
'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class  lowerCAmelCase__   (  lowerCamelCase_    ,						unittest.TestCase     ):
					lowerCAmelCase_        =     DDIMPipeline
					lowerCAmelCase_        =     UNCONDITIONAL_IMAGE_GENERATION_PARAMS
					lowerCAmelCase_        =     PipelineTesterMixin.required_optional_params - {
					    '''num_images_per_prompt''',
					    '''latents''',
					    '''callback''',
					    '''callback_steps''',
					}
					lowerCAmelCase_        =     UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
					lowerCAmelCase_        =     False






					def 	_snake_case    (   self		):


											"""simple docstring"""





											torch.manual_seed(0		)
											lowercase_	:     int													=       UNetaDModel(
											    block_out_channels=(32, 64)				,							layers_per_block=2				,							sample_size=32				,							in_channels=3				,							out_channels=3				,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')				,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')				,							)
											lowercase_	:     List[str]													=       DDIMScheduler()
											lowercase_	:     Dict													=       {'''unet''': unet, '''scheduler''': scheduler}
											return components






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=0		):


											"""simple docstring"""





											if str(__SCREAMING_SNAKE_CASE		).startswith('''mps'''		):
																	lowercase_	:     List[str]													=       torch.manual_seed(__SCREAMING_SNAKE_CASE		)
											else:
																	lowercase_	:     List[Any]													=       torch.Generator(device=__SCREAMING_SNAKE_CASE		).manual_seed(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Dict													=       {
											    '''batch_size''': 1,
											    '''generator''': generator,
											    '''num_inference_steps''': 2,
											    '''output_type''': '''numpy''',
											}
											return inputs






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     int													=       '''cpu'''

											lowercase_	:     Optional[int]													=       self.get_dummy_components()
											lowercase_	:     Union[str, Any]													=       self.pipeline_class(**__SCREAMING_SNAKE_CASE		)
											pipe.to(__SCREAMING_SNAKE_CASE		)
											pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Dict													=       self.get_dummy_inputs(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       pipe(**__SCREAMING_SNAKE_CASE		).images
											lowercase_	:     int													=       image[0, -3:, -3:, -1]

											self.assertEqual(image.shape				,							(1, 32, 32, 3)		)
											lowercase_	:     Union[str, Any]													=       np.array(
											    [1.0_0_0E0_0, 5.7_1_7E-0_1, 4.7_1_7E-0_1, 1.0_0_0E0_0, 0.0_0_0E0_0, 1.0_0_0E0_0, 3.0_0_0E-0_4, 0.0_0_0E0_0, 9.0_0_0E-0_4]		)
											lowercase_	:     Tuple													=       np.abs(image_slice.flatten() - expected_slice		).max()
											self.assertLessEqual(__SCREAMING_SNAKE_CASE				,							1E-3		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											super().test_save_load_local(expected_max_difference=3E-3		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											super().test_save_load_optional_components(expected_max_difference=3E-3		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											super().test_inference_batch_single_identical(expected_max_diff=3E-3		)







@slow
@require_torch_gpu
class  lowerCAmelCase__   (  unittest.TestCase     ):






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       '''google/ddpm-cifar10-32'''

											lowercase_	:     Union[str, Any]													=       UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       DDIMScheduler()

											lowercase_	:     Union[str, Any]													=       DDIMPipeline(unet=__SCREAMING_SNAKE_CASE				,							scheduler=__SCREAMING_SNAKE_CASE		)
											ddim.to(__SCREAMING_SNAKE_CASE		)
											ddim.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     int													=       torch.manual_seed(0		)
											lowercase_	:     Tuple													=       ddim(generator=__SCREAMING_SNAKE_CASE				,							eta=0.0				,							output_type='''numpy'''		).images

											lowercase_	:     Union[str, Any]													=       image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											lowercase_	:     Union[str, Any]													=       np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453]		)

											assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       '''google/ddpm-ema-bedroom-256'''

											lowercase_	:     Dict													=       UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       DDIMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Union[str, Any]													=       DDIMPipeline(unet=__SCREAMING_SNAKE_CASE				,							scheduler=__SCREAMING_SNAKE_CASE		)
											ddpm.to(__SCREAMING_SNAKE_CASE		)
											ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     str													=       torch.manual_seed(0		)
											lowercase_	:     str													=       ddpm(generator=__SCREAMING_SNAKE_CASE				,							output_type='''numpy'''		).images

											lowercase_	:     Optional[Any]													=       image[0, -3:, -3:, -1]

											assert image.shape == (1, 2_56, 2_56, 3)
											lowercase_	:     Tuple													=       np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069]		)

											assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2





'''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


_lowercase :  List[str]    							=       ["text", "image", "audio"]
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[str]							):



						"""simple docstring"""





						lowercase_	:     int													=       []

						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(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							):
																		inputs.append(create_inputs(__SCREAMING_SNAKE_CASE							)							)
												else:
																		raise ValueError(F'''Invalid type requested: {input_type}'''							)

						return inputs
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List							):



						"""simple docstring"""





						lowercase_	:     Optional[Any]													=       []

						for output in outputs:
												if isinstance(__SCREAMING_SNAKE_CASE      ,      (str, AgentText)							):
																		output_types.append('''text'''							)
												elif isinstance(__SCREAMING_SNAKE_CASE      ,      (Image.Image, AgentImage)							):
																		output_types.append('''image'''							)
												elif isinstance(__SCREAMING_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 	_snake_case    (   self		):


											"""simple docstring"""





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

											lowercase_	:     Optional[Any]													=       self.tool.inputs
											for _input in inputs:
																	if isinstance(_input				,							__SCREAMING_SNAKE_CASE		):
																							for __input in _input:
																													self.assertTrue(__input in authorized_types		)
																	else:
																							self.assertTrue(_input in authorized_types		)

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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     int													=       create_inputs(self.tool.inputs		)
											lowercase_	:     Tuple													=       self.tool(*__SCREAMING_SNAKE_CASE		)

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

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






					def 	_snake_case    (   self		):


											"""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 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     int													=       create_inputs(self.tool.inputs		)
											lowercase_	:     int													=       self.tool(*__SCREAMING_SNAKE_CASE		)

											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Optional[Any]													=       [outputs]

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

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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       create_inputs(self.tool.inputs		)

											lowercase_	:     int													=       []

											for _input, input_type in zip(__SCREAMING_SNAKE_CASE				,							self.tool.inputs		):
																	if isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_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
											lowercase_	:     Optional[Any]													=       self.tool(*__SCREAMING_SNAKE_CASE		)

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

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



