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




def  __magic_name__ (   __lowerCAmelCase	:						Dict							)			->   List[str]:
				__lowerCamelCase    					=	MobileViTConfig()

				# size of the architecture
				if "mobilevit_s" in mobilevit_name:
								__lowerCamelCase    					=	[144, 192, 240]
								__lowerCamelCase    					=	[16, 32, 64, 96, 128, 160, 640]
				elif "mobilevit_xs" in mobilevit_name:
								__lowerCamelCase    					=	[96, 120, 144]
								__lowerCamelCase    					=	[16, 32, 48, 64, 80, 96, 384]
				elif "mobilevit_xxs" in mobilevit_name:
								__lowerCamelCase    					=	[64, 80, 96]
								__lowerCamelCase    					=	[16, 16, 24, 48, 64, 80, 320]
								__lowerCamelCase    					=	0.05
								__lowerCamelCase    					=	2.0

				if mobilevit_name.startswith('''deeplabv3_'''							):
								__lowerCamelCase    					=	512
								__lowerCamelCase    					=	16
								__lowerCamelCase    					=	21
								__lowerCamelCase    					=	'''pascal-voc-id2label.json'''
				else:
								__lowerCamelCase    					=	1000
								__lowerCamelCase    					=	'''imagenet-1k-id2label.json'''

				__lowerCamelCase    					=	'''huggingface/label-files'''
				__lowerCamelCase    					=	json.load(open(hf_hub_download(__lowerCAmelCase		,  __lowerCAmelCase		,  repo_type='''dataset'''							)		,  '''r'''							)							)
				__lowerCamelCase    					=	{int(__lowerCAmelCase							): v for k, v in idalabel.items()}
				__lowerCamelCase    					=	idalabel
				__lowerCamelCase    					=	{v: k for k, v in idalabel.items()}

				return config




def  __magic_name__ (   __lowerCAmelCase	:						List[str]		,  __lowerCAmelCase	:						Any=False							)			->   Tuple:
				for i in range(1		,  6							):
								if f'''layer_{i}.''' in name:
												__lowerCamelCase    					=	name.replace(f'''layer_{i}.'''		,  f'''encoder.layer.{i - 1}.'''							)

				if "conv_1." in name:
								__lowerCamelCase    					=	name.replace('''conv_1.'''		,  '''conv_stem.'''							)
				if ".block." in name:
								__lowerCamelCase    					=	name.replace('''.block.'''		,  '''.'''							)

				if "exp_1x1" in name:
								__lowerCamelCase    					=	name.replace('''exp_1x1'''		,  '''expand_1x1'''							)
				if "red_1x1" in name:
								__lowerCamelCase    					=	name.replace('''red_1x1'''		,  '''reduce_1x1'''							)
				if ".local_rep.conv_3x3." in name:
								__lowerCamelCase    					=	name.replace('''.local_rep.conv_3x3.'''		,  '''.conv_kxk.'''							)
				if ".local_rep.conv_1x1." in name:
								__lowerCamelCase    					=	name.replace('''.local_rep.conv_1x1.'''		,  '''.conv_1x1.'''							)
				if ".norm." in name:
								__lowerCamelCase    					=	name.replace('''.norm.'''		,  '''.normalization.'''							)
				if ".conv." in name:
								__lowerCamelCase    					=	name.replace('''.conv.'''		,  '''.convolution.'''							)
				if ".conv_proj." in name:
								__lowerCamelCase    					=	name.replace('''.conv_proj.'''		,  '''.conv_projection.'''							)

				for i in range(0		,  2							):
								for j in range(0		,  4							):
												if f'''.{i}.{j}.''' in name:
																__lowerCamelCase    					=	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:
																__lowerCamelCase    					=	name.replace(f'''.{i}.{j}.'''		,  f'''.{i}.'''							)
																if "expand_1x1" in name:
																				__lowerCamelCase    					=	name.replace('''expand_1x1'''		,  '''downsampling_layer.expand_1x1'''							)
																if "conv_3x3" in name:
																				__lowerCamelCase    					=	name.replace('''conv_3x3'''		,  '''downsampling_layer.conv_3x3'''							)
																if "reduce_1x1" in name:
																				__lowerCamelCase    					=	name.replace('''reduce_1x1'''		,  '''downsampling_layer.reduce_1x1'''							)

				for i in range(2		,  5							):
								if f'''.global_rep.{i}.weight''' in name:
												__lowerCamelCase    					=	name.replace(f'''.global_rep.{i}.weight'''		,  '''.layernorm.weight'''							)
								if f'''.global_rep.{i}.bias''' in name:
												__lowerCamelCase    					=	name.replace(f'''.global_rep.{i}.bias'''		,  '''.layernorm.bias'''							)

				if ".global_rep." in name:
								__lowerCamelCase    					=	name.replace('''.global_rep.'''		,  '''.transformer.'''							)
				if ".pre_norm_mha.0." in name:
								__lowerCamelCase    					=	name.replace('''.pre_norm_mha.0.'''		,  '''.layernorm_before.'''							)
				if ".pre_norm_mha.1.out_proj." in name:
								__lowerCamelCase    					=	name.replace('''.pre_norm_mha.1.out_proj.'''		,  '''.attention.output.dense.'''							)
				if ".pre_norm_ffn.0." in name:
								__lowerCamelCase    					=	name.replace('''.pre_norm_ffn.0.'''		,  '''.layernorm_after.'''							)
				if ".pre_norm_ffn.1." in name:
								__lowerCamelCase    					=	name.replace('''.pre_norm_ffn.1.'''		,  '''.intermediate.dense.'''							)
				if ".pre_norm_ffn.4." in name:
								__lowerCamelCase    					=	name.replace('''.pre_norm_ffn.4.'''		,  '''.output.dense.'''							)
				if ".transformer." in name:
								__lowerCamelCase    					=	name.replace('''.transformer.'''		,  '''.transformer.layer.'''							)

