


'''simple docstring'''

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

import flax
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     __SCREAMING_SNAKE_CASE       :
     UpperCAmelCase			       =					42
     UpperCAmelCase			       =					42

     # setable values
     UpperCAmelCase			       =					42
     UpperCAmelCase			       =					42
     UpperCAmelCase			       =					None






     @classmethod
     def 			a_       (      cls  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				)							->     Optional[int]:
          return cls(
              common=lowercase__  ,				final_alpha_cumprod=lowercase__  ,				init_noise_sigma=lowercase__  ,				timesteps=lowercase__  ,				)







@dataclass
class     __SCREAMING_SNAKE_CASE       (   a__			):
     UpperCAmelCase			       =					42







class     __SCREAMING_SNAKE_CASE       (   a__			,      a__			):
     UpperCAmelCase			       =					[e.name for e in FlaxKarrasDiffusionSchedulers]

     UpperCAmelCase			       =					42






     @property
     def 			a_       (      self		)							->     Tuple:
          return True






     @register_to_config
     def __init__(      self  ,				__UpperCamelCase = 1_000  ,				__UpperCamelCase = 0.00_01  ,				__UpperCamelCase = 0.02  ,				__UpperCamelCase = "linear"  ,				__UpperCamelCase = None  ,				__UpperCamelCase = True  ,				__UpperCamelCase = 0  ,				__UpperCamelCase = "epsilon"  ,				__UpperCamelCase = jnp.floataa  ,				)							->     str:
          _a            =		dtype






     def 			a_       (      self  ,				__UpperCamelCase = None		)							->     DDIMSchedulerState:





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

          # At every step in ddim, we are looking into the previous alphas_cumprod
          # For the final step, there is no previous alphas_cumprod because we are already at 0
          # `set_alpha_to_one` decides whether we set this parameter simply to one or
          # whether we use the final alpha of the "non-previous" one.
          _a            =		(
              jnp.array(1.0  ,				dtype=self.dtype		) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
          )

          # 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 DDIMSchedulerState.create(
              common=lowercase__  ,				final_alpha_cumprod=lowercase__  ,				init_noise_sigma=lowercase__  ,				timesteps=lowercase__  ,				)






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






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase = ()		)							->     DDIMSchedulerState:
          _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  ,				lowercase__		) * step_ratio).round()[::-1] + self.config.steps_offset

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






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase		)							->     Dict:
          _a            =		state.common.alphas_cumprod[timestep]
          _a            =		jnp.where(
              prev_timestep >= 0  ,				state.common.alphas_cumprod[prev_timestep]  ,				state.final_alpha_cumprod		)
          _a            =		1 - alpha_prod_t
          _a            =		1 - alpha_prod_t_prev

          _a            =		(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

          return variance






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase = 0.0  ,				__UpperCamelCase = True  ,				)							->     Union[FlaxDDIMSchedulerOutput, Tuple]:





          if state.num_inference_steps is None:
               raise ValueError(
                   "Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler"		)

          # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
          # Ideally, read DDIM paper in-detail understanding

          # Notation (<variable name> -> <name in paper>
          # - pred_noise_t -> e_theta(x_t, t)
          # - pred_original_sample -> f_theta(x_t, t) or x_0
          # - std_dev_t -> sigma_t
          # - eta -> η
          # - pred_sample_direction -> "direction pointing to x_t"
          # - pred_prev_sample -> "x_t-1"

          # 1. get previous step value (=t-1)
          _a            =		timestep - self.config.num_train_timesteps // state.num_inference_steps

          _a            =		state.common.alphas_cumprod
          _a            =		state.final_alpha_cumprod

          # 2. compute alphas, betas
          _a            =		alphas_cumprod[timestep]
          _a            =		jnp.where(prev_timestep >= 0  ,				alphas_cumprod[prev_timestep]  ,				lowercase__		)

