





"""simple docstring"""




import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


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





def 	a_    (							_lowercase       ):
						print('''Loading config file...'''       )

						def flatten_yaml_as_dict(_lowercase	,						_lowercase=""	,						_lowercase="."       ):
												_UpperCamelCase      : str         =   []
												for k, v in d.items():
																		_UpperCamelCase      : Any         =   parent_key + sep + k if parent_key else k
																		if isinstance(_lowercase	,						collections.abc.MutableMapping       ):
																								items.extend(flatten_yaml_as_dict(_lowercase	,						_lowercase	,						sep=_lowercase       ).items()       )
																		else:
																								items.append((new_key, v)       )
												return dict(_lowercase       )

						_UpperCamelCase      : List[str]         =   argparse.Namespace()
						with open(_lowercase	,						'''r'''       ) as yaml_file:
												try:
																		_UpperCamelCase      : Union[str, Any]         =   yaml.load(_lowercase	,						Loader=yaml.FullLoader       )

																		_UpperCamelCase      : Any         =   flatten_yaml_as_dict(_lowercase       )
																		for k, v in flat_cfg.items():
																								setattr(_lowercase	,						_lowercase	,						_lowercase       )
												except yaml.YAMLError as exc:
																		logger.error('''Error while loading config file: {}. Error message: {}'''.format(_lowercase	,						str(_lowercase       )       )       )
						return config





def 	a_    (							_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Optional[int]         =   MobileViTVaConfig()

						_UpperCamelCase      : int         =   False

						# dataset
						if task_name.startswith('''imagenet1k_'''       ):
												_UpperCamelCase      : Optional[Any]         =   1000
												if int(task_name.strip().split('''_'''       )[-1]       ) == 384:
																		_UpperCamelCase      : Optional[int]         =   384
												else:
																		_UpperCamelCase      : Tuple         =   256
												_UpperCamelCase      : Optional[int]         =   '''imagenet-1k-id2label.json'''
						elif task_name.startswith('''imagenet21k_to_1k_'''       ):
												_UpperCamelCase      : Tuple         =   2_1000
												if int(task_name.strip().split('''_'''       )[-1]       ) == 384:
																		_UpperCamelCase      : int         =   384
												else:
																		_UpperCamelCase      : str         =   256
												_UpperCamelCase      : Optional[int]         =   '''imagenet-22k-id2label.json'''
						elif task_name.startswith('''ade20k_'''       ):
												_UpperCamelCase      : Dict         =   151
												_UpperCamelCase      : Tuple         =   512
												_UpperCamelCase      : Dict         =   '''ade20k-id2label.json'''
												_UpperCamelCase      : int         =   True
						elif task_name.startswith('''voc_'''       ):
												_UpperCamelCase      : Optional[int]         =   21
												_UpperCamelCase      : List[str]         =   512
												_UpperCamelCase      : str         =   '''pascal-voc-id2label.json'''
												_UpperCamelCase      : Any         =   True

						# orig_config
						_UpperCamelCase      : Optional[Any]         =   load_orig_config_file(_lowercase       )
						assert getattr(_lowercase	,						'''model.classification.name'''	,						-1       ) == "mobilevit_v2", "Invalid model"
						_UpperCamelCase      : Union[str, Any]         =   getattr(_lowercase	,						'''model.classification.mitv2.width_multiplier'''	,						1.0       )
						assert (
						    getattr(_lowercase	,						'''model.classification.mitv2.attn_norm_layer'''	,						-1       ) == "layer_norm_2d"
						), "Norm layers other than layer_norm_2d is not supported"
						_UpperCamelCase      : List[Any]         =   getattr(_lowercase	,						'''model.classification.activation.name'''	,						'''swish'''       )
						# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

						if is_segmentation_model:
												_UpperCamelCase      : Union[str, Any]         =   getattr(_lowercase	,						'''model.segmentation.output_stride'''	,						16       )
												if "_deeplabv3" in task_name:
																		_UpperCamelCase      : Any         =   getattr(_lowercase	,						'''model.segmentation.deeplabv3.aspp_rates'''	,						[12, 24, 36]       )
																		_UpperCamelCase      : int         =   getattr(_lowercase	,						'''model.segmentation.deeplabv3.aspp_out_channels'''	,						512       )
																		_UpperCamelCase      : Union[str, Any]         =   getattr(_lowercase	,						'''model.segmentation.deeplabv3.aspp_dropout'''	,						0.1       )

    # id2label
						_UpperCamelCase      : Optional[Any]         =   '''huggingface/label-files'''
						_UpperCamelCase      : Optional[int]         =   json.load(open(hf_hub_download(_lowercase	,						_lowercase	,						repo_type='''dataset'''       )	,						'''r'''       )       )
						_UpperCamelCase      : Any         =   {int(_lowercase       ): v for k, v in idalabel.items()}
						_UpperCamelCase      : Optional[Any]         =   idalabel
						_UpperCamelCase      : Optional[int]         =   {v: k for k, v in idalabel.items()}

						return config





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : List[Any]         =   dct.pop(_lowercase       )
						_UpperCamelCase      : Optional[Any]         =   val





def 	a_    (							_lowercase	,						_lowercase=False       ):
						if base_model:
												_UpperCamelCase      : Tuple         =   ''''''
						else:
												_UpperCamelCase      : str         =   '''mobilevitv2.'''

