
"""simple docstring"""




import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
    from transformers import (
        FlaxBertModel,
        FlaxCLIPVisionModel,
        FlaxVisionTextDualEncoderModel,
        FlaxViTModel,
        VisionTextDualEncoderConfig,
        VisionTextDualEncoderProcessor,
    )
    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )


if is_torch_available():
    import torch

    from transformers import VisionTextDualEncoderModel

if is_vision_available():
    from PIL import Image



def 							a__ (				lowerCAmelCase	:       Dict		):






     '''simple docstring'''
     if isinstance(lowerCAmelCase       ,    collections.abc.Iterable		):
          return x
     return (x, x)







@require_flax
class   _lowercase						:


     '''simple docstring'''





     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					Any:
          pass



     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          pass



     def  lowerCAmelCase__       (      self  )->					Any:
          pass



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Dict:
          UpperCAmelCase__							: List[str] 							=						np.abs((a - b)  ).max()
          self.assertLessEqual(__UpperCamelCase ,			__UpperCamelCase ,			F"Difference between torch and flax is {diff} (>= {tol})."  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=None ,			**__UpperCamelCase  )->					List[Any]:
          UpperCAmelCase__							: Dict 							=						VisionTextDualEncoderConfig.from_vision_text_configs(__UpperCamelCase ,			__UpperCamelCase  )

          UpperCAmelCase__							: int 							=						FlaxVisionTextDualEncoderModel(__UpperCamelCase  )

          UpperCAmelCase__							: Union[str, Any] 							=						model(input_ids=__UpperCamelCase ,			pixel_values=__UpperCamelCase ,			attention_mask=__UpperCamelCase  )

          self.assertEqual(output["text_embeds"].shape ,			(input_ids.shape[0], config.projection_dim)  )
          self.assertEqual(output["image_embeds"].shape ,			(pixel_values.shape[0], config.projection_dim)  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=None ,			**__UpperCamelCase  )->					Optional[Any]:
          UpperCAmelCase__							,	UpperCAmelCase__							: Dict 							=						self.get_vision_text_model(__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: str 							=						{"vision_model": vision_model, "text_model": text_model}
          UpperCAmelCase__							: Any 							=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__UpperCamelCase  )

          UpperCAmelCase__							: Optional[Any] 							=						model(input_ids=__UpperCamelCase ,			pixel_values=__UpperCamelCase ,			attention_mask=__UpperCamelCase  )

          self.assertEqual(output["text_embeds"].shape ,			(input_ids.shape[0], model.config.projection_dim)  )
          self.assertEqual(output["image_embeds"].shape ,			(pixel_values.shape[0], model.config.projection_dim)  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=None ,			**__UpperCamelCase  )->					List[Any]:
          UpperCAmelCase__							,	UpperCAmelCase__							: Dict 							=						self.get_vision_text_model(__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						{"vision_model": vision_model, "text_model": text_model}
          UpperCAmelCase__							: List[Any] 							=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__UpperCamelCase  )

          UpperCAmelCase__							: Tuple 							=						model(input_ids=__UpperCamelCase ,			pixel_values=__UpperCamelCase ,			attention_mask=__UpperCamelCase  )
          UpperCAmelCase__							: Optional[int] 							=						output[0]

          with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(__UpperCamelCase  )
               UpperCAmelCase__							: Union[str, Any] 							=						FlaxVisionTextDualEncoderModel.from_pretrained(__UpperCamelCase  )

               UpperCAmelCase__							: Optional[Any] 							=						model(input_ids=__UpperCamelCase ,			pixel_values=__UpperCamelCase ,			attention_mask=__UpperCamelCase  )
               UpperCAmelCase__							: Dict 							=						after_output[0]
               UpperCAmelCase__							: Optional[int] 							=						np.amax(np.abs(out_a - out_a  )  )
               self.assertLessEqual(__UpperCamelCase ,			1E-3  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=None ,			**__UpperCamelCase  )->					List[str]:
          UpperCAmelCase__							,	UpperCAmelCase__							: Optional[Any] 							=						self.get_vision_text_model(__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: str 							=						{"vision_model": vision_model, "text_model": text_model}
          UpperCAmelCase__							: List[Any] 							=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__UpperCamelCase  )

