






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 						snake_case  (  UpperCAmelCase       ,			unittest.TestCase	):
    __magic_name__   					=					DiTPipeline
    __magic_name__   					=					CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
    __magic_name__   					=					PipelineTesterMixin.required_optional_params - {
        '''latents''',
        '''num_images_per_prompt''',
        '''callback''',
        '''callback_steps''',
    }
    __magic_name__   					=					CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
    __magic_name__   					=					False





    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      torch.manual_seed(0      )
      a					:      str    =       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    ,		)
      a					:      List[Any]    =       AutoencoderKL()
      a					:      Optional[Any]    =       DDIMScheduler()
      a					:      Any    =       {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
      return components





    def     lowerCamelCase__				(							self     : str    ,		A     : Dict    ,		A     : List[Any]=0      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      a					:      List[Any]    =       'cpu'

      a					:      int    =       self.get_dummy_components()
      a					:      int    =       self.pipeline_class(**A      )
      pipe.to(A      )
      pipe.set_progress_bar_config(disable=A      )

      a					:      Union[str, Any]    =       self.get_dummy_inputs(A      )
      a					:      Optional[Any]    =       pipe(**A      ).images
      a					:      int    =       image[0, -3:, -3:, -1]

      self.assertEqual(image.shape    ,		(1, 1_6, 1_6, 3)      )
      a					:      Dict    =       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]      )
      a					:      Union[str, Any]    =       np.abs(image_slice.flatten() - expected_slice      ).max()
      self.assertLessEqual(A    ,		1E-3      )





    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




      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     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )






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





    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




      a					:      Dict    =       torch.manual_seed(0      )

      a					:      Optional[Any]    =       DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'      )
      pipe.to('cuda'      )

      a					:      str    =       ['vase', 'umbrella', 'white shark', 'white wolf']
      a					:      Optional[Any]    =       pipe.get_label_ids(A      )

      a					:      str    =       pipe(A    ,		generator=A    ,		num_inference_steps=4_0    ,		output_type='np'      ).images

      for word, image in zip(A    ,		A      ):
        a					:      Tuple    =       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     lowerCamelCase__				(							self     : int      ):


      '''simple docstring'''




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

      a					:      Dict    =       ['vase', 'umbrella']
      a					:      List[Any]    =       pipe.get_label_ids(A      )

      a					:      List[str]    =       torch.manual_seed(0      )
      a					:      List[Any]    =       pipe(A    ,		generator=A    ,		num_inference_steps=2_5    ,		output_type='np'      ).images

      for word, image in zip(A    ,		A      ):
        a					:      Optional[Any]    =       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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_UpperCamelCase    :   Optional[int]   			=						{
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase    :   Dict   			=						[
       'RagModel',
       'RagPreTrainedModel',
       'RagSequenceForGeneration',
       'RagTokenForGeneration',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase    :   Optional[int]   			=						[
       'TFRagModel',
       'TFRagPreTrainedModel',
       'TFRagSequenceForGeneration',
       'TFRagTokenForGeneration',
   ]


if TYPE_CHECKING:
   from .configuration_rag import RagConfig
   from .retrieval_rag import RagRetriever
   from .tokenization_rag import RagTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_rag import (
          TFRagModel,
          TFRagPreTrainedModel,
          TFRagSequenceForGeneration,
          TFRagTokenForGeneration,
      )

else:
   import sys

   _UpperCamelCase    :   int   			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