						_UpperCamelCase      : List[str]         =   []
						for k in state_dict.keys():
												if k[:8] == "encoder.":
																		_UpperCamelCase      : Optional[int]         =   k[8:]
												else:
																		_UpperCamelCase      : Union[str, Any]         =   k

												if ".block." in k:
																		_UpperCamelCase      : Optional[int]         =   k_new.replace('''.block.'''	,						'''.'''       )
												if ".conv." in k:
																		_UpperCamelCase      : List[Any]         =   k_new.replace('''.conv.'''	,						'''.convolution.'''       )
												if ".norm." in k:
																		_UpperCamelCase      : Optional[int]         =   k_new.replace('''.norm.'''	,						'''.normalization.'''       )

												if "conv_1." in k:
																		_UpperCamelCase      : List[Any]         =   k_new.replace('''conv_1.'''	,						F"""{model_prefix}conv_stem."""       )
												for i in [1, 2]:
																		if F"""layer_{i}.""" in k:
																								_UpperCamelCase      : Union[str, Any]         =   k_new.replace(F"""layer_{i}."""	,						F"""{model_prefix}encoder.layer.{i-1}.layer."""       )
												if ".exp_1x1." in k:
																		_UpperCamelCase      : Optional[Any]         =   k_new.replace('''.exp_1x1.'''	,						'''.expand_1x1.'''       )
												if ".red_1x1." in k:
																		_UpperCamelCase      : Tuple         =   k_new.replace('''.red_1x1.'''	,						'''.reduce_1x1.'''       )

												for i in [3, 4, 5]:
																		if F"""layer_{i}.0.""" in k:
																								_UpperCamelCase      : Optional[int]         =   k_new.replace(F"""layer_{i}.0."""	,						F"""{model_prefix}encoder.layer.{i-1}.downsampling_layer."""       )
																		if F"""layer_{i}.1.local_rep.0.""" in k:
																								_UpperCamelCase      : Optional[int]         =   k_new.replace(F"""layer_{i}.1.local_rep.0."""	,						F"""{model_prefix}encoder.layer.{i-1}.conv_kxk."""       )
																		if F"""layer_{i}.1.local_rep.1.""" in k:
																								_UpperCamelCase      : Union[str, Any]         =   k_new.replace(F"""layer_{i}.1.local_rep.1."""	,						F"""{model_prefix}encoder.layer.{i-1}.conv_1x1."""       )

												for i in [3, 4, 5]:
																		if i == 3:
																								_UpperCamelCase      : Union[str, Any]         =   [0, 1]
																		elif i == 4:
																								_UpperCamelCase      : Tuple         =   [0, 1, 2, 3]
																		elif i == 5:
																								_UpperCamelCase      : Any         =   [0, 1, 2]

																		for j in j_in:
																								if F"""layer_{i}.1.global_rep.{j}.""" in k:
																														_UpperCamelCase      : Optional[int]         =   k_new.replace(
																														    F"""layer_{i}.1.global_rep.{j}."""	,						F"""{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}."""       )
																		if F"""layer_{i}.1.global_rep.{j+1}.""" in k:
																								_UpperCamelCase      : List[Any]         =   k_new.replace(
																								    F"""layer_{i}.1.global_rep.{j+1}."""	,						F"""{model_prefix}encoder.layer.{i-1}.layernorm."""       )

																		if F"""layer_{i}.1.conv_proj.""" in k:
																								_UpperCamelCase      : List[Any]         =   k_new.replace(F"""layer_{i}.1.conv_proj."""	,						F"""{model_prefix}encoder.layer.{i-1}.conv_projection."""       )

												if "pre_norm_attn.0." in k:
																		_UpperCamelCase      : int         =   k_new.replace('''pre_norm_attn.0.'''	,						'''layernorm_before.'''       )
												if "pre_norm_attn.1." in k:
																		_UpperCamelCase      : Optional[int]         =   k_new.replace('''pre_norm_attn.1.'''	,						'''attention.'''       )
												if "pre_norm_ffn.0." in k:
																		_UpperCamelCase      : Optional[int]         =   k_new.replace('''pre_norm_ffn.0.'''	,						'''layernorm_after.'''       )
												if "pre_norm_ffn.1." in k:
																		_UpperCamelCase      : Union[str, Any]         =   k_new.replace('''pre_norm_ffn.1.'''	,						'''ffn.conv1.'''       )
												if "pre_norm_ffn.3." in k:
																		_UpperCamelCase      : Union[str, Any]         =   k_new.replace('''pre_norm_ffn.3.'''	,						'''ffn.conv2.'''       )

