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()
__UpperCamelCase		:       Tuple									=     logging.get_logger(__name__)
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    Tuple							=							MobileViTConfig()

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

						if mobilevit_name.startswith('''deeplabv3_'''						):
												SCREAMING_SNAKE_CASE							:    Dict							=							512
												SCREAMING_SNAKE_CASE							:    List[Any]							=							16
												SCREAMING_SNAKE_CASE							:    List[str]							=							21
												SCREAMING_SNAKE_CASE							:    Dict							=							'''pascal-voc-id2label.json'''
						else:
												SCREAMING_SNAKE_CASE							:    Dict							=							1_000
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''imagenet-1k-id2label.json'''

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

						return config
def       A	(    _lowercase		,  _lowercase=False						):
						for i in range(1		,  6						):
												if f"""layer_{i}.""" in name:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							name.replace(f"""layer_{i}."""		,  f"""encoder.layer.{i - 1}."""						)

						if "conv_1." in name:
												SCREAMING_SNAKE_CASE							:    str							=							name.replace('''conv_1.'''		,  '''conv_stem.'''						)
						if ".block." in name:
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							name.replace('''.block.'''		,  '''.'''						)

						if "exp_1x1" in name:
												SCREAMING_SNAKE_CASE							:    int							=							name.replace('''exp_1x1'''		,  '''expand_1x1'''						)
						if "red_1x1" in name:
												SCREAMING_SNAKE_CASE							:    int							=							name.replace('''red_1x1'''		,  '''reduce_1x1'''						)
						if ".local_rep.conv_3x3." in name:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							name.replace('''.local_rep.conv_3x3.'''		,  '''.conv_kxk.'''						)
						if ".local_rep.conv_1x1." in name:
												SCREAMING_SNAKE_CASE							:    Tuple							=							name.replace('''.local_rep.conv_1x1.'''		,  '''.conv_1x1.'''						)
						if ".norm." in name:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							name.replace('''.norm.'''		,  '''.normalization.'''						)
						if ".conv." in name:
												SCREAMING_SNAKE_CASE							:    int							=							name.replace('''.conv.'''		,  '''.convolution.'''						)
						if ".conv_proj." in name:
												SCREAMING_SNAKE_CASE							:    Union[str, 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:
																								SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							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:
																								SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							name.replace(f""".{i}.{j}."""		,  f""".{i}."""						)
																								if "expand_1x1" in name:
																														SCREAMING_SNAKE_CASE							:    int							=							name.replace('''expand_1x1'''		,  '''downsampling_layer.expand_1x1'''						)
																								if "conv_3x3" in name:
																														SCREAMING_SNAKE_CASE							:    Optional[Any]							=							name.replace('''conv_3x3'''		,  '''downsampling_layer.conv_3x3'''						)
																								if "reduce_1x1" in name:
																														SCREAMING_SNAKE_CASE							:    List[Any]							=							name.replace('''reduce_1x1'''		,  '''downsampling_layer.reduce_1x1'''						)

						for i in range(2		,  5						):
												if f""".global_rep.{i}.weight""" in name:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							name.replace(f""".global_rep.{i}.weight"""		,  '''.layernorm.weight'''						)
												if f""".global_rep.{i}.bias""" in name:
																		SCREAMING_SNAKE_CASE							:    Dict							=							name.replace(f""".global_rep.{i}.bias"""		,  '''.layernorm.bias'''						)

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

						if ".aspp_layer." in name:
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							name.replace('''.aspp_layer.'''		,  '''.'''						)
						if ".aspp_pool." in name:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							name.replace('''.aspp_pool.'''		,  '''.'''						)
						if "seg_head." in name:
												SCREAMING_SNAKE_CASE							:    Tuple							=							name.replace('''seg_head.'''		,  '''segmentation_head.'''						)
						if "segmentation_head.classifier.classifier." in name:
												SCREAMING_SNAKE_CASE							:    Dict							=							name.replace('''segmentation_head.classifier.classifier.'''		,  '''segmentation_head.classifier.'''						)

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

						return name
def       A	(    _lowercase		,  _lowercase		,  _lowercase=False						):
						if base_model:
												SCREAMING_SNAKE_CASE							:    str							=							''''''
						else:
												SCREAMING_SNAKE_CASE							:    Dict							=							'''mobilevit.'''

						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE							:    str							=							orig_state_dict.pop(_lowercase						)

												if key[:8] == "encoder.":
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							key[8:]

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

						return orig_state_dict
def       A	(    ):
						SCREAMING_SNAKE_CASE							:    Any							=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						SCREAMING_SNAKE_CASE							:    Tuple							=							Image.open(requests.get(_lowercase		,  stream=_lowercase						).raw						)
						return im
@torch.no_grad()
def       A	(    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase=False						):
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							get_mobilevit_config(_lowercase						)

						# load original state_dict
						SCREAMING_SNAKE_CASE							:    Tuple							=							torch.load(_lowercase		,  map_location='''cpu'''						)

						# load 🤗 model
						if mobilevit_name.startswith('''deeplabv3_'''						):
												SCREAMING_SNAKE_CASE							:    List[Any]							=							MobileViTForSemanticSegmentation(_lowercase						).eval()
						else:
												SCREAMING_SNAKE_CASE							:    Any							=							MobileViTForImageClassification(_lowercase						).eval()

						SCREAMING_SNAKE_CASE							:    Optional[int]							=							convert_state_dict(_lowercase		,  _lowercase						)
						model.load_state_dict(_lowercase						)

						# Check outputs on an image, prepared by MobileViTImageProcessor
						SCREAMING_SNAKE_CASE							:    str							=							MobileViTImageProcessor(crop_size=config.image_size		,  size=config.image_size + 32						)
						SCREAMING_SNAKE_CASE							:    List[Any]							=							image_processor(images=prepare_img()		,  return_tensors='''pt'''						)
						SCREAMING_SNAKE_CASE							:    Any							=							model(**_lowercase						)
						SCREAMING_SNAKE_CASE							:    List[Any]							=							outputs.logits

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

												if mobilevit_name == "deeplabv3_mobilevit_s":
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							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":
																		SCREAMING_SNAKE_CASE							:    int							=							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":
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							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]		,  _lowercase		,  atol=1e-4						)
						else:
												assert logits.shape == (1, 1_000)

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

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

						Path(_lowercase						).mkdir(exist_ok=_lowercase						)
						print(f"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}"""						)
						model.save_pretrained(_lowercase						)
						print(f"""Saving image processor to {pytorch_dump_folder_path}"""						)
						image_processor.save_pretrained(_lowercase						)

						if push_to_hub:
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							{
												    '''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...'''						)
												SCREAMING_SNAKE_CASE							:    int							=							model_mapping[mobilevit_name]
												image_processor.push_to_hub(_lowercase		,  organization='''apple'''						)
												model.push_to_hub(_lowercase		,  organization='''apple'''						)


if __name__ == "__main__":
					__UpperCamelCase		:       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.'
					)

					__UpperCamelCase		:       List[Any]									=     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_torch_available, is_vision_available


__UpperCamelCase		:       int									=     {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       int									=     ['YolosFeatureExtractor']
					__UpperCamelCase		:       Union[str, Any]									=     ['YolosImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'YolosForObjectDetection',
					    'YolosModel',
					    'YolosPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_yolos import YolosFeatureExtractor
										from .image_processing_yolos import YolosImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_yolos import (
										    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    YolosForObjectDetection,
										    YolosModel,
										    YolosPreTrainedModel,
										)


else:
					import sys

					__UpperCamelCase		:       int									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





