


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


__UpperCAmelCase      			=  """block_records.npy"""


__UpperCAmelCase      			=  logging.get_logger(__name__)
def 	_lowerCamelCase  (		A_  :	str							,  A_  :	int      )			->			List[str]:

   '''simple docstring'''






   import tensorflow.compat.va as tf

   UpperCamelCase__ :			str 						=tf.data.TFRecordDataset(A_							,  buffer_size=5_1_2 * 1_0_2_4 * 1_0_2_4      )
   UpperCamelCase__ :			List[Any] 						=blocks_dataset.batch(A_							,  drop_remainder=A_      )
   UpperCamelCase__ :			Optional[int] 						=next(blocks_dataset.take(1      ).as_numpy_iterator()      )

   return np_record



class 							lowercase__:






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=2   ,			__SCREAMING_SNAKE_CASE=10_00   ,			__SCREAMING_SNAKE_CASE=1_00   ,			__SCREAMING_SNAKE_CASE=10_00_00   ,			)					->						Tuple:







         """simple docstring"""







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

         UpperCamelCase__ :			Union[str, Any] 						=Builder(db=_UpperCAmelCase   ,			num_neighbors=_UpperCAmelCase   ,			distance_measure="dot_product")
         UpperCamelCase__ :			Union[str, Any] 						=builder.tree(
             num_leaves=_UpperCAmelCase   ,			num_leaves_to_search=_UpperCAmelCase   ,			training_sample_size=_UpperCAmelCase)
         UpperCamelCase__ :			List[str] 						=builder.score_ah(dimensions_per_block=_UpperCAmelCase)

         UpperCamelCase__ :			Tuple 						=builder.build()




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						List[Any]:







         """simple docstring"""







         UpperCamelCase__						,	UpperCamelCase__ :			Union[str, Any] 						=self.searcher.search_batched(question_projection.detach().cpu())
         return retrieved_block_ids.astype("int64")




class 							lowercase__:






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						List[str]:







         """simple docstring"""







         super().__init__()
         UpperCamelCase__ :			List[Any] 						=block_records
         UpperCamelCase__ :			Dict 						=tokenizer




      def __call__(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE="pt")					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			str 						=np.take(self.block_records   ,			indices=_UpperCAmelCase   ,			axis=0)

         UpperCamelCase__ :			List[Any] 						=self.tokenizer.decode(question_input_ids[0]   ,			skip_special_tokens=_UpperCAmelCase)

         UpperCamelCase__ :			Optional[int] 						=[]
         UpperCamelCase__ :			List[str] 						=[]
         for retrieved_block in retrieved_blocks:
            text.append(_UpperCAmelCase)
            text_pair.append(retrieved_block.decode())

         UpperCamelCase__ :			Dict 						=self.tokenizer(
             _UpperCAmelCase   ,			_UpperCAmelCase   ,			padding=_UpperCAmelCase   ,			truncation=_UpperCAmelCase   ,			return_special_tokens_mask=_UpperCAmelCase   ,			max_length=_UpperCAmelCase)
         UpperCamelCase__ :			List[Any] 						=concat_inputs.convert_to_tensors(_UpperCAmelCase)

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




      @classmethod
      def    UpperCAmelCase							(		cls   ,			__SCREAMING_SNAKE_CASE   ,			*__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE)					->						Optional[int]:







         """simple docstring"""







         if os.path.isdir(_UpperCAmelCase):
            UpperCamelCase__ :			Any 						=os.path.join(_UpperCAmelCase   ,			_REALM_BLOCK_RECORDS_FILENAME)
         else:
            UpperCamelCase__ :			List[str] 						=hf_hub_download(
                repo_id=_UpperCAmelCase   ,			filename=_REALM_BLOCK_RECORDS_FILENAME   ,			**_UpperCAmelCase)
         UpperCamelCase__ :			Tuple 						=np.load(_UpperCAmelCase   ,			allow_pickle=_UpperCAmelCase)

         UpperCamelCase__ :			Optional[Any] 						=AutoTokenizer.from_pretrained(_UpperCAmelCase   ,			*_UpperCAmelCase   ,			**_UpperCAmelCase)

         return cls(_UpperCAmelCase   ,			_UpperCAmelCase)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						Any:







         """simple docstring"""







