
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


__a         =   'scheduler_config.json'
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Optional[Any]       	=      1
	a     :Any       	=      2
	a     :Dict       	=      3
	a     :Any       	=      4
	a     :Any       	=      5

@dataclass
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :jnp.ndarray

class 					lowercase__:



	"""simple docstring"""




	a     :Dict       	=      SCHEDULER_CONFIG_NAME
	a     :str       	=      ['dtype']
	a     :List[str]       	=      []
	a     :int       	=      True
	@classmethod
	def 		_lowercase    (			cls    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Dict[str, Any] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = None			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=False			,				**SCREAMING_SNAKE_CASE_    :     Any			,				)			->	Dict:
					lowercase_    ,      lowercase_	    =   cls.load_config(
					    pretrained_model_name_or_path=SCREAMING_SNAKE_CASE_			,				subfolder=SCREAMING_SNAKE_CASE_			,				return_unused_kwargs=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			,				)
					lowercase_    ,      lowercase_	    =   cls.from_config(SCREAMING_SNAKE_CASE_			,				return_unused_kwargs=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)

					if hasattr(SCREAMING_SNAKE_CASE_			,				'''create_state'''			) and getattr(SCREAMING_SNAKE_CASE_			,				'''has_state'''			,				SCREAMING_SNAKE_CASE_			):
									lowercase_	    =   scheduler.create_state()

					if return_unused_kwargs:
									return scheduler, state, unused_kwargs

					return scheduler, state
	def 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Union[str, os.PathLike]			,				SCREAMING_SNAKE_CASE_    :     bool = False			,				**SCREAMING_SNAKE_CASE_    :     int			)			->	Dict:
					self.save_config(save_directory=SCREAMING_SNAKE_CASE_			,				push_to_hub=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
	@property
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	List[Any]:
					return self._get_compatibles()




	@classmethod
	def 		_lowercase    (			cls    :     Union[str, Any]			)			->	List[str]:
					lowercase_	    =   list(set([cls.__name__] + cls._compatibles			)			)
					lowercase_	    =   importlib.import_module(__name__.split('''.'''			)[0]			)
					lowercase_	    =   [
					    getattr(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) for c in compatible_classes_str if hasattr(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					]
					return compatible_classes




def 							a		(				snake_case__:   jnp.ndarray		,  snake_case__:   Tuple[int]   ):


				'''simple docstring'''
				assert len(snake_case__   ) >= x.ndim
				return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(snake_case__   ) - x.ndim)   )		,  snake_case__   )




def 							a		(				snake_case__:   int		,  snake_case__:   Tuple=0.9_9_9		,  snake_case__:   Dict=jnp.floataa   ):


				'''simple docstring'''

				def alpha_bar(snake_case__:   Dict   ):
								return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2   ) ** 2

				lowercase_	    =   []
				for i in range(snake_case__   ):
								lowercase_	    =   i / num_diffusion_timesteps
								lowercase_	    =   (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 					lowercase__:



	"""simple docstring"""




	a     :jnp.ndarray
	a     :jnp.ndarray
	a     :jnp.ndarray
	@classmethod
	def 		_lowercase    (			cls    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	List[str]:
					lowercase_	    =   scheduler.config

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

					lowercase_	    =   1.0 - betas

					lowercase_	    =   jnp.cumprod(SCREAMING_SNAKE_CASE_			,				axis=0			)

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




def 							a		(				snake_case__:   CommonSchedulerState		,  snake_case__:   jnp.ndarray		,  snake_case__:   jnp.ndarray		,  snake_case__:   jnp.ndarray   ):


				'''simple docstring'''
				lowercase_	    =   state.alphas_cumprod

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

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

				return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def 							a		(				snake_case__:   CommonSchedulerState		,  snake_case__:   jnp.ndarray		,  snake_case__:   jnp.ndarray		,  snake_case__:   jnp.ndarray   ):


				'''simple docstring'''
				lowercase_    ,      lowercase_	    =   get_sqrt_alpha_prod(snake_case__		,  snake_case__		,  snake_case__		,  snake_case__   )
				lowercase_	    =   sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples




def 							a		(				snake_case__:   CommonSchedulerState		,  snake_case__:   jnp.ndarray		,  snake_case__:   jnp.ndarray		,  snake_case__:   jnp.ndarray   ):


				'''simple docstring'''
				lowercase_    ,      lowercase_	    =   get_sqrt_alpha_prod(snake_case__		,  snake_case__		,  snake_case__		,  snake_case__   )
				lowercase_	    =   sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
				return velocity






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor




def 							a		(				snake_case__:   List[Any]   ):


