



'''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()
a   :			Optional[Any]					  =    logging.get_logger(__name__)



def       __lowerCamelCase  (					_lowercase		)      ->			Any:
							print("""Loading config file..."""		)

							def flatten_yaml_as_dict(_lowercase	,				_lowercase=""	,				_lowercase="."		):
														UpperCAmelCase     :		Optional[Any]							     =		[]
														for k, v in d.items():
																					UpperCAmelCase     :		List[str]							     =		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     :		List[str]							     =		yaml.load(_lowercase	,				Loader=yaml.FullLoader		)

																					UpperCAmelCase     :		str							     =		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       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Dict:
							UpperCAmelCase     :		Any							     =		MobileViTVaConfig()

							UpperCAmelCase     :		int							     =		False

							# dataset
							if task_name.startswith("""imagenet1k_"""		):
														UpperCAmelCase     :		Tuple							     =		1_0_0_0
														if int(task_name.strip().split("""_"""		)[-1]		) == 3_8_4:
																					UpperCAmelCase     :		List[str]							     =		3_8_4
														else:
																					UpperCAmelCase     :		str							     =		2_5_6
														UpperCAmelCase     :		Optional[Any]							     =		"""imagenet-1k-id2label.json"""
							elif task_name.startswith("""imagenet21k_to_1k_"""		):
														UpperCAmelCase     :		int							     =		2_1_0_0_0
														if int(task_name.strip().split("""_"""		)[-1]		) == 3_8_4:
																					UpperCAmelCase     :		Any							     =		3_8_4
														else:
																					UpperCAmelCase     :		int							     =		2_5_6
														UpperCAmelCase     :		Optional[Any]							     =		"""imagenet-22k-id2label.json"""
							elif task_name.startswith("""ade20k_"""		):
														UpperCAmelCase     :		List[str]							     =		1_5_1
														UpperCAmelCase     :		Dict							     =		5_1_2
														UpperCAmelCase     :		List[Any]							     =		"""ade20k-id2label.json"""
														UpperCAmelCase     :		Tuple							     =		True
							elif task_name.startswith("""voc_"""		):
														UpperCAmelCase     :		Tuple							     =		2_1
														UpperCAmelCase     :		Optional[int]							     =		5_1_2
														UpperCAmelCase     :		Any							     =		"""pascal-voc-id2label.json"""
														UpperCAmelCase     :		List[Any]							     =		True

							# orig_config
							UpperCAmelCase     :		Any							     =		load_orig_config_file(_lowercase		)
							assert getattr(_lowercase	,				"""model.classification.name"""	,				-1		) == "mobilevit_v2", "Invalid model"
							UpperCAmelCase     :		str							     =		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     :		Dict							     =		getattr(_lowercase	,				"""model.classification.activation.name"""	,				"""swish"""		)
							# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

							if is_segmentation_model:
														UpperCAmelCase     :		List[str]							     =		getattr(_lowercase	,				"""model.segmentation.output_stride"""	,				1_6		)
														if "_deeplabv3" in task_name:
																					UpperCAmelCase     :		List[Any]							     =		getattr(_lowercase	,				"""model.segmentation.deeplabv3.aspp_rates"""	,				[1_2, 2_4, 3_6]		)
																					UpperCAmelCase     :		Union[str, Any]							     =		getattr(_lowercase	,				"""model.segmentation.deeplabv3.aspp_out_channels"""	,				5_1_2		)
																					UpperCAmelCase     :		Optional[int]							     =		getattr(_lowercase	,				"""model.segmentation.deeplabv3.aspp_dropout"""	,				0.1		)

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

							return config



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			Union[str, Any]:
							UpperCAmelCase     :		List[Any]							     =		dct.pop(_lowercase		)
							UpperCAmelCase     :		Any							     =		val



def       __lowerCamelCase  (					_lowercase	,				_lowercase=False		)      ->			Dict:
							if base_model:
														UpperCAmelCase     :		int							     =		""""""
							else:
														UpperCAmelCase     :		Dict							     =		"""mobilevitv2."""

