


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      lowercase__    (							__A     ,					unittest.TestCase  ):
  __UpperCamelCase	     =  DiTPipeline
  __UpperCamelCase	     =  CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
  __UpperCamelCase	     =  PipelineTesterMixin.required_optional_params - {
      "latents",
      "num_images_per_prompt",
      "callback",
      "callback_steps",
  }
  __UpperCamelCase	     =  CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
  __UpperCamelCase	     =  False







  def 	UpperCAmelCase__		(	self						):
       torch.manual_seed(0						)
       lowerCAmelCase_					:       Optional[int]    =		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=1_000    ,						norm_type="""ada_norm_zero"""    ,						norm_elementwise_affine=UpperCamelCase__    ,						)
       lowerCAmelCase_					:       Dict    =		AutoencoderKL()
       lowerCAmelCase_					:       str    =		DDIMScheduler()
       lowerCAmelCase_					:       Union[str, Any]    =		{"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
       return components







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase=0						):





       if str(UpperCamelCase__						).startswith("""mps"""						):
            lowerCAmelCase_					:       Optional[int]    =		torch.manual_seed(UpperCamelCase__						)
       else:
            lowerCAmelCase_					:       Optional[Any]    =		torch.Generator(device=UpperCamelCase__						).manual_seed(UpperCamelCase__						)
       lowerCAmelCase_					:       Any    =		{
           """class_labels""": [1],
           """generator""": generator,
           """num_inference_steps""": 2,
           """output_type""": """numpy""",
       }
       return inputs







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       int    =		"""cpu"""

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

       lowerCAmelCase_					:       int    =		self.get_dummy_inputs(UpperCamelCase__						)
       lowerCAmelCase_					:       List[Any]    =		pipe(**UpperCamelCase__						).images
       lowerCAmelCase_					:       int    =		image[0, -3:, -3:, -1]

       self.assertEqual(image.shape    ,						(1, 16, 16, 3)						)
       lowerCAmelCase_					:       int    =		np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]						)
       lowerCAmelCase_					:       Dict    =		np.abs(image_slice.flatten() - expected_slice						).max()
       self.assertLessEqual(UpperCamelCase__    ,						1e-3						)







  def 	UpperCAmelCase__		(	self						):
       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						):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)





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







  def 	UpperCAmelCase__		(	self						):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Dict    =		torch.manual_seed(0						)

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

       lowerCAmelCase_					:       Optional[int]    =		["""vase""", """umbrella""", """white shark""", """white wolf"""]
       lowerCAmelCase_					:       Optional[int]    =		pipe.get_label_ids(UpperCamelCase__						)

       lowerCAmelCase_					:       Optional[Any]    =		pipe(UpperCamelCase__    ,						generator=UpperCamelCase__    ,						num_inference_steps=40    ,						output_type="""np"""						).images

       for word, image in zip(UpperCamelCase__    ,						UpperCamelCase__						):
            lowerCAmelCase_					:       int    =		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						):
       lowerCAmelCase_					:       str    =		DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""						)
       lowerCAmelCase_					:       int    =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
       pipe.to("""cuda"""						)

       lowerCAmelCase_					:       Optional[Any]    =		["""vase""", """umbrella"""]
       lowerCAmelCase_					:       str    =		pipe.get_label_ids(UpperCamelCase__						)

       lowerCAmelCase_					:       Union[str, Any]    =		torch.manual_seed(0						)
       lowerCAmelCase_					:       str    =		pipe(UpperCamelCase__    ,						generator=UpperCamelCase__    ,						num_inference_steps=25    ,						output_type="""np"""						).images

       for word, image in zip(UpperCamelCase__    ,						UpperCamelCase__						):
            lowerCAmelCase_					:       Tuple    =		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







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase_					:   List[str] 							=						logging.get_logger(__name__)

UpperCAmelCase_					:   int 							=						{
    """google/bit-50""": """https://huggingface.co/google/bit-50/resolve/main/config.json""",
}





class      lowercase__    (							__A     ,					__A  ):
  __UpperCamelCase	     =  """bit"""
  __UpperCamelCase	     =  ["""preactivation""", """bottleneck"""]
  __UpperCamelCase	     =  ["""SAME""", """VALID"""]







  def __init__(	self    ,						_lowercase=3    ,						_lowercase=64    ,						_lowercase=[256, 512, 1_024, 2_048]    ,						_lowercase=[3, 4, 6, 3]    ,						_lowercase="preactivation"    ,						_lowercase="relu"    ,						_lowercase=None    ,						_lowercase=32    ,						_lowercase=0.0    ,						_lowercase=False    ,						_lowercase=32    ,						_lowercase=1    ,						_lowercase=None    ,						_lowercase=None    ,						**_lowercase    ,						):
       super().__init__(**_lowercase						)
       if layer_type not in self.layer_types:
            raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types						)}'						)
       if global_padding is not None:
            if global_padding.upper() in self.supported_padding:
                 lowerCAmelCase_					:       int    =		global_padding.upper()
            else:
                 raise ValueError(F'Padding strategy {global_padding} not supported'						)
       lowerCAmelCase_					:       Optional[Any]    =		num_channels
       lowerCAmelCase_					:       List[str]    =		embedding_size
       lowerCAmelCase_					:       Dict    =		hidden_sizes
       lowerCAmelCase_					:       Optional[int]    =		depths
       lowerCAmelCase_					:       List[Any]    =		layer_type
       lowerCAmelCase_					:       Union[str, Any]    =		hidden_act
       lowerCAmelCase_					:       Optional[int]    =		global_padding
       lowerCAmelCase_					:       Optional[Any]    =		num_groups
       lowerCAmelCase_					:       Optional[Any]    =		drop_path_rate
       lowerCAmelCase_					:       Tuple    =		embedding_dynamic_padding
       lowerCAmelCase_					:       Any    =		output_stride
       lowerCAmelCase_					:       List[Any]    =		width_factor

       lowerCAmelCase_					:       Union[str, Any]    =		["""stem"""] + [F'stage{idx}' for idx in range(1    ,						len(_lowercase						) + 1						)]
       lowerCAmelCase_   ,  lowerCAmelCase_					:       Tuple    =		get_aligned_output_features_output_indices(
           out_features=_lowercase    ,						out_indices=_lowercase    ,						stage_names=self.stage_names						)
