





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase:	List[Any] = logging.get_logger(__name__)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[Any]			)   ->							List[str]:




		'''simple docstring'''
		lowercase__					:      int      =		'huggingface/label-files'
		lowercase__					:      Tuple      =		'imagenet-1k-id2label.json'
		lowercase__					:      int      =		json.load(open(hf_hub_download(_lowercase   ,    _lowercase   ,    repo_type='dataset'			)   ,    'r'			)			)
		lowercase__					:      List[Any]      =		{int(_lowercase			): v for k, v in idalabel.items()}
		lowercase__					:      Any      =		{v: k for k, v in idalabel.items()}

		lowercase__					:      Union[str, Any]      =		'std_conv' if 'bit' in model_name else False

		# note that when using BiT as backbone for ViT-hybrid checkpoints,
		# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
		# config.conv_layer = "std_conv_same"
		lowercase__					:      int      =		BitConfig(
		    conv_layer=_lowercase   ,    num_labels=1_000   ,    idalabel=_lowercase   ,    labelaid=_lowercase   ,    )

		return config


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : str			)   ->							str:




		'''simple docstring'''
		if "stem.conv" in name:
				lowercase__					:      int      =		name.replace('stem.conv'   ,    'bit.embedder.convolution'			)
		if "blocks" in name:
				lowercase__					:      List[str]      =		name.replace('blocks'   ,    'layers'			)
		if "head.fc" in name:
				lowercase__					:      Dict      =		name.replace('head.fc'   ,    'classifier.1'			)
		if name.startswith('norm'			):
				lowercase__					:      List[Any]      =		'bit.' + name
		if "bit" not in name and "classifier" not in name:
				lowercase__					:      Optional[int]      =		'bit.encoder.' + name

		return name


def       SCREAMING_SNAKE_CASE__  (       )   ->							Dict:




		'''simple docstring'''
		lowercase__					:      Optional[int]      =		'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowercase__					:      Union[str, Any]      =		Image.open(requests.get(_lowercase   ,    stream=_lowercase			).raw			)
		return im


@torch.no_grad()
def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]   ,    _lowercase     : Optional[int]   ,    _lowercase     : List[Any]=False			)   ->							int:




		'''simple docstring'''
		lowercase__					:      Tuple      =		get_config(_lowercase			)

		# load original model from timm
		lowercase__					:      List[Any]      =		create_model(_lowercase   ,    pretrained=_lowercase			)
		timm_model.eval()

		# load state_dict of original model
		lowercase__					:      Union[str, Any]      =		timm_model.state_dict()
		for key in state_dict.copy().keys():
				lowercase__					:      Optional[int]      =		state_dict.pop(_lowercase			)
				lowercase__					:      Union[str, Any]      =		val.squeeze() if 'head' in key else val

		# load HuggingFace model
		lowercase__					:      Union[str, Any]      =		BitForImageClassification(_lowercase			)
		model.eval()
		model.load_state_dict(_lowercase			)

		# create image processor
		lowercase__					:      List[Any]      =		create_transform(**resolve_data_config({}   ,    model=_lowercase			)			)
		lowercase__					:      Tuple      =		transform.transforms

		lowercase__					:      str      =		{
		    'bilinear': PILImageResampling.BILINEAR,
		    'bicubic': PILImageResampling.BICUBIC,
		    'nearest': PILImageResampling.NEAREST,
		}

		lowercase__					:      Union[str, Any]      =		BitImageProcessor(
		    do_resize=_lowercase   ,    size={'shortest_edge': timm_transforms[0].size}   ,    resample=pillow_resamplings[timm_transforms[0].interpolation.value]   ,    do_center_crop=_lowercase   ,    crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}   ,    do_normalize=_lowercase   ,    image_mean=timm_transforms[-1].mean.tolist()   ,    image_std=timm_transforms[-1].std.tolist()   ,    )

		lowercase__					:      Dict      =		prepare_img()
		lowercase__					:      Optional[Any]      =		transform(_lowercase			).unsqueeze(0			)
		lowercase__					:      Dict      =		processor(_lowercase   ,    return_tensors='pt'			).pixel_values

