
"""simple docstring"""

import posixpath
from pathlib import Path
from unittest.mock import patch

import pytest
from fsspec.implementations.local import AbstractFileSystem, LocalFileSystem, stringify_path
from fsspec.registry import _registry as _fsspec_registry
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE				:							int							 =		"mock"




      def __init__(							self				,    *lowercase_				,    lowercase_				,    **lowercase_							)  ->   Union[str, Any]:
          super().__init__()
          UpperCAmelCase       =   LocalFileSystem(*lowercase_				,    **lowercase_							)
          UpperCAmelCase       =   Path(lowercase_							).resolve().as_posix() + '/'




      def       a_ (							self				,    lowercase_				,    *lowercase_				,    **lowercase_							)  ->   List[str]:
          UpperCAmelCase       =   posixpath.join(self.local_root_dir				,    self._strip_protocol(lowercase_							)							)
          return self._fs.mkdir(lowercase_				,    *lowercase_				,    **lowercase_							)




      def       a_ (							self				,    lowercase_				,    *lowercase_				,    **lowercase_							)  ->   List[str]:
          UpperCAmelCase       =   posixpath.join(self.local_root_dir				,    self._strip_protocol(lowercase_							)							)
          return self._fs.makedirs(lowercase_				,    *lowercase_				,    **lowercase_							)




      def       a_ (							self				,    lowercase_							)  ->   Optional[Any]:
          UpperCAmelCase       =   posixpath.join(self.local_root_dir				,    self._strip_protocol(lowercase_							)							)
          return self._fs.rmdir(lowercase_							)




      def       a_ (							self				,    lowercase_				,    lowercase_=True				,    *lowercase_				,    **lowercase_							)  ->   Tuple:
          UpperCAmelCase       =   posixpath.join(self.local_root_dir				,    self._strip_protocol(lowercase_							)							)
          UpperCAmelCase       =   self._fs.ls(lowercase_				,    detail=lowercase_				,    *lowercase_				,    **lowercase_							)
          if detail:
              return [{**info, "name": info["name"][len(self.local_root_dir							) :]} for info in out]
          else:
              return [name[len(self.local_root_dir							) :] for name in out]




      def       a_ (							self				,    lowercase_				,    *lowercase_				,    **lowercase_							)  ->   Tuple:
          UpperCAmelCase       =   posixpath.join(self.local_root_dir				,    self._strip_protocol(lowercase_							)							)
          UpperCAmelCase       =   dict(self._fs.info(lowercase_				,    *lowercase_				,    **lowercase_							)							)
          UpperCAmelCase       =   out['name'][len(self.local_root_dir							) :]
          return out




      def       a_ (							self				,    lowercase_				,    lowercase_				,    *lowercase_				,    **lowercase_							)  ->   Optional[int]:
          UpperCAmelCase       =   posixpath.join(self.local_root_dir				,    self._strip_protocol(lowercase_							)							)
          UpperCAmelCase       =   posixpath.join(self.local_root_dir				,    self._strip_protocol(lowercase_							)							)
          return self._fs.cp_file(lowercase_				,    lowercase_				,    *lowercase_				,    **lowercase_							)




      def       a_ (							self				,    lowercase_				,    *lowercase_				,    **lowercase_							)  ->   int:
          UpperCAmelCase       =   posixpath.join(self.local_root_dir				,    self._strip_protocol(lowercase_							)							)
          return self._fs.rm_file(lowercase_				,    *lowercase_				,    **lowercase_							)




      def       a_ (							self				,    lowercase_				,    *lowercase_				,    **lowercase_							)  ->   List[str]:
          UpperCAmelCase       =   posixpath.join(self.local_root_dir				,    self._strip_protocol(lowercase_							)							)
          return self._fs.rm(lowercase_				,    *lowercase_				,    **lowercase_							)




      def       a_ (							self				,    lowercase_				,    *lowercase_				,    **lowercase_							)  ->   Any:
          UpperCAmelCase       =   posixpath.join(self.local_root_dir				,    self._strip_protocol(lowercase_							)							)
          return self._fs._open(lowercase_				,    *lowercase_				,    **lowercase_							)




      def       a_ (							self				,    lowercase_							)  ->   int:
          UpperCAmelCase       =   posixpath.join(self.local_root_dir				,    self._strip_protocol(lowercase_							)							)
          return self._fs.created(lowercase_							)




      def       a_ (							self				,    lowercase_							)  ->   Optional[int]:
          UpperCAmelCase       =   posixpath.join(self.local_root_dir				,    self._strip_protocol(lowercase_							)							)
          return self._fs.modified(lowercase_							)




