






'''simple docstring'''

import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


UpperCamelCase      					='''scheduler_config.json'''




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    Optional[int]			  =    1
 __a      :    Any			  =    2
 __a      :    List[Any]			  =    3
 __a      :    Optional[int]			  =    4
 __a      :    Optional[Any]			  =    5






@dataclass
class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    List[str]			  =    42






class      A     :






 """simple docstring"""





 __a      :    Dict			  =    SCHEDULER_CONFIG_NAME
 __a      :    str			  =    ['''dtype''']
 __a      :    Optional[Any]			  =    []
 __a      :    str			  =    True
 @classmethod
 def 	_UpperCAmelCase							(				cls      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase=False      ,  **__lowerCAmelCase      ,  ):
       UpperCamelCase_     ,      UpperCamelCase_					: Optional[int]            =				cls.load_config(
           pretrained_model_name_or_path=lowercase__      ,  subfolder=lowercase__      ,  return_unused_kwargs=lowercase__      ,  **lowercase__      ,  )
       UpperCamelCase_     ,      UpperCamelCase_					: List[str]            =				cls.from_config(lowercase__      ,  return_unused_kwargs=lowercase__      ,  **lowercase__			)

       if hasattr(lowercase__      ,  """create_state"""			) and getattr(lowercase__      ,  """has_state"""      ,  lowercase__			):
             UpperCamelCase_					: Dict            =				scheduler.create_state()

       if return_unused_kwargs:
             return scheduler, state, unused_kwargs

       return scheduler, state
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = False      ,  **__lowerCAmelCase			):
       self.save_config(save_directory=lowercase__      ,  push_to_hub=lowercase__      ,  **lowercase__			)
 @property
 def 	_UpperCAmelCase							(				self			):
       return self._get_compatibles()





 @classmethod
 def 	_UpperCAmelCase							(				cls			):
       UpperCamelCase_					: Any            =				list(set([cls.__name__] + cls._compatibles			)			)
       UpperCamelCase_					: List[str]            =				importlib.import_module(__name__.split("""."""			)[0]			)
       UpperCamelCase_					: Any            =				[
           getattr(lowercase__      ,  lowercase__			) for c in compatible_classes_str if hasattr(lowercase__      ,  lowercase__			)
       ]
       return compatible_classes






def 				snake_case       (  a_				:			Optional[Any]     , a_				:			str						)							->					List[str]:





      """simple docstring"""

      assert len(a_						) >= x.ndim
      return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(a_						) - x.ndim)						)     , a_						)



def 				snake_case       (  a_				:			int     , a_				:			Any=0.999     , a_				:			Optional[Any]=jnp.floataa						)							->					Optional[int]:





      """simple docstring"""

      def alpha_bar(a_				:			int						):
            return math.cos((time_step + 0.008) / 1.008 * math.pi / 2						) ** 2

      UpperCamelCase_					: Dict            =				[]
      for i in range(a_						):
            UpperCamelCase_					: int            =				i / num_diffusion_timesteps
            UpperCamelCase_					: int            =				(i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar(a_						) / alpha_bar(a_						)     , a_						)						)
      return jnp.array(a_     , dtype=a_						)




@flax.struct.dataclass
class      A     :






 """simple docstring"""





 __a      :    Dict			  =    42
 __a      :    Union[str, Any]			  =    42
 __a      :    str			  =    42
 @classmethod
 def 	_UpperCAmelCase							(				cls      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Dict            =				scheduler.config

       if config.trained_betas is not None:
             UpperCamelCase_					: Any            =				jnp.asarray(config.trained_betas      ,  dtype=scheduler.dtype			)
       elif config.beta_schedule == "linear":
             UpperCamelCase_					: List[Any]            =				jnp.linspace(config.beta_start      ,  config.beta_end      ,  config.num_train_timesteps      ,  dtype=scheduler.dtype			)
       elif config.beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             UpperCamelCase_					: List[Any]            =				(
                 jnp.linspace(
                     config.beta_start**0.5      ,  config.beta_end**0.5      ,  config.num_train_timesteps      ,  dtype=scheduler.dtype			)
                 ** 2
             )
       elif config.beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             UpperCamelCase_					: str            =				betas_for_alpha_bar(config.num_train_timesteps      ,  dtype=scheduler.dtype			)
       else:
             raise NotImplementedError(
                 F"beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"			)

