
"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)




@flax.struct.dataclass
class 					UpperCamelCase     :

    """simple docstring"""



    UpperCAmelCase_    :				CommonSchedulerState
    UpperCAmelCase_    :				jnp.ndarray

    # setable values
    UpperCAmelCase_    :				jnp.ndarray
    UpperCAmelCase_    :				jnp.ndarray
    UpperCAmelCase_    :				Optional[int]						  =    None


    @classmethod
    def 							A (    cls		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     )   ->   List[str]:
         """simple docstring"""







         return cls(
             common=lowercase__		,     final_alpha_cumprod=lowercase__		,     init_noise_sigma=lowercase__		,     timesteps=lowercase__		,     )






@dataclass
class 					UpperCamelCase     (	__magic_name__				):

    """simple docstring"""



    UpperCAmelCase_    :				DDIMSchedulerState






class 					UpperCamelCase     (	__magic_name__  ,				__magic_name__				):

    """simple docstring"""



    UpperCAmelCase_    :				Optional[int]						  =    [e.name for e in FlaxKarrasDiffusionSchedulers]

    UpperCAmelCase_    :				jnp.dtype


    @property
    def 							A (    self      )   ->   str:
         """simple docstring"""







         return True


    @register_to_config
    def __init__(    self		,     lowercase__ = 1000		,     lowercase__ = 0.0_001		,     lowercase__ = 0.02		,     lowercase__ = "linear"		,     lowercase__ = None		,     lowercase__ = True		,     lowercase__ = 0		,     lowercase__ = "epsilon"		,     lowercase__ = jnp.floataa		,     )   ->   int:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     dtype


    def 							A (    self		,     lowercase__ = None      )   ->   DDIMSchedulerState:
         """simple docstring"""







         if common is None:
              SCREAMING_SNAKE_CASE           =     CommonSchedulerState.create(self      )

         # At every step in ddim, we are looking into the previous alphas_cumprod
         # For the final step, there is no previous alphas_cumprod because we are already at 0
         # `set_alpha_to_one` decides whether we set this parameter simply to one or
         # whether we use the final alpha of the "non-previous" one.
         SCREAMING_SNAKE_CASE           =     (
             jnp.array(1.0		,     dtype=self.dtype      ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
         )

         # standard deviation of the initial noise distribution
         SCREAMING_SNAKE_CASE           =     jnp.array(1.0		,     dtype=self.dtype      )

         SCREAMING_SNAKE_CASE           =     jnp.arange(0		,     self.config.num_train_timesteps      ).round()[::-1]

         return DDIMSchedulerState.create(
             common=lowercase__		,     final_alpha_cumprod=lowercase__		,     init_noise_sigma=lowercase__		,     timesteps=lowercase__		,     )


    def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__ = None      )   ->   jnp.ndarray:
         """simple docstring"""







         return sample


    def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__ = ()      )   ->   DDIMSchedulerState:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     self.config.num_train_timesteps // num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # rounding to avoid issues when num_inference_step is power of 3
         SCREAMING_SNAKE_CASE           =     (jnp.arange(0		,     lowercase__      ) * step_ratio).round()[::-1] + self.config.steps_offset

         return state.replace(
             num_inference_steps=lowercase__		,     timesteps=lowercase__		,     )


    def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   Optional[Any]:
         """simple docstring"""







         SCREAMING_SNAKE_CASE           =     state.common.alphas_cumprod[timestep]
         SCREAMING_SNAKE_CASE           =     jnp.where(
             prev_timestep >= 0		,     state.common.alphas_cumprod[prev_timestep]		,     state.final_alpha_cumprod      )
         SCREAMING_SNAKE_CASE           =     1 - alpha_prod_t
         SCREAMING_SNAKE_CASE           =     1 - alpha_prod_t_prev

         SCREAMING_SNAKE_CASE           =     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

         return variance


    def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__ = 0.0		,     lowercase__ = True		,     )   ->   Union[FlaxDDIMSchedulerOutput, Tuple]:
         """simple docstring"""







         if state.num_inference_steps is None:
              raise ValueError(
                  'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'      )

         # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
         # Ideally, read DDIM paper in-detail understanding

