


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()
__snake_case            =   logging.get_logger(__name__)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			print("""Loading config file..."""	)

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

			UpperCamelCase         =					argparse.Namespace()
			with open(SCREAMING_SNAKE_CASE_       ,  """r"""	) as yaml_file:
						try:
									UpperCamelCase         =					yaml.load(SCREAMING_SNAKE_CASE_       ,  Loader=yaml.FullLoader	)

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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		List[Any]	):





			"""simple docstring"""




			UpperCamelCase         =					MobileViTVaConfig()

			UpperCamelCase         =					False

			# dataset
			if task_name.startswith("""imagenet1k_"""	):
						UpperCamelCase         =					1_000
						if int(task_name.strip().split("""_"""	)[-1]	) == 384:
									UpperCamelCase         =					384
						else:
									UpperCamelCase         =					256
						UpperCamelCase         =					"""imagenet-1k-id2label.json"""
			elif task_name.startswith("""imagenet21k_to_1k_"""	):
						UpperCamelCase         =					21_000
						if int(task_name.strip().split("""_"""	)[-1]	) == 384:
									UpperCamelCase         =					384
						else:
									UpperCamelCase         =					256
						UpperCamelCase         =					"""imagenet-22k-id2label.json"""
			elif task_name.startswith("""ade20k_"""	):
						UpperCamelCase         =					151
						UpperCamelCase         =					512
						UpperCamelCase         =					"""ade20k-id2label.json"""
						UpperCamelCase         =					True
			elif task_name.startswith("""voc_"""	):
						UpperCamelCase         =					21
						UpperCamelCase         =					512
						UpperCamelCase         =					"""pascal-voc-id2label.json"""
						UpperCamelCase         =					True

			# orig_config
			UpperCamelCase         =					load_orig_config_file(SCREAMING_SNAKE_CASE_	)
			assert getattr(SCREAMING_SNAKE_CASE_       ,  """model.classification.name"""       ,  -1	) == "mobilevit_v2", "Invalid model"
			UpperCamelCase         =					getattr(SCREAMING_SNAKE_CASE_       ,  """model.classification.mitv2.width_multiplier"""       ,  1.0	)
			assert (
			    getattr(SCREAMING_SNAKE_CASE_       ,  """model.classification.mitv2.attn_norm_layer"""       ,  -1	) == "layer_norm_2d"
			), "Norm layers other than layer_norm_2d is not supported"
			UpperCamelCase         =					getattr(SCREAMING_SNAKE_CASE_       ,  """model.classification.activation.name"""       ,  """swish"""	)
			# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

			if is_segmentation_model:
						UpperCamelCase         =					getattr(SCREAMING_SNAKE_CASE_       ,  """model.segmentation.output_stride"""       ,  16	)
						if "_deeplabv3" in task_name:
									UpperCamelCase         =					getattr(SCREAMING_SNAKE_CASE_       ,  """model.segmentation.deeplabv3.aspp_rates"""       ,  [12, 24, 36]	)
									UpperCamelCase         =					getattr(SCREAMING_SNAKE_CASE_       ,  """model.segmentation.deeplabv3.aspp_out_channels"""       ,  512	)
									UpperCamelCase         =					getattr(SCREAMING_SNAKE_CASE_       ,  """model.segmentation.deeplabv3.aspp_dropout"""       ,  0.1	)

    # id2label
			UpperCamelCase         =					"""huggingface/label-files"""
			UpperCamelCase         =					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  repo_type="""dataset"""	)       ,  """r"""	)	)
			UpperCamelCase         =					{int(SCREAMING_SNAKE_CASE_	): v for k, v in idalabel.items()}
			UpperCamelCase         =					idalabel
			UpperCamelCase         =					{v: k for k, v in idalabel.items()}

			return config






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		List[str]	):





			"""simple docstring"""




			UpperCamelCase         =					dct.pop(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					val






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]=False	):





			"""simple docstring"""




			if base_model:
						UpperCamelCase         =					""""""
			else:
						UpperCamelCase         =					"""mobilevitv2."""