		# verify pixel values
		assert torch.allclose(_lowercase   ,    _lowercase			)

		# verify logits
		with torch.no_grad():
				lowercase__					:      Union[str, Any]      =		model(_lowercase			)
				lowercase__					:      List[Any]      =		outputs.logits

		print('Logits:'   ,    logits[0, :3]			)
		print('Predicted class:'   ,    model.config.idalabel[logits.argmax(-1			).item()]			)
		lowercase__					:      Union[str, Any]      =		timm_model(_lowercase			)
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(_lowercase   ,    outputs.logits   ,    atol=1e-3			)
		print('Looks ok!'			)

		if pytorch_dump_folder_path is not None:
				Path(_lowercase			).mkdir(exist_ok=_lowercase			)
				print(f"""Saving model {model_name} and processor to {pytorch_dump_folder_path}"""			)
				model.save_pretrained(_lowercase			)
				processor.save_pretrained(_lowercase			)

		if push_to_hub:
				print(f"""Pushing model {model_name} and processor to the hub"""			)
				model.push_to_hub(f"""ybelkada/{model_name}"""			)
				processor.push_to_hub(f"""ybelkada/{model_name}"""			)


if __name__ == "__main__":
			__UpperCamelCase:	str = argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--model_name""",
			    default="""resnetv2_50x1_bitm""",
			    type=str,
			    help="""Name of the BiT timm model you'd like to convert.""",
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
			)
			parser.add_argument(
			    """--push_to_hub""",
			    action="""store_true""",
			    help="""Whether to push the model to the hub.""",
			)

			__UpperCamelCase:	Any = parser.parse_args()
			convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import YolosImageProcessor


class       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	def __init__(						self:						Optional[int],					lowerCamelCase_:						Dict,					lowerCamelCase_:						Tuple=7,					lowerCamelCase_:						Dict=3,					lowerCamelCase_:						Optional[Any]=30,					lowerCamelCase_:						str=400,					lowerCamelCase_:						List[Any]=True,					lowerCamelCase_:						List[Any]=None,					lowerCamelCase_:						Dict=True,					lowerCamelCase_:						Optional[Any]=[0.5, 0.5, 0.5],					lowerCamelCase_:						Dict=[0.5, 0.5, 0.5],					lowerCamelCase_:						Dict=True,					lowerCamelCase_:						List[str]=1 / 255,					lowerCamelCase_:						Dict=True,					):
			# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
			lowercase__					:      str      =		size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
			lowercase__					:      Optional[int]      =		parent
			lowercase__					:      List[Any]      =		batch_size
			lowercase__					:      Optional[int]      =		num_channels
			lowercase__					:      Optional[Any]      =		min_resolution
			lowercase__					:      Union[str, Any]      =		max_resolution
			lowercase__					:      Tuple      =		do_resize
			lowercase__					:      str      =		size
			lowercase__					:      Dict      =		do_normalize
			lowercase__					:      Optional[int]      =		image_mean
			lowercase__					:      Dict      =		image_std
			lowercase__					:      int      =		do_rescale
			lowercase__					:      Any      =		rescale_factor
			lowercase__					:      Union[str, Any]      =		do_pad

	def    snake_case__(						self:						str			):
			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			    "do_rescale": self.do_rescale,
			    "rescale_factor": self.rescale_factor,
			    "do_pad": self.do_pad,
			}





	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						Tuple,					lowerCamelCase_:						int=False			):
			if not batched:
					lowercase__					:      Any      =		image_inputs[0]
					if isinstance(lowerCamelCase_,					Image.Image			):
							lowercase__   ,				lowercase__					:      List[str]      =		image.size
					else:
							lowercase__   ,				lowercase__					:      str      =		image.shape[1], image.shape[2]
					if w < h:
							lowercase__					:      Optional[Any]      =		int(self.size['shortest_edge'] * h / w			)
							lowercase__					:      Optional[int]      =		self.size['shortest_edge']
					elif w > h:
							lowercase__					:      List[str]      =		self.size['shortest_edge']
							lowercase__					:      Any      =		int(self.size['shortest_edge'] * w / h			)
					else:
							lowercase__					:      List[str]      =		self.size['shortest_edge']
							lowercase__					:      List[Any]      =		self.size['shortest_edge']

			else:
					lowercase__					:      int      =		[]
					for image in image_inputs:
							lowercase__   ,				lowercase__					:      Dict      =		self.get_expected_values([image]			)
							expected_values.append((expected_height, expected_width)			)
					lowercase__					:      int      =		max(lowerCamelCase_,					key=lambda lowerCamelCase_			: item[0]			)[0]
					lowercase__					:      Tuple      =		max(lowerCamelCase_,					key=lambda lowerCamelCase_			: item[1]			)[1]

			return expected_height, expected_width


@require_torch
@require_vision
class       __lowerCAmelCase					(       _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





	_A													=				YolosImageProcessor if is_vision_available() else None

	def    snake_case__(						self:						int			):
			lowercase__					:      Optional[int]      =		YolosImageProcessingTester(self			)