         # Notation (<variable name> -> <name in paper>
         # - pred_noise_t -> e_theta(x_t, t)
         # - pred_original_sample -> f_theta(x_t, t) or x_0
         # - std_dev_t -> sigma_t
         # - eta -> η
         # - pred_sample_direction -> "direction pointing to x_t"
         # - pred_prev_sample -> "x_t-1"

         # 1. get previous step value (=t-1)
         SCREAMING_SNAKE_CASE           =     timestep - self.config.num_train_timesteps // state.num_inference_steps

         SCREAMING_SNAKE_CASE           =     state.common.alphas_cumprod
         SCREAMING_SNAKE_CASE           =     state.final_alpha_cumprod

         # 2. compute alphas, betas
         SCREAMING_SNAKE_CASE           =     alphas_cumprod[timestep]
         SCREAMING_SNAKE_CASE           =     jnp.where(prev_timestep >= 0		,     alphas_cumprod[prev_timestep]		,     lowercase__      )

         SCREAMING_SNAKE_CASE           =     1 - alpha_prod_t

         # 3. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         if self.config.prediction_type == "epsilon":
              SCREAMING_SNAKE_CASE           =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
              SCREAMING_SNAKE_CASE           =     model_output
         elif self.config.prediction_type == "sample":
              SCREAMING_SNAKE_CASE           =     model_output
              SCREAMING_SNAKE_CASE           =     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
         elif self.config.prediction_type == "v_prediction":
              SCREAMING_SNAKE_CASE           =     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
              SCREAMING_SNAKE_CASE           =     (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
         else:
              raise ValueError(
                  f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
                  ' `v_prediction`'      )

         # 4. compute variance: "sigma_t(η)" -> see formula (16)
         # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
         SCREAMING_SNAKE_CASE           =     self._get_variance(lowercase__		,     lowercase__		,     lowercase__      )
         SCREAMING_SNAKE_CASE           =     eta * variance ** 0.5

         # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         SCREAMING_SNAKE_CASE           =     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

         # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         SCREAMING_SNAKE_CASE           =     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

         if not return_dict:
              return (prev_sample, state)

         return FlaxDDIMSchedulerOutput(prev_sample=lowercase__		,     state=lowercase__      )


    def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     )   ->   jnp.ndarray:
         """simple docstring"""







         return add_noise_common(state.common		,     lowercase__		,     lowercase__		,     lowercase__      )


    def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     )   ->   jnp.ndarray:
         """simple docstring"""







         return get_velocity_common(state.common		,     lowercase__		,     lowercase__		,     lowercase__      )




    def __len__(    self      )   ->   int:
         """simple docstring"""







         return self.config.num_train_timesteps


"""simple docstring"""




import os
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ... import AutoTokenizer
from ...utils import logging


snake_case			       =		'block_records.npy'


snake_case			       =		logging.get_logger(__name__)

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					import tensorflow.compat.va as tf

					SCREAMING_SNAKE_CASE           =     tf.data.TFRecordDataset(SCREAMING_SNAKE_CASE_,					buffer_size=5_1_2 * 1_0_2_4 * 1_0_2_4      )
					SCREAMING_SNAKE_CASE           =     blocks_dataset.batch(SCREAMING_SNAKE_CASE_,					drop_remainder=SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     next(blocks_dataset.take(1      ).as_numpy_iterator()      )

					return np_record




class 					UpperCamelCase     :

				"""simple docstring"""



				def __init__(    self		,     lowercase__		,     lowercase__		,     lowercase__=2		,     lowercase__=1000		,     lowercase__=100		,     lowercase__=100000		,     )   ->   Dict:
									"""simple docstring"""







									from scann.scann_ops.py.scann_ops_pybind import builder as Builder

									SCREAMING_SNAKE_CASE           =     Builder(db=lowercase__		,     num_neighbors=lowercase__		,     distance_measure='dot_product'      )
									SCREAMING_SNAKE_CASE           =     builder.tree(
									    num_leaves=lowercase__		,     num_leaves_to_search=lowercase__		,     training_sample_size=lowercase__      )
									SCREAMING_SNAKE_CASE           =     builder.score_ah(dimensions_per_block=lowercase__      )

									SCREAMING_SNAKE_CASE           =     builder.build()




				def 							A (    self		,     lowercase__      )   ->   str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     self.searcher.search_batched(question_projection.detach().cpu()      )
									return retrieved_block_ids.astype('int64'      )






class 					UpperCamelCase     :

				"""simple docstring"""



				def __init__(    self		,     lowercase__		,     lowercase__      )   ->   Dict:
									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE           =     block_records
									SCREAMING_SNAKE_CASE           =     tokenizer


				def __call__(    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__=None		,     lowercase__="pt"      )   ->   Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     np.take(self.block_records		,     indices=lowercase__		,     axis=0      )

									SCREAMING_SNAKE_CASE           =     self.tokenizer.decode(question_input_ids[0]		,     skip_special_tokens=lowercase__      )

									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     []
									for retrieved_block in retrieved_blocks:
														text.append(lowercase__      )
														text_pair.append(retrieved_block.decode()      )

									SCREAMING_SNAKE_CASE           =     self.tokenizer(
									    lowercase__		,     lowercase__		,     padding=lowercase__		,     truncation=lowercase__		,     return_special_tokens_mask=lowercase__		,     max_length=lowercase__      )
									SCREAMING_SNAKE_CASE           =     concat_inputs.convert_to_tensors(lowercase__      )

									if answer_ids is not None:
														return self.block_has_answer(lowercase__		,     lowercase__      ) + (concat_inputs_tensors,)
									else:
														return (None, None, None, concat_inputs_tensors)


				@classmethod
				def 							A (    cls		,     lowercase__		,     *lowercase__		,     **lowercase__      )   ->   Optional[Any]:
									"""simple docstring"""







									if os.path.isdir(lowercase__      ):
														SCREAMING_SNAKE_CASE           =     os.path.join(lowercase__		,     _REALM_BLOCK_RECORDS_FILENAME      )
									else:
														SCREAMING_SNAKE_CASE           =     hf_hub_download(
														    repo_id=lowercase__		,     filename=_REALM_BLOCK_RECORDS_FILENAME		,     **lowercase__      )
									SCREAMING_SNAKE_CASE           =     np.load(lowercase__		,     allow_pickle=lowercase__      )

									SCREAMING_SNAKE_CASE           =     AutoTokenizer.from_pretrained(lowercase__		,     *lowercase__		,     **lowercase__      )

									return cls(lowercase__		,     lowercase__      )


				def 							A (    self		,     lowercase__      )   ->   List[str]:
									"""simple docstring"""







									np.save(os.path.join(lowercase__		,     _REALM_BLOCK_RECORDS_FILENAME      )		,     self.block_records      )
									# save tokenizer
									self.tokenizer.save_pretrained(lowercase__      )




				def 							A (    self		,     lowercase__		,     lowercase__      )   ->   Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     0

									for input_id in concat_inputs.input_ids:
														SCREAMING_SNAKE_CASE           =     input_id.tolist()
														# Check answers between two [SEP] tokens
														SCREAMING_SNAKE_CASE           =     input_id_list.index(self.tokenizer.sep_token_id      )
														SCREAMING_SNAKE_CASE           =     first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id      )

														start_pos.append([]      )
														end_pos.append([]      )
														for answer in answer_ids:
																			for idx in range(first_sep_idx + 1		,     lowercase__      ):
																								if answer[0] == input_id_list[idx]:
																													if input_id_list[idx : idx + len(lowercase__      )] == answer:
																																		start_pos[-1].append(lowercase__      )
																																		end_pos[-1].append(idx + len(lowercase__      ) - 1      )

														if len(start_pos[-1]      ) == 0:
																			has_answers.append(lowercase__      )
														else:
																			has_answers.append(lowercase__      )
																			if len(start_pos[-1]      ) > max_answers:
																								SCREAMING_SNAKE_CASE           =     len(start_pos[-1]      )

        # Pad -1 to max_answers
									for start_pos_, end_pos_ in zip(lowercase__		,     lowercase__      ):
														if len(start_pos_      ) < max_answers:
																			SCREAMING_SNAKE_CASE           =     [-1] * (max_answers - len(start_pos_      ))
																			start_pos_ += padded
																			end_pos_ += padded
									return has_answers, start_pos, end_pos