          UpperCAmelCase__							: Tuple 							=						model(
              input_ids=__UpperCamelCase ,			pixel_values=__UpperCamelCase ,			attention_mask=__UpperCamelCase ,			output_attentions=__UpperCamelCase  )

          UpperCAmelCase__							: Optional[int] 							=						output.vision_model_output.attentions
          self.assertEqual(len(__UpperCamelCase  ) ,			vision_config.num_hidden_layers  )

          # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
          UpperCAmelCase__							: List[Any] 							=						to_atuple(vision_model.config.image_size  )
          UpperCAmelCase__							: int 							=						to_atuple(vision_model.config.patch_size  )
          UpperCAmelCase__							: str 							=						(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
          UpperCAmelCase__							: str 							=						num_patches + 1
          self.assertEqual(vision_attentions[0].shape[-3:] ,			(vision_config.num_attention_heads, seq_len, seq_len)  )

          UpperCAmelCase__							: Tuple 							=						output.text_model_output.attentions
          self.assertEqual(len(__UpperCamelCase  ) ,			text_config.num_hidden_layers  )

          self.assertEqual(
              text_attentions[0].shape[-3:] ,			(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) ,			)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					str:
          pt_model.to(__UpperCamelCase  )
          pt_model.eval()

          # prepare inputs
          UpperCAmelCase__							: Union[str, Any] 							=						inputs_dict
          UpperCAmelCase__							: Tuple 							=						{k: torch.tensor(v.tolist()  ) for k, v in flax_inputs.items()}

          with torch.no_grad():
               UpperCAmelCase__							: int 							=						pt_model(**__UpperCamelCase  ).to_tuple()

          UpperCAmelCase__							: List[Any] 							=						fx_model(**__UpperCamelCase  ).to_tuple()
          self.assertEqual(len(__UpperCamelCase  ) ,			len(__UpperCamelCase  ) ,			"Output lengths differ between Flax and PyTorch"  )
          for fx_output, pt_output in zip(fx_outputs[:4] ,			pt_outputs[:4]  ):
               self.assert_almost_equals(__UpperCamelCase ,			pt_output.numpy() ,			4E-2  )

          # PT -> Flax
          with tempfile.TemporaryDirectory() as tmpdirname:
               pt_model.save_pretrained(__UpperCamelCase  )
               UpperCAmelCase__							: Union[str, Any] 							=						FlaxVisionTextDualEncoderModel.from_pretrained(__UpperCamelCase ,			from_pt=__UpperCamelCase  )

          UpperCAmelCase__							: Any 							=						fx_model_loaded(**__UpperCamelCase  ).to_tuple()
          self.assertEqual(len(__UpperCamelCase  ) ,			len(__UpperCamelCase  ) ,			"Output lengths differ between Flax and PyTorch"  )
          for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4] ,			pt_outputs[:4]  ):
               self.assert_almost_equals(__UpperCamelCase ,			pt_output.numpy() ,			4E-2  )

          # Flax -> PT
          with tempfile.TemporaryDirectory() as tmpdirname:
               fx_model.save_pretrained(__UpperCamelCase  )
               UpperCAmelCase__							: str 							=						VisionTextDualEncoderModel.from_pretrained(__UpperCamelCase ,			from_flax=__UpperCamelCase  )

          pt_model_loaded.to(__UpperCamelCase  )
          pt_model_loaded.eval()

          with torch.no_grad():
               UpperCAmelCase__							: List[Any] 							=						pt_model_loaded(**__UpperCamelCase  ).to_tuple()

          self.assertEqual(len(__UpperCamelCase  ) ,			len(__UpperCamelCase  ) ,			"Output lengths differ between Flax and PyTorch"  )
          for fx_output, pt_output_loaded in zip(fx_outputs[:4] ,			pt_outputs_loaded[:4]  ):
               self.assert_almost_equals(__UpperCamelCase ,			pt_output_loaded.numpy() ,			4E-2  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					List[str]:
          UpperCAmelCase__							: Union[str, Any] 							=						VisionTextDualEncoderConfig.from_vision_text_configs(__UpperCamelCase ,			__UpperCamelCase  )

          UpperCAmelCase__							: Dict 							=						VisionTextDualEncoderModel(__UpperCamelCase  )
          UpperCAmelCase__							: List[Any] 							=						FlaxVisionTextDualEncoderModel(__UpperCamelCase  )

