






"""simple docstring"""



# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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

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




					@classmethod
					def   A  (				cls     :						int    ,						A     :						CommonSchedulerState    ,						A     :						jnp.ndarray    ,						A     :						jnp.ndarray   )  ->  Optional[Any]:
							return cls(common=A    ,						init_noise_sigma=A    ,						timesteps=A   )





@dataclass
class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				DDPMSchedulerState





class 					_UpperCAmelCase       ( _A   ,						_A							):
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   [e.name for e in FlaxKarrasDiffusionSchedulers]

					SCREAMING_SNAKE_CASE_		:				jnp.dtype




					@property
					def   A  (				self     :						List[str]   )  ->  Any:
							return True




					@register_to_config
					def __init__(				self     :						str    ,						A     :						int = 10_00    ,						A     :						float = 0.0001    ,						A     :						float = 0.02    ,						A     :						str = "linear"    ,						A     :						Optional[jnp.ndarray] = None    ,						A     :						str = "fixed_small"    ,						A     :						bool = True    ,						A     :						str = "epsilon"    ,						A     :						jnp.dtype = jnp.floataa    ,						)  ->  Any:
							lowercase_ : List[str]							       =   dtype




					def   A  (				self     :						int    ,						A     :						Optional[CommonSchedulerState] = None   )  ->  DDPMSchedulerState:
							if common is None:
									lowercase_ : str							       =   CommonSchedulerState.create(self   )

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

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

							return DDPMSchedulerState.create(
							    common=A    ,						init_noise_sigma=A    ,						timesteps=A    ,						)




					def   A  (				self     :						List[str]    ,						A     :						DDPMSchedulerState    ,						A     :						jnp.ndarray    ,						A     :						Optional[int] = None   )  ->  jnp.ndarray:
							return sample




					def   A  (				self     :						Dict    ,						A     :						DDPMSchedulerState    ,						A     :						int    ,						A     :						Tuple = ()   )  ->  DDPMSchedulerState:
							lowercase_ : Optional[int]							       =   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
							lowercase_ : Tuple							       =   (jnp.arange(0    ,						A   ) * step_ratio).round()[::-1]

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




					def   A  (				self     :						int    ,						A     :						DDPMSchedulerState    ,						A     :						Any    ,						A     :						Optional[int]=None    ,						A     :						Any=None   )  ->  List[Any]:
							lowercase_ : Optional[Any]							       =   state.common.alphas_cumprod[t]
							lowercase_ : int							       =   jnp.where(t > 0    ,						state.common.alphas_cumprod[t - 1]    ,						jnp.array(1.0    ,						dtype=self.dtype   )   )

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
							# and sample from it to get previous sample
							# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
							lowercase_ : int							       =   (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

							if variance_type is None:
									lowercase_ : str							       =   self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small":
									lowercase_ : Dict							       =   jnp.clip(A    ,						a_min=1e-20   )
							# for rl-diffuser https://arxiv.org/abs/2205.09991
							elif variance_type == "fixed_small_log":
									lowercase_ : Union[str, Any]							       =   jnp.log(jnp.clip(A    ,						a_min=1e-20   )   )
							elif variance_type == "fixed_large":
									lowercase_ : Any							       =   state.common.betas[t]
							elif variance_type == "fixed_large_log":
									# Glide max_log
									lowercase_ : Tuple							       =   jnp.log(state.common.betas[t]   )
							elif variance_type == "learned":
									return predicted_variance
							elif variance_type == "learned_range":
									lowercase_ : Union[str, Any]							       =   variance
									lowercase_ : Optional[Any]							       =   state.common.betas[t]
									lowercase_ : List[Any]							       =   (predicted_variance + 1) / 2
									lowercase_ : Optional[int]							       =   frac * max_log + (1 - frac) * min_log

							return variance




					def   A  (				self     :						List[Any]    ,						A     :						DDPMSchedulerState    ,						A     :						jnp.ndarray    ,						A     :						int    ,						A     :						jnp.ndarray    ,						A     :						Optional[jax.random.KeyArray] = None    ,						A     :						bool = True    ,						)  ->  Union[FlaxDDPMSchedulerOutput, Tuple]:
							lowercase_ : Any							       =   timestep

							if key is None:
									lowercase_ : str							       =   jax.random.PRNGKey(0   )

							if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
									lowercase_       ,					lowercase_ : Tuple							       =   jnp.split(A    ,						sample.shape[1]    ,						axis=1   )
							else:
									lowercase_ : Optional[int]							       =   None