				'''simple docstring'''
				if "cls_token" in name:
								lowercase_	    =   name.replace('''cls_token'''		,  '''vit.embeddings.cls_token'''   )
				if "mask_token" in name:
								lowercase_	    =   name.replace('''mask_token'''		,  '''decoder.mask_token'''   )
				if "decoder_pos_embed" in name:
								lowercase_	    =   name.replace('''decoder_pos_embed'''		,  '''decoder.decoder_pos_embed'''   )
				if "pos_embed" in name and "decoder" not in name:
								lowercase_	    =   name.replace('''pos_embed'''		,  '''vit.embeddings.position_embeddings'''   )
				if "patch_embed.proj" in name:
								lowercase_	    =   name.replace('''patch_embed.proj'''		,  '''vit.embeddings.patch_embeddings.projection'''   )
				if "patch_embed.norm" in name:
								lowercase_	    =   name.replace('''patch_embed.norm'''		,  '''vit.embeddings.norm'''   )
				if "decoder_blocks" in name:
								lowercase_	    =   name.replace('''decoder_blocks'''		,  '''decoder.decoder_layers'''   )
				if "blocks" in name:
								lowercase_	    =   name.replace('''blocks'''		,  '''vit.encoder.layer'''   )
				if "attn.proj" in name:
								lowercase_	    =   name.replace('''attn.proj'''		,  '''attention.output.dense'''   )
				if "attn" in name:
								lowercase_	    =   name.replace('''attn'''		,  '''attention.self'''   )
				if "norm1" in name:
								lowercase_	    =   name.replace('''norm1'''		,  '''layernorm_before'''   )
				if "norm2" in name:
								lowercase_	    =   name.replace('''norm2'''		,  '''layernorm_after'''   )
				if "mlp.fc1" in name:
								lowercase_	    =   name.replace('''mlp.fc1'''		,  '''intermediate.dense'''   )
				if "mlp.fc2" in name:
								lowercase_	    =   name.replace('''mlp.fc2'''		,  '''output.dense'''   )
				if "decoder_embed" in name:
								lowercase_	    =   name.replace('''decoder_embed'''		,  '''decoder.decoder_embed'''   )
				if "decoder_norm" in name:
								lowercase_	    =   name.replace('''decoder_norm'''		,  '''decoder.decoder_norm'''   )
				if "decoder_pred" in name:
								lowercase_	    =   name.replace('''decoder_pred'''		,  '''decoder.decoder_pred'''   )
				if "norm.weight" in name and "decoder" not in name:
								lowercase_	    =   name.replace('''norm.weight'''		,  '''vit.layernorm.weight'''   )
				if "norm.bias" in name and "decoder" not in name:
								lowercase_	    =   name.replace('''norm.bias'''		,  '''vit.layernorm.bias'''   )

				return name




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Any   ):


				'''simple docstring'''
				for key in orig_state_dict.copy().keys():
								lowercase_	    =   orig_state_dict.pop(snake_case__   )

								if "qkv" in key:
												lowercase_	    =   key.split('''.'''   )
												lowercase_	    =   int(key_split[1]   )
												if "decoder_blocks" in key:
																lowercase_	    =   config.decoder_hidden_size
																lowercase_	    =   '''decoder.decoder_layers.'''
																if "weight" in key:
																				lowercase_	    =   val[:dim, :]
																				lowercase_	    =   val[dim : dim * 2, :]
																				lowercase_	    =   val[-dim:, :]
																elif "bias" in key:
																				lowercase_	    =   val[:dim]
																				lowercase_	    =   val[dim : dim * 2]
																				lowercase_	    =   val[-dim:]
												else:
																lowercase_	    =   config.hidden_size
																lowercase_	    =   '''vit.encoder.layer.'''
																if "weight" in key:
																				lowercase_	    =   val[:dim, :]
																				lowercase_	    =   val[dim : dim * 2, :]
																				lowercase_	    =   val[-dim:, :]
																elif "bias" in key:
																				lowercase_	    =   val[:dim]
																				lowercase_	    =   val[dim : dim * 2]
																				lowercase_	    =   val[-dim:]

								else:
												lowercase_	    =   val

				return orig_state_dict




def 							a		(				snake_case__:   str		,  snake_case__:   int   ):


				'''simple docstring'''
				lowercase_	    =   ViTMAEConfig()
				if "large" in checkpoint_url:
								lowercase_	    =   1_024
								lowercase_	    =   4_096
								lowercase_	    =   24
								lowercase_	    =   16
				elif "huge" in checkpoint_url:
								lowercase_	    =   14
								lowercase_	    =   1_280
								lowercase_	    =   5_120
								lowercase_	    =   32
								lowercase_	    =   16

				lowercase_	    =   ViTMAEForPreTraining(snake_case__   )

				lowercase_	    =   torch.hub.load_state_dict_from_url(snake_case__		,  map_location='''cpu'''   )['''model''']

				lowercase_	    =   ViTMAEImageProcessor(size=config.image_size   )

				lowercase_	    =   convert_state_dict(snake_case__		,  snake_case__   )

				model.load_state_dict(snake_case__   )
				model.eval()

				lowercase_	    =   '''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

				lowercase_	    =   Image.open(requests.get(snake_case__		,  stream=snake_case__   ).raw   )
				lowercase_	    =   ViTMAEImageProcessor(size=config.image_size   )
				lowercase_	    =   image_processor(images=snake_case__		,  return_tensors='''pt'''   )

				# forward pass
				torch.manual_seed(2   )
				lowercase_	    =   model(**snake_case__   )
				lowercase_	    =   outputs.logits

				if "large" in checkpoint_url:
								lowercase_	    =   torch.tensor(
								    [[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]]   )
				elif "huge" in checkpoint_url:
								lowercase_	    =   torch.tensor(
								    [[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]]   )
				else:
								lowercase_	    =   torch.tensor(
								    [[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]]   )

				# verify logits
				assert torch.allclose(logits[0, :3, :3]		,  snake_case__		,  atol=1e-4   )

				print(F'''Saving model 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         =   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--checkpoint_url',
						    default='https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth',
						    type=str,
						    help='URL of the checkpoint you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
						)

						__a         =   parser.parse_args()
						convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