          UpperCAmelCase__							: Tuple 							=						convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,			__UpperCamelCase  )
          UpperCAmelCase__							: Tuple 							=						fx_state

          self.check_pt_flax_equivalence(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					List[str]:
          UpperCAmelCase__							: Union[str, Any] 							=						VisionTextDualEncoderConfig.from_vision_text_configs(__UpperCamelCase ,			__UpperCamelCase  )

          UpperCAmelCase__							: List[str] 							=						VisionTextDualEncoderModel(__UpperCamelCase  )
          UpperCAmelCase__							: str 							=						FlaxVisionTextDualEncoderModel(__UpperCamelCase  )

          UpperCAmelCase__							: str 							=						load_flax_weights_in_pytorch_model(__UpperCamelCase ,			fx_model.params  )

          self.check_pt_flax_equivalence(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          UpperCAmelCase__							: Union[str, Any] 							=						self.prepare_config_and_inputs()
          self.check_model_from_pretrained_configs(**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          UpperCAmelCase__							: Optional[Any] 							=						self.prepare_config_and_inputs()
          self.check_vision_text_dual_encoder_from_pretrained(**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          UpperCAmelCase__							: Optional[int] 							=						self.prepare_config_and_inputs()
          self.check_save_load(**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					str:
          UpperCAmelCase__							: List[str] 							=						self.prepare_config_and_inputs()
          self.check_vision_text_output_attention(**__UpperCamelCase  )



     @is_pt_flax_cross_test
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          UpperCAmelCase__							: Optional[Any] 							=						self.prepare_config_and_inputs()
          UpperCAmelCase__							: Optional[int] 							=						config_inputs_dict.pop("vision_config"  )
          UpperCAmelCase__							: Optional[Any] 							=						config_inputs_dict.pop("text_config"  )

          UpperCAmelCase__							: Union[str, Any] 							=						config_inputs_dict

          self.check_equivalence_pt_to_flax(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )
          self.check_equivalence_flax_to_pt(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     @slow
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          UpperCAmelCase__							,	UpperCAmelCase__							: Any 							=						self.get_pretrained_model_and_inputs()

          UpperCAmelCase__							: str 							=						model_a(**__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						outputs[0]

          with tempfile.TemporaryDirectory() as tmp_dirname:
               model_a.save_pretrained(__UpperCamelCase  )
               UpperCAmelCase__							: List[str] 							=						FlaxVisionTextDualEncoderModel.from_pretrained(__UpperCamelCase  )

               UpperCAmelCase__							: Union[str, Any] 							=						model_a(**__UpperCamelCase  )
               UpperCAmelCase__							: List[str] 							=						after_outputs[0]
               UpperCAmelCase__							: Optional[Any] 							=						np.amax(np.abs(out_a - out_a  )  )
               self.assertLessEqual(__UpperCamelCase ,			1E-5  )







@require_flax
class   _lowercase						(       lowerCAmelCase_	,   unittest.TestCase				):


     '''simple docstring'''





     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: Any 							=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
              "hf-internal-testing/tiny-random-vit" ,			"hf-internal-testing/tiny-bert" ,			vision_from_pt=__UpperCamelCase ,			text_from_pt=__UpperCamelCase ,			)
          UpperCAmelCase__							: Tuple 							=						13
          UpperCAmelCase__							: Union[str, Any] 							=						floats_tensor(
              [
                  batch_size,
                  model.config.vision_config.num_channels,
                  model.config.vision_config.image_size,
                  model.config.vision_config.image_size,
              ]  )
          UpperCAmelCase__							: List[str] 							=						ids_tensor([batch_size, 4] ,			model.config.text_config.vocab_size  )
          UpperCAmelCase__							: int 							=						random_attention_mask([batch_size, 4]  )
          UpperCAmelCase__							: Optional[int] 							=						{"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

          return model, inputs



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					Dict:
          UpperCAmelCase__							: int 							=						FlaxViTModel(__UpperCamelCase  )
          UpperCAmelCase__							: Optional[Any] 							=						FlaxBertModel(__UpperCamelCase  )
          return vision_model, text_model



     def  lowerCAmelCase__       (      self  )->					str:
          UpperCAmelCase__							: Tuple 							=						FlaxViTModelTester(self  )
          UpperCAmelCase__							: Tuple 							=						FlaxBertModelTester(self  )
          UpperCAmelCase__							: List[Any] 							=						vit_model_tester.prepare_config_and_inputs()
          UpperCAmelCase__							: Union[str, Any] 							=						bert_model_tester.prepare_config_and_inputs()

