
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import MobileNetVaImageProcessor







class 				lowercase		(					unittest.TestCase		):




							def __init__(				self   ,A__   ,A__=7   ,A__=3   ,A__=1_8   ,A__=3_0   ,A__=4_0_0   ,A__=True   ,A__=None   ,A__=True   ,A__=None   ,):
												lowercase							=						size if size is not None else {'''shortest_edge''': 2_0}
												lowercase							=						crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
												lowercase							=						parent
												lowercase							=						batch_size
												lowercase							=						num_channels
												lowercase							=						image_size
												lowercase							=						min_resolution
												lowercase							=						max_resolution
												lowercase							=						do_resize
												lowercase							=						size
												lowercase							=						do_center_crop
												lowercase							=						crop_size





							def  A__      (				self):
												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_center_crop": self.do_center_crop,
												    "crop_size": self.crop_size,
												}







@require_torch
@require_vision
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Tuple       					=MobileNetVaImageProcessor if is_vision_available() else None




							def  A__      (				self):
												lowercase							=						MobileNetVaImageProcessingTester(self)




							@property
							def  A__      (				self):
												return self.image_processor_tester.prepare_image_processor_dict()




							def  A__      (				self):
												lowercase							=						self.image_processing_class(**self.image_processor_dict)
												self.assertTrue(hasattr(A__   ,'''do_resize'''))
												self.assertTrue(hasattr(A__   ,'''size'''))
												self.assertTrue(hasattr(A__   ,'''do_center_crop'''))
												self.assertTrue(hasattr(A__   ,'''crop_size'''))




							def  A__      (				self):
												lowercase							=						self.image_processing_class.from_dict(self.image_processor_dict)
												self.assertEqual(image_processor.size   ,{'''shortest_edge''': 2_0})
												self.assertEqual(image_processor.crop_size   ,{'''height''': 1_8, '''width''': 1_8})

												lowercase							=						self.image_processing_class.from_dict(self.image_processor_dict   ,size=4_2   ,crop_size=8_4)
												self.assertEqual(image_processor.size   ,{'''shortest_edge''': 4_2})
												self.assertEqual(image_processor.crop_size   ,{'''height''': 8_4, '''width''': 8_4})




							def  A__      (				self):
												pass




							def  A__      (				self):
												# Initialize image_processing
												lowercase							=						self.image_processing_class(**self.image_processor_dict)
												# create random PIL images
												lowercase							=						prepare_image_inputs(self.image_processor_tester   ,equal_resolution=A__)
												for image in image_inputs:
																	self.assertIsInstance(A__   ,Image.Image)

												# Test not batched input
												lowercase							=						image_processing(image_inputs[0]   ,return_tensors='''pt''').pixel_values
												self.assertEqual(
												    encoded_images.shape   ,(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_size['''width'''],
												    )   ,)

												# Test batched
												lowercase							=						image_processing(A__   ,return_tensors='''pt''').pixel_values
												self.assertEqual(
												    encoded_images.shape   ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_size['''width'''],
												    )   ,)




							def  A__      (				self):
												# Initialize image_processing
												lowercase							=						self.image_processing_class(**self.image_processor_dict)
												# create random numpy tensors
												lowercase							=						prepare_image_inputs(self.image_processor_tester   ,equal_resolution=A__   ,numpify=A__)
												for image in image_inputs:
																	self.assertIsInstance(A__   ,np.ndarray)

												# Test not batched input
												lowercase							=						image_processing(image_inputs[0]   ,return_tensors='''pt''').pixel_values
												self.assertEqual(
												    encoded_images.shape   ,(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_size['''width'''],
												    )   ,)

												# Test batched
												lowercase							=						image_processing(A__   ,return_tensors='''pt''').pixel_values
												self.assertEqual(
												    encoded_images.shape   ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_size['''width'''],
												    )   ,)





							def  A__      (				self):
												# Initialize image_processing
												lowercase							=						self.image_processing_class(**self.image_processor_dict)
												# create random PyTorch tensors
												lowercase							=						prepare_image_inputs(self.image_processor_tester   ,equal_resolution=A__   ,torchify=A__)
												for image in image_inputs:
																	self.assertIsInstance(A__   ,torch.Tensor)

												# Test not batched input
												lowercase							=						image_processing(image_inputs[0]   ,return_tensors='''pt''').pixel_values
												self.assertEqual(
												    encoded_images.shape   ,(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_size['''width'''],
												    )   ,)

												# Test batched
												lowercase							=						image_processing(A__   ,return_tensors='''pt''').pixel_values
												self.assertEqual(
												    encoded_images.shape   ,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size['''height'''],
												        self.image_processor_tester.crop_size['''width'''],
												    )   ,)









'''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 __A			:



 '''simple docstring'''







 __lowerCamelCase    :       CommonSchedulerState

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



 @classmethod
 def 					a__  (cls ,   A ,   A ,   A       )    ->     str:


      """simple docstring"""



      return cls(common=A ,   init_noise_sigma=A ,   timesteps=A       )






@dataclass
class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       DDPMSchedulerState






class __A			(  A   ,   A						):



 '''simple docstring'''







 __lowerCamelCase    :       Dict          =       [e.name for e in FlaxKarrasDiffusionSchedulers]

 __lowerCamelCase    :       jnp.dtype



 @property
 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      return True



 @register_to_config
 def __init__(self ,   A = 1_000 ,   A = 0.0001 ,   A = 0.02 ,   A = "linear" ,   A = None ,   A = "fixed_small" ,   A = True ,   A = "epsilon" ,   A = jnp.floataa ,   )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  dtype



 def 					a__  (self ,   A = None       )    ->     DDPMSchedulerState:


      """simple docstring"""



      if common is None:
           _a      =  CommonSchedulerState.create(self       )

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

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

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



 def 					a__  (self ,   A ,   A ,   A = None       )    ->     jnp.ndarray:


      """simple docstring"""



      return sample



 def 					a__  (self ,   A ,   A ,   A = ()       )    ->     DDPMSchedulerState:


      """simple docstring"""



      _a      =  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
      _a      =  (jnp.arange(0 ,   A       ) * step_ratio).round()[::-1]

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



 def 					a__  (self ,   A ,   A ,   A=None ,   A=None       )    ->     int:


      """simple docstring"""



      _a      =  state.common.alphas_cumprod[t]
      _a      =  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
      _a      =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

      if variance_type is None:
           _a      =  self.config.variance_type

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

      return variance



 def 					a__  (self ,   A ,   A ,   A ,   A ,   A = None ,   A = True ,   )    ->     Union[FlaxDDPMSchedulerOutput, Tuple]:


      """simple docstring"""



      _a      =  timestep

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

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

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

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

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

      _a      =  pred_prev_sample + variance

      if not return_dict:
           return (pred_prev_sample, state)

      return FlaxDDPMSchedulerOutput(prev_sample=A ,   state=A       )



 def 					a__  (self ,   A ,   A ,   A ,   A ,   )    ->     jnp.ndarray:


      """simple docstring"""



      return add_noise_common(state.common ,   A ,   A ,   A       )



 def 					a__  (self ,   A ,   A ,   A ,   A ,   )    ->     jnp.ndarray:


      """simple docstring"""



      return get_velocity_common(state.common ,   A ,   A ,   A       )



 def __len__(self       )    ->     Tuple:


      """simple docstring"""



      return self.config.num_train_timesteps