	@property
	def    snake_case__(						self:						Union[str, Any]			):
			return self.image_processor_tester.prepare_image_processor_dict()

	def    snake_case__(						self:						Dict			):
			lowercase__					:      Optional[Any]      =		self.image_processing_class(**self.image_processor_dict			)
			self.assertTrue(hasattr(lowerCamelCase_,					'image_mean'			)			)
			self.assertTrue(hasattr(lowerCamelCase_,					'image_std'			)			)
			self.assertTrue(hasattr(lowerCamelCase_,					'do_normalize'			)			)
			self.assertTrue(hasattr(lowerCamelCase_,					'do_resize'			)			)
			self.assertTrue(hasattr(lowerCamelCase_,					'size'			)			)

	def    snake_case__(						self:						List[Any]			):
			lowercase__					:      Tuple      =		self.image_processing_class.from_dict(self.image_processor_dict			)
			self.assertEqual(image_processor.size,					{'shortest_edge': 18, 'longest_edge': 1333}			)
			self.assertEqual(image_processor.do_pad,					lowerCamelCase_			)

			lowercase__					:      Optional[int]      =		self.image_processing_class.from_dict(
			    self.image_processor_dict,					size=42,					max_size=84,					pad_and_return_pixel_mask=lowerCamelCase_			)
			self.assertEqual(image_processor.size,					{'shortest_edge': 42, 'longest_edge': 84}			)
			self.assertEqual(image_processor.do_pad,					lowerCamelCase_			)

	def    snake_case__(						self:						str			):
			pass

	def    snake_case__(						self:						Optional[Any]			):
			# Initialize image_processing
			lowercase__					:      Optional[int]      =		self.image_processing_class(**self.image_processor_dict			)
			# create random PIL images
			lowercase__					:      Union[str, Any]      =		prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowerCamelCase_			)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,					Image.Image			)

			# Test not batched input
			lowercase__					:      Tuple      =		image_processing(image_inputs[0],					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      List[Any]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_			)

			self.assertEqual(
			    encoded_images.shape,					(1, self.image_processor_tester.num_channels, expected_height, expected_width),					)

			# Test batched
			lowercase__   ,				lowercase__					:      Optional[Any]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_,					batched=lowerCamelCase_			)

			lowercase__					:      Dict      =		image_processing(lowerCamelCase_,					return_tensors='pt'			).pixel_values
			self.assertEqual(
			    encoded_images.shape,					(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    ),					)

	def    snake_case__(						self:						str			):
			# Initialize image_processing
			lowercase__					:      str      =		self.image_processing_class(**self.image_processor_dict			)
			# create random numpy tensors
			lowercase__					:      int      =		prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowerCamelCase_,					numpify=lowerCamelCase_			)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,					np.ndarray			)

			# Test not batched input
			lowercase__					:      List[Any]      =		image_processing(image_inputs[0],					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      Optional[int]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_			)

			self.assertEqual(
			    encoded_images.shape,					(1, self.image_processor_tester.num_channels, expected_height, expected_width),					)

			# Test batched
			lowercase__					:      Any      =		image_processing(lowerCamelCase_,					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      Optional[int]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_,					batched=lowerCamelCase_			)

			self.assertEqual(
			    encoded_images.shape,					(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    ),					)

	def    snake_case__(						self:						Union[str, Any]			):
			# Initialize image_processing
			lowercase__					:      Optional[int]      =		self.image_processing_class(**self.image_processor_dict			)
			# create random PyTorch tensors
			lowercase__					:      Dict      =		prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowerCamelCase_,					torchify=lowerCamelCase_			)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,					torch.Tensor			)

			# Test not batched input
			lowercase__					:      List[str]      =		image_processing(image_inputs[0],					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      Union[str, Any]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_			)

			self.assertEqual(
			    encoded_images.shape,					(1, self.image_processor_tester.num_channels, expected_height, expected_width),					)

			# Test batched
			lowercase__					:      List[Any]      =		image_processing(lowerCamelCase_,					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      Union[str, Any]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_,					batched=lowerCamelCase_			)

			self.assertEqual(
			    encoded_images.shape,					(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    ),					)

	def    snake_case__(						self:						int			):
			# Initialize image_processings
			lowercase__					:      Tuple      =		self.image_processing_class(**self.image_processor_dict			)
			lowercase__					:      List[str]      =		self.image_processing_class(do_resize=lowerCamelCase_,					do_normalize=lowerCamelCase_,					do_rescale=lowerCamelCase_			)
			# create random PyTorch tensors
			lowercase__					:      Tuple      =		prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowerCamelCase_,					torchify=lowerCamelCase_			)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,					torch.Tensor			)

