


# 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      UpperCAmelCase_ :

   '''simple docstring'''




   __A			:				Optional[jnp.ndarray]							=		None
   __A			:				Optional[jnp.ndarray]							=		None
   __A			:				Optional[jnp.ndarray]							=		None






   @classmethod
   def _snake_case   (		cls			):






     """simple docstring"""





     return cls()



@dataclass
class      UpperCAmelCase_ ( UpperCamelCase   ):

   '''simple docstring'''




   __A			:				ScoreSdeVeSchedulerState
   __A			:				jnp.ndarray
   __A			:				Optional[jnp.ndarray]							=		None



class      UpperCAmelCase_ ( UpperCamelCase		,			UpperCamelCase   ):

   '''simple docstring'''




   @property
   def _snake_case   (		self			):






     """simple docstring"""





     return True






   @register_to_config
   def __init__(		self						,    __A = 2000						,    __A = 0.15						,    __A = 0.01						,    __A = 1348.0						,    __A = 1e-5						,    __A = 1						,    ):






     """simple docstring"""





     pass






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    ScoreSdeVeSchedulerState.create()
     return self.set_sigmas(
         __A						,    self.config.num_train_timesteps						,    self.config.sigma_min						,    self.config.sigma_max						,    self.config.sampling_eps						,    )






   def _snake_case   (		self						,    __A						,    __A						,    __A = ()						,    __A = None			):






     """simple docstring"""





     lowerCamelCase			: List[str]   =    sampling_eps if sampling_eps is not None else self.config.sampling_eps

     lowerCamelCase			: str   =    jnp.linspace(1						,    __A						,    __A			)
     return state.replace(timesteps=__A			)






   def _snake_case   (		self						,    __A						,    __A						,    __A = None						,    __A = None						,    __A = None						,    ):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    sigma_min if sigma_min is not None else self.config.sigma_min
     lowerCamelCase			: int   =    sigma_max if sigma_max is not None else self.config.sigma_max
     lowerCamelCase			: int   =    sampling_eps if sampling_eps is not None else self.config.sampling_eps
     if state.timesteps is None:
       lowerCamelCase			: Optional[Any]   =    self.set_timesteps(__A						,    __A						,    __A			)

     lowerCamelCase			: Dict   =    jnp.exp(jnp.linspace(jnp.log(__A			)						,    jnp.log(__A			)						,    __A			)			)
     lowerCamelCase			: List[Any]   =    jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]			)

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






   def _snake_case   (		self						,    __A						,    __A						,    __A			):






     """simple docstring"""





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






   def _snake_case   (		self						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A = True						,    ):






     """simple docstring"""





     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			: Optional[int]   =    (timestep * (len(state.timesteps			) - 1)).long()

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

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






   def _snake_case   (		self						,    __A						,    __A						,    __A						,    __A						,    __A = True						,    ):






     """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
     lowerCamelCase			: List[Any]   =    random.split(__A						,    num=1			)
     lowerCamelCase			: Union[str, Any]   =    random.normal(key=__A						,    shape=sample.shape			)

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

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

     if not return_dict:
       return (prev_sample, state)

     return FlaxSdeVeOutput(prev_sample=__A						,    state=__A			)






   def __len__(		self			):






     """simple docstring"""





     return self.config.num_train_timesteps







import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
@require_vision
class      UpperCAmelCase_ ( unittest.TestCase   ):

   '''simple docstring'''




   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Tuple   =    tempfile.mkdtemp()

     lowerCamelCase			: List[Any]   =    CLIPImageProcessor()
     lowerCamelCase			: str   =    BertTokenizer.from_pretrained(
         "hf-internal-testing/tiny-random-BertModel"						,    model_input_names=["input_ids", "attention_mask"]			)

     lowerCamelCase			: List[str]   =    GitProcessor(__A						,    __A			)

     processor.save_pretrained(self.tmpdirname			)






   def _snake_case   (		self						,    **__A			):






     """simple docstring"""





     return AutoProcessor.from_pretrained(self.tmpdirname						,    **__A			).tokenizer






   def _snake_case   (		self						,    **__A			):






