





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				(						__UpperCamelCase   ):
       __snake_case    : str   =		1
       __snake_case    : List[Any]   =		2
       __snake_case    : Tuple   =		3
       __snake_case    : Any   =		4
       __snake_case    : Optional[Any]   =		5






@dataclass
class 		__a				(						__UpperCamelCase   ):
       __snake_case    : List[str]   =		42






class 		__a				:
       __snake_case    : List[str]   =		SCHEDULER_CONFIG_NAME
       __snake_case    : Union[str, Any]   =		["""dtype"""]
       __snake_case    : Any   =		[]
       __snake_case    : int   =		True



       @classmethod
       def 			A				(  cls						: Optional[int]       ,      UpperCAmelCase						: Dict[str, Any] = None       ,      UpperCAmelCase						: Optional[str] = None       ,      UpperCAmelCase						: Union[str, Any]=False       ,      **UpperCAmelCase						: Optional[Any]       ,      ):
           lowerCAmelCase_       :						int							= cls.load_config(
               pretrained_model_name_or_path=lowerCamelCase__       ,      subfolder=lowerCamelCase__       ,      return_unused_kwargs=lowerCamelCase__       ,      **lowerCamelCase__       ,      )
           lowerCAmelCase_       :						Any							= cls.from_config(lowerCamelCase__       ,      return_unused_kwargs=lowerCamelCase__       ,      **lowerCamelCase__      )

           if hasattr(lowerCamelCase__       ,      """create_state"""      ) and getattr(lowerCamelCase__       ,      """has_state"""       ,      lowerCamelCase__      ):
               lowerCAmelCase_       :						int							= scheduler.create_state()

           if return_unused_kwargs:
               return scheduler, state, unused_kwargs

           return scheduler, state



       def 			A				(  self						: Tuple       ,      UpperCAmelCase						: Union[str, os.PathLike]       ,      UpperCAmelCase						: bool = False       ,      **UpperCAmelCase						: List[Any]      ):
           self.save_config(save_directory=lowerCamelCase__       ,      push_to_hub=lowerCamelCase__       ,      **lowerCamelCase__      )



       @property
       def 			A				(  self						: List[Any]      ):
           return self._get_compatibles()



       @classmethod
       def 			A				(  cls						: Dict      ):
           lowerCAmelCase_       :						int							= list(set([cls.__name__] + cls._compatibles      )      )
           lowerCAmelCase_       :						Tuple							= importlib.import_module(__name__.split("""."""      )[0]      )
           lowerCAmelCase_       :						List[Any]							= [
               getattr(lowerCamelCase__       ,      lowerCamelCase__      ) for c in compatible_classes_str if hasattr(lowerCamelCase__       ,      lowerCamelCase__      )
           ]
           return compatible_classes






def       __UpperCamelCase					(      lowercase__						:							Optional[Any]		,     lowercase__						:							int		)						->   List[Any]:
    '''simple docstring'''





    assert len(UpperCAmelCase_		) >= x.ndim
    return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(UpperCAmelCase_		) - x.ndim)		)		,     UpperCAmelCase_		)
def       __UpperCamelCase					(      lowercase__						:							Dict		,     lowercase__						:							List[Any]=0.999		,     lowercase__						:							List[Any]=jnp.floataa		)						->   List[Any]:
    '''simple docstring'''





    def alpha_bar(lowercase__						:							Optional[int]		):
        return math.cos((time_step + 0.008) / 1.008 * math.pi / 2		) ** 2

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

@flax.struct.dataclass
class 		__a				:
       __snake_case    : Optional[int]   =		42
       __snake_case    : Union[str, Any]   =		42
       __snake_case    : Dict   =		42



       @classmethod
       def 			A				(  cls						: str       ,      UpperCAmelCase						: List[str]      ):
           lowerCAmelCase_       :						Union[str, Any]							= scheduler.config

           if config.trained_betas is not None:
               lowerCAmelCase_       :						Dict							= jnp.asarray(config.trained_betas       ,      dtype=scheduler.dtype      )
           elif config.beta_schedule == "linear":
               lowerCAmelCase_       :						int							= 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_       :						Optional[int]							= (
                   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_       :						Optional[Any]							= 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_       :						str							= 1.0 - betas

           lowerCAmelCase_       :						Dict							= jnp.cumprod(lowerCamelCase__       ,      axis=0      )