          _a            =		1 - alpha_prod_t

          # 3. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          if self.config.prediction_type == "epsilon":
               _a            =		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
               _a            =		model_output
          elif self.config.prediction_type == "sample":
               _a            =		model_output
               _a            =		(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
          elif self.config.prediction_type == "v_prediction":
               _a            =		(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
               _a            =		(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
          else:
               raise ValueError(
                   f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
                   " `v_prediction`"		)

          # 4. compute variance: "sigma_t(η)" -> see formula (16)
          # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
          _a            =		self._get_variance(lowercase__  ,				lowercase__  ,				lowercase__		)
          _a            =		eta * variance ** 0.5

          # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          _a            =		(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

          # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          _a            =		alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

          if not return_dict:
               return (prev_sample, state)

          return FlaxDDIMSchedulerOutput(prev_sample=lowercase__  ,				state=lowercase__		)






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






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






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








'''simple docstring'''

from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
   from PIL import Image

   from transformers import ConvNextImageProcessor





class lowerCamelCase    :







  def __init__(		self  ,  lowercase__  ,  lowercase__=13  ,  lowercase__=32  ,  lowercase__=3  ,  lowercase__=4  ,  lowercase__=[10, 20, 30, 40]  ,  lowercase__=[2, 2, 3, 2]  ,  lowercase__=True  ,  lowercase__=True  ,  lowercase__=37  ,  lowercase__="gelu"  ,  lowercase__=10  ,  lowercase__=0.02  ,  lowercase__=3  ,  lowercase__=None  ,  )		->    Any:






        """simple docstring"""





        _snake_case		:						str         =  parent
        _snake_case		:						Any         =  batch_size
        _snake_case		:						Optional[Any]         =  image_size
        _snake_case		:						List[Any]         =  num_channels
        _snake_case		:						int         =  num_stages
        _snake_case		:						Any         =  hidden_sizes
        _snake_case		:						Dict         =  depths
        _snake_case		:						List[Any]         =  is_training
        _snake_case		:						Union[str, Any]         =  use_labels
        _snake_case		:						Dict         =  intermediate_size
        _snake_case		:						List[str]         =  hidden_act
        _snake_case		:						Optional[int]         =  type_sequence_label_size
        _snake_case		:						str         =  initializer_range
        _snake_case		:						Dict         =  scope







  def 					UpperCAmelCase_					(		self		)		->    List[str]:






        """simple docstring"""





        _snake_case		:						Dict         =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        _snake_case		:						int         =  None
        if self.use_labels:
              _snake_case		:						Optional[int]         =  ids_tensor([self.batch_size]  ,  self.type_sequence_label_size		)

        _snake_case		:						Union[str, Any]         =  self.get_config()

        return config, pixel_values, labels







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






        """simple docstring"""





        return ConvNextConfig(
            num_channels=self.num_channels  ,  hidden_sizes=self.hidden_sizes  ,  depths=self.depths  ,  num_stages=self.num_stages  ,  hidden_act=self.hidden_act  ,  is_decoder=lowercase__  ,  initializer_range=self.initializer_range  ,  )







  def 					UpperCAmelCase_					(		self  ,  lowercase__  ,  lowercase__  ,  lowercase__		)		->    int:






        """simple docstring"""





        _snake_case		:						Tuple         =  TFConvNextModel(config=lowercase__		)
        _snake_case		:						Optional[int]         =  model(lowercase__  ,  training=lowercase__		)
        # expected last hidden states: B, C, H // 32, W // 32
        self.parent.assertEqual(
            result.last_hidden_state.shape  ,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)  ,  )







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






        """simple docstring"""





        _snake_case		:						int         =  self.type_sequence_label_size
        _snake_case		:						Optional[int]         =  TFConvNextForImageClassification(lowercase__		)
        _snake_case		:						Dict         =  model(lowercase__  ,  labels=lowercase__  ,  training=lowercase__		)
        self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.type_sequence_label_size)		)







  def 					UpperCAmelCase_					(		self		)		->    List[str]:






        """simple docstring"""





        _snake_case		:						Union[str, Any]         =  self.prepare_config_and_inputs()
        _snake_case  ,				_snake_case  ,				_snake_case		:						Union[str, Any]         =  config_and_inputs
        _snake_case		:						Optional[Any]         =  {'''pixel_values''': pixel_values}
        return config, inputs_dict