       UpperCamelCase_					: Union[str, Any]            =				1.0 - betas

       UpperCamelCase_					: List[Any]            =				jnp.cumprod(lowercase__      ,  axis=0			)

       return cls(
           alphas=lowercase__      ,  betas=lowercase__      ,  alphas_cumprod=lowercase__      ,  )






def 				snake_case       (  a_				:			str     , a_				:			int     , a_				:			str     , a_				:			List[Any]						)							->					str:





      """simple docstring"""

      UpperCamelCase_					: Any            =				state.alphas_cumprod

      UpperCamelCase_					: Any            =				alphas_cumprod[timesteps] ** 0.5
      UpperCamelCase_					: str            =				sqrt_alpha_prod.flatten()
      UpperCamelCase_					: List[Any]            =				broadcast_to_shape_from_left(a_     , original_samples.shape						)

      UpperCamelCase_					: Any            =				(1 - alphas_cumprod[timesteps]) ** 0.5
      UpperCamelCase_					: Any            =				sqrt_one_minus_alpha_prod.flatten()
      UpperCamelCase_					: int            =				broadcast_to_shape_from_left(a_     , original_samples.shape						)

      return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 				snake_case       (  a_				:			str     , a_				:			Optional[int]     , a_				:			Tuple     , a_				:			Dict						)							->					Optional[Any]:





      """simple docstring"""

      UpperCamelCase_     ,      UpperCamelCase_					: Dict            =				get_sqrt_alpha_prod(a_     , a_     , a_     , a_						)
      UpperCamelCase_					: Dict            =				sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples



def 				snake_case       (  a_				:			Dict     , a_				:			Any     , a_				:			Optional[Any]     , a_				:			Union[str, Any]						)							->					Dict:





      """simple docstring"""

      UpperCamelCase_     ,      UpperCamelCase_					: List[str]            =				get_sqrt_alpha_prod(a_     , a_     , a_     , a_						)
      UpperCamelCase_					: str            =				sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
      return velocity







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase:	Dict       			=		logging.get_logger(__name__)






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							MobileViTConfig()

							# size of the architecture
							if "mobilevit_s" in mobilevit_name:
														_lowerCAmelCase							=							[144, 192, 240]
														_lowerCAmelCase							=							[16, 32, 64, 96, 128, 160, 640]
							elif "mobilevit_xs" in mobilevit_name:
														_lowerCAmelCase							=							[96, 120, 144]
														_lowerCAmelCase							=							[16, 32, 48, 64, 80, 96, 384]
							elif "mobilevit_xxs" in mobilevit_name:
														_lowerCAmelCase							=							[64, 80, 96]
														_lowerCAmelCase							=							[16, 16, 24, 48, 64, 80, 320]
														_lowerCAmelCase							=							0.05
														_lowerCAmelCase							=							2.0

							if mobilevit_name.startswith('deeplabv3_'  ):
														_lowerCAmelCase							=							512
														_lowerCAmelCase							=							16
														_lowerCAmelCase							=							21
														_lowerCAmelCase							=							'pascal-voc-id2label.json'
							else:
														_lowerCAmelCase							=							1_000
														_lowerCAmelCase							=							'imagenet-1k-id2label.json'

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

							return config






def _lowerCamelCase  (		snake_case     , snake_case=False  ):
							for i in range(1     , 6  ):
														if F'layer_{i}.' in name:
																					_lowerCAmelCase							=							name.replace(F'layer_{i}.'     , F'encoder.layer.{i - 1}.'  )

							if "conv_1." in name:
														_lowerCAmelCase							=							name.replace('conv_1.'     , 'conv_stem.'  )
							if ".block." in name:
														_lowerCAmelCase							=							name.replace('.block.'     , '.'  )

							if "exp_1x1" in name:
														_lowerCAmelCase							=							name.replace('exp_1x1'     , 'expand_1x1'  )
							if "red_1x1" in name:
														_lowerCAmelCase							=							name.replace('red_1x1'     , 'reduce_1x1'  )
							if ".local_rep.conv_3x3." in name:
														_lowerCAmelCase							=							name.replace('.local_rep.conv_3x3.'     , '.conv_kxk.'  )
							if ".local_rep.conv_1x1." in name:
														_lowerCAmelCase							=							name.replace('.local_rep.conv_1x1.'     , '.conv_1x1.'  )
							if ".norm." in name:
														_lowerCAmelCase							=							name.replace('.norm.'     , '.normalization.'  )
							if ".conv." in name:
														_lowerCAmelCase							=							name.replace('.conv.'     , '.convolution.'  )
							if ".conv_proj." in name:
														_lowerCAmelCase							=							name.replace('.conv_proj.'     , '.conv_projection.'  )