							# 1. compute alphas, betas
							lowercase_ : Union[str, Any]							       =   state.common.alphas_cumprod[t]
							lowercase_ : int							       =   jnp.where(t > 0    ,						state.common.alphas_cumprod[t - 1]    ,						jnp.array(1.0    ,						dtype=self.dtype   )   )
							lowercase_ : Union[str, Any]							       =   1 - alpha_prod_t
							lowercase_ : Tuple							       =   1 - alpha_prod_t_prev

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if self.config.prediction_type == "epsilon":
									lowercase_ : List[str]							       =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
									lowercase_ : Dict							       =   model_output
							elif self.config.prediction_type == "v_prediction":
									lowercase_ : List[str]							       =   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
							else:
									raise ValueError(
									    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
									    ''' for the FlaxDDPMScheduler.'''   )

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
									lowercase_ : List[str]							       =   jnp.clip(A    ,						-1    ,						1   )

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							lowercase_ : Dict							       =   (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
							lowercase_ : List[str]							       =   state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							lowercase_ : Tuple							       =   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							def random_variance():
									lowercase_ : str							       =   jax.random.split(A    ,						num=1   )
									lowercase_ : List[Any]							       =   jax.random.normal(A    ,						shape=model_output.shape    ,						dtype=self.dtype   )
									return (self._get_variance(A    ,						A    ,						predicted_variance=A   ) ** 0.5) * noise

							lowercase_ : int							       =   jnp.where(t > 0    ,						random_variance()    ,						jnp.zeros(model_output.shape    ,						dtype=self.dtype   )   )

							lowercase_ : Union[str, Any]							       =   pred_prev_sample + variance

							if not return_dict:
									return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=A    ,						state=A   )




					def   A  (				self     :						int    ,						A     :						DDPMSchedulerState    ,						A     :						jnp.ndarray    ,						A     :						jnp.ndarray    ,						A     :						jnp.ndarray    ,						)  ->  jnp.ndarray:
							return add_noise_common(state.common    ,						A    ,						A    ,						A   )




					def   A  (				self     :						Dict    ,						A     :						DDPMSchedulerState    ,						A     :						jnp.ndarray    ,						A     :						jnp.ndarray    ,						A     :						jnp.ndarray    ,						)  ->  jnp.ndarray:
							return get_velocity_common(state.common    ,						A    ,						A    ,						A   )






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











"""simple docstring"""



import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


__A		:       Dict  				=		'''
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 					_UpperCAmelCase       ( unittest.TestCase   ,						_A							):




					def   A  (				self     :						List[Any]   )  ->  Dict:
							lowercase_ : Optional[int]							       =   load_tool('''text-question-answering'''   )
							self.tool.setup()
							lowercase_ : Union[str, Any]							       =   load_tool('''text-question-answering'''    ,						remote=A   )




					def   A  (				self     :						Any   )  ->  List[str]:
							lowercase_ : Union[str, Any]							       =   self.tool(A    ,						'''What did Hugging Face do in April 2021?'''   )
							self.assertEqual(A    ,						'''launched the BigScience Research Workshop'''   )




					def   A  (				self     :						str   )  ->  List[str]:
							lowercase_ : int							       =   self.remote_tool(A    ,						'''What did Hugging Face do in April 2021?'''   )
							self.assertEqual(A    ,						'''launched the BigScience Research Workshop'''   )




					def   A  (				self     :						List[Any]   )  ->  int:
							lowercase_ : Optional[Any]							       =   self.tool(text=A    ,						question='''What did Hugging Face do in April 2021?'''   )
							self.assertEqual(A    ,						'''launched the BigScience Research Workshop'''   )






					def   A  (				self     :						List[str]   )  ->  Optional[int]:
							lowercase_ : int							       =   self.remote_tool(text=A    ,						question='''What did Hugging Face do in April 2021?'''   )
							self.assertEqual(A    ,						'''launched the BigScience Research Workshop'''   )