			UpperCamelCase         =					[]
			for k in state_dict.keys():
						if k[:8] == "encoder.":
									UpperCamelCase         =					k[8:]
						else:
									UpperCamelCase         =					k

						if ".block." in k:
									UpperCamelCase         =					k_new.replace(""".block."""       ,  """."""	)
						if ".conv." in k:
									UpperCamelCase         =					k_new.replace(""".conv."""       ,  """.convolution."""	)
						if ".norm." in k:
									UpperCamelCase         =					k_new.replace(""".norm."""       ,  """.normalization."""	)

						if "conv_1." in k:
									UpperCamelCase         =					k_new.replace("""conv_1."""       ,  f'{model_prefix}conv_stem.'	)
						for i in [1, 2]:
									if f'layer_{i}.' in k:
												UpperCamelCase         =					k_new.replace(f'layer_{i}.'       ,  f'{model_prefix}encoder.layer.{i-1}.layer.'	)
						if ".exp_1x1." in k:
									UpperCamelCase         =					k_new.replace(""".exp_1x1."""       ,  """.expand_1x1."""	)
						if ".red_1x1." in k:
									UpperCamelCase         =					k_new.replace(""".red_1x1."""       ,  """.reduce_1x1."""	)

						for i in [3, 4, 5]:
									if f'layer_{i}.0.' in k:
												UpperCamelCase         =					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         =					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         =					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         =					[0, 1]
									elif i == 4:
												UpperCamelCase         =					[0, 1, 2, 3]
									elif i == 5:
												UpperCamelCase         =					[0, 1, 2]

									for j in j_in:
												if f'layer_{i}.1.global_rep.{j}.' in k:
															UpperCamelCase         =					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         =					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         =					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         =					k_new.replace("""pre_norm_attn.0."""       ,  """layernorm_before."""	)
						if "pre_norm_attn.1." in k:
									UpperCamelCase         =					k_new.replace("""pre_norm_attn.1."""       ,  """attention."""	)
						if "pre_norm_ffn.0." in k:
									UpperCamelCase         =					k_new.replace("""pre_norm_ffn.0."""       ,  """layernorm_after."""	)
						if "pre_norm_ffn.1." in k:
									UpperCamelCase         =					k_new.replace("""pre_norm_ffn.1."""       ,  """ffn.conv1."""	)
						if "pre_norm_ffn.3." in k:
									UpperCamelCase         =					k_new.replace("""pre_norm_ffn.3."""       ,  """ffn.conv2."""	)

						if "classifier.1." in k:
									UpperCamelCase         =					k_new.replace("""classifier.1."""       ,  """classifier."""	)

						if "seg_head." in k:
									UpperCamelCase         =					k_new.replace("""seg_head."""       ,  """segmentation_head."""	)
						if ".aspp_layer." in k:
									UpperCamelCase         =					k_new.replace(""".aspp_layer."""       ,  """."""	)
						if ".aspp_pool." in k:
									UpperCamelCase         =					k_new.replace(""".aspp_pool."""       ,  """."""	)

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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[int]	):





			"""simple docstring"""




			UpperCamelCase         =					[]
			for k in state_dict.keys():
						if k.startswith("""seg_head.aux_head."""	):
									keys_to_ignore.append(SCREAMING_SNAKE_CASE_	)
			for k in keys_to_ignore:
						state_dict.pop(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
			UpperCamelCase         =					Image.open(requests.get(SCREAMING_SNAKE_CASE_       ,  stream=SCREAMING_SNAKE_CASE_	).raw	)
			return im






@torch.no_grad()
def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		List[Any]	):





			"""simple docstring"""




			UpperCamelCase         =					get_mobilevitva_config(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			# load original state_dict
			UpperCamelCase         =					torch.load(SCREAMING_SNAKE_CASE_       ,  map_location="""cpu"""	)

			# load huggingface model
			if task_name.startswith("""ade20k_"""	) or task_name.startswith("""voc_"""	):
						UpperCamelCase         =					MobileViTVaForSemanticSegmentation(SCREAMING_SNAKE_CASE_	).eval()
						UpperCamelCase         =					False
			else:
						UpperCamelCase         =					MobileViTVaForImageClassification(SCREAMING_SNAKE_CASE_	).eval()
						UpperCamelCase         =					False