												if "classifier.1." in k:
																		_UpperCamelCase      : Optional[Any]         =   k_new.replace('''classifier.1.'''	,						'''classifier.'''       )

												if "seg_head." in k:
																		_UpperCamelCase      : Union[str, Any]         =   k_new.replace('''seg_head.'''	,						'''segmentation_head.'''       )
												if ".aspp_layer." in k:
																		_UpperCamelCase      : List[str]         =   k_new.replace('''.aspp_layer.'''	,						'''.'''       )
												if ".aspp_pool." in k:
																		_UpperCamelCase      : List[Any]         =   k_new.replace('''.aspp_pool.'''	,						'''.'''       )

												rename_keys.append((k, k_new)       )
						return rename_keys





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : List[Any]         =   []
						for k in state_dict.keys():
												if k.startswith('''seg_head.aux_head.'''       ):
																		keys_to_ignore.append(_lowercase       )
						for k in keys_to_ignore:
												state_dict.pop(_lowercase	,						_lowercase       )





def 	a_    (							):
						_UpperCamelCase      : Any         =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
						_UpperCamelCase      : Union[str, Any]         =   Image.open(requests.get(_lowercase	,						stream=_lowercase       ).raw       )
						return im





@torch.no_grad()
def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : List[str]         =   get_mobilevitva_config(_lowercase	,						_lowercase       )

						# load original state_dict
						_UpperCamelCase      : Optional[int]         =   torch.load(_lowercase	,						map_location='''cpu'''       )

						# load huggingface model
						if task_name.startswith('''ade20k_'''       ) or task_name.startswith('''voc_'''       ):
												_UpperCamelCase      : Union[str, Any]         =   MobileViTVaForSemanticSegmentation(_lowercase       ).eval()
												_UpperCamelCase      : List[str]         =   False
						else:
												_UpperCamelCase      : Any         =   MobileViTVaForImageClassification(_lowercase       ).eval()
												_UpperCamelCase      : List[Any]         =   False

						# remove and rename some keys of load the original model
						_UpperCamelCase      : str         =   checkpoint
						remove_unused_keys(_lowercase       )
						_UpperCamelCase      : Optional[int]         =   create_rename_keys(_lowercase	,						base_model=_lowercase       )
						for rename_key_src, rename_key_dest in rename_keys:
												rename_key(_lowercase	,						_lowercase	,						_lowercase       )

						# load modified state_dict
						model.load_state_dict(_lowercase       )

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

						# verify classification model
						if task_name.startswith('''imagenet'''       ):
												_UpperCamelCase      : int         =   outputs.logits
												_UpperCamelCase      : Optional[Any]         =   logits.argmax(-1       ).item()
												print('''Predicted class:'''	,						model.config.idalabel[predicted_class_idx]       )
												if task_name.startswith('''imagenet1k_256'''       ) and config.width_multiplier == 1.0:
																		# expected_logits for base variant
																		_UpperCamelCase      : Dict         =   torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01]       )
																		assert torch.allclose(logits[0, :3]	,						_lowercase	,						atol=1E-4       )

						Path(_lowercase       ).mkdir(exist_ok=_lowercase       )
						print(F"""Saving model {task_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 __name__ == "__main__":
						UpperCamelCase_									=argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--task""",
						    default="""imagenet1k_256""",
						    type=str,
						    help=(
						        """Name of the task for which the MobileViTV2 model you'd like to convert is trained on . """
						        """
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            """
						    ),
						    choices=[
						        """imagenet1k_256""",
						        """imagenet1k_384""",
						        """imagenet21k_to_1k_256""",
						        """imagenet21k_to_1k_384""",
						        """ade20k_deeplabv3""",
						        """voc_deeplabv3""",
						    ],
						)

						parser.add_argument(
						    """--orig_checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
						)
						parser.add_argument("""--orig_config_path""", required=True, type=str, help="""Path to the original config file.""")
						parser.add_argument(
						    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
						)

						UpperCamelCase_									=parser.parse_args()
						convert_mobilevitva_checkpoint(
						    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
						)










"""simple docstring"""




UpperCamelCase_									=[
    """DownloadConfig""",
    """DownloadManager""",
    """DownloadMode""",
    """StreamingDownloadManager""",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager



