

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[int]   =      logging.get_logger(__name__)


def 					__a				(      SCREAMING_SNAKE_CASE					)					->   List[Any]:






    '''simple docstring'''
    print('''Loading config file...'''					)

    def flatten_yaml_as_dict(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE=""				,      SCREAMING_SNAKE_CASE="."					):
        __UpperCAmelCase     =	[]
        for k, v in d.items():
            __UpperCAmelCase     =	parent_key + sep + k if parent_key else k
            if isinstance(snake_case_				,      collections.abc.MutableMapping					):
                items.extend(flatten_yaml_as_dict(snake_case_				,      snake_case_				,      sep=snake_case_					).items()					)
            else:
                items.append((new_key, v)					)
        return dict(snake_case_					)

    __UpperCAmelCase     =	argparse.Namespace()
    with open(snake_case_				,      '''r'''					) as yaml_file:
        try:
            __UpperCAmelCase     =	yaml.load(snake_case_				,      Loader=yaml.FullLoader					)

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


def 					__a				(      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE					)					->   Dict:






    '''simple docstring'''
    __UpperCAmelCase     =	MobileViTVaConfig()

    __UpperCAmelCase     =	False

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

    # orig_config
    __UpperCAmelCase     =	load_orig_config_file(snake_case_					)
    assert getattr(snake_case_				,      '''model.classification.name'''				,      -1					) == "mobilevit_v2", "Invalid model"
    __UpperCAmelCase     =	getattr(snake_case_				,      '''model.classification.mitv2.width_multiplier'''				,      1.0					)
    assert (
        getattr(snake_case_				,      '''model.classification.mitv2.attn_norm_layer'''				,      -1					) == "layer_norm_2d"
    ), "Norm layers other than layer_norm_2d is not supported"
    __UpperCAmelCase     =	getattr(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(snake_case_				,      '''model.segmentation.output_stride'''				,      1_6					)
        if "_deeplabv3" in task_name:
            __UpperCAmelCase     =	getattr(snake_case_				,      '''model.segmentation.deeplabv3.aspp_rates'''				,      [1_2, 2_4, 3_6]					)
            __UpperCAmelCase     =	getattr(snake_case_				,      '''model.segmentation.deeplabv3.aspp_out_channels'''				,      5_1_2					)
            __UpperCAmelCase     =	getattr(snake_case_				,      '''model.segmentation.deeplabv3.aspp_dropout'''				,      0.1					)

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

    return config


def 					__a				(      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE					)					->   Tuple:






    '''simple docstring'''
    __UpperCAmelCase     =	dct.pop(snake_case_					)
    __UpperCAmelCase     =	val


def 					__a				(      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE=False					)					->   int:






    '''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 					__a				(      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(snake_case_					)
    for k in keys_to_ignore:
        state_dict.pop(snake_case_				,      snake_case_					)


def 					__a				(      )					->   Optional[int]:






    '''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(snake_case_				,      stream=snake_case_					).raw					)
    return im


@torch.no_grad()
def 					__a				(      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE					)					->   Tuple:






    '''simple docstring'''
    __UpperCAmelCase     =	get_mobilevitva_config(snake_case_				,      snake_case_					)

    # load original state_dict
    __UpperCAmelCase     =	torch.load(snake_case_				,      map_location='''cpu'''					)

    # load huggingface model
    if task_name.startswith('''ade20k_'''					) or task_name.startswith('''voc_'''					):
        __UpperCAmelCase     =	MobileViTVaForSemanticSegmentation(snake_case_					).eval()
        __UpperCAmelCase     =	False
    else:
        __UpperCAmelCase     =	MobileViTVaForImageClassification(snake_case_					).eval()
        __UpperCAmelCase     =	False

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

    # load modified state_dict
    model.load_state_dict(snake_case_					)

    # Check outputs on an image, prepared by MobileViTImageProcessor
    __UpperCAmelCase     =	MobileViTImageProcessor(crop_size=config.image_size				,      size=config.image_size + 3_2					)
    __UpperCAmelCase     =	image_processor(images=prepare_img()				,      return_tensors='''pt'''					)
    __UpperCAmelCase     =	model(**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]				,      snake_case_				,      atol=1e-4					)

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


if __name__ == "__main__":
       A_						:	str   =      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.'
       )

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










# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class 						lowercase							(   UpperCamelCase__,UpperCamelCase__		):
     _a							 =						1

     @register_to_config
     def __init__(  self		,     _a=2000		,     _a=0.1		,     _a=20		,     _a=1e-3				)						->							List[Any]:
           _A							:     Dict			=						None
           _A							:     List[Any]			=						None
           _A							:     Dict			=						None

     def 					a__	(  self		,     _a		,     _a = None				)						->							Union[str, Any]:
           _A							:     Union[str, Any]			=						torch.linspace(1		,     self.config.sampling_eps		,     _a		,     device=_a				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a=None				)						->							Dict:
           if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""				)

           # TODO(Patrick) better comments + non-PyTorch
           # postprocess model score
           _A							:     Any			=						(
               -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
           )
           _A							:     List[Any]			=						torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff				)				)
           _A							:     List[str]			=						std.flatten()
           while len(std.shape				) < len(score.shape				):
                 _A							:     List[Any]			=						std.unsqueeze(-1				)
           _A							:     int			=						-score / std

           # compute
           _A							:     Tuple			=						-1.0 / len(self.timesteps				)

           _A							:     str			=						self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
           _A							:     List[str]			=						beta_t.flatten()
           while len(beta_t.shape				) < len(x.shape				):
                 _A							:     Union[str, Any]			=						beta_t.unsqueeze(-1				)
           _A							:     Tuple			=						-0.5 * beta_t * x

           _A							:     Tuple			=						torch.sqrt(_a				)
           _A							:     Dict			=						drift - diffusion**2 * score
           _A							:     Dict			=						x + drift * dt

           # add noise
           _A							:     Any			=						randn_tensor(x.shape		,     layout=x.layout		,     generator=_a		,     device=x.device		,     dtype=x.dtype				)
           _A							:     str			=						x_mean + diffusion * math.sqrt(-dt				) * noise

           return x, x_mean



     def __len__(  self				)						->							Optional[Any]:
           return self.config.num_train_timesteps