      @classmethod
      def       a_ (							cls				,    lowercase_							)  ->   List[Any]:
          UpperCAmelCase       =   stringify_path(lowercase_							)
          if path.startswith('mock://'							):
              UpperCAmelCase       =   path[7:]
          return path

class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE				:							int							 =		"tmp"
      __SCREAMING_SNAKE_CASE				:							Union[str, Any]							 =		None




      def __init__(							self				,    *lowercase_				,    **lowercase_							)  ->   Union[str, Any]:
          assert self.tmp_dir is not None, "TmpDirFileSystem.tmp_dir is not set"
          super().__init__(*lowercase_				,    **lowercase_				,    local_root_dir=self.tmp_dir				,    auto_mkdir=lowercase_							)




      @classmethod
      def       a_ (							cls				,    lowercase_							)  ->   Optional[int]:
          UpperCAmelCase       =   stringify_path(lowercase_							)
          if path.startswith('tmp://'							):
              UpperCAmelCase       =   path[6:]
          return path




@pytest.fixture
def       lowercase__  (   )	-> Dict:






    """simple docstring"""




    UpperCAmelCase       =   MockFileSystem
    UpperCAmelCase       =   TmpDirFileSystem
    yield
    del _fsspec_registry["mock"]
    del _fsspec_registry["tmp"]




@pytest.fixture
def       lowercase__  (   lowerCAmelCase			:   Tuple	,			lowerCAmelCase			:   int	)	-> str:






    """simple docstring"""




    UpperCAmelCase       =   tmp_path_factory.mktemp('mockfs'	)
    return MockFileSystem(local_root_dir=lowerCAmelCase	,			auto_mkdir=lowerCAmelCase	)







@pytest.fixture
def       lowercase__  (   lowerCAmelCase			:   Union[str, Any]	,			lowerCAmelCase			:   Union[str, Any]	)	-> Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase       =   tmp_path_factory.mktemp('tmpfs'	)
    with patch.object(lowerCAmelCase	,			'tmp_dir'	,			lowerCAmelCase	):
        yield TmpDirFileSystem()





"""simple docstring"""

# 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							:
      __SCREAMING_SNAKE_CASE				:							CommonSchedulerState
      __SCREAMING_SNAKE_CASE				:							jnp.ndarray

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




      @classmethod
      def       a_ (							cls				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    )  ->   Dict:
          return cls(
              common=lowercase_				,    final_alpha_cumprod=lowercase_				,    init_noise_sigma=lowercase_				,    timesteps=lowercase_				,    )

@dataclass
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE				:							DDIMSchedulerState

class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE				:							Optional[Any]							 =		[e.name for e in FlaxKarrasDiffusionSchedulers]

      __SCREAMING_SNAKE_CASE				:							jnp.dtype




      @property
      def       a_ (							self							)  ->   Union[str, Any]:
          return True




      @register_to_config
      def __init__(							self				,    lowercase_ = 1_0_0_0				,    lowercase_ = 0.0_0_0_1				,    lowercase_ = 0.0_2				,    lowercase_ = "linear"				,    lowercase_ = None				,    lowercase_ = True				,    lowercase_ = 0				,    lowercase_ = "epsilon"				,    lowercase_ = jnp.floataa				,    )  ->   Optional[Any]:
          UpperCAmelCase       =   dtype




      def       a_ (							self				,    lowercase_ = None							)  ->   DDIMSchedulerState:
          if common is None:
              UpperCAmelCase       =   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.
          UpperCAmelCase       =   (
              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
          UpperCAmelCase       =   jnp.array(1.0				,    dtype=self.dtype							)

          UpperCAmelCase       =   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:
          return sample




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_ = ()							)  ->   DDIMSchedulerState:
          UpperCAmelCase       =   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
          UpperCAmelCase       =   (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_							)  ->   Union[str, Any]:
          UpperCAmelCase       =   state.common.alphas_cumprod[timestep]
          UpperCAmelCase       =   jnp.where(
              prev_timestep >= 0				,    state.common.alphas_cumprod[prev_timestep]				,    state.final_alpha_cumprod							)
          UpperCAmelCase       =   1 - alpha_prod_t
          UpperCAmelCase       =   1 - alpha_prod_t_prev

          UpperCAmelCase       =   (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]:
          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)
          UpperCAmelCase       =   timestep - self.config.num_train_timesteps // state.num_inference_steps

          UpperCAmelCase       =   state.common.alphas_cumprod
          UpperCAmelCase       =   state.final_alpha_cumprod

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

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

          # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          UpperCAmelCase       =   (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
          UpperCAmelCase       =   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:
          return add_noise_common(state.common				,    lowercase_				,    lowercase_				,    lowercase_							)




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    )  ->   jnp.ndarray:
          return get_velocity_common(state.common				,    lowercase_				,    lowercase_				,    lowercase_							)




      def __len__(							self							)  ->   Optional[Any]:
          return self.config.num_train_timesteps



