




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      _snake_case												=						(DDIMParallelScheduler,)
      _snake_case												=						(("""eta""", 0.0), ("""num_inference_steps""", 50))

      def      UpperCAmelCase				(				self    ,				**A     )		->    Optional[int]:
         snake_case				:    Union[str, Any]           =	{
             """num_train_timesteps""": 1_0_0_0,
             """beta_start""": 0.00_01,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
             """clip_sample""": True,
         }

         config.update(**A     )
         return config

      def      UpperCAmelCase				(				self    ,				**A     )		->    int:
         snake_case				:    str           =	self.scheduler_classes[0]
         snake_case				:    Union[str, Any]           =	self.get_scheduler_config(**A     )
         snake_case				:    List[str]           =	scheduler_class(**A     )

         snake_case     , snake_case				:    int           =	1_0, 0.0

         snake_case				:    Union[str, Any]           =	self.dummy_model()
         snake_case				:    List[str]           =	self.dummy_sample_deter

         scheduler.set_timesteps(A     )

         for t in scheduler.timesteps:
            snake_case				:    int           =	model(A    ,				A     )
            snake_case				:    Tuple           =	scheduler.step(A    ,				A    ,				A    ,				A     ).prev_sample

         return sample

      def      UpperCAmelCase				(				self     )		->    Optional[Any]:
         for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=A     )

      def      UpperCAmelCase				(				self     )		->    Optional[int]:
         for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=A     )

         snake_case				:    Optional[int]           =	self.scheduler_classes[0]
         snake_case				:    List[Any]           =	self.get_scheduler_config(steps_offset=1     )
         snake_case				:    Optional[Any]           =	scheduler_class(**A     )
         scheduler.set_timesteps(5     )
         assert torch.equal(scheduler.timesteps    ,				torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]     )     )

      def      UpperCAmelCase				(				self     )		->    str:
         for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]    ,				[0.0_02, 0.02, 0.2, 2]     ):
            self.check_over_configs(beta_start=A    ,				beta_end=A     )

      def      UpperCAmelCase				(				self     )		->    int:
         for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=A     )

      def      UpperCAmelCase				(				self     )		->    Optional[Any]:
         for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=A     )

      def      UpperCAmelCase				(				self     )		->    Union[str, Any]:
         for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=A     )

      def      UpperCAmelCase				(				self     )		->    Optional[int]:
         for timestep_spacing in ["trailing", "leading"]:
            self.check_over_configs(timestep_spacing=A     )

      def      UpperCAmelCase				(				self     )		->    List[Any]:
         for rescale_betas_zero_snr in [True, False]:
            self.check_over_configs(rescale_betas_zero_snr=A     )

      def      UpperCAmelCase				(				self     )		->    int:
         self.check_over_configs(thresholding=A     )
         for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(
                   thresholding=A    ,				prediction_type=A    ,				sample_max_value=A    ,				)

      def      UpperCAmelCase				(				self     )		->    Optional[Any]:
         for t in [1, 1_0, 4_9]:
            self.check_over_forward(time_step=A     )

      def      UpperCAmelCase				(				self     )		->    Union[str, Any]:
         for t, num_inference_steps in zip([1, 1_0, 5_0]    ,				[1_0, 5_0, 5_0_0]     ):
            self.check_over_forward(time_step=A    ,				num_inference_steps=A     )

      def      UpperCAmelCase				(				self     )		->    List[Any]:
         for t, eta in zip([1, 1_0, 4_9]    ,				[0.0, 0.5, 1.0]     ):
            self.check_over_forward(time_step=A    ,				eta=A     )

      def      UpperCAmelCase				(				self     )		->    List[str]:
         snake_case				:    List[str]           =	self.scheduler_classes[0]
         snake_case				:    int           =	self.get_scheduler_config()
         snake_case				:    List[str]           =	scheduler_class(**A     )

         assert torch.sum(torch.abs(scheduler._get_variance(0    ,				0     ) - 0.0     )     ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_2_0    ,				4_0_0     ) - 0.1_47_71     )     ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_8_0    ,				9_6_0     ) - 0.3_24_60     )     ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(0    ,				0     ) - 0.0     )     ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7    ,				4_8_6     ) - 0.0_09_79     )     ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9    ,				9_9_8     ) - 0.02     )     ) < 1e-5

      def      UpperCAmelCase				(				self     )		->    List[str]:
         snake_case				:    str           =	self.scheduler_classes[0]
         snake_case				:    Union[str, Any]           =	self.get_scheduler_config()
         snake_case				:    List[str]           =	scheduler_class(**A     )

         snake_case     , snake_case				:    str           =	1_0, 0.0
         scheduler.set_timesteps(A     )

         snake_case				:    List[str]           =	self.dummy_model()
         snake_case				:    Union[str, Any]           =	self.dummy_sample_deter
         snake_case				:    Dict           =	self.dummy_sample_deter + 0.1
         snake_case				:    Any           =	self.dummy_sample_deter - 0.1

         snake_case				:    Optional[Any]           =	samplea.shape[0]
         snake_case				:    Union[str, Any]           =	torch.stack([samplea, samplea, samplea]    ,				dim=0     )
         snake_case				:    Optional[int]           =	torch.arange(A     )[0:3, None].repeat(1    ,				A     )