@require_tf
class lowerCamelCase    (a__    ,   a__    ,   unittest.TestCase       ):
  _lowercase	:						str							       =					(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
  _lowercase	:						List[str]							       =					(
      {"""feature-extraction""": TFConvNextModel, """image-classification""": TFConvNextForImageClassification}
      if is_tf_available()
      else {}
  )

  _lowercase	:						Optional[Any]							       =					False
  _lowercase	:						str							       =					False
  _lowercase	:						Tuple							       =					False
  _lowercase	:						Union[str, Any]							       =					False
  _lowercase	:						Dict							       =					False







  def 					UpperCAmelCase_					(		self		)		->    Any:






        """simple docstring"""





        _snake_case		:						List[str]         =  TFConvNextModelTester(self		)
        _snake_case		:						Tuple         =  ConfigTester(
            self  ,  config_class=lowercase__  ,  has_text_modality=lowercase__  ,  hidden_size=37  ,  )







  @unittest.skip(reason='''ConvNext does not use inputs_embeds'''		)
  def 					UpperCAmelCase_					(		self		)		->    int:






        """simple docstring"""





        pass







  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''		)		) == 0  ,  reason='''TF does not support backprop for grouped convolutions on CPU.'''  ,  )
  @slow
  def 					UpperCAmelCase_					(		self		)		->    int:






        """simple docstring"""





        super().test_keras_fit()







  @unittest.skip(reason='''ConvNext does not support input and output embeddings'''		)
  def 					UpperCAmelCase_					(		self		)		->    Any:






        """simple docstring"""





        pass







  def 					UpperCAmelCase_					(		self		)		->    Any:






        """simple docstring"""





        _snake_case  ,				_snake_case		:						Dict         =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              _snake_case		:						int         =  model_class(lowercase__		)
              _snake_case		:						Dict         =  inspect.signature(model.call		)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              _snake_case		:						Optional[int]         =  [*signature.parameters.keys()]

              _snake_case		:						int         =  ['''pixel_values''']
              self.assertListEqual(arg_names[:1]  ,  lowercase__		)







  def 					UpperCAmelCase_					(		self		)		->    str:






        """simple docstring"""





        _snake_case		:						Dict         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase__		)







  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''		)		) == 0  ,  reason='''TF does not support backprop for grouped convolutions on CPU.'''  ,  )
  def 					UpperCAmelCase_					(		self		)		->    Optional[Any]:






        """simple docstring"""





        super().test_dataset_conversion()







  def 					UpperCAmelCase_					(		self		)		->    List[str]:






        """simple docstring"""





        def check_hidden_states_output(lowercase__  ,  lowercase__  ,  lowercase__		):
              _snake_case		:						List[Any]         =  model_class(lowercase__		)

              _snake_case		:						Dict         =  model(**self._prepare_for_class(lowercase__  ,  lowercase__		)		)
              _snake_case		:						str         =  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              _snake_case		:						List[Any]         =  self.model_tester.num_stages
              self.assertEqual(len(lowercase__		)  ,  expected_num_stages + 1		)

              # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]		)  ,  [self.model_tester.image_size // 4, self.model_tester.image_size // 4]  ,  )

        _snake_case  ,				_snake_case		:						Optional[Any]         =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              _snake_case		:						int         =  True
              check_hidden_states_output(lowercase__  ,  lowercase__  ,  lowercase__		)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              _snake_case		:						Any         =  True

              check_hidden_states_output(lowercase__  ,  lowercase__  ,  lowercase__		)







  def 					UpperCAmelCase_					(		self		)		->    Any:






        """simple docstring"""





