



import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory



def    snake_case_				(    ):
	raise RuntimeError("CUDA out of memory."   )

class   _A   ( nn.Module	):


						'''simple docstring'''





						def __init__(	self	:							str	):






							'''simple docstring'''



							super().__init__()
							__lowercase		       =			nn.Linear(3							,      4	)
							__lowercase		       =			nn.BatchNormad(4	)
							__lowercase		       =			nn.Linear(4							,      5	)



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							List[Any]	):






							'''simple docstring'''



							return self.lineara(self.batchnorm(self.lineara(lowerCamelCase	)	)	)

class   _A   ( unittest.TestCase	):


						'''simple docstring'''





						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							__lowercase		       =			[]

							@find_executable_batch_size(starting_batch_size=128	)
							def mock_training_loop_function(lowerCamelCase	:							Tuple	):
								nonlocal batch_sizes
								batch_sizes.append(lowerCamelCase	)
								if batch_size != 8:
									raise_fake_out_of_memory()

							mock_training_loop_function()
							self.assertListEqual(lowerCamelCase							,      [128, 64, 32, 16, 8]	)



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			[]

							@find_executable_batch_size(starting_batch_size=128	)
							def mock_training_loop_function(lowerCamelCase	:							str							,      lowerCamelCase	:							Dict	):
								nonlocal batch_sizes
								batch_sizes.append(lowerCamelCase	)
								if batch_size != 8:
									raise_fake_out_of_memory()
								return batch_size, arga

							__lowercase       ,    __lowercase		       =			mock_training_loop_function("hello"	)
							self.assertListEqual(lowerCamelCase							,      [128, 64, 32, 16, 8]	)
							self.assertListEqual([bs, arga]							,      [8, "hello"]	)



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							@find_executable_batch_size(starting_batch_size=0	)
							def mock_training_loop_function(lowerCamelCase	:							List[Any]	):
								pass

							with self.assertRaises(lowerCamelCase	) as cm:
								mock_training_loop_function()
								self.assertIn("No executable batch size found, reached zero."							,      cm.exception.args[0]	)



						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							@find_executable_batch_size(starting_batch_size=16	)
							def mock_training_loop_function(lowerCamelCase	:							str	):
								if batch_size > 0:
									raise_fake_out_of_memory()
								pass

							with self.assertRaises(lowerCamelCase	) as cm:
								mock_training_loop_function()
								self.assertIn("No executable batch size found, reached zero."							,      cm.exception.args[0]	)



						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							@find_executable_batch_size(starting_batch_size=128	)
							def mock_training_loop_function(lowerCamelCase	:							List[str]							,      lowerCamelCase	:							Any							,      lowerCamelCase	:							List[Any]	):
								if batch_size != 8:
									raise raise_fake_out_of_memory()

							with self.assertRaises(lowerCamelCase	) as cm:
								mock_training_loop_function(128							,      "hello"							,      "world"	)
								self.assertIn("Batch size was passed into `f`"							,      cm.exception.args[0]	)
								self.assertIn("`f(arg1='hello', arg2='world')"							,      cm.exception.args[0]	)



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							@find_executable_batch_size(starting_batch_size=16	)
							def mock_training_loop_function(lowerCamelCase	:							str	):
								raise ValueError("Oops, we had an error!"	)

							with self.assertRaises(lowerCamelCase	) as cm:
								mock_training_loop_function()
								self.assertIn("Oops, we had an error!"							,      cm.exception.args[0]	)



						@require_cuda
						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							__lowercase		       =			torch.cuda.memory_allocated()
							__lowercase		       =			ModelForTest()
							model.cuda()
							self.assertGreater(torch.cuda.memory_allocated()							,      lowerCamelCase	)
							__lowercase		       =			release_memory(lowerCamelCase	)
							self.assertEqual(torch.cuda.memory_allocated()							,      lowerCamelCase	)








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()
snake_case__					:     Optional[int]    		=   logging.get_logger(__name__)



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			MobileNetVaConfig(layer_norm_eps=0.0_0_1   )

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

	__lowercase		       =			re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$"  ,	_SCREAMING_SNAKE_CASE   )
	if matches:
		__lowercase		       =			float(matches[1]   )
		__lowercase		       =			int(matches[2]   )

	# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
	# the usual 1000. The first class (index 0) is "background".
	__lowercase		       =			1_0_0_1
	__lowercase		       =			"imagenet-1k-id2label.json"
	__lowercase		       =			"huggingface/label-files"
	__lowercase		       =			json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"   )  ,	"r"   )   )
	__lowercase		       =			{int(_SCREAMING_SNAKE_CASE   ) + 1: v for k, v in idalabel.items()}
	__lowercase		       =			"background"
	__lowercase		       =			idalabel
	__lowercase		       =			{v: k for k, v in idalabel.items()}

	return config



def    snake_case_				(    ):
	__lowercase		       =			"http://images.cocodataset.org/val2017/000000039769.jpg"
	__lowercase		       =			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   ):
	__lowercase		       =			get_mobilenet_va_config(_SCREAMING_SNAKE_CASE   )

	# Load 🤗 model
	__lowercase		       =			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 MobileNetV1ImageProcessor
	__lowercase		       =			MobileNetVaImageProcessor(
	    crop_size={"width": config.image_size, "height": config.image_size}  ,	size={"shortest_edge": config.image_size + 3_2}  ,	)
	__lowercase		       =			image_processor(images=prepare_img()  ,	return_tensors="pt"   )
	__lowercase		       =			model(**_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			outputs.logits

	assert logits.shape == (1, 1_0_0_1)

	if model_name == "mobilenet_v1_1.0_224":
		__lowercase		       =			torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]   )
	elif model_name == "mobilenet_v1_0.75_192":
		__lowercase		       =			torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]   )
	else:
		__lowercase		       =			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..."   )
		__lowercase		       =			"google/" + model_name
		image_processor.push_to_hub(_SCREAMING_SNAKE_CASE   )
		model.push_to_hub(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	snake_case__					:     Tuple    		=   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."""
	)

	snake_case__					:     Dict    		=   parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)



