

'''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'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowercase__         =      logging.get_logger("transformers.models.speecht5")




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 hf_model.apply_weight_norm()

 UpperCAmelCase	:     Dict						 =			checkpoint['input_conv.weight_g']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.weight_v']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.bias']

 for i in range(len(config.upsample_rates     )     ):
  UpperCAmelCase	:     Any						 =			checkpoint[F"""upsamples.{i}.1.weight_g"""]
  UpperCAmelCase	:     Union[str, Any]						 =			checkpoint[F"""upsamples.{i}.1.weight_v"""]
  UpperCAmelCase	:     str						 =			checkpoint[F"""upsamples.{i}.1.bias"""]

 for i in range(len(config.upsample_rates     ) * len(config.resblock_kernel_sizes     )     ):
  for j in range(len(config.resblock_dilation_sizes     )     ):
   UpperCAmelCase	:     Any						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
   UpperCAmelCase	:     Optional[int]						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
   UpperCAmelCase	:     Dict						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

   UpperCAmelCase	:     Union[str, Any]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   UpperCAmelCase	:     int						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   UpperCAmelCase	:     Optional[int]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCAmelCase	:     Dict						 =			checkpoint['output_conv.1.weight_g']
 UpperCAmelCase	:     str						 =			checkpoint['output_conv.1.weight_v']
 UpperCAmelCase	:     Union[str, Any]						 =			checkpoint['output_conv.1.bias']

 hf_model.remove_weight_norm()




@torch.no_grad()
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=None  ,	UpperCAmelCase_=None  ,	):
 if config_path is not None:
  UpperCAmelCase	:     Any						 =			SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase_     )
 else:
  UpperCAmelCase	:     Optional[Any]						 =			SpeechTaHifiGanConfig()

 UpperCAmelCase	:     List[Any]						 =			SpeechTaHifiGan(UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			torch.load(UpperCAmelCase_     )
 load_weights(orig_checkpoint['model']['generator']  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			np.load(UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			stats[0].reshape(-1     )
 UpperCAmelCase	:     List[str]						 =			stats[1].reshape(-1     )
 UpperCAmelCase	:     Union[str, Any]						 =			torch.from_numpy(UpperCAmelCase_     ).float()
 UpperCAmelCase	:     List[str]						 =			torch.from_numpy(UpperCAmelCase_     ).float()

 model.save_pretrained(UpperCAmelCase_     )

 if repo_id:
  print('Pushing to the hub...'     )
  model.push_to_hub(UpperCAmelCase_     )


if __name__ == "__main__":
 lowercase__         =      argparse.ArgumentParser()
 parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
 parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 parser.add_argument(
     "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
 )
 parser.add_argument(
     "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
 )

 lowercase__         =      parser.parse_args()
 convert_hifigan_checkpoint(
     args.checkpoint_path,
     args.stats_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )




