






"""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


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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[str]      )		->List[str]:
						A__						:  Union[str, Any]		  =  []

						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((5_1_2, 5_1_2)      )      )
												elif input_type == "audio":
																		inputs.append(torch.ones(3_0_0_0      )      )
												elif isinstance(UpperCAmelCase__,						UpperCAmelCase__      ):
																		inputs.append(create_inputs(UpperCAmelCase__      )      )
												else:
																		raise ValueError(f'Invalid type requested: {input_type}'      )

						return inputs




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List      )		->int:
						A__						:  Union[str, Any]		  =  []

						for output in outputs:
												if isinstance(UpperCAmelCase__,						(str, AgentText)      ):
																		output_types.append("""text"""      )
												elif isinstance(UpperCAmelCase__,						(Image.Image, AgentImage)      ):
																		output_types.append("""image"""      )
												elif isinstance(UpperCAmelCase__,						(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 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													self.assertTrue(hasattr(self.tool			,    """inputs"""							)							)
													self.assertTrue(hasattr(self.tool			,    """outputs"""							)							)

													A__						:  List[str]		  =  self.tool.inputs
													for _input in inputs:
																			if isinstance(_input			,    snake_case							):
																									for __input in _input:
																															self.assertTrue(__input in authorized_types							)
																			else:
																									self.assertTrue(_input in authorized_types							)

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


							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  create_inputs(self.tool.inputs							)
													A__						:  str		  =  self.tool(*snake_case							)

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

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


							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													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      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  List[Any]		  =  create_inputs(self.tool.inputs							)
													A__						:  Optional[Any]		  =  self.tool(*snake_case							)

													if not isinstance(snake_case			,    snake_case							):
																			A__						:  Optional[Any]		  =  [outputs]

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

													for output, output_type in zip(snake_case			,    self.tool.outputs							):
																			A__						:  Dict		  =  AGENT_TYPE_MAPPING[output_type]
																			self.assertTrue(isinstance(snake_case			,    snake_case							)							)


							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													A__						:  str		  =  create_inputs(self.tool.inputs							)

													A__						:  Union[str, Any]		  =  []

													for _input, input_type in zip(snake_case			,    self.tool.inputs							):
																			if isinstance(snake_case			,    snake_case							):
																									_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
													A__						:  str		  =  self.tool(*snake_case							)

													if not isinstance(snake_case			,    snake_case							):
																			A__						:  Any		  =  [outputs]

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







"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							42
							snake_case_     =							42


							def __init__(			self      :						Union[str, Any]			,    snake_case      :						UNetaDModel			,    snake_case      :						KarrasVeScheduler							):
													'''simple docstring'''





													super().__init__()
													self.register_modules(unet=snake_case			,    scheduler=snake_case							)


							@torch.no_grad()
							def __call__(			self      :						Any			,    snake_case      :						int = 1			,    snake_case      :						int = 50			,    snake_case      :						Optional[Union[torch.Generator, List[torch.Generator]]] = None			,    snake_case      :						Optional[str] = "pil"			,    snake_case      :						bool = True			,    **snake_case      :						Dict			,    ):
													'''simple docstring'''





													A__						:  Optional[int]		  =  self.unet.config.sample_size
													A__						:  Dict		  =  (batch_size, 3, img_size, img_size)

													A__						:  Tuple		  =  self.unet

													# sample x_0 ~ N(0, sigma_0^2 * I)
													A__						:  Union[str, Any]		  =  randn_tensor(snake_case			,    generator=snake_case			,    device=self.device							) * self.scheduler.init_noise_sigma

													self.scheduler.set_timesteps(snake_case							)

													for t in self.progress_bar(self.scheduler.timesteps							):
																			# here sigma_t == t_i from the paper
																			A__						:  Optional[Any]		  =  self.scheduler.schedule[t]
																			A__						:  Union[str, Any]		  =  self.scheduler.schedule[t - 1] if t > 0 else 0

																			# 1. Select temporarily increased noise level sigma_hat
																			# 2. Add new noise to move from sample_i to sample_hat
																			A__  ,  A__						:  Dict		  =  self.scheduler.add_noise_to_input(snake_case			,    snake_case			,    generator=snake_case							)

																			# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																			# The model inputs and output are adjusted by following eq. (213) in [1].
																			A__						:  int		  =  (sigma_hat / 2) * model((sample_hat + 1) / 2			,    sigma_hat / 2							).sample

																			# 4. Evaluate dx/dt at sigma_hat
																			# 5. Take Euler step from sigma to sigma_prev
																			A__						:  Tuple		  =  self.scheduler.step(snake_case			,    snake_case			,    snake_case			,    snake_case							)

																			if sigma_prev != 0:
																									# 6. Apply 2nd order correction
																									# The model inputs and output are adjusted by following eq. (213) in [1].
																									A__						:  Dict		  =  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2			,    sigma_prev / 2							).sample
																									A__						:  Tuple		  =  self.scheduler.step_correct(
																									    snake_case			,    snake_case			,    snake_case			,    snake_case			,    step_output.prev_sample			,    step_output["""derivative"""]			,    )
																			A__						:  Tuple		  =  step_output.prev_sample

													A__						:  Dict		  =  (sample / 2 + 0.5).clamp(0			,    1							)
													A__						:  Dict		  =  sample.cpu().permute(0			,    2			,    3			,    1							).numpy()
													if output_type == "pil":
																			A__						:  Optional[Any]		  =  self.numpy_to_pil(snake_case							)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=snake_case							)






