

'''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()
lowercase__         =      logging.get_logger(__name__)




def 				UpperCamelCase(					UpperCAmelCase_     ):
 print('Loading config file...'     )

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

 UpperCAmelCase	:     int						 =			argparse.Namespace()
 with open(UpperCAmelCase_  ,	'r'     ) as yaml_file:
  try:
   UpperCAmelCase	:     int						 =			yaml.load(UpperCAmelCase_  ,	Loader=yaml.FullLoader     )

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




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Dict						 =			MobileViTVaConfig()

 UpperCAmelCase	:     str						 =			False

 # dataset
 if task_name.startswith('imagenet1k_'     ):
  UpperCAmelCase	:     Optional[int]						 =			10_00
  if int(task_name.strip().split('_'     )[-1]     ) == 3_84:
   UpperCAmelCase	:     Union[str, Any]						 =			3_84
  else:
   UpperCAmelCase	:     Optional[Any]						 =			2_56
  UpperCAmelCase	:     List[Any]						 =			'imagenet-1k-id2label.json'
 elif task_name.startswith('imagenet21k_to_1k_'     ):
  UpperCAmelCase	:     Optional[int]						 =			2_10_00
  if int(task_name.strip().split('_'     )[-1]     ) == 3_84:
   UpperCAmelCase	:     List[Any]						 =			3_84
  else:
   UpperCAmelCase	:     Optional[int]						 =			2_56
  UpperCAmelCase	:     str						 =			'imagenet-22k-id2label.json'
 elif task_name.startswith('ade20k_'     ):
  UpperCAmelCase	:     List[str]						 =			1_51
  UpperCAmelCase	:     Union[str, Any]						 =			5_12
  UpperCAmelCase	:     Optional[int]						 =			'ade20k-id2label.json'
  UpperCAmelCase	:     Tuple						 =			True
 elif task_name.startswith('voc_'     ):
  UpperCAmelCase	:     Union[str, Any]						 =			21
  UpperCAmelCase	:     int						 =			5_12
  UpperCAmelCase	:     List[str]						 =			'pascal-voc-id2label.json'
  UpperCAmelCase	:     Any						 =			True

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

 if is_segmentation_model:
  UpperCAmelCase	:     str						 =			getattr(UpperCAmelCase_  ,	'model.segmentation.output_stride'  ,	16     )
  if "_deeplabv3" in task_name:
   UpperCAmelCase	:     List[Any]						 =			getattr(UpperCAmelCase_  ,	'model.segmentation.deeplabv3.aspp_rates'  ,	[12, 24, 36]     )
   UpperCAmelCase	:     int						 =			getattr(UpperCAmelCase_  ,	'model.segmentation.deeplabv3.aspp_out_channels'  ,	5_12     )
   UpperCAmelCase	:     Optional[Any]						 =			getattr(UpperCAmelCase_  ,	'model.segmentation.deeplabv3.aspp_dropout'  ,	0.1     )

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

 return config




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Dict						 =			dct.pop(UpperCAmelCase_     )
 UpperCAmelCase	:     List[Any]						 =			val




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_=False     ):
 if base_model:
  UpperCAmelCase	:     Optional[Any]						 =			''
 else:
  UpperCAmelCase	:     List[str]						 =			'mobilevitv2.'

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

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

  if "conv_1." in k:
   UpperCAmelCase	:     Optional[Any]						 =			k_new.replace('conv_1.'  ,	F"""{model_prefix}conv_stem."""     )
  for i in [1, 2]:
   if F"""layer_{i}.""" in k:
    UpperCAmelCase	:     Tuple						 =			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	:     Optional[Any]						 =			k_new.replace('.red_1x1.'  ,	'.reduce_1x1.'     )