          UpperCAmelCase__							,	UpperCAmelCase__							: Dict 							=						vision_config_and_inputs

          UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: Union[str, Any] 							=						text_config_and_inputs

          # make sure that cross attention layers are added
          return {
              "text_config": text_config,
              "vision_config": vision_config,
              "pixel_values": pixel_values,
              "attention_mask": attention_mask,
              "input_ids": input_ids,
              "token_type_ids": token_type_ids,
          }







@require_torch
class   _lowercase						(       lowerCAmelCase_	,   unittest.TestCase				):


     '''simple docstring'''





     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: Tuple 							=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
              "hf-internal-testing/tiny-random-clip" ,			"hf-internal-testing/tiny-bert" ,			vision_from_pt=__UpperCamelCase ,			text_from_pt=__UpperCamelCase ,			)
          UpperCAmelCase__							: Optional[int] 							=						13
          UpperCAmelCase__							: List[str] 							=						floats_tensor(
              [
                  batch_size,
                  model.config.vision_config.num_channels,
                  model.config.vision_config.image_size,
                  model.config.vision_config.image_size,
              ]  )
          UpperCAmelCase__							: Dict 							=						ids_tensor([batch_size, 4] ,			model.config.text_config.vocab_size  )
          UpperCAmelCase__							: Union[str, Any] 							=						random_attention_mask([batch_size, 4]  )
          UpperCAmelCase__							: List[str] 							=						{"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

          return model, inputs



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					str:
          UpperCAmelCase__							: Any 							=						FlaxCLIPVisionModel(__UpperCamelCase  )
          UpperCAmelCase__							: List[Any] 							=						FlaxBertModel(__UpperCamelCase  )
          return vision_model, text_model



     def  lowerCAmelCase__       (      self  )->					str:
          UpperCAmelCase__							: Tuple 							=						FlaxCLIPVisionModelTester(self  )
          UpperCAmelCase__							: Tuple 							=						FlaxBertModelTester(self  )
          UpperCAmelCase__							: int 							=						clip_model_tester.prepare_config_and_inputs()
          UpperCAmelCase__							: Tuple 							=						bert_model_tester.prepare_config_and_inputs()

          UpperCAmelCase__							,	UpperCAmelCase__							: Any 							=						vision_config_and_inputs

          UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: Any 							=						text_config_and_inputs

          # make sure that cross attention layers are added
          return {
              "text_config": text_config,
              "vision_config": vision_config,
              "pixel_values": pixel_values,
              "attention_mask": attention_mask,
              "input_ids": input_ids,
              "token_type_ids": token_type_ids,
          }







@require_flax
@require_vision
class   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     @slow
     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: Optional[int] 							=						FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian" ,			logit_scale_init_value=1.0  )
          UpperCAmelCase__							: List[Any] 							=						VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian"  )

          UpperCAmelCase__							: int 							=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
          UpperCAmelCase__							: List[Any] 							=						processor(
              text=["una foto di un gatto", "una foto di un cane"] ,			images=__UpperCamelCase ,			padding=__UpperCamelCase ,			return_tensors="np"  )

          UpperCAmelCase__							: Dict 							=						model(**__UpperCamelCase  )

          # verify the logits
          self.assertEqual(outputs.logits_per_image.shape ,			(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])  )
          self.assertEqual(
              outputs.logits_per_text.shape ,			(inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) ,			)

          UpperCAmelCase__							: Optional[Any] 							=						np.array([[1.228_4727, 0.310_4122]]  )

          self.assertTrue(np.allclose(outputs.logits_per_image ,			__UpperCamelCase ,			atol=1E-3  )  )





"""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   _lowercase						:


     '''simple docstring'''





     _A								=							42

     # setable values
     _A								=							42
     _A								=							42
     _A								=							None



     @classmethod
     def  lowerCAmelCase__       (      cls ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Union[str, Any]:
          return cls(common=__UpperCamelCase ,			init_noise_sigma=__UpperCamelCase ,			timesteps=__UpperCamelCase  )







