

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__								=		logging.get_logger(__name__)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[Any]	)						->				List[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						MobileViTConfig()

	# size of the architecture
	if "mobilevit_s" in mobilevit_name:
		_UpperCamelCase   :		Any			=						[1_4_4, 1_9_2, 2_4_0]
		_UpperCamelCase   :		Any			=						[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
	elif "mobilevit_xs" in mobilevit_name:
		_UpperCamelCase   :		Union[str, Any]			=						[9_6, 1_2_0, 1_4_4]
		_UpperCamelCase   :		Dict			=						[1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
	elif "mobilevit_xxs" in mobilevit_name:
		_UpperCamelCase   :		Dict			=						[6_4, 8_0, 9_6]
		_UpperCamelCase   :		List[Any]			=						[1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
		_UpperCamelCase   :		List[str]			=						0.0_5
		_UpperCamelCase   :		int			=						2.0

	if mobilevit_name.startswith('deeplabv3_'	):
		_UpperCamelCase   :		Tuple			=						5_1_2
		_UpperCamelCase   :		Optional[int]			=						1_6
		_UpperCamelCase   :		List[str]			=						2_1
		_UpperCamelCase   :		Dict			=						'pascal-voc-id2label.json'
	else:
		_UpperCamelCase   :		Optional[Any]			=						1_0_0_0
		_UpperCamelCase   :		Union[str, Any]			=						'imagenet-1k-id2label.json'

	_UpperCamelCase   :		int			=						'huggingface/label-files'
	_UpperCamelCase   :		List[str]			=						json.load(open(hf_hub_download(UpperCAmelCase_							,       UpperCAmelCase_							,       repo_type='dataset'	)							,       'r'	)	)
	_UpperCamelCase   :		Dict			=						{int(UpperCAmelCase_	): v for k, v in idalabel.items()}
	_UpperCamelCase   :		int			=						idalabel
	_UpperCamelCase   :		List[Any]			=						{v: k for k, v in idalabel.items()}

	return config






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     Tuple=False	)						->				Union[str, Any]:




	'''simple docstring'''




	for i in range(1							,       6	):
		if F'''layer_{i}.''' in name:
			_UpperCamelCase   :		int			=						name.replace(F'''layer_{i}.'''							,       F'''encoder.layer.{i - 1}.'''	)

	if "conv_1." in name:
		_UpperCamelCase   :		List[Any]			=						name.replace('conv_1.'							,       'conv_stem.'	)
	if ".block." in name:
		_UpperCamelCase   :		Optional[int]			=						name.replace('.block.'							,       '.'	)

	if "exp_1x1" in name:
		_UpperCamelCase   :		str			=						name.replace('exp_1x1'							,       'expand_1x1'	)
	if "red_1x1" in name:
		_UpperCamelCase   :		Dict			=						name.replace('red_1x1'							,       'reduce_1x1'	)
	if ".local_rep.conv_3x3." in name:
		_UpperCamelCase   :		List[Any]			=						name.replace('.local_rep.conv_3x3.'							,       '.conv_kxk.'	)
	if ".local_rep.conv_1x1." in name:
		_UpperCamelCase   :		Optional[int]			=						name.replace('.local_rep.conv_1x1.'							,       '.conv_1x1.'	)
	if ".norm." in name:
		_UpperCamelCase   :		List[str]			=						name.replace('.norm.'							,       '.normalization.'	)
	if ".conv." in name:
		_UpperCamelCase   :		Tuple			=						name.replace('.conv.'							,       '.convolution.'	)
	if ".conv_proj." in name:
		_UpperCamelCase   :		List[Any]			=						name.replace('.conv_proj.'							,       '.conv_projection.'	)

	for i in range(0							,       2	):
		for j in range(0							,       4	):
			if F'''.{i}.{j}.''' in name:
				_UpperCamelCase   :		Optional[int]			=						name.replace(F'''.{i}.{j}.'''							,       F'''.{i}.layer.{j}.'''	)

	for i in range(2							,       6	):
		for j in range(0							,       4	):
			if F'''.{i}.{j}.''' in name:
				_UpperCamelCase   :		Tuple			=						name.replace(F'''.{i}.{j}.'''							,       F'''.{i}.'''	)
				if "expand_1x1" in name:
					_UpperCamelCase   :		Dict			=						name.replace('expand_1x1'							,       'downsampling_layer.expand_1x1'	)
				if "conv_3x3" in name:
					_UpperCamelCase   :		List[str]			=						name.replace('conv_3x3'							,       'downsampling_layer.conv_3x3'	)
				if "reduce_1x1" in name:
					_UpperCamelCase   :		List[Any]			=						name.replace('reduce_1x1'							,       'downsampling_layer.reduce_1x1'	)