     """simple docstring"""





     return AutoProcessor.from_pretrained(self.tmpdirname						,    **__A			).image_processor






   def _snake_case   (		self			):






     """simple docstring"""





     shutil.rmtree(self.tmpdirname			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: str   =    [np.random.randint(255						,    size=(3, 30, 400)						,    dtype=np.uinta			)]

     lowerCamelCase			: Any   =    [Image.fromarray(np.moveaxis(__A						,    0						,    -1			)			) for x in image_inputs]

     return image_inputs






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Tuple   =    GitProcessor(tokenizer=self.get_tokenizer()						,    image_processor=self.get_image_processor()			)
     processor.save_pretrained(self.tmpdirname			)

     lowerCamelCase			: Optional[Any]   =    self.get_tokenizer(bos_token="(BOS)"						,    eos_token="(EOS)"			)
     lowerCamelCase			: List[str]   =    self.get_image_processor(do_normalize=__A						,    padding_value=1.0			)

     lowerCamelCase			: Dict   =    GitProcessor.from_pretrained(
         self.tmpdirname						,    bos_token="(BOS)"						,    eos_token="(EOS)"						,    do_normalize=__A						,    padding_value=1.0			)

     self.assertEqual(processor.tokenizer.get_vocab()						,    tokenizer_add_kwargs.get_vocab()			)
     self.assertIsInstance(processor.tokenizer						,    __A			)

     self.assertEqual(processor.image_processor.to_json_string()						,    image_processor_add_kwargs.to_json_string()			)
     self.assertIsInstance(processor.image_processor						,    __A			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Any   =    self.get_image_processor()
     lowerCamelCase			: Optional[int]   =    self.get_tokenizer()

     lowerCamelCase			: Optional[Any]   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

     lowerCamelCase			: List[str]   =    self.prepare_image_inputs()

     lowerCamelCase			: str   =    image_processor(__A						,    return_tensors="np"			)
     lowerCamelCase			: Union[str, Any]   =    processor(images=__A						,    return_tensors="np"			)

     for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()						,    input_processor[key].sum()						,    delta=1e-2			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: int   =    self.get_image_processor()
     lowerCamelCase			: Dict   =    self.get_tokenizer()

     lowerCamelCase			: Optional[int]   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

     lowerCamelCase			: int   =    "lower newer"

     lowerCamelCase			: str   =    processor(text=__A			)

     lowerCamelCase			: Union[str, Any]   =    tokenizer(__A						,    return_token_type_ids=__A			)

     for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]						,    encoded_processor[key]			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Dict   =    self.get_image_processor()
     lowerCamelCase			: Optional[int]   =    self.get_tokenizer()

     lowerCamelCase			: List[str]   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

     lowerCamelCase			: str   =    "lower newer"
     lowerCamelCase			: Tuple   =    self.prepare_image_inputs()

     lowerCamelCase			: str   =    processor(text=__A						,    images=__A			)

     self.assertListEqual(list(inputs.keys()			)						,    ["input_ids", "attention_mask", "pixel_values"]			)

     # test if it raises when no input is passed
     with pytest.raises(__A			):
       processor()






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Union[str, Any]   =    self.get_image_processor()
     lowerCamelCase			: Tuple   =    self.get_tokenizer()

     lowerCamelCase			: Tuple   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

     lowerCamelCase			: Optional[Any]   =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     lowerCamelCase			: str   =    processor.batch_decode(__A			)
     lowerCamelCase			: int   =    tokenizer.batch_decode(__A			)

     self.assertListEqual(__A						,    __A			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    self.get_image_processor()
     lowerCamelCase			: int   =    self.get_tokenizer()

     lowerCamelCase			: Optional[int]   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

     lowerCamelCase			: Optional[Any]   =    "lower newer"
     lowerCamelCase			: List[Any]   =    self.prepare_image_inputs()

     lowerCamelCase			: Optional[int]   =    processor(text=__A						,    images=__A			)

     # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
     self.assertListEqual(list(inputs.keys()			)						,    ["input_ids", "attention_mask", "pixel_values"]			)