			# remove and rename some keys of load the original model
			UpperCamelCase         =					checkpoint
			remove_unused_keys(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					create_rename_keys(SCREAMING_SNAKE_CASE_       ,  base_model=SCREAMING_SNAKE_CASE_	)
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			# load modified state_dict
			model.load_state_dict(SCREAMING_SNAKE_CASE_	)

			# Check outputs on an image, prepared by MobileViTImageProcessor
			UpperCamelCase         =					MobileViTImageProcessor(crop_size=config.image_size       ,  size=config.image_size + 32	)
			UpperCamelCase         =					image_processor(images=prepare_img()       ,  return_tensors="""pt"""	)
			UpperCamelCase         =					model(**SCREAMING_SNAKE_CASE_	)

			# verify classification model
			if task_name.startswith("""imagenet"""	):
						UpperCamelCase         =					outputs.logits
						UpperCamelCase         =					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         =					torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]	)
									assert torch.allclose(logits[0, :3]       ,  SCREAMING_SNAKE_CASE_       ,  atol=1e-4	)

			Path(SCREAMING_SNAKE_CASE_	).mkdir(exist_ok=SCREAMING_SNAKE_CASE_	)
			print(f'Saving model {task_name} to {pytorch_dump_folder_path}'	)
			model.save_pretrained(SCREAMING_SNAKE_CASE_	)
			print(f'Saving image processor to {pytorch_dump_folder_path}'	)
			image_processor.save_pretrained(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
			__snake_case            =   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 . "
			        "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
			    ),
			    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."
			)

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








import torch

from transformers import AutoModel




class    UpperCAmelCase					(       torch.nn.Module    ):




			def __init__(						self							:		int       ,       __magic_name__							:		List[Any]="sayef/fsner-bert-base-uncased"						):







						"""simple docstring"""







						super(__magic_name__       ,       self						).__init__()

						UpperCamelCase         =					AutoModel.from_pretrained(__magic_name__       ,       return_dict=__magic_name__						)
						UpperCamelCase         =					torch.nn.CosineSimilarity(3       ,       1e-08						)
						UpperCamelCase         =					torch.nn.Softmax(dim=1						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       **__magic_name__							:		List[Any]						):







						"""simple docstring"""







						return self.bert(**__magic_name__						).last_hidden_state




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		int						):







						"""simple docstring"""







						return token_embeddings.sum(2       ,       keepdim=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		Optional[Any]=1						):







						"""simple docstring"""







						return self.softmax(T * self.cos(__magic_name__       ,       __magic_name__						)						)





			def   lowerCamelCase_					(						self							:		Union[str, Any]       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					W_supports["""sizes"""].tolist()
						UpperCamelCase         =					W_supports["""start_token_id"""].item()
						UpperCamelCase         =					W_supports["""end_token_id"""].item()

						del W_supports["sizes"]
						del W_supports["start_token_id"]
						del W_supports["end_token_id"]

						UpperCamelCase         =					self.BERT(**__magic_name__						)
						UpperCamelCase         =					self.BERT(**__magic_name__						)

						UpperCamelCase         =					None
						UpperCamelCase         =					None

						UpperCamelCase         =					W_supports["""input_ids"""] == start_token_id
						UpperCamelCase         =					W_supports["""input_ids"""] == end_token_id

						for i, size in enumerate(__magic_name__						):
									if i == 0:
												UpperCamelCase         =					0
									else:
												UpperCamelCase         =					support_sizes[i - 1]

									UpperCamelCase         =					S[s : s + size][start_token_masks[s : s + size]]
									UpperCamelCase         =					S[s : s + size][end_token_masks[s : s + size]]

									UpperCamelCase         =					torch.matmul(q[i]       ,       s_start.T						).sum(1						).softmax(0						)
									UpperCamelCase         =					torch.matmul(q[i]       ,       s_end.T						).sum(1						).softmax(0						)

									if p_starts is not None:
												UpperCamelCase         =					torch.vstack((p_starts, p_start)						)
												UpperCamelCase         =					torch.vstack((p_ends, p_end)						)
									else:
												UpperCamelCase         =					p_start
												UpperCamelCase         =					p_end

						return p_starts, p_ends




