


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()
lowerCAmelCase__			:      List[str]								=logging.get_logger(__name__)






def 						a__			(				A__				):
			print('Loading config file...'				)

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

			SCREAMING_SNAKE_CASE_		:     Dict					=					argparse.Namespace()
			with open(A__,      'r'				) as yaml_file:
						try:
									SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					yaml.load(A__,      Loader=yaml.FullLoader				)

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






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					MobileViTVaConfig()

			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					False

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

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

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

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

			return config






def 						a__			(				A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Dict					=					dct.pop(A__				)
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					val






def 						a__			(				A__,      A__=False				):
			if base_model:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					''
			else:
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					'mobilevitv2.'

			SCREAMING_SNAKE_CASE_		:     Any					=					[]
			for k in state_dict.keys():
						if k[:8] == "encoder.":
									SCREAMING_SNAKE_CASE_		:     str					=					k[8:]
						else:
									SCREAMING_SNAKE_CASE_		:     Dict					=					k

						if ".block." in k:
									SCREAMING_SNAKE_CASE_		:     Tuple					=					k_new.replace('.block.',      '.'				)
						if ".conv." in k:
									SCREAMING_SNAKE_CASE_		:     Any					=					k_new.replace('.conv.',      '.convolution.'				)
						if ".norm." in k:
									SCREAMING_SNAKE_CASE_		:     Dict					=					k_new.replace('.norm.',      '.normalization.'				)

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

						for i in [3, 4, 5]:
									if F'''layer_{i}.0.''' in k:
												SCREAMING_SNAKE_CASE_		:     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:
												SCREAMING_SNAKE_CASE_		:     Union[str, 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:
												SCREAMING_SNAKE_CASE_		:     Dict					=					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:
												SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[0, 1]
									elif i == 4:
												SCREAMING_SNAKE_CASE_		:     Any					=					[0, 1, 2, 3]
									elif i == 5:
												SCREAMING_SNAKE_CASE_		:     Any					=					[0, 1, 2]

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

						if "classifier.1." in k:
									SCREAMING_SNAKE_CASE_		:     int					=					k_new.replace('classifier.1.',      'classifier.'				)

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

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






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					[]
			for k in state_dict.keys():
						if k.startswith('seg_head.aux_head.'				):
									keys_to_ignore.append(A__				)
			for k in keys_to_ignore:
						state_dict.pop(A__,      A__				)






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






@torch.no_grad()
def 						a__			(				A__,      A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     List[str]					=					get_mobilevitva_config(A__,      A__				)

			# load original state_dict
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.load(A__,      map_location='cpu'				)

			# load huggingface model
			if task_name.startswith('ade20k_'				) or task_name.startswith('voc_'				):
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					MobileViTVaForSemanticSegmentation(A__				).eval()
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					False
			else:
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					MobileViTVaForImageClassification(A__				).eval()
						SCREAMING_SNAKE_CASE_		:     str					=					False

			# remove and rename some keys of load the original model
			SCREAMING_SNAKE_CASE_		:     Dict					=					checkpoint
			remove_unused_keys(A__				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					create_rename_keys(A__,      base_model=A__				)
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(A__,      A__,      A__				)

			# load modified state_dict
			model.load_state_dict(A__				)

			# Check outputs on an image, prepared by MobileViTImageProcessor
			SCREAMING_SNAKE_CASE_		:     List[str]					=					MobileViTImageProcessor(crop_size=config.image_size,      size=config.image_size + 3_2				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					image_processor(images=prepare_img(),      return_tensors='pt'				)
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					model(**A__				)

			# verify classification model
			if task_name.startswith('imagenet'				):
						SCREAMING_SNAKE_CASE_		:     Any					=					outputs.logits
						SCREAMING_SNAKE_CASE_		:     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
									SCREAMING_SNAKE_CASE_		:     Dict					=					torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01]				)
									assert torch.allclose(logits[0, :3],      A__,      atol=1E-4				)

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


if __name__ == "__main__":
				lowerCAmelCase__			:      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 . '
				        '\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.'
				)

				lowerCAmelCase__			:      Any								=parser.parse_args()
				convert_mobilevitva_checkpoint(
				    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
				)








import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    ["""image_processor""", """tokenizer"""]
       lowercase_						=    """LayoutLMv2ImageProcessor"""
       lowercase_						=    ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")

       def __init__(							self ,   lowercase__=None ,   lowercase__=None ,   **lowercase__     ):







             '''simple docstring'''



             if "feature_extractor" in kwargs:
                   warnings.warn(
                       '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                       ''' instead.''' ,   lowercase__ ,   )
                   __A    =kwargs.pop('''feature_extractor'''     )

             __A    =image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                   raise ValueError('''You need to specify an `image_processor`.'''     )
             if tokenizer is None:
                   raise ValueError('''You need to specify a `tokenizer`.'''     )

             super().__init__(lowercase__ ,   lowercase__     )

       def __call__(							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = True ,   lowercase__ = False ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = 0 ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = True ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



             if self.image_processor.apply_ocr and (boxes is not None):
                   raise ValueError(
                       '''You cannot provide bounding boxes '''
                       '''if you initialized the image processor with apply_ocr set to True.'''     )

             if self.image_processor.apply_ocr and (word_labels is not None):
                   raise ValueError(
                       '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''     )

             if return_overflowing_tokens is True and return_offsets_mapping is False:
                   raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''     )

             # first, apply the image processor
             __A    =self.image_processor(images=lowercase__ ,   return_tensors=lowercase__     )

             # second, apply the tokenizer
             if text is not None and self.image_processor.apply_ocr and text_pair is None:
                   if isinstance(lowercase__ ,   lowercase__     ):
                         __A    =[text]  # add batch dimension (as the image processor always adds a batch dimension)
                   __A    =features['''words''']

             __A    =self.tokenizer(
                 text=text if text is not None else features['''words'''] ,   text_pair=text_pair if text_pair is not None else None ,   boxes=boxes if boxes is not None else features['''boxes'''] ,   word_labels=lowercase__ ,   add_special_tokens=lowercase__ ,   padding=lowercase__ ,   truncation=lowercase__ ,   max_length=lowercase__ ,   stride=lowercase__ ,   pad_to_multiple_of=lowercase__ ,   return_token_type_ids=lowercase__ ,   return_attention_mask=lowercase__ ,   return_overflowing_tokens=lowercase__ ,   return_special_tokens_mask=lowercase__ ,   return_offsets_mapping=lowercase__ ,   return_length=lowercase__ ,   verbose=lowercase__ ,   return_tensors=lowercase__ ,   **lowercase__ ,   )

             # add pixel values
             __A    =features.pop('''pixel_values'''     )
             if return_overflowing_tokens is True:
                   __A    =self.get_overflowing_images(lowercase__ ,   encoded_inputs['''overflow_to_sample_mapping''']     )
             __A    =images

             return encoded_inputs

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =[]
             for sample_idx in overflow_to_sample_mapping:
                   images_with_overflow.append(images[sample_idx]     )

             if len(lowercase__     ) != len(lowercase__     ):
                   raise ValueError(
                       '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
                       f''' {len(lowercase__     )} and {len(lowercase__     )}'''     )

             return images_with_overflow

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             return self.tokenizer.batch_decode(*lowercase__ ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             return self.tokenizer.decode(*lowercase__ ,   **lowercase__     )

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return ["input_ids", "bbox", "attention_mask", "image"]

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             warnings.warn(
                 '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,   lowercase__ ,   )
             return self.image_processor_class


       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             warnings.warn(
                 '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,   lowercase__ ,   )
             return self.image_processor