							for i in range(0     , 2  ):
														for j in range(0     , 4  ):
																					if F'.{i}.{j}.' in name:
																												_lowerCAmelCase							=							name.replace(F'.{i}.{j}.'     , F'.{i}.layer.{j}.'  )

							for i in range(2     , 6  ):
														for j in range(0     , 4  ):
																					if F'.{i}.{j}.' in name:
																												_lowerCAmelCase							=							name.replace(F'.{i}.{j}.'     , F'.{i}.'  )
																												if "expand_1x1" in name:
																																			_lowerCAmelCase							=							name.replace('expand_1x1'     , 'downsampling_layer.expand_1x1'  )
																												if "conv_3x3" in name:
																																			_lowerCAmelCase							=							name.replace('conv_3x3'     , 'downsampling_layer.conv_3x3'  )
																												if "reduce_1x1" in name:
																																			_lowerCAmelCase							=							name.replace('reduce_1x1'     , 'downsampling_layer.reduce_1x1'  )

							for i in range(2     , 5  ):
														if F'.global_rep.{i}.weight' in name:
																					_lowerCAmelCase							=							name.replace(F'.global_rep.{i}.weight'     , '.layernorm.weight'  )
														if F'.global_rep.{i}.bias' in name:
																					_lowerCAmelCase							=							name.replace(F'.global_rep.{i}.bias'     , '.layernorm.bias'  )

							if ".global_rep." in name:
														_lowerCAmelCase							=							name.replace('.global_rep.'     , '.transformer.'  )
							if ".pre_norm_mha.0." in name:
														_lowerCAmelCase							=							name.replace('.pre_norm_mha.0.'     , '.layernorm_before.'  )
							if ".pre_norm_mha.1.out_proj." in name:
														_lowerCAmelCase							=							name.replace('.pre_norm_mha.1.out_proj.'     , '.attention.output.dense.'  )
							if ".pre_norm_ffn.0." in name:
														_lowerCAmelCase							=							name.replace('.pre_norm_ffn.0.'     , '.layernorm_after.'  )
							if ".pre_norm_ffn.1." in name:
														_lowerCAmelCase							=							name.replace('.pre_norm_ffn.1.'     , '.intermediate.dense.'  )
							if ".pre_norm_ffn.4." in name:
														_lowerCAmelCase							=							name.replace('.pre_norm_ffn.4.'     , '.output.dense.'  )
							if ".transformer." in name:
														_lowerCAmelCase							=							name.replace('.transformer.'     , '.transformer.layer.'  )

							if ".aspp_layer." in name:
														_lowerCAmelCase							=							name.replace('.aspp_layer.'     , '.'  )
							if ".aspp_pool." in name:
														_lowerCAmelCase							=							name.replace('.aspp_pool.'     , '.'  )
							if "seg_head." in name:
														_lowerCAmelCase							=							name.replace('seg_head.'     , 'segmentation_head.'  )
							if "segmentation_head.classifier.classifier." in name:
														_lowerCAmelCase							=							name.replace('segmentation_head.classifier.classifier.'     , 'segmentation_head.classifier.'  )

							if "classifier.fc." in name:
														_lowerCAmelCase							=							name.replace('classifier.fc.'     , 'classifier.'  )
							elif (not base_model) and ("segmentation_head." not in name):
														_lowerCAmelCase							=							'mobilevit.' + name

							return name






def _lowerCamelCase  (		snake_case     , snake_case     , snake_case=False  ):
							if base_model:
														_lowerCAmelCase							=							''
							else:
														_lowerCAmelCase							=							'mobilevit.'

							for key in orig_state_dict.copy().keys():
														_lowerCAmelCase							=							orig_state_dict.pop(snake_case  )

														if key[:8] == "encoder.":
																					_lowerCAmelCase							=							key[8:]

														if "qkv" in key:
																					_lowerCAmelCase							=							key.split('.'  )
																					_lowerCAmelCase							=							int(key_split[0][6:]  ) - 1
																					_lowerCAmelCase							=							int(key_split[3]  )
																					_lowerCAmelCase							=							model.get_submodule(F'{model_prefix}encoder.layer.{layer_num}'  )
																					_lowerCAmelCase							=							layer.transformer.layer[transformer_num].attention.attention.all_head_size
																					_lowerCAmelCase							=							(
																					    F'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
																					)
																					if "weight" in key:
																												_lowerCAmelCase							=							val[:dim, :]
																												_lowerCAmelCase							=							val[dim : dim * 2, :]
																												_lowerCAmelCase							=							val[-dim:, :]
																					else:
																												_lowerCAmelCase							=							val[:dim]
																												_lowerCAmelCase							=							val[dim : dim * 2]
																												_lowerCAmelCase							=							val[-dim:]
														else:
																					_lowerCAmelCase							=							val

							return orig_state_dict






def _lowerCamelCase  (		):
							_lowerCAmelCase							=							'http://images.cocodataset.org/val2017/000000039769.jpg'
							_lowerCAmelCase							=							Image.open(requests.get(snake_case     , stream=snake_case  ).raw  )
							return im






