






"""simple docstring"""







import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_UpperCamelCase					      =  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_UpperCamelCase					      =  """    \"\"\"
    Output class for the scheduler's step function output.

    Args:
        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
            denoising loop.
        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
            `pred_original_sample` can be used to preview progress or for guidance.
    \"\"\"

    prev_sample: torch.FloatTensor
    pred_original_sample: Optional[torch.FloatTensor] = None
"""







class  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	tempfile.mkdtemp()
          os.makedirs(os.path.join(self.diffusers_dir     ,"""schedulers/""" ) )
          UpperCAmelCase          =	self.diffusers_dir
          shutil.copy(
              os.path.join(A     ,"""src/diffusers/schedulers/scheduling_ddpm.py""" )     ,os.path.join(self.diffusers_dir     ,"""schedulers/scheduling_ddpm.py""" )     ,)






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""src/diffusers"""
          shutil.rmtree(self.diffusers_dir )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A=None ):
          UpperCAmelCase          =	comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
          if overwrite_result is not None:
                UpperCAmelCase          =	comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
          UpperCAmelCase          =	black.Mode(target_versions={black.TargetVersion.PYaa}     ,line_length=119 )
          UpperCAmelCase          =	black.format_str(A     ,mode=A )
          UpperCAmelCase          =	os.path.join(self.diffusers_dir     ,"""new_code.py""" )
          with open(A     ,"""w"""     ,newline="""\n""" ) as f:
                f.write(A )
          if overwrite_result is None:
                self.assertTrue(len(check_copies.is_copy_consistent(A ) ) == 0 )
          else:
                check_copies.is_copy_consistent(f.name     ,overwrite=A )
                with open(A     ,"""r""" ) as f:
                      self.assertTrue(f.read()     ,A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	check_copies.find_code_in_diffusers("""schedulers.scheduling_ddpm.DDPMSchedulerOutput""" )
          self.assertEqual(A     ,A )






    def     _UpperCamelCase							(			self ):
          # Base copy consistency
          self.check_copy_consistency(
              """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"""     ,"""DDPMSchedulerOutput"""     ,REFERENCE_CODE + """\n"""     ,)

          # With no empty line at the end
          self.check_copy_consistency(
              """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"""     ,"""DDPMSchedulerOutput"""     ,A     ,)

          # Copy consistency with rename
          self.check_copy_consistency(
              """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"""     ,"""TestSchedulerOutput"""     ,re.sub("""DDPM"""     ,"""Test"""     ,A )     ,)

          # Copy consistency with a really long name
          UpperCAmelCase          =	"""TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"""
          self.check_copy_consistency(
              F'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}'''     ,F'''{long_class_name}SchedulerOutput'''     ,re.sub("""Bert"""     ,A     ,A )     ,)

          # Copy consistency with overwrite
          self.check_copy_consistency(
              """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"""     ,"""TestSchedulerOutput"""     ,A     ,overwrite_result=re.sub("""DDPM"""     ,"""Test"""     ,A )     ,)











"""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


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

def _a			(		_snake_case							):







      """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(3000							)							)
            elif isinstance(_snake_case				,	_snake_case							):
                  inputs.append(create_inputs(_snake_case							)							)
            else:
                  raise ValueError(F'''Invalid type requested: {input_type}'''							)

      return inputs

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[]

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

      return output_types







@is_tool_test
class  lowerCamelCase__ :






    def     _UpperCamelCase							(			self ):
          self.assertTrue(hasattr(self.tool     ,"""inputs""" ) )
          self.assertTrue(hasattr(self.tool     ,"""outputs""" ) )

          UpperCAmelCase          =	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 )

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






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	create_inputs(self.tool.inputs )
          UpperCAmelCase          =	self.tool(*A )

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

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






    def     _UpperCamelCase							(			self ):
          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     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	create_inputs(self.tool.inputs )
          UpperCAmelCase          =	self.tool(*A )

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

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

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






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	create_inputs(self.tool.inputs )

          UpperCAmelCase          =	[]

          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
          UpperCAmelCase          =	self.tool(*A )

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

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