  for i in [3, 4, 5]:
   if F"""layer_{i}.0.""" in k:
    UpperCAmelCase	:     Any						 =			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	:     Dict						 =			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	:     List[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	:     int						 =			[0, 1]
   elif i == 4:
    UpperCAmelCase	:     Optional[Any]						 =			[0, 1, 2, 3]
   elif i == 5:
    UpperCAmelCase	:     Union[str, 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	:     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	:     Tuple						 =			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	:     Union[str, Any]						 =			k_new.replace('pre_norm_attn.1.'  ,	'attention.'     )
  if "pre_norm_ffn.0." in k:
   UpperCAmelCase	:     Any						 =			k_new.replace('pre_norm_ffn.0.'  ,	'layernorm_after.'     )
  if "pre_norm_ffn.1." in k:
   UpperCAmelCase	:     Optional[Any]						 =			k_new.replace('pre_norm_ffn.1.'  ,	'ffn.conv1.'     )
  if "pre_norm_ffn.3." in k:
   UpperCAmelCase	:     int						 =			k_new.replace('pre_norm_ffn.3.'  ,	'ffn.conv2.'     )

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

  if "seg_head." in k:
   UpperCAmelCase	:     List[str]						 =			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 				UpperCamelCase(					UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[int]						 =			[]
 for k in state_dict.keys():
  if k.startswith('seg_head.aux_head.'     ):
   keys_to_ignore.append(UpperCAmelCase_     )
 for k in keys_to_ignore:
  state_dict.pop(UpperCAmelCase_  ,	UpperCAmelCase_     )




def 				UpperCamelCase(					):
 UpperCAmelCase	:     Union[str, 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(UpperCAmelCase_  ,	stream=UpperCAmelCase_     ).raw     )
 return im




@torch.no_grad()
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Dict						 =			get_mobilevitva_config(UpperCAmelCase_  ,	UpperCAmelCase_     )

 # load original state_dict
 UpperCAmelCase	:     str						 =			torch.load(UpperCAmelCase_  ,	map_location='cpu'     )

 # load huggingface model
 if task_name.startswith('ade20k_'     ) or task_name.startswith('voc_'     ):
  UpperCAmelCase	:     Any						 =			MobileViTVaForSemanticSegmentation(UpperCAmelCase_     ).eval()
  UpperCAmelCase	:     Any						 =			False
 else:
  UpperCAmelCase	:     str						 =			MobileViTVaForImageClassification(UpperCAmelCase_     ).eval()
  UpperCAmelCase	:     Tuple						 =			False

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

 # load modified state_dict
 model.load_state_dict(UpperCAmelCase_     )

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

 # 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	:     Tuple						 =			torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01]     )
   assert torch.allclose(logits[0, :3]  ,	UpperCAmelCase_  ,	atol=1E-4     )

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


if __name__ == "__main__":
  lowercase__         =      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."
  )

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







'''simple docstring'''

from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowercase__         =      logging.get_logger(__name__)

lowercase__         =      {
    "deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Optional[int]  		=  """perceiver"""


     def __init__(		self			:					str					,      lowercase_			:					List[str]=256					,      lowercase_			:					List[str]=1_280					,      lowercase_			:					str=768					,      lowercase_			:					Tuple=1					,      lowercase_			:					str=26					,      lowercase_			:					List[Any]=8					,      lowercase_			:					int=8					,      lowercase_			:					List[str]=None					,      lowercase_			:					Dict=None					,      lowercase_			:					int="kv"					,      lowercase_			:					Union[str, Any]=1					,      lowercase_			:					List[str]=1					,      lowercase_			:					Any="gelu"					,      lowercase_			:					Optional[Any]=0.1					,      lowercase_			:					str=0.02					,      lowercase_			:					Optional[Any]=1E-12					,      lowercase_			:					Any=True					,      lowercase_			:					Union[str, Any]=262					,      lowercase_			:					Union[str, Any]=2_048					,      lowercase_			:					Optional[int]=56					,      lowercase_			:					int=[368, 496]					,      lowercase_			:					str=16					,      lowercase_			:					Optional[int]=1_920					,      lowercase_			:					Tuple=16					,      lowercase_			:					int=[1, 16, 224, 224]					,      **lowercase_			:					Union[str, Any]					,      )						->     List[str]:
      super().__init__(**lowercase_	)

