
"""simple docstring"""

import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ,  unittest.TestCase      ):
					lowercase						       =						DiTPipeline
					lowercase						       =						CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
					lowercase						       =						PipelineTesterMixin.required_optional_params - {
					    "latents",
					    "num_images_per_prompt",
					    "callback",
					    "callback_steps",
					}
					lowercase						       =						CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
					lowercase						       =						False





					def 						UpperCAmelCase (     self				):

						'''simple docstring'''




						torch.manual_seed(0				)
						__UpperCamelCase      =	TransformeraDModel(
						    sample_size=16     ,					num_layers=2     ,					patch_size=4     ,					attention_head_dim=8     ,					num_attention_heads=2     ,					in_channels=4     ,					out_channels=8     ,					attention_bias=__UpperCAmelCase     ,					activation_fn='gelu-approximate'     ,					num_embeds_ada_norm=1000     ,					norm_type='ada_norm_zero'     ,					norm_elementwise_affine=__UpperCAmelCase     ,					)
						__UpperCamelCase      =	AutoencoderKL()
						__UpperCamelCase      =	DDIMScheduler()
						__UpperCamelCase      =	{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
						return components





					def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=0				):

						'''simple docstring'''




						if str(__UpperCAmelCase				).startswith('mps'				):
							__UpperCamelCase      =	torch.manual_seed(__UpperCAmelCase				)
						else:
							__UpperCamelCase      =	torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)
						__UpperCamelCase      =	{
						    'class_labels': [1],
						    'generator': generator,
						    'num_inference_steps': 2,
						    'output_type': 'numpy',
						}
						return inputs





					def 						UpperCAmelCase (     self				):

						'''simple docstring'''




						__UpperCamelCase      =	'cpu'

						__UpperCamelCase      =	self.get_dummy_components()
						__UpperCamelCase      =	self.pipeline_class(**__UpperCAmelCase				)
						pipe.to(__UpperCAmelCase				)
						pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

						__UpperCamelCase      =	self.get_dummy_inputs(__UpperCAmelCase				)
						__UpperCamelCase      =	pipe(**__UpperCAmelCase				).images
						__UpperCamelCase      =	image[0, -3:, -3:, -1]

						self.assertEqual(image.shape     ,					(1, 16, 16, 3)				)
						__UpperCamelCase      =	np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]				)
						__UpperCamelCase      =	np.abs(image_slice.flatten() - expected_slice				).max()
						self.assertLessEqual(__UpperCAmelCase     ,					1E-3				)





					def 						UpperCAmelCase (     self				):

						'''simple docstring'''




						self._test_inference_batch_single_identical(relax_max_difference=__UpperCAmelCase     ,					expected_max_diff=1E-3				)







					@unittest.skipIf(
					    torch_device != 'cuda' or not is_xformers_available()     ,					reason='XFormers attention is only available with CUDA and `xformers` installed'     ,					)
					def 						UpperCAmelCase (     self				):

						'''simple docstring'''




						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)


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





					def 						UpperCAmelCase (     self				):

						'''simple docstring'''




						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





					def 						UpperCAmelCase (     self				):

						'''simple docstring'''




						__UpperCamelCase      =	torch.manual_seed(0				)

						__UpperCamelCase      =	DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'				)
						pipe.to('cuda'				)

						__UpperCamelCase      =	['vase', 'umbrella', 'white shark', 'white wolf']
						__UpperCamelCase      =	pipe.get_label_ids(__UpperCAmelCase				)

						__UpperCamelCase      =	pipe(__UpperCAmelCase     ,					generator=__UpperCAmelCase     ,					num_inference_steps=40     ,					output_type='np'				).images

						for word, image in zip(__UpperCAmelCase     ,					__UpperCAmelCase				):
							__UpperCamelCase      =	load_numpy(
							    F'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'				)
							assert np.abs((expected_image - image).max()				) < 1E-2







					def 						UpperCAmelCase (     self				):

						'''simple docstring'''




						__UpperCamelCase      =	DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'				)
						__UpperCamelCase      =	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
						pipe.to('cuda'				)

						__UpperCamelCase      =	['vase', 'umbrella']
						__UpperCamelCase      =	pipe.get_label_ids(__UpperCAmelCase				)

						__UpperCamelCase      =	torch.manual_seed(0				)
						__UpperCamelCase      =	pipe(__UpperCAmelCase     ,					generator=__UpperCAmelCase     ,					num_inference_steps=25     ,					output_type='np'				).images

						for word, image in zip(__UpperCAmelCase     ,					__UpperCAmelCase				):
							__UpperCamelCase      =	load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    F'/dit/{word}_512.npy'				)

							assert np.abs((expected_image - image).max()				) < 1E-1





"""simple docstring"""

from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands






def 							A						(    )				->     Union[str, Any]:
 __UpperCamelCase      =	ArgumentParser('Transformers CLI tool'    , usage='transformers-cli <command> [<args>]'		)
 __UpperCamelCase      =	parser.add_subparsers(help='transformers-cli command helpers'		)

 # Register commands
 ConvertCommand.register_subcommand(snake_case		)
 DownloadCommand.register_subcommand(snake_case		)
 EnvironmentCommand.register_subcommand(snake_case		)
 RunCommand.register_subcommand(snake_case		)
 ServeCommand.register_subcommand(snake_case		)
 UserCommands.register_subcommand(snake_case		)
 AddNewModelCommand.register_subcommand(snake_case		)
 AddNewModelLikeCommand.register_subcommand(snake_case		)
 LfsCommands.register_subcommand(snake_case		)
 PTtoTFCommand.register_subcommand(snake_case		)

 # Let's go
 __UpperCamelCase      =	parser.parse_args()

 if not hasattr(snake_case    , 'func'		):
  parser.print_help()
  exit(1		)

 # Run
 __UpperCamelCase      =	args.func(snake_case		)
 service.run()


if __name__ == "__main__":
      main()