							UpperCAmelCase     :		Optional[int]							     =		[]
							for k in state_dict.keys():
														if k[:8] == "encoder.":
																					UpperCAmelCase     :		Optional[Any]							     =		k[8:]
														else:
																					UpperCAmelCase     :		Optional[Any]							     =		k

														if ".block." in k:
																					UpperCAmelCase     :		List[Any]							     =		k_new.replace(""".block."""	,				"""."""		)
														if ".conv." in k:
																					UpperCAmelCase     :		Any							     =		k_new.replace(""".conv."""	,				""".convolution."""		)
														if ".norm." in k:
																					UpperCAmelCase     :		List[str]							     =		k_new.replace(""".norm."""	,				""".normalization."""		)

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

														for i in [3, 4, 5]:
																					if F'''layer_{i}.0.''' in k:
																												UpperCAmelCase     :		str							     =		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[Any]							     =		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     :		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     :		List[Any]							     =		[0, 1]
																					elif i == 4:
																												UpperCAmelCase     :		Optional[Any]							     =		[0, 1, 2, 3]
																					elif i == 5:
																												UpperCAmelCase     :		Optional[int]							     =		[0, 1, 2]

																					for j in j_in:
																												if F'''layer_{i}.1.global_rep.{j}.''' in k:
																																			UpperCAmelCase     :		Dict							     =		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     :		Union[str, 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     :		Optional[int]							     =		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     :		Dict							     =		k_new.replace("""pre_norm_attn.0."""	,				"""layernorm_before."""		)
														if "pre_norm_attn.1." in k:
																					UpperCAmelCase     :		int							     =		k_new.replace("""pre_norm_attn.1."""	,				"""attention."""		)
														if "pre_norm_ffn.0." in k:
																					UpperCAmelCase     :		List[Any]							     =		k_new.replace("""pre_norm_ffn.0."""	,				"""layernorm_after."""		)
														if "pre_norm_ffn.1." in k:
																					UpperCAmelCase     :		Tuple							     =		k_new.replace("""pre_norm_ffn.1."""	,				"""ffn.conv1."""		)
														if "pre_norm_ffn.3." in k:
																					UpperCAmelCase     :		str							     =		k_new.replace("""pre_norm_ffn.3."""	,				"""ffn.conv2."""		)

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

														if "seg_head." in k:
																					UpperCAmelCase     :		Tuple							     =		k_new.replace("""seg_head."""	,				"""segmentation_head."""		)
														if ".aspp_layer." in k:
																					UpperCAmelCase     :		Optional[int]							     =		k_new.replace(""".aspp_layer."""	,				"""."""		)
														if ".aspp_pool." in k:
																					UpperCAmelCase     :		int							     =		k_new.replace(""".aspp_pool."""	,				"""."""		)

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



def       __lowerCamelCase  (					_lowercase		)      ->			Dict:
							UpperCAmelCase     :		Dict							     =		[]
							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       __lowerCamelCase  (					)      ->			Dict:
							UpperCAmelCase     :		List[str]							     =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
							UpperCAmelCase     :		Optional[int]							     =		Image.open(requests.get(_lowercase	,				stream=_lowercase		).raw		)
							return im



@torch.no_grad()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		List[Any]							     =		get_mobilevitva_config(_lowercase	,				_lowercase		)

							# load original state_dict
							UpperCAmelCase     :		Any							     =		torch.load(_lowercase	,				map_location="""cpu"""		)

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

							# remove and rename some keys of load the original model
							UpperCAmelCase     :		List[str]							     =		checkpoint
							remove_unused_keys(_lowercase		)
							UpperCAmelCase     :		Union[str, Any]							     =		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     :		Dict							     =		MobileViTImageProcessor(crop_size=config.image_size	,				size=config.image_size + 3_2		)
							UpperCAmelCase     :		Any							     =		image_processor(images=prepare_img()	,				return_tensors="""pt"""		)
							UpperCAmelCase     :		Optional[int]							     =		model(**_lowercase		)

							# verify classification model
							if task_name.startswith("""imagenet"""		):
														UpperCAmelCase     :		Dict							     =		outputs.logits
														UpperCAmelCase     :		Union[str, 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([-1.6_336e00, -7.3_204e-02, -5.1_883e-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__":
							a   :			int					  =    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."""
							)

							a   :			Optional[int]					  =    parser.parse_args()
							convert_mobilevitva_checkpoint(
							    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
							)






