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

import flax
import jax.numpy as jnp
from scipy import integrate

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


@flax.struct.dataclass
class 						snake_case_ :
   __A			:	List[Any]        =       42

   # setable values
   __A			:	Optional[int]        =       42
   __A			:	Union[str, Any]        =       42
   __A			:	Union[str, Any]        =       42
   __A			:	List[Any]        =       None

   # running values
   __A			:	Any        =       None






   @classmethod
   def    __UpperCamelCase  (   cls       :  Dict			,		lowercase_       :  str			,		lowercase_       :  Dict			,		lowercase_       :  Any			,		lowercase_       :  List[str]						)      ->		Dict:
       return cls(common=lowerCamelCase__			,		init_noise_sigma=lowerCamelCase__			,		timesteps=lowerCamelCase__			,		sigmas=lowerCamelCase__						)



@dataclass
class 						snake_case_ (	_a ):
   __A			:	List[Any]        =       42



class 						snake_case_ (	_a						,_a ):
   __A			:	Dict        =       [e.name for e in FlaxKarrasDiffusionSchedulers]

   __A			:	int        =       42






   @property
   def    __UpperCamelCase  (   self       :  Dict						)      ->		str:
       return True






   @register_to_config
   def __init__(   self       :  str			,		lowercase_       :  List[str] = 10_00			,		lowercase_       :  int = 0.00_01			,		lowercase_       :  Any = 0.02			,		lowercase_       :  Tuple = "linear"			,		lowercase_       :  Union[str, Any] = None			,		lowercase_       :  Union[str, Any] = "epsilon"			,		lowercase_       :  List[str] = jnp.floataa			,		)      ->		int:
       lowercase__				:     Optional[Any]					      =       dtype






   def    __UpperCamelCase  (   self       :  Dict			,		lowercase_       :  Tuple = None						)      ->		Optional[Any]:
       if common is None:
           lowercase__				:     Union[str, Any]					      =       CommonSchedulerState.create(self						)

       lowercase__				:     int					      =       jnp.arange(0			,		self.config.num_train_timesteps						).round()[::-1]
       lowercase__				:     Dict					      =       ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

       # standard deviation of the initial noise distribution
       lowercase__				:     Optional[Any]					      =       sigmas.max()

       return LMSDiscreteSchedulerState.create(
           common=lowerCamelCase__			,		init_noise_sigma=lowerCamelCase__			,		timesteps=lowerCamelCase__			,		sigmas=lowerCamelCase__			,		)






   def    __UpperCamelCase  (   self       :  int			,		lowercase_       :  int			,		lowercase_       :  Optional[int]			,		lowercase_       :  Any						)      ->		List[Any]:
       (lowercase__	)				:     Optional[Any]					      =       jnp.where(state.timesteps == timestep			,		size=1						)
       lowercase__				:     Optional[Any]					      =       step_index[0]

       lowercase__				:     Dict					      =       state.sigmas[step_index]
       lowercase__				:     Optional[Any]					      =       sample / ((sigma**2 + 1) ** 0.5)
       return sample






   def    __UpperCamelCase  (   self       :  Optional[int]			,		lowercase_       :  List[Any]			,		lowercase_       :  str			,		lowercase_       :  str			,		lowercase_       :  int						)      ->		int:

       def lms_derivative(lowercase_       :  List[str]						):
           lowercase__				:     Optional[Any]					      =       1.0
           for k in range(lowerCamelCase__						):
               if current_order == k:
                   continue
               prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
           return prod

       lowercase__				:     Tuple					      =       integrate.quad(lowerCamelCase__			,		state.sigmas[t]			,		state.sigmas[t + 1]			,		epsrel=1E-4						)[0]

       return integrated_coeff






   def    __UpperCamelCase  (   self       :  Dict			,		lowercase_       :  Optional[int]			,		lowercase_       :  List[str]			,		lowercase_       :  Optional[int] = ()						)      ->		Dict:
       lowercase__				:     int					      =       jnp.linspace(self.config.num_train_timesteps - 1			,		0			,		lowerCamelCase__			,		dtype=self.dtype						)

       lowercase__				:     Optional[Any]					      =       jnp.floor(lowerCamelCase__						).astype(jnp.intaa						)
       lowercase__				:     Union[str, Any]					      =       jnp.ceil(lowerCamelCase__						).astype(jnp.intaa						)

       lowercase__				:     List[str]					      =       jnp.mod(lowerCamelCase__			,		1.0						)

       lowercase__				:     Union[str, Any]					      =       ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
       lowercase__				:     Union[str, Any]					      =       (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
       lowercase__				:     Optional[Any]					      =       jnp.concatenate([sigmas, jnp.array([0.0]			,		dtype=self.dtype						)]						)

       lowercase__				:     str					      =       timesteps.astype(jnp.intaa						)

