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


_lowercase:	Dict       			=		'''scheduler_config.json'''



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=1
			UpperCamelCase__    							=2
			UpperCamelCase__    							=3
			UpperCamelCase__    							=4
			UpperCamelCase__    							=5







@dataclass
class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=42







class        lowerCamelCase__				:
			UpperCamelCase__    							=SCHEDULER_CONFIG_NAME
			UpperCamelCase__    							=["dtype"]
			UpperCamelCase__    							=[]
			UpperCamelCase__    							=True





			@classmethod
			def   SCREAMING_SNAKE_CASE__     (       cls	:	int  ,  lowercase__	:	Dict[str, Any] = None  ,  lowercase__	:	Optional[str] = None  ,  lowercase__	:	Dict=False  ,  **lowercase__	:	List[str]  ,  ):
										_lowerCAmelCase			,		_lowerCAmelCase							=							cls.load_config(
										    pretrained_model_name_or_path=lowercase__  ,  subfolder=lowercase__  ,  return_unused_kwargs=lowercase__  ,  **lowercase__  ,  )
										_lowerCAmelCase			,		_lowerCAmelCase							=							cls.from_config(lowercase__  ,  return_unused_kwargs=lowercase__  ,  **lowercase__						)

										if hasattr(lowercase__  ,  'create_state'						) and getattr(lowercase__  ,  'has_state'  ,  lowercase__						):
																	_lowerCAmelCase							=							scheduler.create_state()

										if return_unused_kwargs:
																	return scheduler, state, unused_kwargs

										return scheduler, state





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  lowercase__	:	Union[str, os.PathLike]  ,  lowercase__	:	bool = False  ,  **lowercase__	:	int						):
										self.save_config(save_directory=lowercase__  ,  push_to_hub=lowercase__  ,  **lowercase__						)





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										return self._get_compatibles()





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







def _lowerCamelCase  (		snake_case     , snake_case  ):
							assert len(snake_case  ) >= x.ndim
							return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(snake_case  ) - x.ndim)  )     , snake_case  )






def _lowerCamelCase  (		snake_case     , snake_case=0.9_99     , snake_case=jnp.floataa  ):

							def alpha_bar(snake_case  ):
														return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2  ) ** 2

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



@flax.struct.dataclass
class        lowerCamelCase__				:
			UpperCamelCase__    							=42
			UpperCamelCase__    							=42
			UpperCamelCase__    							=42





			@classmethod
			def   SCREAMING_SNAKE_CASE__     (       cls	:	Optional[int]  ,  lowercase__	:	Optional[int]						):
										_lowerCAmelCase							=							scheduler.config

										if config.trained_betas is not None:
																	_lowerCAmelCase							=							jnp.asarray(config.trained_betas  ,  dtype=scheduler.dtype						)
										elif config.beta_schedule == "linear":
																	_lowerCAmelCase							=							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.
																	_lowerCAmelCase							=							(
																	    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
																	_lowerCAmelCase							=							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__}'						)

										_lowerCAmelCase							=							1.0 - betas

										_lowerCAmelCase							=							jnp.cumprod(lowercase__  ,  axis=0						)

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







def _lowerCamelCase  (		snake_case     , snake_case     , snake_case     , snake_case  ):
							_lowerCAmelCase							=							state.alphas_cumprod

							_lowerCAmelCase							=							alphas_cumprod[timesteps] ** 0.5
							_lowerCAmelCase							=							sqrt_alpha_prod.flatten()
							_lowerCAmelCase							=							broadcast_to_shape_from_left(snake_case     , original_samples.shape  )

							_lowerCAmelCase							=							(1 - alphas_cumprod[timesteps]) ** 0.5
							_lowerCAmelCase							=							sqrt_one_minus_alpha_prod.flatten()
							_lowerCAmelCase							=							broadcast_to_shape_from_left(snake_case     , original_samples.shape  )

							return sqrt_alpha_prod, sqrt_one_minus_alpha_prod






def _lowerCamelCase  (		snake_case     , snake_case     , snake_case     , snake_case  ):
							_lowerCAmelCase			,		_lowerCAmelCase							=							get_sqrt_alpha_prod(snake_case     , snake_case     , snake_case     , snake_case  )
							_lowerCAmelCase							=							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples






def _lowerCamelCase  (		snake_case     , snake_case     , snake_case     , snake_case  ):
							_lowerCAmelCase			,		_lowerCAmelCase							=							get_sqrt_alpha_prod(snake_case     , snake_case     , snake_case     , snake_case  )
							_lowerCAmelCase							=							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
				)