				if ".aspp_layer." in name:
								__lowerCamelCase    					=	name.replace('''.aspp_layer.'''		,  '''.'''							)
				if ".aspp_pool." in name:
								__lowerCamelCase    					=	name.replace('''.aspp_pool.'''		,  '''.'''							)
				if "seg_head." in name:
								__lowerCamelCase    					=	name.replace('''seg_head.'''		,  '''segmentation_head.'''							)
				if "segmentation_head.classifier.classifier." in name:
								__lowerCamelCase    					=	name.replace('''segmentation_head.classifier.classifier.'''		,  '''segmentation_head.classifier.'''							)

				if "classifier.fc." in name:
								__lowerCamelCase    					=	name.replace('''classifier.fc.'''		,  '''classifier.'''							)
				elif (not base_model) and ("segmentation_head." not in name):
								__lowerCamelCase    					=	'''mobilevit.''' + name

				return name




def  __magic_name__ (   __lowerCAmelCase	:						Optional[Any]		,  __lowerCAmelCase	:						Tuple		,  __lowerCAmelCase	:						Dict=False							)			->   Dict:
				if base_model:
								__lowerCamelCase    					=	''''''
				else:
								__lowerCamelCase    					=	'''mobilevit.'''

				for key in orig_state_dict.copy().keys():
								__lowerCamelCase    					=	orig_state_dict.pop(__lowerCAmelCase							)

								if key[:8] == "encoder.":
												__lowerCamelCase    					=	key[8:]

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

				return orig_state_dict




def  __magic_name__ (   )			->   List[Any]:
				__lowerCamelCase    					=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowerCamelCase    					=	Image.open(requests.get(__lowerCAmelCase		,  stream=__lowerCAmelCase							).raw							)
				return im




@torch.no_grad()
def  __magic_name__ (   __lowerCAmelCase	:						List[str]		,  __lowerCAmelCase	:						Union[str, Any]		,  __lowerCAmelCase	:						Optional[Any]		,  __lowerCAmelCase	:						Optional[Any]=False							)			->   Optional[Any]:
				__lowerCamelCase    					=	get_mobilevit_config(__lowerCAmelCase							)

				# load original state_dict
				__lowerCamelCase    					=	torch.load(__lowerCAmelCase		,  map_location='''cpu'''							)

				# load 🤗 model
				if mobilevit_name.startswith('''deeplabv3_'''							):
								__lowerCamelCase    					=	MobileViTForSemanticSegmentation(__lowerCAmelCase							).eval()
				else:
								__lowerCamelCase    					=	MobileViTForImageClassification(__lowerCAmelCase							).eval()

				__lowerCamelCase    					=	convert_state_dict(__lowerCAmelCase		,  __lowerCAmelCase							)
				model.load_state_dict(__lowerCAmelCase							)

				# Check outputs on an image, prepared by MobileViTImageProcessor
				__lowerCamelCase    					=	MobileViTImageProcessor(crop_size=config.image_size		,  size=config.image_size + 32							)
				__lowerCamelCase    					=	image_processor(images=prepare_img()		,  return_tensors='''pt'''							)
				__lowerCamelCase    					=	model(**__lowerCAmelCase							)
				__lowerCamelCase    					=	outputs.logits

				if mobilevit_name.startswith('''deeplabv3_'''							):
								assert logits.shape == (1, 21, 32, 32)

								if mobilevit_name == "deeplabv3_mobilevit_s":
												__lowerCamelCase    					=	torch.tensor(
												    [
												        [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
												        [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
												        [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
												    ]							)
								elif mobilevit_name == "deeplabv3_mobilevit_xs":
												__lowerCamelCase    					=	torch.tensor(
												    [
												        [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
												        [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
												        [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
												    ]							)
								elif mobilevit_name == "deeplabv3_mobilevit_xxs":
												__lowerCamelCase    					=	torch.tensor(
												    [
												        [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
												        [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
												        [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
												    ]							)
								else:
												raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''							)

								assert torch.allclose(logits[0, :3, :3, :3]		,  __lowerCAmelCase		,  atol=1E-4							)
				else:
								assert logits.shape == (1, 1000)

								if mobilevit_name == "mobilevit_s":
												__lowerCamelCase    					=	torch.tensor([-0.9866, 0.2392, -1.1241]							)
								elif mobilevit_name == "mobilevit_xs":
												__lowerCamelCase    					=	torch.tensor([-2.4761, -0.9399, -1.9587]							)
								elif mobilevit_name == "mobilevit_xxs":
												__lowerCamelCase    					=	torch.tensor([-1.9364, -1.2327, -0.4653]							)
								else:
												raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''							)

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

				Path(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
				print(f'''Saving model {mobilevit_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:
								__lowerCamelCase    					=	{
								    '''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...'''							)
								__lowerCamelCase    					=	model_mapping[mobilevit_name]
								image_processor.push_to_hub(__lowerCAmelCase		,  organization='''apple'''							)
								model.push_to_hub(__lowerCAmelCase		,  organization='''apple'''							)


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

					SCREAMING_SNAKE_CASE__   :			Optional[int]												=    parser.parse_args()
					convert_movilevit_checkpoint(
					    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)





import pprint

import requests

SCREAMING_SNAKE_CASE__   :			str												=    "https://zenquotes.io/api"




def  __magic_name__ (   )			->   list:
				return requests.get(API_ENDPOINT_URL + '''/today'''							).json()




def  __magic_name__ (   )			->   list:
				return requests.get(API_ENDPOINT_URL + '''/random'''							).json()


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :			int												=    random_quotes()
					pprint.pprint(response)



