

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__  :      Optional[Any]			=    'scheduler_config.json'



class       __snake_case  (    __lowercase       ):
		__lowerCAmelCase     :	List[Any]						 =  1
		__lowerCAmelCase     :	Tuple						 =  2
		__lowerCAmelCase     :	Dict						 =  3
		__lowerCAmelCase     :	Tuple						 =  4
		__lowerCAmelCase     :	Tuple						 =  5






@dataclass
class       __snake_case  (    __lowercase       ):
		__lowerCAmelCase     :	jnp.ndarray






class       __snake_case  :
		__lowerCAmelCase     :	Tuple						 =  SCHEDULER_CONFIG_NAME
		__lowerCAmelCase     :	Union[str, Any]						 =  ['''dtype''']
		__lowerCAmelCase     :	Union[str, Any]						 =  []
		__lowerCAmelCase     :	Any						 =  True

		@classmethod
		def 						lowerCAmelCase__     (					cls						,		_A = None						,		_A = None						,		_A=False						,		**_A						,		):
						SCREAMING_SNAKE_CASE_									=						cls.load_config(
						    pretrained_model_name_or_path=__a						,		subfolder=__a						,		return_unused_kwargs=__a						,		**__a						,		)
						SCREAMING_SNAKE_CASE_									=						cls.from_config(__a						,		return_unused_kwargs=__a						,		**__a)

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

						if return_unused_kwargs:
										return scheduler, state, unused_kwargs

						return scheduler, state

		def 						lowerCAmelCase__     (					self						,		_A						,		_A = False						,		**_A):
						self.save_config(save_directory=__a						,		push_to_hub=__a						,		**__a)

		@property
		def 						lowerCAmelCase__     (					self):
						return self._get_compatibles()


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






def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		jnp.ndarray ,   _SCREAMING_SNAKE_CASE					:		Tuple[int]       ):





				"""simple docstring"""


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


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		str=0.999 ,   _SCREAMING_SNAKE_CASE					:		Dict=jnp.floataa       ):





				"""simple docstring"""


				def alpha_bar(_SCREAMING_SNAKE_CASE					:		List[Any]       ):
								return math.cos((time_step + 0.008) / 1.008 * math.pi / 2       ) ** 2

				SCREAMING_SNAKE_CASE_									=						[]
				for i in range(UpperCamelCase__       ):
								SCREAMING_SNAKE_CASE_									=						i / num_diffusion_timesteps
								SCREAMING_SNAKE_CASE_									=						(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       __snake_case  :
		__lowerCAmelCase     :	jnp.ndarray
		__lowerCAmelCase     :	jnp.ndarray
		__lowerCAmelCase     :	jnp.ndarray

		@classmethod
		def 						lowerCAmelCase__     (					cls						,		_A):
						SCREAMING_SNAKE_CASE_									=						scheduler.config

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

						SCREAMING_SNAKE_CASE_									=						1.0 - betas

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

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






def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		CommonSchedulerState ,   _SCREAMING_SNAKE_CASE					:		jnp.ndarray ,   _SCREAMING_SNAKE_CASE					:		jnp.ndarray ,   _SCREAMING_SNAKE_CASE					:		jnp.ndarray       ):





				"""simple docstring"""


				SCREAMING_SNAKE_CASE_									=						state.alphas_cumprod

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

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

				return sqrt_alpha_prod, sqrt_one_minus_alpha_prod


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		CommonSchedulerState ,   _SCREAMING_SNAKE_CASE					:		jnp.ndarray ,   _SCREAMING_SNAKE_CASE					:		jnp.ndarray ,   _SCREAMING_SNAKE_CASE					:		jnp.ndarray       ):





				"""simple docstring"""


				SCREAMING_SNAKE_CASE_									=						get_sqrt_alpha_prod(UpperCamelCase__ ,   UpperCamelCase__ ,   UpperCamelCase__ ,   UpperCamelCase__       )
				SCREAMING_SNAKE_CASE_									=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		CommonSchedulerState ,   _SCREAMING_SNAKE_CASE					:		jnp.ndarray ,   _SCREAMING_SNAKE_CASE					:		jnp.ndarray ,   _SCREAMING_SNAKE_CASE					:		jnp.ndarray       ):





				"""simple docstring"""


				SCREAMING_SNAKE_CASE_									=						get_sqrt_alpha_prod(UpperCamelCase__ ,   UpperCamelCase__ ,   UpperCamelCase__ ,   UpperCamelCase__       )
				SCREAMING_SNAKE_CASE_									=						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
				return velocity





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__  :      str			=    logging.get_logger(__name__)

UpperCamelCase__  :      Optional[int]			=    {
    "facebook/dpr-ctx_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-single-nq-base": (
        "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-ctx_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-multiset-base": (
        "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
    ),
}



class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	Optional[int]						 =  'dpr'

  def __init__(					self						,		_A=30522						,		_A=768						,		_A=12						,		_A=12						,		_A=3072						,		_A="gelu"						,		_A=0.1						,		_A=0.1						,		_A=512						,		_A=2						,		_A=0.0_2						,		_A=1E-12						,		_A=0						,		_A="absolute"						,		_A = 0						,		**_A						,		):
      super().__init__(pad_token_id=_A						,		**_A)

      SCREAMING_SNAKE_CASE_									=						vocab_size
      SCREAMING_SNAKE_CASE_									=						hidden_size
      SCREAMING_SNAKE_CASE_									=						num_hidden_layers
      SCREAMING_SNAKE_CASE_									=						num_attention_heads
      SCREAMING_SNAKE_CASE_									=						hidden_act
      SCREAMING_SNAKE_CASE_									=						intermediate_size
      SCREAMING_SNAKE_CASE_									=						hidden_dropout_prob
      SCREAMING_SNAKE_CASE_									=						attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE_									=						max_position_embeddings
      SCREAMING_SNAKE_CASE_									=						type_vocab_size
      SCREAMING_SNAKE_CASE_									=						initializer_range
      SCREAMING_SNAKE_CASE_									=						layer_norm_eps
      SCREAMING_SNAKE_CASE_									=						projection_dim
      SCREAMING_SNAKE_CASE_									=						position_embedding_type