      UpperCAmelCase	:     Union[str, Any]						 =			num_latents
      UpperCAmelCase	:     List[Any]						 =			d_latents
      UpperCAmelCase	:     Dict						 =			d_model
      UpperCAmelCase	:     Dict						 =			num_blocks
      UpperCAmelCase	:     Optional[int]						 =			num_self_attends_per_block
      UpperCAmelCase	:     Optional[Any]						 =			num_self_attention_heads
      UpperCAmelCase	:     Optional[Any]						 =			num_cross_attention_heads
      UpperCAmelCase	:     Tuple						 =			qk_channels
      UpperCAmelCase	:     Optional[int]						 =			v_channels
      UpperCAmelCase	:     str						 =			cross_attention_shape_for_attention
      UpperCAmelCase	:     Union[str, Any]						 =			self_attention_widening_factor
      UpperCAmelCase	:     List[Any]						 =			cross_attention_widening_factor
      UpperCAmelCase	:     Optional[Any]						 =			hidden_act
      UpperCAmelCase	:     int						 =			attention_probs_dropout_prob
      UpperCAmelCase	:     Optional[int]						 =			initializer_range
      UpperCAmelCase	:     Dict						 =			layer_norm_eps
      UpperCAmelCase	:     Any						 =			use_query_residual
      # masked language modeling attributes
      UpperCAmelCase	:     Any						 =			vocab_size
      UpperCAmelCase	:     List[Any]						 =			max_position_embeddings
      # image classification attributes
      UpperCAmelCase	:     str						 =			image_size
      # flow attributes
      UpperCAmelCase	:     Any						 =			train_size
      # multimodal autoencoding attributes
      UpperCAmelCase	:     Any						 =			num_frames
      UpperCAmelCase	:     List[Any]						 =			audio_samples_per_frame
      UpperCAmelCase	:     Tuple						 =			samples_per_patch
      UpperCAmelCase	:     Union[str, Any]						 =			output_shape




class 	A_			(    _snake_case							):






     '''simple docstring'''



     @property
     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
       UpperCAmelCase	:     Tuple						 =			{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
       UpperCAmelCase	:     Tuple						 =			{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('inputs', dynamic_axis),
              ('attention_mask', dynamic_axis),
          ]	)


     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     float:
      return 1E-4




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,      lowercase_			:					int = -1					,      lowercase_			:					int = -1					,      lowercase_			:					int = -1					,      lowercase_			:					bool = False					,      lowercase_			:					Optional[TensorType] = None					,      lowercase_			:					int = 3					,      lowercase_			:					int = 40					,      lowercase_			:					int = 40					,      )						->     Mapping[str, Any]:
      # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

      if isinstance(lowercase_					,      lowercase_	):
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCAmelCase	:     Tuple						 =			compute_effective_axis_dimension(
           lowercase_					,      fixed_dimension=OnnxConfig.default_fixed_batch					,      num_token_to_add=0	)
       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCAmelCase	:     int						 =			preprocessor.num_special_tokens_to_add(lowercase_	)
       UpperCAmelCase	:     Union[str, Any]						 =			compute_effective_axis_dimension(
           lowercase_					,      fixed_dimension=OnnxConfig.default_fixed_sequence					,      num_token_to_add=lowercase_	)
       # Generate dummy inputs according to compute batch and sequence
       UpperCAmelCase	:     List[Any]						 =			[' '.join(['a']	) * seq_length] * batch_size
       UpperCAmelCase	:     Union[str, Any]						 =			dict(preprocessor(lowercase_					,      return_tensors=lowercase_	)	)
       UpperCAmelCase	:     Union[str, Any]						 =			inputs.pop('input_ids'	)
       return inputs
      elif isinstance(lowercase_					,      lowercase_	) and preprocessor.model_input_names[0] == "pixel_values":
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCAmelCase	:     Tuple						 =			compute_effective_axis_dimension(lowercase_					,      fixed_dimension=OnnxConfig.default_fixed_batch	)
       UpperCAmelCase	:     Any						 =			self._generate_dummy_images(lowercase_					,      lowercase_					,      lowercase_					,      lowercase_	)
       UpperCAmelCase	:     Tuple						 =			dict(preprocessor(images=lowercase_					,      return_tensors=lowercase_	)	)
       UpperCAmelCase	:     Dict						 =			inputs.pop('pixel_values'	)
       return inputs
      else:
       raise ValueError(
           'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'	)