       # initial running values
       lowercase__				:     Dict					      =       jnp.zeros((0,) + shape			,		dtype=self.dtype						)

       return state.replace(
           timesteps=lowerCamelCase__			,		sigmas=lowerCamelCase__			,		num_inference_steps=lowerCamelCase__			,		derivatives=lowerCamelCase__			,		)






   def    __UpperCamelCase  (   self       :  Any			,		lowercase_       :  Optional[int]			,		lowercase_       :  Optional[Any]			,		lowercase_       :  List[str]			,		lowercase_       :  Tuple			,		lowercase_       :  str = 4			,		lowercase_       :  Any = True			,		)      ->		Union[str, Any]:
       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"						)

       lowercase__				:     Optional[int]					      =       state.sigmas[timestep]

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
           lowercase__				:     Union[str, Any]					      =       sample - sigma * model_output
       elif self.config.prediction_type == "v_prediction":
           # * c_out + input * c_skip
           lowercase__				:     Dict					      =       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
       else:
           raise ValueError(
               F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''						)

       # 2. Convert to an ODE derivative
       lowercase__				:     Optional[int]					      =       (sample - pred_original_sample) / sigma
       lowercase__				:     List[Any]					      =       state.replace(derivatives=jnp.append(state.derivatives			,		lowerCamelCase__						)						)
       if len(state.derivatives						) > order:
           lowercase__				:     Tuple					      =       state.replace(derivatives=jnp.delete(state.derivatives			,		0						)						)

       # 3. Compute linear multistep coefficients
       lowercase__				:     Union[str, Any]					      =       min(timestep + 1			,		lowerCamelCase__						)
       lowercase__				:     Any					      =       [self.get_lms_coefficient(lowerCamelCase__			,		lowerCamelCase__			,		lowerCamelCase__			,		lowerCamelCase__						) for curr_order in range(lowerCamelCase__						)]

       # 4. Compute previous sample based on the derivatives path
       lowercase__				:     Optional[int]					      =       sample + sum(
           coeff * derivative for coeff, derivative in zip(lowerCamelCase__			,		reversed(state.derivatives						)						)						)

       if not return_dict:
           return (prev_sample, state)

       return FlaxLMSSchedulerOutput(prev_sample=lowerCamelCase__			,		state=lowerCamelCase__						)






   def    __UpperCamelCase  (   self       :  str			,		lowercase_       :  int			,		lowercase_       :  Dict			,		lowercase_       :  Optional[Any]			,		lowercase_       :  int			,		)      ->		int:
       lowercase__				:     Tuple					      =       state.sigmas[timesteps].flatten()
       lowercase__				:     int					      =       broadcast_to_shape_from_left(lowerCamelCase__			,		noise.shape						)

       lowercase__				:     str					      =       original_samples + noise * sigma

       return noisy_samples







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




import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin



class 						snake_case_ (	unittest.TestCase						,__A ):






   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		List[Any]:
       lowercase__				:     List[Any]					      =       load_tool("document-question-answering"						)
       self.tool.setup()
       lowercase__				:     Optional[int]					      =       load_tool("document-question-answering"			,		remote=lowercase_						)






   def    __UpperCamelCase  (   self       :  int						)      ->		Optional[int]:
       lowercase__				:     List[Any]					      =       load_dataset("hf-internal-testing/example-documents"			,		split="test"						)
       lowercase__				:     List[Any]					      =       dataset[0]["image"]

       lowercase__				:     int					      =       self.tool(lowercase_			,		"When is the coffee break?"						)
       self.assertEqual(lowercase_			,		"11-14 to 11:39 a.m."						)






   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		List[Any]:
       lowercase__				:     str					      =       load_dataset("hf-internal-testing/example-documents"			,		split="test"						)
       lowercase__				:     str					      =       dataset[0]["image"]

       lowercase__				:     List[str]					      =       self.remote_tool(lowercase_			,		"When is the coffee break?"						)
       self.assertEqual(lowercase_			,		"11-14 to 11:39 a.m."						)






   def    __UpperCamelCase  (   self       :  List[Any]						)      ->		Dict:
       lowercase__				:     List[str]					      =       load_dataset("hf-internal-testing/example-documents"			,		split="test"						)
       lowercase__				:     List[str]					      =       dataset[0]["image"]

       self.tool(document=lowercase_			,		question="When is the coffee break?"						)







   def    __UpperCamelCase  (   self       :  str						)      ->		Optional[Any]:
       lowercase__				:     Optional[Any]					      =       load_dataset("hf-internal-testing/example-documents"			,		split="test"						)
       lowercase__				:     int					      =       dataset[0]["image"]

       lowercase__				:     Tuple					      =       self.remote_tool(document=lowercase_			,		question="When is the coffee break?"						)
       self.assertEqual(lowercase_			,		"11-14 to 11:39 a.m."						)