         snake_case				:    Union[str, Any]           =	model(samples.flatten(0    ,				1     )    ,				timesteps.flatten(0    ,				1     )     )
         snake_case				:    List[str]           =	scheduler.batch_step_no_noise(A    ,				timesteps.flatten(0    ,				1     )    ,				samples.flatten(0    ,				1     )    ,				A     )

         snake_case				:    int           =	torch.sum(torch.abs(A     )     )
         snake_case				:    List[Any]           =	torch.mean(torch.abs(A     )     )

         assert abs(result_sum.item() - 11_47.79_04     ) < 1e-2
         assert abs(result_mean.item() - 0.49_82     ) < 1e-3

      def      UpperCAmelCase				(				self     )		->    Optional[int]:
         snake_case				:    List[str]           =	self.full_loop()

         snake_case				:    Tuple           =	torch.sum(torch.abs(A     )     )
         snake_case				:    int           =	torch.mean(torch.abs(A     )     )

         assert abs(result_sum.item() - 1_72.00_67     ) < 1e-2
         assert abs(result_mean.item() - 0.22_39_67     ) < 1e-3

      def      UpperCAmelCase				(				self     )		->    Union[str, Any]:
         snake_case				:    List[str]           =	self.full_loop(prediction_type="""v_prediction"""     )

         snake_case				:    str           =	torch.sum(torch.abs(A     )     )
         snake_case				:    Union[str, Any]           =	torch.mean(torch.abs(A     )     )

         assert abs(result_sum.item() - 52.53_02     ) < 1e-2
         assert abs(result_mean.item() - 0.06_84     ) < 1e-3

      def      UpperCAmelCase				(				self     )		->    List[Any]:
         # We specify different beta, so that the first alpha is 0.99
         snake_case				:    Dict           =	self.full_loop(set_alpha_to_one=A    ,				beta_start=0.01     )
         snake_case				:    Union[str, Any]           =	torch.sum(torch.abs(A     )     )
         snake_case				:    Optional[int]           =	torch.mean(torch.abs(A     )     )

         assert abs(result_sum.item() - 1_49.82_95     ) < 1e-2
         assert abs(result_mean.item() - 0.19_51     ) < 1e-3

      def      UpperCAmelCase				(				self     )		->    Union[str, Any]:
         # We specify different beta, so that the first alpha is 0.99
         snake_case				:    Union[str, Any]           =	self.full_loop(set_alpha_to_one=A    ,				beta_start=0.01     )
         snake_case				:    Dict           =	torch.sum(torch.abs(A     )     )
         snake_case				:    Optional[int]           =	torch.mean(torch.abs(A     )     )

         assert abs(result_sum.item() - 1_49.07_84     ) < 1e-2
         assert abs(result_mean.item() - 0.19_41     ) < 1e-3











from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
       import numpy as np
       from decord import VideoReader


if is_torch_available():
       from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

lowerCamelCase	:	int				=   logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase__							)
class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      def __init__(				self    ,				*A    ,				**A     )		->    Optional[int]:
         super().__init__(*A    ,				**A     )
         requires_backends(self    ,				"""decord"""     )
         self.check_model_type(A     )

      def      UpperCAmelCase				(				self    ,				A=None    ,				A=None    ,				A=None     )		->    int:
         snake_case				:    Any           =	{}
         if frame_sampling_rate is not None:
            snake_case				:    int           =	frame_sampling_rate
         if num_frames is not None:
            snake_case				:    Union[str, Any]           =	num_frames

         snake_case				:    str           =	{}
         if top_k is not None:
            snake_case				:    Optional[int]           =	top_k
         return preprocess_params, {}, postprocess_params

      def __call__(				self    ,				A    ,				**A     )		->    Dict:
         return super().__call__(A    ,				**A     )

      def      UpperCAmelCase				(				self    ,				A    ,				A=None    ,				A=1     )		->    Tuple:
         if num_frames is None:
            snake_case				:    Tuple           =	self.model.config.num_frames

         if video.startswith("""http://"""     ) or video.startswith("""https://"""     ):
            snake_case				:    Optional[int]           =	BytesIO(requests.get(A     ).content     )

         snake_case				:    Optional[Any]           =	VideoReader(A     )
         videoreader.seek(0     )

         snake_case				:    Optional[Any]           =	0
         snake_case				:    Optional[Any]           =	num_frames * frame_sampling_rate - 1
         snake_case				:    Any           =	np.linspace(A    ,				A    ,				num=A    ,				dtype=np.intaa     )

         snake_case				:    int           =	videoreader.get_batch(A     ).asnumpy()
         snake_case				:    List[Any]           =	list(A     )

         snake_case				:    Optional[Any]           =	self.image_processor(A    ,				return_tensors=self.framework     )
         return model_inputs

      def      UpperCAmelCase				(				self    ,				A     )		->    List[str]:
         snake_case				:    Dict           =	self.model(**A     )
         return model_outputs

      def      UpperCAmelCase				(				self    ,				A    ,				A=5     )		->    int:
         if top_k > self.model.config.num_labels:
            snake_case				:    str           =	self.model.config.num_labels

         if self.framework == "pt":
            snake_case				:    List[Any]           =	model_outputs.logits.softmax(-1     )[0]
            snake_case     , snake_case				:    Tuple           =	probs.topk(A     )
         else:
            raise ValueError(f"""Unsupported framework: {self.framework}"""     )

         snake_case				:    List[Any]           =	scores.tolist()
         snake_case				:    str           =	ids.tolist()
         return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(A    ,				A     )]