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




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Union[str, Any]:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=[]
         UpperCamelCase__ :			Dict 						=[]
         UpperCamelCase__ :			int 						=[]
         UpperCamelCase__ :			int 						=0

         for input_id in concat_inputs.input_ids:
            UpperCamelCase__ :			Any 						=input_id.tolist()
            # Check answers between two [SEP] tokens
            UpperCamelCase__ :			Dict 						=input_id_list.index(self.tokenizer.sep_token_id)
            UpperCamelCase__ :			List[str] 						=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   ,			_UpperCAmelCase):
                  if answer[0] == input_id_list[idx]:
                     if input_id_list[idx : idx + len(_UpperCAmelCase)] == answer:
                        start_pos[-1].append(_UpperCAmelCase)
                        end_pos[-1].append(idx + len(_UpperCAmelCase) - 1)

            if len(start_pos[-1]) == 0:
               has_answers.append(_UpperCAmelCase)
            else:
               has_answers.append(_UpperCAmelCase)
               if len(start_pos[-1]) > max_answers:
                  UpperCamelCase__ :			str 						=len(start_pos[-1])

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



# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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'''







      a		:CommonSchedulerState
      a		:jnp.ndarray

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







      @classmethod
      def lowercase__		( cls					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					):
            return cls(
                common=_UpperCAmelCase					,					final_alpha_cumprod=_UpperCAmelCase					,					init_noise_sigma=_UpperCAmelCase					,					timesteps=_UpperCAmelCase					,					)


@dataclass
class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      a		:DDIMSchedulerState


class 			UpperCamelCase_				(  A					,       A     ):




      '''simple docstring'''







      a		:int						 =      [e.name for e in FlaxKarrasDiffusionSchedulers]

      a		:jnp.dtype







      @property
      def lowercase__		( self):
            return True







      @register_to_config
      def __init__( self					,					_UpperCAmelCase = 1_000					,					_UpperCAmelCase = 0.0001					,					_UpperCAmelCase = 0.02					,					_UpperCAmelCase = "linear"					,					_UpperCAmelCase = None					,					_UpperCAmelCase = True					,					_UpperCAmelCase = 0					,					_UpperCAmelCase = "epsilon"					,					_UpperCAmelCase = jnp.floataa					,					):
            lowerCAmelCase_						=      dtype







      def lowercase__		( self					,					_UpperCAmelCase = None):
            if common is None:
                  lowerCAmelCase_						=      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.
            lowerCAmelCase_						=      (
                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
            lowerCAmelCase_						=      jnp.array(1.0					,					dtype=self.dtype)

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

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







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase = None):
            return sample







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase = ()):
            lowerCAmelCase_						=      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
            lowerCAmelCase_						=      (jnp.arange(0					,					_UpperCAmelCase) * step_ratio).round()[::-1] + self.config.steps_offset

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







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      state.common.alphas_cumprod[timestep]
            lowerCAmelCase_						=      jnp.where(
                prev_timestep >= 0					,					state.common.alphas_cumprod[prev_timestep]					,					state.final_alpha_cumprod)
            lowerCAmelCase_						=      1 - alpha_prod_t
            lowerCAmelCase_						=      1 - alpha_prod_t_prev

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

            return variance







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase = 0.0					,					_UpperCAmelCase = True					,					):
            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)
            lowerCAmelCase_						=      timestep - self.config.num_train_timesteps // state.num_inference_steps

            lowerCAmelCase_						=      state.common.alphas_cumprod
            lowerCAmelCase_						=      state.final_alpha_cumprod

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

            lowerCAmelCase_						=      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":
                  lowerCAmelCase_						=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                  lowerCAmelCase_						=      model_output
            elif self.config.prediction_type == "sample":
                  lowerCAmelCase_						=      model_output
                  lowerCAmelCase_						=      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
            elif self.config.prediction_type == "v_prediction":
                  lowerCAmelCase_						=      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                  lowerCAmelCase_						=      (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)
            lowerCAmelCase_						=      self._get_variance(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
            lowerCAmelCase_						=      eta * variance ** 0.5

            # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            lowerCAmelCase_						=      (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
            lowerCAmelCase_						=      alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

            if not return_dict:
                  return (prev_sample, state)

            return FlaxDDIMSchedulerOutput(prev_sample=_UpperCAmelCase					,					state=_UpperCAmelCase)







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					):
            return add_noise_common(state.common					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					):
            return get_velocity_common(state.common					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)







      def __len__( self):
            return self.config.num_train_timesteps



