


import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


__lowerCamelCase   	=  '''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''


class 	a__						(    unittest.TestCase    ,							lowerCAmelCase_     ):

       def     UpperCAmelCase( self   :  Optional[int]     ):
            a_     :  List[Any]				      =     load_tool("""summarization"""     )
            self.tool.setup()
            a_     :  Any				      =     load_tool("""summarization"""   ,				remote=lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Any     ):
            a_     :  int				      =     self.tool(lowerCamelCase_     )
            self.assertEqual(
                lowerCamelCase_   ,				"""Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""   ,				)

       def     UpperCAmelCase( self   :  Union[str, Any]     ):
            a_     :  str				      =     self.remote_tool(lowerCamelCase_     )
            self.assertEqual(
                lowerCamelCase_   ,				"""Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""   ,				)

       def     UpperCAmelCase( self   :  Tuple     ):
            a_     :  Any				      =     self.tool(text=lowerCamelCase_     )
            self.assertEqual(
                lowerCamelCase_   ,				"""Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""   ,				)

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            a_     :  Optional[Any]				      =     self.remote_tool(text=lowerCamelCase_     )
            self.assertEqual(
                lowerCamelCase_   ,				"""Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""   ,				)





# 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



