
"""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


SCREAMING_SNAKE_CASE_    					=						['''text''', '''image''', '''audio''']




def     lowercase__						(					lowerCAmelCase				:					List[str] ) ->      Any:






    """simple docstring"""




    UpperCAmelCase									=					[]

    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(3_000 ) )
        elif isinstance(lowerCAmelCase ,				lowerCAmelCase ):
            inputs.append(create_inputs(lowerCAmelCase ) )
        else:
            raise ValueError(F"Invalid type requested: {input_type}" )

    return inputs




def     lowercase__						(					lowerCAmelCase				:					List ) ->      int:






    """simple docstring"""




    UpperCAmelCase									=					[]

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

    return output_types







@is_tool_test
class      _UpperCAmelCase							:




      def     a_      (							self							)						->					Optional[Any]:
          self.assertTrue(hasattr(self.tool   ,       'inputs'							)							)
          self.assertTrue(hasattr(self.tool   ,       'outputs'							)							)

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

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




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					create_inputs(self.tool.inputs							)
          UpperCAmelCase									=					self.tool(*lowercase_							)

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

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




      def     a_      (							self							)						->					Any:
          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     a_      (							self							)						->					Dict:
          UpperCAmelCase									=					create_inputs(self.tool.inputs							)
          UpperCAmelCase									=					self.tool(*lowercase_							)

          if not isinstance(lowercase_   ,       lowercase_							):
              UpperCAmelCase									=					[outputs]

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

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




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					create_inputs(self.tool.inputs							)

          UpperCAmelCase									=					[]

          for _input, input_type in zip(lowercase_   ,       self.tool.inputs							):
              if isinstance(lowercase_   ,       lowercase_							):
                  _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
          UpperCAmelCase									=					self.tool(*lowercase_							)

          if not isinstance(lowercase_   ,       lowercase_							):
              UpperCAmelCase									=					[outputs]

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





"""simple docstring"""

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							str							      =	(DDPMScheduler,)




      def     a_      (							self   ,       **lowercase_							)						->					Union[str, Any]:
          UpperCAmelCase									=					{
              'num_train_timesteps': 1_0_0_0,
              'beta_start': 0.0_0_0_1,
              'beta_end': 0.0_2,
              'beta_schedule': 'linear',
              'variance_type': 'fixed_small',
              'clip_sample': True,
          }

          config.update(**lowercase_							)
          return config




      def     a_      (							self							)						->					int:
          for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
              self.check_over_configs(num_train_timesteps=lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]   ,       [0.0_0_2, 0.0_2, 0.2, 2]							):
              self.check_over_configs(beta_start=lowercase_   ,       beta_end=lowercase_							)




      def     a_      (							self							)						->					Dict:
          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=lowercase_							)




      def     a_      (							self							)						->					Union[str, Any]:
          for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=lowercase_							)




      def     a_      (							self							)						->					Optional[Any]:
          for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=lowercase_							)




      def     a_      (							self							)						->					Tuple:
          self.check_over_configs(thresholding=lowercase_							)
          for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                  self.check_over_configs(
                      thresholding=lowercase_   ,       prediction_type=lowercase_   ,       sample_max_value=lowercase_   ,       )




      def     a_      (							self							)						->					Any:
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=lowercase_							)




      def     a_      (							self							)						->					Union[str, Any]:
          for t in [0, 5_0_0, 9_9_9]:
              self.check_over_forward(time_step=lowercase_							)




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.0_0_9_7_9							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.0_2							)							) < 1E-5




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					len(lowercase_							)

          UpperCAmelCase									=					self.dummy_model()
          UpperCAmelCase									=					self.dummy_sample_deter
          UpperCAmelCase									=					torch.manual_seed(0							)

          for t in reversed(range(lowercase_							)							):
              # 1. predict noise residual
              UpperCAmelCase									=					model(lowercase_   ,       lowercase_							)

              # 2. predict previous mean of sample x_t-1
              UpperCAmelCase									=					scheduler.step(lowercase_   ,       lowercase_   ,       lowercase_   ,       generator=lowercase_							).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              UpperCAmelCase									=					pred_prev_sample

          UpperCAmelCase									=					torch.sum(torch.abs(lowercase_							)							)
          UpperCAmelCase									=					torch.mean(torch.abs(lowercase_							)							)

          assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1E-2
          assert abs(result_mean.item() - 0.3_3_7_2							) < 1E-3




      def     a_      (							self							)						->					Union[str, Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config(prediction_type='v_prediction'							)
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					len(lowercase_							)

          UpperCAmelCase									=					self.dummy_model()
          UpperCAmelCase									=					self.dummy_sample_deter
          UpperCAmelCase									=					torch.manual_seed(0							)

          for t in reversed(range(lowercase_							)							):
              # 1. predict noise residual
              UpperCAmelCase									=					model(lowercase_   ,       lowercase_							)

              # 2. predict previous mean of sample x_t-1
              UpperCAmelCase									=					scheduler.step(lowercase_   ,       lowercase_   ,       lowercase_   ,       generator=lowercase_							).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              UpperCAmelCase									=					pred_prev_sample

          UpperCAmelCase									=					torch.sum(torch.abs(lowercase_							)							)
          UpperCAmelCase									=					torch.mean(torch.abs(lowercase_							)							)

          assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1E-2
          assert abs(result_mean.item() - 0.2_6_3_1							) < 1E-3




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[1_0_0, 8_7, 5_0, 1, 0]

          scheduler.set_timesteps(timesteps=lowercase_							)

          UpperCAmelCase									=					scheduler.timesteps

          for i, timestep in enumerate(lowercase_							):
              if i == len(lowercase_							) - 1:
                  UpperCAmelCase									=					-1
              else:
                  UpperCAmelCase									=					timesteps[i + 1]

              UpperCAmelCase									=					scheduler.previous_timestep(lowercase_							)
              UpperCAmelCase									=					prev_t.item()

              self.assertEqual(lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[1_0_0, 8_7, 5_0, 5_1, 0]

          with self.assertRaises(lowercase_   ,       msg='`custom_timesteps` must be in descending order.'							):
              scheduler.set_timesteps(timesteps=lowercase_							)




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[1_0_0, 8_7, 5_0, 1, 0]
          UpperCAmelCase									=					len(lowercase_							)

          with self.assertRaises(lowercase_   ,       msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'							):
              scheduler.set_timesteps(num_inference_steps=lowercase_   ,       timesteps=lowercase_							)




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[scheduler.config.num_train_timesteps]

          with self.assertRaises(
              lowercase_   ,       msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'   ,       ):
              scheduler.set_timesteps(timesteps=lowercase_							)



