


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()
SCREAMING_SNAKE_CASE__       			=				logging.get_logger(__name__)




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Tuple  ):







	'''simple docstring'''



	lowercase_			 = MobileNetVaConfig(layer_norm_eps=0.001  )

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

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

	if model_name.startswith("deeplabv3_"  ):
		lowercase_			 = 8
		lowercase_			 = 21
		lowercase_			 = "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".
		lowercase_			 = 1001
		lowercase_			 = "imagenet-1k-id2label.json"

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

	if config.num_labels == 1001:
		lowercase_			 = {int(__lowerCamelCase  ) + 1: v for k, v in idalabel.items()}
		lowercase_			 = "background"
	else:
		lowercase_			 = {int(__lowerCamelCase  ): v for k, v in idalabel.items()}

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

	return config




def SCREAMING_SNAKE_CASE_  (				):







	'''simple docstring'''



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




@torch.no_grad()
def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Optional[Any]  ,			__lowerCamelCase:		Optional[Any]  ,			__lowerCamelCase:		List[Any]  ,			__lowerCamelCase:		int=False  ):







	'''simple docstring'''



	lowercase_			 = get_mobilenet_va_config(__lowerCamelCase  )

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

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

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

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

		if model_name == "deeplabv3_mobilenet_v2_1.0_513":
			lowercase_			 = 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]  ,			__lowerCamelCase  ,			atol=1E-4  )
	else:
		assert logits.shape == (1, 1001)

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

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

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

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       			=				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."""
							)

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





import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       			=				argparse.ArgumentParser()

							# Hyperparameters sent by the client are passed as command-line arguments to the script.
							parser.add_argument("""--epochs""", type=int, default=1)
							parser.add_argument("""--per_device_train_batch_size""", type=int, default=1_6)
							parser.add_argument("""--per_device_eval_batch_size""", type=int, default=8)
							parser.add_argument("""--model_name_or_path""", type=str)
							parser.add_argument("""--learning_rate""", type=str, default=5E-5)
							parser.add_argument("""--do_train""", type=bool, default=True)
							parser.add_argument("""--do_eval""", type=bool, default=True)
							parser.add_argument("""--output_dir""", type=str)

							SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				parser.parse_known_args()

							# overwrite batch size until we have tf_glue.py
							SCREAMING_SNAKE_CASE__       			=				1_6
							SCREAMING_SNAKE_CASE__       			=				1_6

							# Set up logging
							SCREAMING_SNAKE_CASE__       			=				logging.getLogger(__name__)

							logging.basicConfig(
							    level=logging.getLevelName("""INFO"""),
							    handlers=[logging.StreamHandler(sys.stdout)],
							    format="""%(asctime)s - %(name)s - %(levelname)s - %(message)s""",
							)

							# Load model and tokenizer
							SCREAMING_SNAKE_CASE__       			=				TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
							SCREAMING_SNAKE_CASE__       			=				AutoTokenizer.from_pretrained(args.model_name_or_path)

							# Load dataset
							SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				load_dataset("""imdb""", split=["""train""", """test"""])
							SCREAMING_SNAKE_CASE__       			=				train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
							SCREAMING_SNAKE_CASE__       			=				test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

							# Preprocess train dataset
							SCREAMING_SNAKE_CASE__       			=				train_dataset.map(
							    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
							)
							train_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

							SCREAMING_SNAKE_CASE__       			=				{
							    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
							    for x in ["""input_ids""", """attention_mask"""]
							}
							SCREAMING_SNAKE_CASE__       			=				tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])).batch(
							    args.per_device_train_batch_size
							)

							# Preprocess test dataset
							SCREAMING_SNAKE_CASE__       			=				test_dataset.map(
							    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
							)
							test_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

							SCREAMING_SNAKE_CASE__       			=				{
							    x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
							    for x in ["""input_ids""", """attention_mask"""]
							}
							SCREAMING_SNAKE_CASE__       			=				tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])).batch(
							    args.per_device_eval_batch_size
							)

							# fine optimizer and loss
							SCREAMING_SNAKE_CASE__       			=				tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
							SCREAMING_SNAKE_CASE__       			=				tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
							SCREAMING_SNAKE_CASE__       			=				[tf.keras.metrics.SparseCategoricalAccuracy()]
							model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

							SCREAMING_SNAKE_CASE__       			=				time.time()
							SCREAMING_SNAKE_CASE__       			=				model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
							SCREAMING_SNAKE_CASE__       			=				time.time() - start_train_time

							logger.info("""*** Train ***""")
							logger.info(f"""train_runtime = {end_train_time}""")
							for key, value in train_results.history.items():
														logger.info(f"""  {key} = {value}""")

