





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"""


  a_      =			DiTPipeline
  a_      =			CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
  a_      =			PipelineTesterMixin.required_optional_params - {
      """latents""",
      """num_images_per_prompt""",
      """callback""",
      """callback_steps""",
  }
  a_      =			CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
  a_      =			False






  def     lowercase		(    self			:		Dict					)							->						Optional[int]:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     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=__a ,  activation_fn='gelu-approximate' ,  num_embeds_ada_norm=1_0_0_0 ,  norm_type='ada_norm_zero' ,  norm_elementwise_affine=__a ,  )
     __lowerCAmelCase							     =     AutoencoderKL()
     __lowerCAmelCase							     =     DDIMScheduler()
     __lowerCAmelCase							     =     {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
     return components






  def     lowercase		(    self			:		str ,  lowerCAmelCase_			:		Any ,  lowerCAmelCase_			:		Optional[int]=0					)							->						Dict:
     if str(__a					).startswith('mps'					):
        __lowerCAmelCase							     =     torch.manual_seed(__a					)
     else:
        __lowerCAmelCase							     =     torch.Generator(device=__a					).manual_seed(__a					)
     __lowerCAmelCase							     =     {
         'class_labels': [1],
         'generator': generator,
         'num_inference_steps': 2,
         'output_type': 'numpy',
     }
     return inputs






  def     lowercase		(    self			:		Optional[Any]					)							->						Optional[int]:
     __lowerCAmelCase							     =     'cpu'

     __lowerCAmelCase							     =     self.get_dummy_components()
     __lowerCAmelCase							     =     self.pipeline_class(**__a					)
     pipe.to(__a					)
     pipe.set_progress_bar_config(disable=__a					)

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

     self.assertEqual(image.shape ,  (1, 1_6, 1_6, 3)					)
     __lowerCAmelCase							     =     np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57]					)
     __lowerCAmelCase							     =     np.abs(image_slice.flatten() - expected_slice					).max()
     self.assertLessEqual(__a ,  1e-3					)






  def     lowercase		(    self			:		Dict					)							->						Optional[int]:
     self._test_inference_batch_single_identical(relax_max_difference=__a ,  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     lowercase		(    self			:		Optional[Any]					)							->						int:
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)





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

  """simple docstring"""


  def     lowercase		(    self			:		Union[str, Any]					)							->						List[Any]:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






  def     lowercase		(    self			:		str					)							->						Optional[Any]:
     __lowerCAmelCase							     =     torch.manual_seed(0					)

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

     __lowerCAmelCase							     =     ['vase', 'umbrella', 'white shark', 'white wolf']
     __lowerCAmelCase							     =     pipe.get_label_ids(__a					)

     __lowerCAmelCase							     =     pipe(__a ,  generator=__a ,  num_inference_steps=4_0 ,  output_type='np'					).images

     for word, image in zip(__a ,  __a					):
        __lowerCAmelCase							     =     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     lowercase		(    self			:		List[str]					)							->						Dict:
     __lowerCAmelCase							     =     DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'					)
     __lowerCAmelCase							     =     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
     pipe.to('cuda'					)

     __lowerCAmelCase							     =     ['vase', 'umbrella']
     __lowerCAmelCase							     =     pipe.get_label_ids(__a					)

     __lowerCAmelCase							     =     torch.manual_seed(0					)
     __lowerCAmelCase							     =     pipe(__a ,  generator=__a ,  num_inference_steps=2_5 ,  output_type='np'					).images

     for word, image in zip(__a ,  __a					):
        __lowerCAmelCase							     =     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 dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin



@dataclass
class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42





class     _UpperCAmelCase   (   _UpperCamelCase		,   _UpperCamelCase   ):

  """simple docstring"""


  @register_to_config
  def __init__(    self			:		Optional[int] ,  lowerCAmelCase_			:		int = 1_6 ,  lowerCAmelCase_			:		int = 8_8 ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		int = 1 ,  lowerCAmelCase_			:		float = 0.0 ,  lowerCAmelCase_			:		int = 3_2 ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		str = "geglu" ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		bool = True ,  )							->						Dict:
     super().__init__()
     __lowerCAmelCase							     =     num_attention_heads
     __lowerCAmelCase							     =     attention_head_dim
     __lowerCAmelCase							     =     num_attention_heads * attention_head_dim

     __lowerCAmelCase							     =     in_channels

     __lowerCAmelCase							     =     torch.nn.GroupNorm(num_groups=lowerCAmelCase_ ,  num_channels=lowerCAmelCase_ ,  eps=1e-6 ,  affine=lowerCAmelCase_					)
     __lowerCAmelCase							     =     nn.Linear(lowerCAmelCase_ ,  lowerCAmelCase_					)

     # 3. Define transformers blocks
     __lowerCAmelCase							     =     nn.ModuleList(
         [
             BasicTransformerBlock(
                 lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  dropout=lowerCAmelCase_ ,  cross_attention_dim=lowerCAmelCase_ ,  activation_fn=lowerCAmelCase_ ,  attention_bias=lowerCAmelCase_ ,  double_self_attention=lowerCAmelCase_ ,  norm_elementwise_affine=lowerCAmelCase_ ,  )
             for d in range(lowerCAmelCase_					)
         ]					)

     __lowerCAmelCase							     =     nn.Linear(lowerCAmelCase_ ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		List[Any]=None ,  lowerCAmelCase_			:		Optional[int]=None ,  lowerCAmelCase_			:		Union[str, Any]=None ,  lowerCAmelCase_			:		Optional[int]=1 ,  lowerCAmelCase_			:		Optional[int]=None ,  lowerCAmelCase_			:		bool = True ,  )							->						str:
     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     hidden_states.shape
     __lowerCAmelCase							     =     batch_frames // num_frames

     __lowerCAmelCase							     =     hidden_states

     __lowerCAmelCase							     =     hidden_states[None, :].reshape(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     hidden_states.permute(0 ,  2 ,  1 ,  3 ,  4					)

     __lowerCAmelCase							     =     self.norm(lowerCAmelCase_					)
     __lowerCAmelCase							     =     hidden_states.permute(0 ,  3 ,  4 ,  2 ,  1					).reshape(batch_size * height * width ,  lowerCAmelCase_ ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     self.proj_in(lowerCAmelCase_					)

     # 2. Blocks
     for block in self.transformer_blocks:
        __lowerCAmelCase							     =     block(
            lowerCAmelCase_ ,  encoder_hidden_states=lowerCAmelCase_ ,  timestep=lowerCAmelCase_ ,  cross_attention_kwargs=lowerCAmelCase_ ,  class_labels=lowerCAmelCase_ ,  )

     # 3. Output
     __lowerCAmelCase							     =     self.proj_out(lowerCAmelCase_					)
     __lowerCAmelCase							     =     (
         hidden_states[None, None, :]
         .reshape(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
         .permute(0 ,  3 ,  4 ,  1 ,  2					)
         .contiguous()
     )
     __lowerCAmelCase							     =     hidden_states.reshape(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     hidden_states + residual

     if not return_dict:
        return (output,)

     return TransformerTemporalModelOutput(sample=lowerCAmelCase_					)