	for i in range(2							,       5	):
		if F'''.global_rep.{i}.weight''' in name:
			_UpperCamelCase   :		str			=						name.replace(F'''.global_rep.{i}.weight'''							,       '.layernorm.weight'	)
		if F'''.global_rep.{i}.bias''' in name:
			_UpperCamelCase   :		List[str]			=						name.replace(F'''.global_rep.{i}.bias'''							,       '.layernorm.bias'	)

	if ".global_rep." in name:
		_UpperCamelCase   :		Optional[int]			=						name.replace('.global_rep.'							,       '.transformer.'	)
	if ".pre_norm_mha.0." in name:
		_UpperCamelCase   :		Optional[Any]			=						name.replace('.pre_norm_mha.0.'							,       '.layernorm_before.'	)
	if ".pre_norm_mha.1.out_proj." in name:
		_UpperCamelCase   :		str			=						name.replace('.pre_norm_mha.1.out_proj.'							,       '.attention.output.dense.'	)
	if ".pre_norm_ffn.0." in name:
		_UpperCamelCase   :		List[Any]			=						name.replace('.pre_norm_ffn.0.'							,       '.layernorm_after.'	)
	if ".pre_norm_ffn.1." in name:
		_UpperCamelCase   :		int			=						name.replace('.pre_norm_ffn.1.'							,       '.intermediate.dense.'	)
	if ".pre_norm_ffn.4." in name:
		_UpperCamelCase   :		Union[str, Any]			=						name.replace('.pre_norm_ffn.4.'							,       '.output.dense.'	)
	if ".transformer." in name:
		_UpperCamelCase   :		Tuple			=						name.replace('.transformer.'							,       '.transformer.layer.'	)

	if ".aspp_layer." in name:
		_UpperCamelCase   :		List[Any]			=						name.replace('.aspp_layer.'							,       '.'	)
	if ".aspp_pool." in name:
		_UpperCamelCase   :		Optional[int]			=						name.replace('.aspp_pool.'							,       '.'	)
	if "seg_head." in name:
		_UpperCamelCase   :		Optional[Any]			=						name.replace('seg_head.'							,       'segmentation_head.'	)
	if "segmentation_head.classifier.classifier." in name:
		_UpperCamelCase   :		Optional[int]			=						name.replace('segmentation_head.classifier.classifier.'							,       'segmentation_head.classifier.'	)

	if "classifier.fc." in name:
		_UpperCamelCase   :		List[str]			=						name.replace('classifier.fc.'							,       'classifier.'	)
	elif (not base_model) and ("segmentation_head." not in name):
		_UpperCamelCase   :		Dict			=						'mobilevit.' + name

	return name






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     Tuple=False	)						->				Union[str, Any]:




	'''simple docstring'''




	if base_model:
		_UpperCamelCase   :		Union[str, Any]			=						''
	else:
		_UpperCamelCase   :		Union[str, Any]			=						'mobilevit.'

	for key in orig_state_dict.copy().keys():
		_UpperCamelCase   :		Union[str, Any]			=						orig_state_dict.pop(UpperCAmelCase_	)

		if key[:8] == "encoder.":
			_UpperCamelCase   :		Optional[int]			=						key[8:]

		if "qkv" in key:
			_UpperCamelCase   :		Optional[int]			=						key.split('.'	)
			_UpperCamelCase   :		Optional[Any]			=						int(key_split[0][6:]	) - 1
			_UpperCamelCase   :		int			=						int(key_split[3]	)
			_UpperCamelCase   :		Tuple			=						model.get_submodule(F'''{model_prefix}encoder.layer.{layer_num}'''	)
			_UpperCamelCase   :		Dict			=						layer.transformer.layer[transformer_num].attention.attention.all_head_size
			_UpperCamelCase   :		str			=						(
			    F'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
			)
			if "weight" in key:
				_UpperCamelCase   :		Union[str, Any]			=						val[:dim, :]
				_UpperCamelCase   :		Optional[Any]			=						val[dim : dim * 2, :]
				_UpperCamelCase   :		Optional[int]			=						val[-dim:, :]
			else:
				_UpperCamelCase   :		Dict			=						val[:dim]
				_UpperCamelCase   :		str			=						val[dim : dim * 2]
				_UpperCamelCase   :		Optional[int]			=						val[-dim:]
		else:
			_UpperCamelCase   :		Union[str, Any]			=						val

	return orig_state_dict






def 					lowerCamelCase_     (							)						->				Any:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						'http://images.cocodataset.org/val2017/000000039769.jpg'
	_UpperCamelCase   :		List[Any]			=						Image.open(requests.get(UpperCAmelCase_							,       stream=UpperCAmelCase_	).raw	)
	return im






@torch.no_grad()
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     List[str]							,       UpperCAmelCase_							:     Any=False	)						->				int:




	'''simple docstring'''




	_UpperCamelCase   :		Tuple			=						get_mobilevit_config(UpperCAmelCase_	)