			# Test whether the method "pad" and calling the image processor return the same tensors
			lowercase__					:      Optional[int]      =		image_processing_a.pad(lowerCamelCase_,					return_tensors='pt'			)
			lowercase__					:      int      =		image_processing_a(lowerCamelCase_,					return_tensors='pt'			)

			self.assertTrue(
			    torch.allclose(encoded_images_with_method['pixel_values'],					encoded_images['pixel_values'],					atol=1E-4			)			)

	@slow
	def    snake_case__(						self:						List[str]			):
			# prepare image and target
			lowercase__					:      Any      =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
			with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt',					'r'			) as f:
					lowercase__					:      List[str]      =		json.loads(f.read()			)

			lowercase__					:      List[str]      =		{'image_id': 39769, 'annotations': target}

			# encode them
			lowercase__					:      Any      =		YolosImageProcessor.from_pretrained('hustvl/yolos-small'			)
			lowercase__					:      Optional[Any]      =		image_processing(images=lowerCamelCase_,					annotations=lowerCamelCase_,					return_tensors='pt'			)

			# verify pixel values
			lowercase__					:      Optional[Any]      =		torch.Size([1, 3, 800, 1066]			)
			self.assertEqual(encoding['pixel_values'].shape,					lowerCamelCase_			)

			lowercase__					:      int      =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
			self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3],					lowerCamelCase_,					atol=1E-4			)			)

			# verify area
			lowercase__					:      Optional[int]      =		torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['area'],					lowerCamelCase_			)			)
			# verify boxes
			lowercase__					:      int      =		torch.Size([6, 4]			)
			self.assertEqual(encoding['labels'][0]['boxes'].shape,					lowerCamelCase_			)
			lowercase__					:      Tuple      =		torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0],					lowerCamelCase_,					atol=1E-3			)			)
			# verify image_id
			lowercase__					:      Tuple      =		torch.tensor([39769]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],					lowerCamelCase_			)			)
			# verify is_crowd
			lowercase__					:      Union[str, Any]      =		torch.tensor([0, 0, 0, 0, 0, 0]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],					lowerCamelCase_			)			)
			# verify class_labels
			lowercase__					:      Tuple      =		torch.tensor([75, 75, 63, 65, 17, 17]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],					lowerCamelCase_			)			)
			# verify orig_size
			lowercase__					:      Optional[int]      =		torch.tensor([480, 640]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],					lowerCamelCase_			)			)
			# verify size
			lowercase__					:      List[Any]      =		torch.tensor([800, 1066]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['size'],					lowerCamelCase_			)			)





	@slow
	def    snake_case__(						self:						Dict			):
			# prepare image, target and masks_path
			lowercase__					:      Optional[int]      =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
			with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt',					'r'			) as f:
					lowercase__					:      Optional[Any]      =		json.loads(f.read()			)

			lowercase__					:      List[Any]      =		{'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

			lowercase__					:      Optional[int]      =		pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'			)

			# encode them
			lowercase__					:      List[Any]      =		YolosImageProcessor(format='coco_panoptic'			)
			lowercase__					:      Dict      =		image_processing(images=lowerCamelCase_,					annotations=lowerCamelCase_,					masks_path=lowerCamelCase_,					return_tensors='pt'			)

			# verify pixel values
			lowercase__					:      str      =		torch.Size([1, 3, 800, 1066]			)
			self.assertEqual(encoding['pixel_values'].shape,					lowerCamelCase_			)

			lowercase__					:      List[str]      =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
			self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3],					lowerCamelCase_,					atol=1E-4			)			)

			# verify area
			lowercase__					:      Optional[int]      =		torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['area'],					lowerCamelCase_			)			)
			# verify boxes
			lowercase__					:      Dict      =		torch.Size([6, 4]			)
			self.assertEqual(encoding['labels'][0]['boxes'].shape,					lowerCamelCase_			)
			lowercase__					:      List[str]      =		torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0],					lowerCamelCase_,					atol=1E-3			)			)
			# verify image_id
			lowercase__					:      Any      =		torch.tensor([39769]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],					lowerCamelCase_			)			)
			# verify is_crowd
			lowercase__					:      List[str]      =		torch.tensor([0, 0, 0, 0, 0, 0]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],					lowerCamelCase_			)			)
			# verify class_labels
			lowercase__					:      List[str]      =		torch.tensor([17, 17, 63, 75, 75, 93]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],					lowerCamelCase_			)			)
			# verify masks
			lowercase__					:      Optional[Any]      =		822873
			self.assertEqual(encoding['labels'][0]['masks'].sum().item(),					lowerCamelCase_			)
			# verify orig_size
			lowercase__					:      Optional[int]      =		torch.tensor([480, 640]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],					lowerCamelCase_			)			)
			# verify size
			lowercase__					:      Dict      =		torch.tensor([800, 1066]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['size'],					lowerCamelCase_			)			)