'''simple docstring'''




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce



def       __lowerCamelCase  (					_lowercase		)      ->			Tuple:
							return (torch.arange(state.num_processes		) + 1.0 + (state.num_processes * state.process_index)).to(state.device		)



def       __lowerCamelCase  (					_lowercase		)      ->			List[Any]:
							UpperCAmelCase     :		Any							     =		create_tensor(_lowercase		)
							UpperCAmelCase     :		Union[str, Any]							     =		gather(_lowercase		)
							assert gathered_tensor.tolist() == list(range(1	,				state.num_processes**2 + 1		)		)



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		Any							     =		[state.process_index]
							UpperCAmelCase     :		Union[str, Any]							     =		gather_object(_lowercase		)
							assert len(_lowercase		) == state.num_processes, F'''{gathered_obj}, {len(_lowercase		)} != {state.num_processes}'''
							assert gathered_obj == list(range(state.num_processes		)		), F'''{gathered_obj} != {list(range(state.num_processes		)		)}'''



def       __lowerCamelCase  (					_lowercase		)      ->			List[Any]:
							UpperCAmelCase     :		Optional[int]							     =		create_tensor(_lowercase		)
							UpperCAmelCase     :		List[str]							     =		broadcast(_lowercase		)
							assert broadcasted_tensor.shape == torch.Size([state.num_processes]		)
							assert broadcasted_tensor.tolist() == list(range(1	,				state.num_processes + 1		)		)



def       __lowerCamelCase  (					_lowercase		)      ->			Tuple:
							# We need to pad the tensor with one more element if we are the main process
							# to ensure that we can pad
							if state.is_main_process:
														UpperCAmelCase     :		Optional[Any]							     =		torch.arange(state.num_processes + 1		).to(state.device		)
							else:
														UpperCAmelCase     :		Tuple							     =		torch.arange(state.num_processes		).to(state.device		)
							UpperCAmelCase     :		Optional[Any]							     =		pad_across_processes(_lowercase		)
							assert padded_tensor.shape == torch.Size([state.num_processes + 1]		)
							if not state.is_main_process:
														assert padded_tensor.tolist() == list(range(0	,				state.num_processes		)		) + [0]



def       __lowerCamelCase  (					_lowercase		)      ->			Dict:
							# For now runs on only two processes
							if state.num_processes != 2:
														return
							UpperCAmelCase     :		Optional[Any]							     =		create_tensor(_lowercase		)
							UpperCAmelCase     :		Optional[Any]							     =		reduce(_lowercase	,				"""sum"""		)
							UpperCAmelCase     :		Optional[Any]							     =		torch.tensor([4.0, 6]		).to(state.device		)
							assert torch.allclose(_lowercase	,				_lowercase		), F'''{reduced_tensor} != {truth_tensor}'''



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[Any]:
							# For now runs on only two processes
							if state.num_processes != 2:
														return
							UpperCAmelCase     :		Tuple							     =		create_tensor(_lowercase		)
							UpperCAmelCase     :		Optional[int]							     =		reduce(_lowercase	,				"""mean"""		)
							UpperCAmelCase     :		str							     =		torch.tensor([2.0, 3]		).to(state.device		)
							assert torch.allclose(_lowercase	,				_lowercase		), F'''{reduced_tensor} != {truth_tensor}'''



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							# For xla_spawn (TPUs)
							main()



def       __lowerCamelCase  (					)      ->			int:
							UpperCAmelCase     :		List[Any]							     =		PartialState()
							state.print(F'''State: {state}'''		)
							state.print("""testing gather"""		)
							test_gather(_lowercase		)
							state.print("""testing gather_object"""		)
							test_gather_object(_lowercase		)
							state.print("""testing broadcast"""		)
							test_broadcast(_lowercase		)
							state.print("""testing pad_across_processes"""		)
							test_pad_across_processes(_lowercase		)
							state.print("""testing reduce_sum"""		)
							test_reduce_sum(_lowercase		)
							state.print("""testing reduce_mean"""		)
							test_reduce_mean(_lowercase		)


if __name__ == "__main__":
							main()