	# load original state_dict
	_UpperCamelCase   :		List[Any]			=						torch.load(UpperCAmelCase_							,       map_location='cpu'	)

	# load 🤗 model
	if mobilevit_name.startswith('deeplabv3_'	):
		_UpperCamelCase   :		Tuple			=						MobileViTForSemanticSegmentation(UpperCAmelCase_	).eval()
	else:
		_UpperCamelCase   :		List[str]			=						MobileViTForImageClassification(UpperCAmelCase_	).eval()

	_UpperCamelCase   :		int			=						convert_state_dict(UpperCAmelCase_							,       UpperCAmelCase_	)
	model.load_state_dict(UpperCAmelCase_	)

	# Check outputs on an image, prepared by MobileViTImageProcessor
	_UpperCamelCase   :		Optional[Any]			=						MobileViTImageProcessor(crop_size=config.image_size							,       size=config.image_size + 3_2	)
	_UpperCamelCase   :		List[str]			=						image_processor(images=prepare_img()							,       return_tensors='pt'	)
	_UpperCamelCase   :		List[str]			=						model(**UpperCAmelCase_	)
	_UpperCamelCase   :		List[str]			=						outputs.logits

	if mobilevit_name.startswith('deeplabv3_'	):
		assert logits.shape == (1, 2_1, 3_2, 3_2)

		if mobilevit_name == "deeplabv3_mobilevit_s":
			_UpperCamelCase   :		List[str]			=						torch.tensor(
			    [
			        [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
			        [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
			        [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
			    ]	)
		elif mobilevit_name == "deeplabv3_mobilevit_xs":
			_UpperCamelCase   :		Optional[Any]			=						torch.tensor(
			    [
			        [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
			        [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
			        [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
			    ]	)
		elif mobilevit_name == "deeplabv3_mobilevit_xxs":
			_UpperCamelCase   :		Optional[Any]			=						torch.tensor(
			    [
			        [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
			        [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
			        [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
			    ]	)
		else:
			raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''	)

		assert torch.allclose(logits[0, :3, :3, :3]							,       UpperCAmelCase_							,       atol=1e-4	)
	else:
		assert logits.shape == (1, 1_0_0_0)

		if mobilevit_name == "mobilevit_s":
			_UpperCamelCase   :		Tuple			=						torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1]	)
		elif mobilevit_name == "mobilevit_xs":
			_UpperCamelCase   :		Optional[Any]			=						torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7]	)
		elif mobilevit_name == "mobilevit_xxs":
			_UpperCamelCase   :		List[Any]			=						torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]	)
		else:
			raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''	)

		assert torch.allclose(logits[0, :3]							,       UpperCAmelCase_							,       atol=1e-4	)

	Path(UpperCAmelCase_	).mkdir(exist_ok=UpperCAmelCase_	)
	print(F'''Saving model {mobilevit_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:
		_UpperCamelCase   :		Tuple			=						{
		    'mobilevit_s': 'mobilevit-small',
		    'mobilevit_xs': 'mobilevit-x-small',
		    'mobilevit_xxs': 'mobilevit-xx-small',
		    'deeplabv3_mobilevit_s': 'deeplabv3-mobilevit-small',
		    'deeplabv3_mobilevit_xs': 'deeplabv3-mobilevit-x-small',
		    'deeplabv3_mobilevit_xxs': 'deeplabv3-mobilevit-xx-small',
		}

		print('Pushing to the hub...'	)
		_UpperCamelCase   :		int			=						model_mapping[mobilevit_name]
		image_processor.push_to_hub(UpperCAmelCase_							,       organization='apple'	)
		model.push_to_hub(UpperCAmelCase_							,       organization='apple'	)


if __name__ == "__main__":
							lowerCAmelCase__								=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--mobilevit_name""",
							    default="""mobilevit_s""",
							    type=str,
							    help=(
							        """Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
							        """ 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
							    ),
							)
							parser.add_argument(
							    """--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt 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."""
							)

							lowerCAmelCase__								=		parser.parse_args()
							convert_movilevit_checkpoint(
							    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase__								=		{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CanineForMultipleChoice""",
							    """CanineForQuestionAnswering""",
							    """CanineForSequenceClassification""",
							    """CanineForTokenClassification""",
							    """CanineLayer""",
							    """CanineModel""",
							    """CaninePreTrainedModel""",
							    """load_tf_weights_in_canine""",
							]


if TYPE_CHECKING:
							from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
							from .tokenization_canine import CanineTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_canine import (
														    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CanineForMultipleChoice,
														    CanineForQuestionAnswering,
														    CanineForSequenceClassification,
														    CanineForTokenClassification,
														    CanineLayer,
														    CanineModel,
														    CaninePreTrainedModel,
														    load_tf_weights_in_canine,
														)


else:
							import sys

							lowerCAmelCase__								=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




