelCase							)





def 						a__     (						_UpperCamelCase     :	int	,_UpperCamelCase     :	Dict	,_UpperCamelCase     :	Dict							):
   __lowerCamelCase  							=      []
   for ground_truth in ground_truths:
      __lowerCamelCase  							=      metric_fn(_UpperCamelCase	,_UpperCamelCase							)
      scores_for_ground_truths.append(_UpperCamelCase							)
   return max(_UpperCamelCase							)





def 						a__     (						_UpperCamelCase     :	Optional[int]	,_UpperCamelCase     :	Optional[Any]							):
   __lowerCamelCase  							=      __lowerCamelCase  							=      __lowerCamelCase  							=      0
   __lowerCamelCase  							=      []
   for passage in dataset:
      for qa in passage["qas"]:
         total += 1
         if qa["id"] not in predictions:
            __lowerCamelCase  							=      F"""Unanswered question {qa["id"]} will receive score 0."""
            print(_UpperCamelCase	,file=sys.stderr							)
            continue

         __lowerCamelCase  							=      [x['''text'''] for x in qa['''answers''']]
         __lowerCamelCase  							=      predictions[qa['''id''']]

         __lowerCamelCase  							=      metric_max_over_ground_truths(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase							)
         if int(_exact_match							) == 1:
            correct_ids.append(qa['''id''']							)
         exact_match += _exact_match

         fa += metric_max_over_ground_truths(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase							)

   __lowerCamelCase  							=      exact_match / total
   __lowerCamelCase  							=      fa / total

   return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
  a_								=   """1.0"""
  a_								=   argparse.ArgumentParser("""Official evaluation script for ReCoRD v1.0.""")
  parser.add_argument("""data_file""", help="""The dataset file in JSON format.""")
  parser.add_argument("""pred_file""", help="""The model prediction file in JSON format.""")
  parser.add_argument("""--output_correct_ids""", action="""store_true""", help="""Output the correctly answered query IDs.""")
  a_								=   parser.parse_args()

  with open(args.data_file) as data_file:
    a_								=   json.load(data_file)
    if dataset_json["version"] != expected_version:
      print(
          f"Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}",
          file=sys.stderr,
      )
    a_								=   dataset_json["""data"""]

  with open(args.pred_file) as pred_file:
    a_								=   json.load(pred_file)

  a_						,  a_								=   evaluate(dataset, predictions)

  if args.output_correct_ids:
    print(f"Output {len(correct_ids)} correctly answered question IDs.")
    with open("""correct_ids.json""", """w""") as f:
      json.dump(correct_ids, f)






