





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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    _A   :
		lowercase__:      Optional[jnp.ndarray]							    =      None
		lowercase__:      Optional[jnp.ndarray]							    =      None
		lowercase__:      Optional[jnp.ndarray]							    =      None

		@classmethod
		def   lowercase__    (      cls				:      Union[str, Any] )				->						Optional[int]:


					"""simple docstring"""

					return cls()





@dataclass
class    _A   (				UpperCAmelCase__		):
		lowercase__:      ScoreSdeVeSchedulerState
		lowercase__:      jnp.ndarray
		lowercase__:      Optional[jnp.ndarray]							    =      None





class    _A   (				UpperCAmelCase__	,				UpperCAmelCase__		):

		@property
		def   lowercase__    (      self				:      Any )				->						int:


					"""simple docstring"""

					return True

		@register_to_config
		def __init__(      self				:      Union[str, Any]     ,			__magic_name__				:      int = 20_00     ,			__magic_name__				:      Tuple = 0.15     ,			__magic_name__				:      List[Any] = 0.01     ,			__magic_name__				:      Optional[int] = 13_48.0     ,			__magic_name__				:      Tuple = 1E-5     ,			__magic_name__				:      Dict = 1     ,			)				->						Dict:


					"""simple docstring"""

					pass

		def   lowercase__    (      self				:      Any )				->						Tuple:


					"""simple docstring"""

					__snake_case				:			Tuple       			=    ScoreSdeVeSchedulerState.create()
					return self.set_sigmas(
					    lowerCamelCase__     ,			self.config.num_train_timesteps     ,			self.config.sigma_min     ,			self.config.sigma_max     ,			self.config.sampling_eps     ,			)

		def   lowercase__    (      self				:      Tuple     ,			__magic_name__				:      Union[str, Any]     ,			__magic_name__				:      Tuple     ,			__magic_name__				:      str = ()     ,			__magic_name__				:      Tuple = None )				->						List[str]:


					"""simple docstring"""

					__snake_case				:			str       			=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

					__snake_case				:			List[str]       			=    jnp.linspace(1     ,			lowerCamelCase__     ,			lowerCamelCase__ )
					return state.replace(timesteps=lowerCamelCase__ )

		def   lowercase__    (      self				:      Optional[int]     ,			__magic_name__				:      Tuple     ,			__magic_name__				:      str     ,			__magic_name__				:      List[Any] = None     ,			__magic_name__				:      Any = None     ,			__magic_name__				:      Optional[int] = None     ,			)				->						Optional[Any]:


					"""simple docstring"""

					__snake_case				:			Dict       			=    sigma_min if sigma_min is not None else self.config.sigma_min
					__snake_case				:			Dict       			=    sigma_max if sigma_max is not None else self.config.sigma_max
					__snake_case				:			int       			=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
					if state.timesteps is None:
								__snake_case				:			Dict       			=    self.set_timesteps(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__ )

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

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

		def   lowercase__    (      self				:      Any     ,			__magic_name__				:      Union[str, Any]     ,			__magic_name__				:      Tuple     ,			__magic_name__				:      str )				->						Any:


					"""simple docstring"""

					return jnp.where(timesteps == 0     ,			jnp.zeros_like(lowerCamelCase__ )     ,			state.discrete_sigmas[timesteps - 1] )

		def   lowercase__    (      self				:      List[str]     ,			__magic_name__				:      Any     ,			__magic_name__				:      str     ,			__magic_name__				:      int     ,			__magic_name__				:      List[Any]     ,			__magic_name__				:      Optional[Any]     ,			__magic_name__				:      int = True     ,			)				->						List[str]:


					"""simple docstring"""

					if state.timesteps is None:
								raise ValueError(
								    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )

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

					__snake_case				:			Optional[int]       			=    state.discrete_sigmas[timesteps]
					__snake_case				:			int       			=    self.get_adjacent_sigma(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__ )
					__snake_case				:			Optional[Any]       			=    jnp.zeros_like(lowerCamelCase__ )
					__snake_case				:			int       			=    (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
					__snake_case				:			Any       			=    diffusion.flatten()
					__snake_case				:			Union[str, Any]       			=    broadcast_to_shape_from_left(lowerCamelCase__     ,			sample.shape )
					__snake_case				:			Union[str, Any]       			=    drift - diffusion**2 * model_output

					#  equation 6: sample noise for the diffusion term of
					__snake_case				:			Optional[Any]       			=    random.split(lowerCamelCase__     ,			num=1 )
					__snake_case				:			Union[str, Any]       			=    random.normal(key=lowerCamelCase__     ,			shape=sample.shape )
					__snake_case				:			Dict       			=    sample - drift  # subtract because `dt` is a small negative timestep
					# TODO is the variable diffusion the correct scaling term for the noise?
					__snake_case				:			Any       			=    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=lowerCamelCase__     ,			prev_sample_mean=lowerCamelCase__     ,			state=lowerCamelCase__ )

		def   lowercase__    (      self				:      Union[str, Any]     ,			__magic_name__				:      List[str]     ,			__magic_name__				:      Tuple     ,			__magic_name__				:      Union[str, Any]     ,			__magic_name__				:      Dict     ,			__magic_name__				:      Dict = True     ,			)				->						List[Any]:


					"""simple docstring"""

					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
					__snake_case				:			Tuple       			=    random.split(lowerCamelCase__     ,			num=1 )
					__snake_case				:			Any       			=    random.normal(key=lowerCamelCase__     ,			shape=sample.shape )

					# compute step size from the model_output, the noise, and the snr
					__snake_case				:			Tuple       			=    jnp.linalg.norm(lowerCamelCase__ )
					__snake_case				:			Any       			=    jnp.linalg.norm(lowerCamelCase__ )
					__snake_case				:			Dict       			=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
					__snake_case				:			Dict       			=    step_size * jnp.ones(sample.shape[0] )

					# compute corrected sample: model_output term and noise term
					__snake_case				:			List[Any]       			=    step_size.flatten()
					__snake_case				:			str       			=    broadcast_to_shape_from_left(lowerCamelCase__     ,			sample.shape )
					__snake_case				:			str       			=    sample + step_size * model_output
					__snake_case				:			str       			=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

					if not return_dict:
								return (prev_sample, state)

					return FlaxSdeVeOutput(prev_sample=lowerCamelCase__     ,			state=lowerCamelCase__ )

		def __len__(      self				:      List[str] )				->						int:


					"""simple docstring"""

					return self.config.num_train_timesteps









'''simple docstring'''






import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




@require_tokenizers
class    _A   (				unittest.TestCase		):
		lowercase__:      Tuple							    =      EsmTokenizer

		def   lowercase__    (      self				:      Tuple )				->						Optional[int]:


					"""simple docstring"""

					super().setUp()
					__snake_case				:			str       			=    tempfile.mkdtemp()
					# fmt: off
					__snake_case				:			List[str]       			=    ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
					# fmt: on
					__snake_case				:			Tuple       			=    os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES["""vocab_file"""] )
					with open(self.vocab_file     ,			"""w"""     ,			encoding="""utf-8""" ) as vocab_writer:
								vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )

		def   lowercase__    (      self				:      List[Any]     ,			**__magic_name__				:      Tuple )				->						List[PreTrainedTokenizerBase]:


					"""simple docstring"""

					return [self.get_tokenizer(**__magic_name__ )]

		def   lowercase__    (      self				:      int     ,			**__magic_name__				:      Dict )				->						PreTrainedTokenizer:


					"""simple docstring"""

					return self.tokenizer_class.from_pretrained(self.tmpdirname     ,			**__magic_name__ )

		def   lowercase__    (      self				:      Union[str, Any] )				->						Dict:


					"""simple docstring"""

					__snake_case				:			Dict       			=    self.tokenizer_class(self.vocab_file )

					__snake_case				:			Optional[int]       			=    tokenizer.tokenize("""LAGVS""" )
					self.assertListEqual(__magic_name__     ,			["""L""", """A""", """G""", """V""", """S"""] )
					self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__ )     ,			[4, 5, 6, 7, 8] )

		def   lowercase__    (      self				:      List[str] )				->						Optional[Any]:


					"""simple docstring"""

					__snake_case				:			str       			=    self.tokenizer_class(self.vocab_file )

					__snake_case				:			Optional[int]       			=    """LAGVS"""
					self.assertListEqual(tokenizer.encode(__magic_name__ )     ,			[0, 4, 5, 6, 7, 8, 2] )

		def   lowercase__    (      self				:      str )				->						Optional[Any]:


					"""simple docstring"""

					__snake_case				:			Dict       			=    self.tokenizer_class(self.vocab_file )

					__snake_case				:			Tuple       			=    ["""LAGVS""", """WCB"""]
					__snake_case				:			Dict       			=    tokenizer(__magic_name__     ,			padding=__magic_name__ )["""input_ids"""]

					self.assertListEqual(__magic_name__     ,			[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]] )

		def   lowercase__    (      self				:      str )				->						List[Any]:


					"""simple docstring"""

					__snake_case				:			int       			=    self.tokenizer_class(self.vocab_file )

					__snake_case				:			List[Any]       			=    ["""LAGVS""", """WCB"""]
					__snake_case				:			Any       			=    tokenizer(__magic_name__     ,			padding=__magic_name__ )["""input_ids"""]

					self.assertListEqual(__magic_name__     ,			[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]] )

		def   lowercase__    (      self				:      List[str] )				->						Tuple:


					"""simple docstring"""

					__snake_case				:			Any       			=    self.get_tokenizers(fast=__magic_name__ )
					for tokenizer in tokenizers:
								with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
											__snake_case				:			int       			=    """<unk>"""
											__snake_case				:			Optional[Any]       			=    """<mask>"""

											__snake_case				:			Tuple       			=    tokenizer.tokenize(__magic_name__ )
											__snake_case				:			List[Any]       			=    tokenizer.tokenize(__magic_name__ )

											self.assertEqual(len(__magic_name__ )     ,			1 )
											self.assertEqual(len(__magic_name__ )     ,			1 )
											self.assertEqual(token_a[0]     ,			__magic_name__ )
											self.assertEqual(token_a[0]     ,			__magic_name__ )