        _snake_case  ,				_snake_case		:						Dict         =  self.model_tester.prepare_config_and_inputs_for_common()

        def check_equivalence(lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__={}		):
              _snake_case		:						Tuple         =  model(lowercase__  ,  return_dict=lowercase__  ,  **lowercase__		)
              _snake_case		:						Any         =  model(lowercase__  ,  return_dict=lowercase__  ,  **lowercase__		).to_tuple()

              def recursive_check(lowercase__  ,  lowercase__		):
                    if isinstance(lowercase__  ,  (List, Tuple)		):
                          for tuple_iterable_value, dict_iterable_value in zip(lowercase__  ,  lowercase__		):
                                recursive_check(lowercase__  ,  lowercase__		)
                    elif tuple_object is None:
                          return
                    else:
                          self.assertTrue(
                              all(tf.equal(lowercase__  ,  lowercase__		)		)  ,  msg=(
                                  '''Tuple and dict output are not equal. Difference:'''
                                  F''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object		)		)}'''
                              )  ,  )

                    recursive_check(lowercase__  ,  lowercase__		)

        for model_class in self.all_model_classes:
              _snake_case		:						Dict         =  model_class(lowercase__		)

              _snake_case		:						Union[str, Any]         =  self._prepare_for_class(lowercase__  ,  lowercase__		)
              _snake_case		:						str         =  self._prepare_for_class(lowercase__  ,  lowercase__		)
              check_equivalence(lowercase__  ,  lowercase__  ,  lowercase__		)

              _snake_case		:						List[Any]         =  self._prepare_for_class(lowercase__  ,  lowercase__  ,  return_labels=lowercase__		)
              _snake_case		:						Any         =  self._prepare_for_class(lowercase__  ,  lowercase__  ,  return_labels=lowercase__		)
              check_equivalence(lowercase__  ,  lowercase__  ,  lowercase__		)

              _snake_case		:						List[str]         =  self._prepare_for_class(lowercase__  ,  lowercase__		)
              _snake_case		:						Union[str, Any]         =  self._prepare_for_class(lowercase__  ,  lowercase__		)
              check_equivalence(lowercase__  ,  lowercase__  ,  lowercase__  ,  {'''output_hidden_states''': True}		)

              _snake_case		:						Optional[int]         =  self._prepare_for_class(lowercase__  ,  lowercase__  ,  return_labels=lowercase__		)
              _snake_case		:						Any         =  self._prepare_for_class(lowercase__  ,  lowercase__  ,  return_labels=lowercase__		)
              check_equivalence(lowercase__  ,  lowercase__  ,  lowercase__  ,  {'''output_hidden_states''': True}		)







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






        """simple docstring"""





        _snake_case		:						str         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase__		)







  @slow
  def 					UpperCAmelCase_					(		self		)		->    Optional[Any]:






        """simple docstring"""





        _snake_case		:						List[Any]         =  TFConvNextModel.from_pretrained('''facebook/convnext-tiny-224'''		)
        self.assertIsNotNone(lowercase__		)




def  _a     (				):





      """simple docstring"""



      _snake_case		:						List[str]         =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
      return image





@require_tf
@require_vision
class lowerCamelCase    (unittest.TestCase       ):







  @cached_property
  def 					UpperCAmelCase_					(		self		)		->    Optional[int]:






        """simple docstring"""





        return ConvNextImageProcessor.from_pretrained('''facebook/convnext-tiny-224'''		) if is_vision_available() else None







  @slow
  def 					UpperCAmelCase_					(		self		)		->    Any:






        """simple docstring"""





        _snake_case		:						Dict         =  TFConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224'''		)

        _snake_case		:						str         =  self.default_image_processor
        _snake_case		:						Optional[int]         =  prepare_img()
        _snake_case		:						str         =  image_processor(images=lowercase__  ,  return_tensors='''tf'''		)

        # forward pass
        _snake_case		:						Union[str, Any]         =  model(**lowercase__		)

        # verify the logits
        _snake_case		:						Union[str, Any]         =  tf.TensorShape((1, 1_000)		)
        self.assertEqual(outputs.logits.shape  ,  lowercase__		)

        _snake_case		:						List[str]         =  tf.constant([-0.0_260, -0.4_739, 0.1_911]		)

        tf.debugging.assert_near(outputs.logits[0, :3]  ,  lowercase__  ,  atol=1E-4		)


