
"""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"""

import re

from filelock import FileLock


try:
      import nltk

      UpperCamelCase			:       int									=				True
except (ImportError, ModuleNotFoundError):
      UpperCamelCase			:       List[str]									=				False

if NLTK_AVAILABLE:
      with FileLock(".lock") as lock:
            nltk.download("punkt", quiet=True)






def 							A		(       snake_case   :str	)   ->			str:
 re.sub('<n>'       ,      ''       ,      snake_case	)  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(snake_case	)	)



