




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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left




@flax.struct.dataclass
class 	UpperCamelCase__			:

				'''simple docstring'''



				lowerCamelCase_   :			Tuple			  =   None
				lowerCamelCase_   :			Tuple			  =   None
				lowerCamelCase_   :			str			  =   None







				@classmethod
				def 					_lowercase  (					cls  )  ->   List[str]:
											return cls()




@dataclass
class 	UpperCamelCase__			(UpperCAmelCase_  ):

				'''simple docstring'''



				lowerCamelCase_   :			int			  =   4_2
				lowerCamelCase_   :			Any			  =   4_2
				lowerCamelCase_   :			Optional[Any]			  =   None




class 	UpperCamelCase__			(UpperCAmelCase_					,       UpperCAmelCase_  ):

				'''simple docstring'''



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







				@register_to_config
				def __init__(					self  ,						UpperCamelCase__ = 2000  ,						UpperCamelCase__ = 0.15  ,						UpperCamelCase__ = 0.01  ,						UpperCamelCase__ = 1348.0  ,						UpperCamelCase__ = 1e-5  ,						UpperCamelCase__ = 1  ,						)  ->   Dict:
											pass







				def 					_lowercase  (					self  )  ->   Any:
											lowerCamelCase     :   Any    				=      ScoreSdeVeSchedulerState.create()
											return self.set_sigmas(
											    __UpperCamelCase  ,						self.config.num_train_timesteps  ,						self.config.sigma_min  ,						self.config.sigma_max  ,						self.config.sampling_eps  ,						)







				def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ = ()  ,						UpperCamelCase__ = None  )  ->   Any:
											lowerCamelCase     :   int    				=      sampling_eps if sampling_eps is not None else self.config.sampling_eps

											lowerCamelCase     :   Any    				=      jnp.linspace(1  ,						__UpperCamelCase  ,						__UpperCamelCase  )
											return state.replace(timesteps=__UpperCamelCase  )







				def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = None  ,						)  ->   List[str]:
											lowerCamelCase     :   Union[str, Any]    				=      sigma_min if sigma_min is not None else self.config.sigma_min
											lowerCamelCase     :   Optional[int]    				=      sigma_max if sigma_max is not None else self.config.sigma_max
											lowerCamelCase     :   Union[str, Any]    				=      sampling_eps if sampling_eps is not None else self.config.sampling_eps
											if state.timesteps is None:
																		lowerCamelCase     :   Tuple    				=      self.set_timesteps(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  )

											lowerCamelCase     :   Optional[Any]    				=      jnp.exp(jnp.linspace(jnp.log(__UpperCamelCase  )  ,						jnp.log(__UpperCamelCase  )  ,						__UpperCamelCase  )  )
											lowerCamelCase     :   Union[str, Any]    				=      jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]  )

											return state.replace(discrete_sigmas=__UpperCamelCase  ,						sigmas=__UpperCamelCase  )







				def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   Dict:
											return jnp.where(timesteps == 0  ,						jnp.zeros_like(__UpperCamelCase  )  ,						state.discrete_sigmas[timesteps - 1]  )







				def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ = True  ,						)  ->   Any:
											if state.timesteps is None:
																		raise ValueError(
																		    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"  )

											lowerCamelCase     :   Optional[int]    				=      timestep * jnp.ones(
											    sample.shape[0]  ,						)
											lowerCamelCase     :   int    				=      (timestep * (len(state.timesteps  ) - 1)).long()

											lowerCamelCase     :   int    				=      state.discrete_sigmas[timesteps]
											lowerCamelCase     :   Union[str, Any]    				=      self.get_adjacent_sigma(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  )
											lowerCamelCase     :   int    				=      jnp.zeros_like(__UpperCamelCase  )
											lowerCamelCase     :   Any    				=      (sigma**2 - adjacent_sigma**2) ** 0.5

											# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
											# also equation 47 shows the analog from SDE models to ancestral sampling methods
											lowerCamelCase     :   List[str]    				=      diffusion.flatten()
											lowerCamelCase     :   str    				=      broadcast_to_shape_from_left(__UpperCamelCase  ,						sample.shape  )
											lowerCamelCase     :   Dict    				=      drift - diffusion**2 * model_output

