


"""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 			UpperCamelCase_     (			UpperCamelCase      ,  unittest.TestCase):

	"""simple docstring"""





	snake_case__    :  Dict    =      DiTPipeline
	snake_case__    :  Dict    =      CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
	snake_case__    :  Optional[int]    =      PipelineTesterMixin.required_optional_params - {
	    "latents",
	    "num_images_per_prompt",
	    "callback",
	    "callback_steps",
	}
	snake_case__    :  List[Any]    =      CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
	snake_case__    :  str    =      False




	def   UpperCAmelCase_				(			self      :    List[str]       )	->     Optional[Any]:
						torch.manual_seed(0       )
						__SCREAMING_SNAKE_CASE						       =    TransformeraDModel(
						    sample_size=1_6      ,      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=1_0_0_0      ,      norm_type="ada_norm_zero"      ,      norm_elementwise_affine=UpperCAmelCase__      ,      )
						__SCREAMING_SNAKE_CASE						       =    AutoencoderKL()
						__SCREAMING_SNAKE_CASE						       =    DDIMScheduler()
						__SCREAMING_SNAKE_CASE						       =    {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
						return components




	def   UpperCAmelCase_				(			self      :    Union[str, Any]      ,      UpperCAmelCase__      :    int      ,      UpperCAmelCase__      :    str=0       )	->     Dict:
						if str(UpperCAmelCase__       ).startswith("mps"       ):
											__SCREAMING_SNAKE_CASE						       =    torch.manual_seed(UpperCAmelCase__       )
						else:
											__SCREAMING_SNAKE_CASE						       =    torch.Generator(device=UpperCAmelCase__       ).manual_seed(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    {
						    "class_labels": [1],
						    "generator": generator,
						    "num_inference_steps": 2,
						    "output_type": "numpy",
						}
						return inputs




	def   UpperCAmelCase_				(			self      :    Optional[int]       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    "cpu"

						__SCREAMING_SNAKE_CASE						       =    self.get_dummy_components()
						__SCREAMING_SNAKE_CASE						       =    self.pipeline_class(**UpperCAmelCase__       )
						pipe.to(UpperCAmelCase__       )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__       )

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

						self.assertEqual(image.shape      ,      (1, 1_6, 1_6, 3)       )
						__SCREAMING_SNAKE_CASE						       =    np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]       )
						__SCREAMING_SNAKE_CASE						       =    np.abs(image_slice.flatten() - expected_slice       ).max()
						self.assertLessEqual(UpperCAmelCase__      ,      1E-3       )




	def   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     List[str]:
						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      :    Any       )	->     Tuple:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )




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

	"""simple docstring"""





	def   UpperCAmelCase_				(			self      :    Tuple       )	->     str:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




	def   UpperCAmelCase_				(			self      :    int       )	->     List[str]:
						__SCREAMING_SNAKE_CASE						       =    torch.manual_seed(0       )

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

						__SCREAMING_SNAKE_CASE						       =    ["vase", "umbrella", "white shark", "white wolf"]
						__SCREAMING_SNAKE_CASE						       =    pipe.get_label_ids(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    pipe(UpperCAmelCase__      ,      generator=UpperCAmelCase__      ,      num_inference_steps=4_0      ,      output_type="np"       ).images

						for word, image in zip(UpperCAmelCase__      ,      UpperCAmelCase__       ):
											__SCREAMING_SNAKE_CASE						       =    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      :    Optional[int]       )	->     Optional[Any]:
						__SCREAMING_SNAKE_CASE						       =    DiTPipeline.from_pretrained("facebook/DiT-XL-2-512"       )
						__SCREAMING_SNAKE_CASE						       =    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
						pipe.to("cuda"       )

						__SCREAMING_SNAKE_CASE						       =    ["vase", "umbrella"]
						__SCREAMING_SNAKE_CASE						       =    pipe.get_label_ids(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    torch.manual_seed(0       )
						__SCREAMING_SNAKE_CASE						       =    pipe(UpperCAmelCase__      ,      generator=UpperCAmelCase__      ,      num_inference_steps=2_5      ,      output_type="np"       ).images

						for word, image in zip(UpperCAmelCase__      ,      UpperCAmelCase__       ):
											__SCREAMING_SNAKE_CASE						       =    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 ...configuration_utils import PretrainedConfig
from ...utils import logging


a__    :							int            =					logging.get_logger(__name__)

a__    :							Tuple            =					{'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  Dict    =      "ctrl"
	snake_case__    :  int    =      ["past_key_values"]
	snake_case__    :  Tuple    =      {
	    "max_position_embeddings": "n_positions",
	    "hidden_size": "n_embd",
	    "num_attention_heads": "n_head",
	    "num_hidden_layers": "n_layer",
	}




	def __init__(			self      :    List[Any]      ,      UpperCAmelCase__      :    Any=2_4_6_5_3_4      ,      UpperCAmelCase__      :    List[Any]=2_5_6      ,      UpperCAmelCase__      :    Optional[int]=1_2_8_0      ,      UpperCAmelCase__      :    Optional[Any]=8_1_9_2      ,      UpperCAmelCase__      :    int=4_8      ,      UpperCAmelCase__      :    Optional[int]=1_6      ,      UpperCAmelCase__      :    Tuple=0.1      ,      UpperCAmelCase__      :    Optional[Any]=0.1      ,      UpperCAmelCase__      :    Any=1E-6      ,      UpperCAmelCase__      :    List[str]=0.02      ,      UpperCAmelCase__      :    Union[str, Any]=True      ,      **UpperCAmelCase__      :    Tuple      ,      )	->     str:
						__SCREAMING_SNAKE_CASE						       =    vocab_size
						__SCREAMING_SNAKE_CASE						       =    n_positions
						__SCREAMING_SNAKE_CASE						       =    n_embd
						__SCREAMING_SNAKE_CASE						       =    n_layer
						__SCREAMING_SNAKE_CASE						       =    n_head
						__SCREAMING_SNAKE_CASE						       =    dff
						__SCREAMING_SNAKE_CASE						       =    resid_pdrop
						__SCREAMING_SNAKE_CASE						       =    embd_pdrop
						__SCREAMING_SNAKE_CASE						       =    layer_norm_epsilon
						__SCREAMING_SNAKE_CASE						       =    initializer_range

						__SCREAMING_SNAKE_CASE						       =    use_cache

						super().__init__(**UpperCAmelCase__       )