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






def       __UpperCamelCase					(      lowercase__						:							int		,     lowercase__						:							Tuple		,     lowercase__						:							Any		,     lowercase__						:							List[Any]		)						->   Optional[int]:
    '''simple docstring'''





    lowerCAmelCase_       :						List[Any]							= state.alphas_cumprod

    lowerCAmelCase_       :						Optional[int]							= alphas_cumprod[timesteps] ** 0.5
    lowerCAmelCase_       :						List[Any]							= sqrt_alpha_prod.flatten()
    lowerCAmelCase_       :						Any							= broadcast_to_shape_from_left(UpperCAmelCase_		,     original_samples.shape		)

    lowerCAmelCase_       :						str							= (1 - alphas_cumprod[timesteps]) ** 0.5
    lowerCAmelCase_       :						Optional[int]							= sqrt_one_minus_alpha_prod.flatten()
    lowerCAmelCase_       :						Optional[Any]							= broadcast_to_shape_from_left(UpperCAmelCase_		,     original_samples.shape		)

    return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def       __UpperCamelCase					(      lowercase__						:							Dict		,     lowercase__						:							int		,     lowercase__						:							Optional[Any]		,     lowercase__						:							Union[str, Any]		)						->   List[str]:
    '''simple docstring'''





    lowerCAmelCase_       :						Dict							= get_sqrt_alpha_prod(UpperCAmelCase_		,     UpperCAmelCase_		,     UpperCAmelCase_		,     UpperCAmelCase_		)
    lowerCAmelCase_       :						Tuple							= sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples

def       __UpperCamelCase					(      lowercase__						:							Tuple		,     lowercase__						:							int		,     lowercase__						:							Dict		,     lowercase__						:							Union[str, Any]		)						->   int:
    '''simple docstring'''





    lowerCAmelCase_       :						List[str]							= get_sqrt_alpha_prod(UpperCAmelCase_		,     UpperCAmelCase_		,     UpperCAmelCase_		,     UpperCAmelCase_		)
    lowerCAmelCase_       :						Optional[Any]							= sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
    return velocity







'''simple docstring'''
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def A__       (	UpperCAmelCase_				):
						if "model" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('model.'    ,    ''				)
						if "norm1" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('norm1'    ,    'attention.output.LayerNorm'				)
						if "norm2" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.replace('norm2'    ,    'output.LayerNorm'				)
						if "norm" in orig_key:
												_UpperCamelCase   :						str												=  orig_key.replace('norm'    ,    'LayerNorm'				)
						if "transformer" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.split('.'				)[0].split('_'				)[-1]
												_UpperCamelCase   :						Tuple												=  orig_key.replace(f'transformer_{layer_num}'    ,    f'encoder.layer.{layer_num}'				)
						if "mha.attn" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('mha.attn'    ,    'attention.self'				)
						if "mha" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('mha'    ,    'attention'				)
						if "W_q" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('W_q'    ,    'self.query'				)
						if "W_k" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('W_k'    ,    'self.key'				)
						if "W_v" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('W_v'    ,    'self.value'				)
						if "ff1" in orig_key:
												_UpperCamelCase   :						Optional[Any]												=  orig_key.replace('ff1'    ,    'intermediate.dense'				)
						if "ff2" in orig_key:
												_UpperCamelCase   :						List[str]												=  orig_key.replace('ff2'    ,    'output.dense'				)
						if "ff" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('ff'    ,    'output.dense'				)
						if "mlm_class" in orig_key:
												_UpperCamelCase   :						Tuple												=  orig_key.replace('mlm.mlm_class'    ,    'cls.predictions.decoder'				)
						if "mlm" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.replace('mlm'    ,    'cls.predictions.transform'				)
						if "cls" not in orig_key:
												_UpperCamelCase   :						Optional[int]												=  'yoso.' + orig_key

						return orig_key






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						for key in orig_state_dict.copy().keys():
												_UpperCamelCase   :						Any												=  orig_state_dict.pop(UpperCAmelCase_				)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		_UpperCamelCase   :						Union[str, Any]												=  val

						_UpperCamelCase   :						Any												=  orig_state_dict['cls.predictions.decoder.bias']
						_UpperCamelCase   :						Union[str, Any]												=  torch.arange(UpperCAmelCase_				).expand((1, -1)				) + 2

						return orig_state_dict






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						List[Any]												=  torch.load(UpperCAmelCase_    ,    map_location='cpu'				)['model_state_dict']
						_UpperCamelCase   :						List[str]												=  YosoConfig.from_json_file(UpperCAmelCase_				)
						_UpperCamelCase   :						Union[str, Any]												=  YosoForMaskedLM(UpperCAmelCase_				)

						_UpperCamelCase   :						Tuple												=  convert_checkpoint_helper(config.max_position_embeddings    ,    UpperCAmelCase_				)

						print(model.load_state_dict(UpperCAmelCase_				)				)
						model.eval()
						model.save_pretrained(UpperCAmelCase_				)

						print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'				)


if __name__ == "__main__":
					snake_case_					:      int    			=    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					snake_case_					:      List[Any]    			=    parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


