'''simple docstring'''

import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Union[str, Any]      							=   (IPNDMScheduler,)
      UpperCAmelCase_      :      Union[str, Any]      							=   (('''num_inference_steps''', 50),)




      def 		lowerCAmelCase__					(				self					,**lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  {'''num_train_timesteps''': 1000}
         config.update(**lowerCamelCase_							)
         return config




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_=0					,**lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  dict(self.forward_default_kwargs							)
         UpperCAmelCase__							:				Any   				=  kwargs.pop('''num_inference_steps'''					,lowerCamelCase_							)
         UpperCAmelCase__							:				Tuple   				=  self.dummy_sample
         UpperCAmelCase__							:				Dict   				=  0.1 * sample
         UpperCAmelCase__							:				int   				=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
            UpperCAmelCase__							:				Tuple   				=  self.get_scheduler_config(**lowerCamelCase_							)
            UpperCAmelCase__							:				str   				=  scheduler_class(**lowerCamelCase_							)
            scheduler.set_timesteps(lowerCamelCase_							)
            # copy over dummy past residuals
            UpperCAmelCase__							:				str   				=  dummy_past_residuals[:]

            if time_step is None:
               UpperCAmelCase__							:				Optional[int]   				=  scheduler.timesteps[len(scheduler.timesteps							) // 2]

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(lowerCamelCase_							)
               UpperCAmelCase__							:				Any   				=  scheduler_class.from_pretrained(lowerCamelCase_							)
               new_scheduler.set_timesteps(lowerCamelCase_							)
               # copy over dummy past residuals
               UpperCAmelCase__							:				Union[str, Any]   				=  dummy_past_residuals[:]

            UpperCAmelCase__							:				Union[str, Any]   				=  scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							).prev_sample
            UpperCAmelCase__							:				Any   				=  new_scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							).prev_sample

            assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"

            UpperCAmelCase__							:				str   				=  scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							).prev_sample
            UpperCAmelCase__							:				Dict   				=  new_scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							).prev_sample

            assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"




      def 		lowerCAmelCase__					(				self							)			->			Optional[int]:



         '''simple docstring'''



         pass




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_=0					,**lowerCamelCase_							)			->			Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  dict(self.forward_default_kwargs							)
         UpperCAmelCase__							:				str   				=  kwargs.pop('''num_inference_steps'''					,lowerCamelCase_							)
         UpperCAmelCase__							:				Dict   				=  self.dummy_sample
         UpperCAmelCase__							:				Dict   				=  0.1 * sample
         UpperCAmelCase__							:				List[str]   				=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
            UpperCAmelCase__							:				Optional[int]   				=  self.get_scheduler_config()
            UpperCAmelCase__							:				List[str]   				=  scheduler_class(**lowerCamelCase_							)
            scheduler.set_timesteps(lowerCamelCase_							)

            # copy over dummy past residuals (must be after setting timesteps)
            UpperCAmelCase__							:				Dict   				=  dummy_past_residuals[:]

            if time_step is None:
               UpperCAmelCase__							:				int   				=  scheduler.timesteps[len(scheduler.timesteps							) // 2]

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(lowerCamelCase_							)
               UpperCAmelCase__							:				Dict   				=  scheduler_class.from_pretrained(lowerCamelCase_							)
               # copy over dummy past residuals
               new_scheduler.set_timesteps(lowerCamelCase_							)

               # copy over dummy past residual (must be after setting timesteps)
               UpperCAmelCase__							:				List[Any]   				=  dummy_past_residuals[:]

            UpperCAmelCase__							:				int   				=  scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							).prev_sample
            UpperCAmelCase__							:				List[str]   				=  new_scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							).prev_sample

            assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"

            UpperCAmelCase__							:				Dict   				=  scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							).prev_sample
            UpperCAmelCase__							:				List[str]   				=  new_scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							).prev_sample

            assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"




      def 		lowerCAmelCase__					(				self					,**lowerCamelCase_							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  self.scheduler_classes[0]
         UpperCAmelCase__							:				List[Any]   				=  self.get_scheduler_config(**lowerCamelCase_							)
         UpperCAmelCase__							:				List[Any]   				=  scheduler_class(**lowerCamelCase_							)

         UpperCAmelCase__							:				List[str]   				=  10
         UpperCAmelCase__							:				Union[str, Any]   				=  self.dummy_model()
         UpperCAmelCase__							:				Optional[int]   				=  self.dummy_sample_deter
         scheduler.set_timesteps(lowerCamelCase_							)

         for i, t in enumerate(scheduler.timesteps							):
            UpperCAmelCase__							:				str   				=  model(lowerCamelCase_					,lowerCamelCase_							)
            UpperCAmelCase__							:				Dict   				=  scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							).prev_sample

         for i, t in enumerate(scheduler.timesteps							):
            UpperCAmelCase__							:				Optional[Any]   				=  model(lowerCamelCase_					,lowerCamelCase_							)
            UpperCAmelCase__							:				Optional[Any]   				=  scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							).prev_sample

         return sample




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



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  dict(self.forward_default_kwargs							)

