




'''simple docstring'''



# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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 	lowercase_					:


   """simple docstring"""

   lowerCamelCase_       						=		42
   lowerCamelCase_       						=		42

   # setable values
   lowerCamelCase_       						=		42
   lowerCamelCase_       						=		42
   lowerCamelCase_       						=		None



   @classmethod
   def 							lowerCAmelCase_    (					cls      :			int						,   __lowerCamelCase      :			CommonSchedulerState						,   __lowerCamelCase      :			jnp.ndarray						,   __lowerCamelCase      :			jnp.ndarray						,   __lowerCamelCase      :			jnp.ndarray						,   ):

      """simple docstring"""
      return cls(
          common=__lowerCamelCase						,   final_alpha_cumprod=__lowerCamelCase						,   init_noise_sigma=__lowerCamelCase						,   timesteps=__lowerCamelCase						,   )







@dataclass
class 	lowercase_					(				A     ):


   """simple docstring"""

   lowerCamelCase_       						=		42







class 	lowercase_					(				A  ,						A     ):


   """simple docstring"""

   lowerCamelCase_       						=		[e.name for e in FlaxKarrasDiffusionSchedulers]

   lowerCamelCase_       						=		42



   @property
   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      return True



   @register_to_config
   def __init__(					self      :			Dict						,   __lowerCamelCase      :			int = 1_0_0_0						,   __lowerCamelCase      :			float = 0.0_0_0_1						,   __lowerCamelCase      :			float = 0.0_2						,   __lowerCamelCase      :			str = "linear"						,   __lowerCamelCase      :			Optional[jnp.ndarray] = None						,   __lowerCamelCase      :			bool = True						,   __lowerCamelCase      :			int = 0						,   __lowerCamelCase      :			str = "epsilon"						,   __lowerCamelCase      :			jnp.dtype = jnp.floataa						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   dtype



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]						,   __lowerCamelCase      :			Optional[CommonSchedulerState] = None					):

      """simple docstring"""
      if common is None:
         _SCREAMING_SNAKE_CASE =   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.
      _SCREAMING_SNAKE_CASE =   (
          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
      _SCREAMING_SNAKE_CASE =   jnp.array(1.0						,   dtype=self.dtype					)

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

      return DDIMSchedulerState.create(
          common=__lowerCamelCase						,   final_alpha_cumprod=__lowerCamelCase						,   init_noise_sigma=__lowerCamelCase						,   timesteps=__lowerCamelCase						,   )



   def 							lowerCAmelCase_    (					self      :			Optional[int]						,   __lowerCamelCase      :			DDIMSchedulerState						,   __lowerCamelCase      :			jnp.ndarray						,   __lowerCamelCase      :			Optional[int] = None					):

      """simple docstring"""
      return sample



   def 							lowerCAmelCase_    (					self      :			Dict						,   __lowerCamelCase      :			DDIMSchedulerState						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Tuple = ()					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   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
      _SCREAMING_SNAKE_CASE =   (jnp.arange(0						,   __lowerCamelCase					) * step_ratio).round()[::-1] + self.config.steps_offset

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



   def 							lowerCAmelCase_    (					self      :			Optional[Any]						,   __lowerCamelCase      :			DDIMSchedulerState						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   state.common.alphas_cumprod[timestep]
      _SCREAMING_SNAKE_CASE =   jnp.where(
          prev_timestep >= 0						,   state.common.alphas_cumprod[prev_timestep]						,   state.final_alpha_cumprod					)
      _SCREAMING_SNAKE_CASE =   1 - alpha_prod_t
      _SCREAMING_SNAKE_CASE =   1 - alpha_prod_t_prev

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

      return variance



   def 							lowerCAmelCase_    (					self      :			str						,   __lowerCamelCase      :			DDIMSchedulerState						,   __lowerCamelCase      :			jnp.ndarray						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			jnp.ndarray						,   __lowerCamelCase      :			float = 0.0						,   __lowerCamelCase      :			bool = True						,   ):

      """simple docstring"""
      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)
      _SCREAMING_SNAKE_CASE =   timestep - self.config.num_train_timesteps // state.num_inference_steps

      _SCREAMING_SNAKE_CASE =   state.common.alphas_cumprod
      _SCREAMING_SNAKE_CASE =   state.final_alpha_cumprod

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

      _SCREAMING_SNAKE_CASE =   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":
         _SCREAMING_SNAKE_CASE =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         _SCREAMING_SNAKE_CASE =   model_output
      elif self.config.prediction_type == "sample":
         _SCREAMING_SNAKE_CASE =   model_output
         _SCREAMING_SNAKE_CASE =   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
      elif self.config.prediction_type == "v_prediction":
         _SCREAMING_SNAKE_CASE =   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
         _SCREAMING_SNAKE_CASE =   (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)
      _SCREAMING_SNAKE_CASE =   self._get_variance(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   eta * variance ** 0.5

      # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      _SCREAMING_SNAKE_CASE =   (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
      _SCREAMING_SNAKE_CASE =   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

      if not return_dict:
         return (prev_sample, state)

      return FlaxDDIMSchedulerOutput(prev_sample=__lowerCamelCase						,   state=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			DDIMSchedulerState						,   __lowerCamelCase      :			jnp.ndarray						,   __lowerCamelCase      :			jnp.ndarray						,   __lowerCamelCase      :			jnp.ndarray						,   ):

      """simple docstring"""
      return add_noise_common(state.common						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]						,   __lowerCamelCase      :			DDIMSchedulerState						,   __lowerCamelCase      :			jnp.ndarray						,   __lowerCamelCase      :			jnp.ndarray						,   __lowerCamelCase      :			jnp.ndarray						,   ):

      """simple docstring"""
      return get_velocity_common(state.common						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase					)



   def __len__(					self      :			Union[str, Any]					):

      """simple docstring"""
      return self.config.num_train_timesteps







'''simple docstring'''



import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:







 class 	lowercase_					:


    """simple docstring"""

    @staticmethod
    def 							lowerCAmelCase_    (					*__lowerCamelCase      :			Union[str, Any]						,   **__lowerCamelCase      :			str					):

       """simple docstring"""
       pass







@is_pipeline_test
@require_torch_or_tf
@require_vision
class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   lowerCamelCase_       						=		MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
   lowerCamelCase_       						=		TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   ImageClassificationPipeline(model=__lowerCamelCase						,   image_processor=__lowerCamelCase						,   top_k=2					)
      _SCREAMING_SNAKE_CASE =   [
          Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					),
          "http://images.cocodataset.org/val2017/000000039769.jpg",
      ]
      return image_classifier, examples



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			Optional[Any]						,   __lowerCamelCase      :			Tuple					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"					)

      self.assertEqual(
          __lowerCamelCase						,   [
              {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
          ]						,   )

      import datasets

      _SCREAMING_SNAKE_CASE =   datasets.load_dataset("hf-internal-testing/fixtures_image_utils"						,   "image"						,   split="test"					)

      # Accepts URL + PIL.Image + lists
      _SCREAMING_SNAKE_CASE =   image_classifier(
          [
              Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					),
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              # RGBA
              dataset[0]["file"],
              # LA
              dataset[1]["file"],
              # L
              dataset[2]["file"],
          ]					)
      self.assertEqual(
          __lowerCamelCase						,   [
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
          ]						,   )



   @require_torch
   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "hf-internal-testing/tiny-random-vit"
      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]						,   )

      _SCREAMING_SNAKE_CASE =   image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]						,   top_k=2						,   )
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
          ]						,   )



   @require_tf
   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "hf-internal-testing/tiny-random-vit"
      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model=__lowerCamelCase						,   framework="tf"					)

      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]						,   )

      _SCREAMING_SNAKE_CASE =   image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]						,   top_k=2						,   )
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
          ]						,   )



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   PreTrainedTokenizer()

      # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
      _SCREAMING_SNAKE_CASE =   pipeline(
          "image-classification"						,   model="hf-internal-testing/tiny-random-vit"						,   tokenizer=__lowerCamelCase					)

      self.assertIs(image_classifier.tokenizer						,   __lowerCamelCase					)



   @slow
   @require_torch
   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      # Perceiver is not tested by `run_pipeline_test` properly.
      # That is because the type of feature_extractor and model preprocessor need to be kept
      # in sync, which is not the case in the current design
      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model="deepmind/vision-perceiver-conv"					)
      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              {"score": 0.4_3_8_5, "label": "tabby, tabby cat"},
              {"score": 0.3_2_1, "label": "tiger cat"},
              {"score": 0.0_5_0_2, "label": "Egyptian cat"},
              {"score": 0.0_1_3_7, "label": "crib, cot"},
              {"score": 0.0_0_7, "label": "radiator"},
          ]						,   )

      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model="deepmind/vision-perceiver-fourier"					)
      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              {"score": 0.5_6_5_8, "label": "tabby, tabby cat"},
              {"score": 0.1_3_0_9, "label": "tiger cat"},
              {"score": 0.0_7_2_2, "label": "Egyptian cat"},
              {"score": 0.0_7_0_7, "label": "remote control, remote"},
              {"score": 0.0_0_8_2, "label": "computer keyboard, keypad"},
          ]						,   )

      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model="deepmind/vision-perceiver-learned"					)
      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              {"score": 0.3_0_2_2, "label": "tabby, tabby cat"},
              {"score": 0.2_3_6_2, "label": "Egyptian cat"},
              {"score": 0.1_8_5_6, "label": "tiger cat"},
              {"score": 0.0_3_2_4, "label": "remote control, remote"},
              {"score": 0.0_0_9_6, "label": "quilt, comforter, comfort, puff"},
          ]						,   )