@dataclass
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							42







class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							[e.name for e in FlaxKarrasDiffusionSchedulers]

     _A								=							42



     @property
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          return True



     @register_to_config
     def __init__(      self ,			__UpperCamelCase = 10_00 ,			__UpperCamelCase = 0.0001 ,			__UpperCamelCase = 0.02 ,			__UpperCamelCase = "linear" ,			__UpperCamelCase = None ,			__UpperCamelCase = "fixed_small" ,			__UpperCamelCase = True ,			__UpperCamelCase = "epsilon" ,			__UpperCamelCase = jnp.floataa ,			)->					List[str]:
          UpperCAmelCase__							: int 							=						dtype



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase = None  )->					DDPMSchedulerState:
          if common is None:
               UpperCAmelCase__							: int 							=						CommonSchedulerState.create(self  )

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

          UpperCAmelCase__							: Tuple 							=						jnp.arange(0 ,			self.config.num_train_timesteps  ).round()[::-1]

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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					jnp.ndarray:
          return sample



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = ()  )->					DDPMSchedulerState:
          UpperCAmelCase__							: Dict 							=						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
          UpperCAmelCase__							: Optional[int] 							=						(jnp.arange(0 ,			__UpperCamelCase  ) * step_ratio).round()[::-1]

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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=None ,			__UpperCamelCase=None  )->					Union[str, Any]:
          UpperCAmelCase__							: Optional[Any] 							=						state.common.alphas_cumprod[t]
          UpperCAmelCase__							: 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
          UpperCAmelCase__							: Any 							=						(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

          if variance_type is None:
               UpperCAmelCase__							: Union[str, Any] 							=						self.config.variance_type

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

          return variance



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = True ,			)->					Union[FlaxDDPMSchedulerOutput, Tuple]:
          UpperCAmelCase__							: List[str] 							=						timestep

          if key is None:
               UpperCAmelCase__							: int 							=						jax.random.PRNGKey(0  )

          if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
               UpperCAmelCase__							,	UpperCAmelCase__							: List[str] 							=						jnp.split(__UpperCamelCase ,			sample.shape[1] ,			axis=1  )
          else:
               UpperCAmelCase__							: Optional[Any] 							=						None

          # 1. compute alphas, betas
          UpperCAmelCase__							: Union[str, Any] 							=						state.common.alphas_cumprod[t]
          UpperCAmelCase__							: Tuple 							=						jnp.where(t > 0 ,			state.common.alphas_cumprod[t - 1] ,			jnp.array(1.0 ,			dtype=self.dtype  )  )
          UpperCAmelCase__							: Union[str, Any] 							=						1 - alpha_prod_t
          UpperCAmelCase__							: 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":
               UpperCAmelCase__							: List[Any] 							=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          elif self.config.prediction_type == "sample":
               UpperCAmelCase__							: Any 							=						model_output
          elif self.config.prediction_type == "v_prediction":
               UpperCAmelCase__							: Union[str, Any] 							=						(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:
               UpperCAmelCase__							: List[Any] 							=						jnp.clip(__UpperCamelCase ,			-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
          UpperCAmelCase__							: List[str] 							=						(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
          UpperCAmelCase__							: List[Any] 							=						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
          UpperCAmelCase__							: Tuple 							=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

          # 6. Add noise
          def random_variance():
               UpperCAmelCase__							: Any 							=						jax.random.split(__UpperCamelCase ,			num=1  )
               UpperCAmelCase__							: int 							=						jax.random.normal(__UpperCamelCase ,			shape=model_output.shape ,			dtype=self.dtype  )
               return (self._get_variance(__UpperCamelCase ,			__UpperCamelCase ,			predicted_variance=__UpperCamelCase  ) ** 0.5) * noise

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

          UpperCAmelCase__							: Any 							=						pred_prev_sample + variance

          if not return_dict:
               return (pred_prev_sample, state)

          return FlaxDDPMSchedulerOutput(prev_sample=__UpperCamelCase ,			state=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					jnp.ndarray:
          return add_noise_common(state.common ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					jnp.ndarray:
          return get_velocity_common(state.common ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def __len__(      self  )->					Tuple:
          return self.config.num_train_timesteps