         UpperCAmelCase__							:				Dict   				=  kwargs.pop('''num_inference_steps'''					,lowerCamelCase_							)

         for scheduler_class in self.scheduler_classes:
            UpperCAmelCase__							:				Optional[int]   				=  self.get_scheduler_config()
            UpperCAmelCase__							:				Optional[int]   				=  scheduler_class(**lowerCamelCase_							)

            UpperCAmelCase__							:				Optional[Any]   				=  self.dummy_sample
            UpperCAmelCase__							:				List[str]   				=  0.1 * sample

            if num_inference_steps is not None and hasattr(lowerCamelCase_					,'''set_timesteps'''							):
               scheduler.set_timesteps(lowerCamelCase_							)
            elif num_inference_steps is not None and not hasattr(lowerCamelCase_					,'''set_timesteps'''							):
               UpperCAmelCase__							:				List[Any]   				=  num_inference_steps

            # copy over dummy past residuals (must be done after set_timesteps)
            UpperCAmelCase__							:				Optional[Any]   				=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
            UpperCAmelCase__							:				Optional[int]   				=  dummy_past_residuals[:]

            UpperCAmelCase__							:				Dict   				=  scheduler.timesteps[5]
            UpperCAmelCase__							:				Dict   				=  scheduler.timesteps[6]

            UpperCAmelCase__							:				List[str]   				=  scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							).prev_sample
            UpperCAmelCase__							:				List[Any]   				=  scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							).prev_sample

            self.assertEqual(output_a.shape					,sample.shape							)
            self.assertEqual(output_a.shape					,output_a.shape							)

            UpperCAmelCase__							:				List[str]   				=  scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							).prev_sample
            UpperCAmelCase__							:				Dict   				=  scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							).prev_sample

            self.assertEqual(output_a.shape					,sample.shape							)
            self.assertEqual(output_a.shape					,output_a.shape							)




      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         for timesteps in [100, 1000]:
            self.check_over_configs(num_train_timesteps=lowerCamelCase_					,time_step=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			int:



         '''simple docstring'''



         for t, num_inference_steps in zip([1, 5, 10]					,[10, 50, 100]							):
            self.check_over_forward(num_inference_steps=lowerCamelCase_					,time_step=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  self.full_loop()
         UpperCAmelCase__							:				Tuple   				=  torch.mean(torch.abs(lowerCamelCase_							)							)

         assert abs(result_mean.item() - 2540529							) < 10





'''simple docstring'''

import torch

from transformers import AutoModel


class 				_lowercase	(      torch.nn.Module   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_="sayef/fsner-bert-base-uncased"							)			->			Dict:



         '''simple docstring'''



         super(lowerCamelCase_					,self							).__init__()

         UpperCAmelCase__							:				Union[str, Any]   				=  AutoModel.from_pretrained(lowerCamelCase_					,return_dict=lowerCamelCase_							)
         UpperCAmelCase__							:				str   				=  torch.nn.CosineSimilarity(3					,1e-08							)
         UpperCAmelCase__							:				int   				=  torch.nn.Softmax(dim=1							)




      def 		lowerCAmelCase__					(				self					,**lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         return self.bert(**lowerCamelCase_							).last_hidden_state




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return token_embeddings.sum(2					,keepdim=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_=1							)			->			int:



         '''simple docstring'''



         return self.softmax(T * self.cos(lowerCamelCase_					,lowerCamelCase_							)							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  W_supports['''sizes'''].tolist()
         UpperCAmelCase__							:				Union[str, Any]   				=  W_supports['''start_token_id'''].item()
         UpperCAmelCase__							:				int   				=  W_supports['''end_token_id'''].item()

         del W_supports["sizes"]
         del W_supports["start_token_id"]
         del W_supports["end_token_id"]

         UpperCAmelCase__							:				Any   				=  self.BERT(**lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  self.BERT(**lowerCamelCase_							)

         UpperCAmelCase__							:				Any   				=  None
         UpperCAmelCase__							:				Tuple   				=  None

         UpperCAmelCase__							:				Dict   				=  W_supports['''input_ids'''] == start_token_id
         UpperCAmelCase__							:				Any   				=  W_supports['''input_ids'''] == end_token_id

         for i, size in enumerate(lowerCamelCase_							):
            if i == 0:
               UpperCAmelCase__							:				int   				=  0
            else:
               UpperCAmelCase__							:				str   				=  support_sizes[i - 1]

            UpperCAmelCase__							:				Dict   				=  S[s : s + size][start_token_masks[s : s + size]]
            UpperCAmelCase__							:				Union[str, Any]   				=  S[s : s + size][end_token_masks[s : s + size]]

            UpperCAmelCase__							:				int   				=  torch.matmul(q[i]					,s_start.T							).sum(1							).softmax(0							)
            UpperCAmelCase__							:				int   				=  torch.matmul(q[i]					,s_end.T							).sum(1							).softmax(0							)

            if p_starts is not None:
               UpperCAmelCase__							:				str   				=  torch.vstack((p_starts, p_start)							)
               UpperCAmelCase__							:				Optional[int]   				=  torch.vstack((p_ends, p_end)							)
            else:
               UpperCAmelCase__							:				Union[str, Any]   				=  p_start
               UpperCAmelCase__							:				int   				=  p_end

         return p_starts, p_ends




