

'''simple docstring'''




from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image


_lowerCAmelCase         =						["text", "image", "audio"]




def 						UpperCamelCase	(			a			)				->     Optional[int]:






     '''simple docstring'''
     __magic_name__						       =    []

     for input_type in input_types:
          if input_type == "text":
               inputs.append('''Text input'''			)
          elif input_type == "image":
               inputs.append(
                   Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''			)			) / '''000000039769.png'''			).resize((512, 512)			)			)
          elif input_type == "audio":
               inputs.append(torch.ones(3000			)			)
          elif isinstance(a		,						a			):
               inputs.append(create_inputs(a			)			)
          else:
               raise ValueError(F'''Invalid type requested: {input_type}'''			)

     return inputs




def 						UpperCamelCase	(			a			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    []

     for output in outputs:
          if isinstance(a		,						(str, AgentText)			):
               output_types.append('''text'''			)
          elif isinstance(a		,						(Image.Image, AgentImage)			):
               output_types.append('''image'''			)
          elif isinstance(a		,						(torch.Tensor, AgentAudio)			):
               output_types.append('''audio'''			)
          else:
               raise ValueError(F'''Invalid output: {output}'''			)

     return output_types






@is_tool_test
class   _SCREAMING_SNAKE_CASE   :



 def 			snake_case__  (   self		:   Optional[int]      ):
      self.assertTrue(hasattr(self.tool			,				'''inputs'''      )      )
      self.assertTrue(hasattr(self.tool			,				'''outputs'''      )      )

      __magic_name__						       =    self.tool.inputs
      for _input in inputs:
           if isinstance(_input			,				a__      ):
                for __input in _input:
                     self.assertTrue(__input in authorized_types      )
           else:
                self.assertTrue(_input in authorized_types      )

      __magic_name__						       =    self.tool.outputs
      for _output in outputs:
           self.assertTrue(_output in authorized_types      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    create_inputs(self.tool.inputs      )
      __magic_name__						       =    self.tool(*a__      )

      # There is a single output
      if len(self.tool.outputs      ) == 1:
           __magic_name__						       =    [outputs]

      self.assertListEqual(output_types(a__      )			,				self.tool.outputs      )



 def 			snake_case__  (   self		:   List[str]      ):
      self.assertTrue(hasattr(self.tool			,				'''description'''      )      )
      self.assertTrue(hasattr(self.tool			,				'''default_checkpoint'''      )      )
      self.assertTrue(self.tool.description.startswith('''This is a tool that'''      )      )



 def 			snake_case__  (   self		:   Tuple      ):
      __magic_name__						       =    create_inputs(self.tool.inputs      )
      __magic_name__						       =    self.tool(*a__      )

      if not isinstance(a__			,				a__      ):
           __magic_name__						       =    [outputs]

      self.assertEqual(len(a__      )			,				len(self.tool.outputs      )      )

      for output, output_type in zip(a__			,				self.tool.outputs      ):
           __magic_name__						       =    AGENT_TYPE_MAPPING[output_type]
           self.assertTrue(isinstance(a__			,				a__      )      )



 def 			snake_case__  (   self		:   Tuple      ):
      __magic_name__						       =    create_inputs(self.tool.inputs      )

      __magic_name__						       =    []

      for _input, input_type in zip(a__			,				self.tool.inputs      ):
           if isinstance(a__			,				a__      ):
                _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input      ) for _input_type in input_type]      )
           else:
                _inputs.append(AGENT_TYPE_MAPPING[input_type](_input      )      )

        # Should not raise an error
      __magic_name__						       =    self.tool(*a__      )

      if not isinstance(a__			,				a__      ):
           __magic_name__						       =    [outputs]

      self.assertEqual(len(a__      )			,				len(self.tool.outputs      )      )









'''simple docstring'''




import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class   _SCREAMING_SNAKE_CASE   (   __a		,__a     ):
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					1



 @register_to_config
 def __init__(   self		:   Optional[int]			,				a__		:   int = 1000			,				a__		:   Optional[Union[np.ndarray, List[float]]] = None      ):
      # set `betas`, `alphas`, `timesteps`
      self.set_timesteps(a__      )

      # standard deviation of the initial noise distribution
      __magic_name__						       =    1.0

      # For now we only support F-PNDM, i.e. the runge-kutta method
      # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
      # mainly at formula (9), (12), (13) and the Algorithm 2.
      __magic_name__						       =    4

      # running values
      __magic_name__						       =    []



 def 			snake_case__  (   self		:   Dict			,				a__		:   int			,				a__		:   Union[str, torch.device] = None      ):
      __magic_name__						       =    num_inference_steps
      __magic_name__						       =    torch.linspace(1			,				0			,				num_inference_steps + 1      )[:-1]
      __magic_name__						       =    torch.cat([steps, torch.tensor([0.0]      )]      )

      if self.config.trained_betas is not None:
           __magic_name__						       =    torch.tensor(self.config.trained_betas			,				dtype=torch.floataa      )
      else:
           __magic_name__						       =    torch.sin(steps * math.pi / 2      ) ** 2

      __magic_name__						       =    (1.0 - self.betas**2) ** 0.5

      __magic_name__						       =    (torch.atana(self.betas			,				self.alphas      ) / math.pi * 2)[:-1]
      __magic_name__						       =    timesteps.to(a__      )

      __magic_name__						       =    []



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   torch.FloatTensor			,				a__		:   int			,				a__		:   torch.FloatTensor			,				a__		:   bool = True			,				):
      if self.num_inference_steps is None:
           raise ValueError(
               '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''      )

      __magic_name__						       =    (self.timesteps == timestep).nonzero().item()
      __magic_name__						       =    timestep_index + 1

      __magic_name__						       =    sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
      self.ets.append(a__      )

      if len(self.ets      ) == 1:
           __magic_name__						       =    self.ets[-1]
      elif len(self.ets      ) == 2:
           __magic_name__						       =    (3 * self.ets[-1] - self.ets[-2]) / 2
      elif len(self.ets      ) == 3:
           __magic_name__						       =    (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
      else:
           __magic_name__						       =    (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

      __magic_name__						       =    self._get_prev_sample(a__			,				a__			,				a__			,				a__      )

      if not return_dict:
           return (prev_sample,)

      return SchedulerOutput(prev_sample=a__      )



 def 			snake_case__  (   self		:   Any			,				a__		:   torch.FloatTensor			,				*a__		:   Optional[int]			,				**a__		:   Union[str, Any]      ):
      return sample



 def 			snake_case__  (   self		:   str			,				a__		:   Optional[int]			,				a__		:   int			,				a__		:   List[Any]			,				a__		:   List[str]      ):
      __magic_name__						       =    self.alphas[timestep_index]
      __magic_name__						       =    self.betas[timestep_index]

      __magic_name__						       =    self.alphas[prev_timestep_index]
      __magic_name__						       =    self.betas[prev_timestep_index]

      __magic_name__						       =    (sample - sigma * ets) / max(a__			,				1E-8      )
      __magic_name__						       =    next_alpha * pred + ets * next_sigma

      return prev_sample



 def __len__(   self		:   int      ):
      return self.config.num_train_timesteps