											#  equation 6: sample noise for the diffusion term of
											lowerCamelCase     :   Union[str, Any]    				=      random.split(__UpperCamelCase  ,						num=1  )
											lowerCamelCase     :   Dict    				=      random.normal(key=__UpperCamelCase  ,						shape=sample.shape  )
											lowerCamelCase     :   Union[str, Any]    				=      sample - drift  # subtract because `dt` is a small negative timestep
											# TODO is the variable diffusion the correct scaling term for the noise?
											lowerCamelCase     :   int    				=      prev_sample_mean + diffusion * noise  # add impact of diffusion field g

											if not return_dict:
																		return (prev_sample, prev_sample_mean, state)

											return FlaxSdeVeOutput(prev_sample=__UpperCamelCase  ,						prev_sample_mean=__UpperCamelCase  ,						state=__UpperCamelCase  )







				def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ = True  ,						)  ->   Optional[int]:
											if state.timesteps is None:
																		raise ValueError(
																		    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"  )

											# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
											# sample noise for correction
											lowerCamelCase     :   str    				=      random.split(__UpperCamelCase  ,						num=1  )
											lowerCamelCase     :   int    				=      random.normal(key=__UpperCamelCase  ,						shape=sample.shape  )

											# compute step size from the model_output, the noise, and the snr
											lowerCamelCase     :   List[str]    				=      jnp.linalg.norm(__UpperCamelCase  )
											lowerCamelCase     :   Dict    				=      jnp.linalg.norm(__UpperCamelCase  )
											lowerCamelCase     :   Union[str, Any]    				=      (self.config.snr * noise_norm / grad_norm) ** 2 * 2
											lowerCamelCase     :   int    				=      step_size * jnp.ones(sample.shape[0]  )

											# compute corrected sample: model_output term and noise term
											lowerCamelCase     :   Tuple    				=      step_size.flatten()
											lowerCamelCase     :   Tuple    				=      broadcast_to_shape_from_left(__UpperCamelCase  ,						sample.shape  )
											lowerCamelCase     :   int    				=      sample + step_size * model_output
											lowerCamelCase     :   Any    				=      prev_sample_mean + ((step_size * 2) ** 0.5) * noise

											if not return_dict:
																		return (prev_sample, state)

											return FlaxSdeVeOutput(prev_sample=__UpperCamelCase  ,						state=__UpperCamelCase  )







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












import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       import torch

       from transformers import GPTaLMHeadModel




@require_torch
@require_sentencepiece
@require_tokenizers
class 	UpperCamelCase__			(unittest.TestCase  ):

    '''simple docstring'''



    @slow
    @unittest.skip("Model is not available."  )
    def 					_lowercase  (					self  )  ->   List[Any]:
           lowerCamelCase     :   List[Any]    				=      "nvidia/megatron-gpt2-345m/"
           if "MYDIR" in os.environ:
                  lowerCamelCase     :   Union[str, Any]    				=      os.path.join(os.environ["MYDIR"]  ,						UpperCamelCase__  )
           lowerCamelCase     :   Any    				=      GPTaLMHeadModel.from_pretrained(UpperCamelCase__  )
           model.to(UpperCamelCase__  )
           model.half()

           lowerCamelCase     :   Dict    				=      torch.tensor(
               [[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]  ,						device=UpperCamelCase__  ,						dtype=torch.long  ,						)

           with torch.no_grad():
                  lowerCamelCase     :   Any    				=      model(UpperCamelCase__  ).logits

           lowerCamelCase     :   Union[str, Any]    				=      torch.Size((1, 9, 5_0257)  )
           self.assertEqual(output.shape  ,						UpperCamelCase__  )

           lowerCamelCase     :   List[str]    				=      torch.tensor(
               [
                   4.9414,
                   -0.2920,
                   -1.2148,
                   -4.0273,
                   -0.5161,
                   -5.2109,
                   -1.2412,
                   -1.8301,
                   -1.7734,
                   -4.7148,
                   -0.2317,
                   -1.0811,
                   -2.1777,
                   0.4141,
                   -3.7969,
                   -4.0586,
                   -2.5332,
                   -3.3809,
                   4.3867,
               ]  ,						device=UpperCamelCase__  ,						dtype=torch.half  ,						)

           for i in range(19  ):
                  lowerCamelCase				,   lowerCamelCase     :   Optional[int]    				=      8 * i // 17, 2792 * i  # along the diagonal
                  lowerCamelCase				,   lowerCamelCase     :   Tuple    				=      output[0, r, c], expected_diag[i]
                  lowerCamelCase     :   List[str]    				=      F'''row={r} col={c} computed={computed} expected={expected}'''
                  self.assertAlmostEqual(UpperCamelCase__  ,						UpperCamelCase__  ,						delta=1e-4  ,						msg=UpperCamelCase__  )