@torch.no_grad()
def _lowerCamelCase  (		snake_case     , snake_case     , snake_case     , snake_case=False  ):
							_lowerCAmelCase							=							get_mobilevit_config(snake_case  )

							# load original state_dict
							_lowerCAmelCase							=							torch.load(snake_case     , map_location='cpu'  )

							# load 🤗 model
							if mobilevit_name.startswith('deeplabv3_'  ):
														_lowerCAmelCase							=							MobileViTForSemanticSegmentation(snake_case  ).eval()
							else:
														_lowerCAmelCase							=							MobileViTForImageClassification(snake_case  ).eval()

							_lowerCAmelCase							=							convert_state_dict(snake_case     , snake_case  )
							model.load_state_dict(snake_case  )

							# Check outputs on an image, prepared by MobileViTImageProcessor
							_lowerCAmelCase							=							MobileViTImageProcessor(crop_size=config.image_size     , size=config.image_size + 32  )
							_lowerCAmelCase							=							image_processor(images=prepare_img()     , return_tensors='pt'  )
							_lowerCAmelCase							=							model(**snake_case  )
							_lowerCAmelCase							=							outputs.logits

							if mobilevit_name.startswith('deeplabv3_'  ):
														assert logits.shape == (1, 21, 32, 32)

														if mobilevit_name == "deeplabv3_mobilevit_s":
																					_lowerCAmelCase							=							torch.tensor(
																					    [
																					        [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
																					        [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
																					        [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
																					    ]  )
														elif mobilevit_name == "deeplabv3_mobilevit_xs":
																					_lowerCAmelCase							=							torch.tensor(
																					    [
																					        [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
																					        [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
																					        [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
																					    ]  )
														elif mobilevit_name == "deeplabv3_mobilevit_xxs":
																					_lowerCAmelCase							=							torch.tensor(
																					    [
																					        [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
																					        [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
																					        [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
																					    ]  )
														else:
																					raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}'  )

														assert torch.allclose(logits[0, :3, :3, :3]     , snake_case     , atol=1E-4  )
							else:
														assert logits.shape == (1, 1_000)

														if mobilevit_name == "mobilevit_s":
																					_lowerCAmelCase							=							torch.tensor([-0.98_66, 0.23_92, -1.12_41]  )
														elif mobilevit_name == "mobilevit_xs":
																					_lowerCAmelCase							=							torch.tensor([-2.47_61, -0.93_99, -1.95_87]  )
														elif mobilevit_name == "mobilevit_xxs":
																					_lowerCAmelCase							=							torch.tensor([-1.93_64, -1.23_27, -0.46_53]  )
														else:
																					raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}'  )

														assert torch.allclose(logits[0, :3]     , snake_case     , atol=1E-4  )

							Path(snake_case  ).mkdir(exist_ok=snake_case  )
							print(F'Saving model {mobilevit_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 push_to_hub:
														_lowerCAmelCase							=							{
														    'mobilevit_s': 'mobilevit-small',
														    'mobilevit_xs': 'mobilevit-x-small',
														    'mobilevit_xxs': 'mobilevit-xx-small',
														    'deeplabv3_mobilevit_s': 'deeplabv3-mobilevit-small',
														    'deeplabv3_mobilevit_xs': 'deeplabv3-mobilevit-x-small',
														    'deeplabv3_mobilevit_xxs': 'deeplabv3-mobilevit-xx-small',
														}

														print('Pushing to the hub...'  )
														_lowerCAmelCase							=							model_mapping[mobilevit_name]
														image_processor.push_to_hub(snake_case     , organization='apple'  )
														model.push_to_hub(snake_case     , organization='apple'  )


if __name__ == "__main__":
				_lowercase:	Union[str, Any]       			=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--mobilevit_name''',
				    default='''mobilevit_s''',
				    type=str,
				    help=(
				        '''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
				        ''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
				    ),
				)
				parser.add_argument(
				    '''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
				)
				parser.add_argument(
				    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
				)

				_lowercase:	List[str]       			=		parser.parse_args()
				convert_movilevit_checkpoint(
				    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)






