






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     a    ( lowercase__							,       unittest.TestCase   ):



      """simple docstring"""


      a  : Union[str, Any]							       =	DiTPipeline
      a  : Union[str, Any]							       =	CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
      a  : int							       =	PipelineTesterMixin.required_optional_params - {
          'latents',
          'num_images_per_prompt',
          'callback',
          'callback_steps',
      }
      a  : int							       =	CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
      a  : List[str]							       =	False
      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      Optional[Any]:
        torch.manual_seed(0					)
        __UpperCAmelCase							:	Tuple           =  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=__lowercase		,      activation_fn="""gelu-approximate"""		,      num_embeds_ada_norm=1000		,      norm_type="""ada_norm_zero"""		,      norm_elementwise_affine=__lowercase		,      )
        __UpperCAmelCase							:	List[Any]           =  AutoencoderKL()
        __UpperCAmelCase							:	List[Any]           =  DDIMScheduler()
        __UpperCAmelCase							:	List[Any]           =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
        return components
      def 	UpperCAmelCase      (      self					:			Any		,      __lowercase					:			Optional[Any]		,      __lowercase					:			List[Any]=0					)							->      Union[str, Any]:
        if str(__lowercase					).startswith("""mps"""					):
          __UpperCAmelCase							:	int           =  torch.manual_seed(__lowercase					)
        else:
          __UpperCAmelCase							:	List[str]           =  torch.Generator(device=__lowercase					).manual_seed(__lowercase					)
        __UpperCAmelCase							:	List[Any]           =  {
            """class_labels""": [1],
            """generator""": generator,
            """num_inference_steps""": 2,
            """output_type""": """numpy""",
        }
        return inputs
      def 	UpperCAmelCase      (      self					:			Dict					)							->      List[Any]:
        __UpperCAmelCase							:	str           =  """cpu"""

        __UpperCAmelCase							:	Dict           =  self.get_dummy_components()
        __UpperCAmelCase							:	Optional[Any]           =  self.pipeline_class(**__lowercase					)
        pipe.to(__lowercase					)
        pipe.set_progress_bar_config(disable=__lowercase					)

        __UpperCAmelCase							:	Dict           =  self.get_dummy_inputs(__lowercase					)
        __UpperCAmelCase							:	Union[str, Any]           =  pipe(**__lowercase					).images
        __UpperCAmelCase							:	Tuple           =  image[0, -3:, -3:, -1]

        self.assertEqual(image.shape		,      (1, 16, 16, 3)					)
        __UpperCAmelCase							:	str           =  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]					)
        __UpperCAmelCase							:	List[Any]           =  np.abs(image_slice.flatten() - expected_slice					).max()
        self.assertLessEqual(__lowercase		,      1e-3					)
      def 	UpperCAmelCase      (      self					:			Dict					)							->      Tuple:
        self._test_inference_batch_single_identical(relax_max_difference=__lowercase		,      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					:			List[Any]					)							->      Optional[int]:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)


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



      """simple docstring"""


      def 	UpperCAmelCase      (      self					:			str					)							->      str:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
      def 	UpperCAmelCase      (      self					:			str					)							->      List[str]:
        __UpperCAmelCase							:	Union[str, Any]           =  torch.manual_seed(0					)

        __UpperCAmelCase							:	Union[str, Any]           =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""					)
        pipe.to("""cuda"""					)

        __UpperCAmelCase							:	str           =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
        __UpperCAmelCase							:	Optional[int]           =  pipe.get_label_ids(__lowercase					)

        __UpperCAmelCase							:	List[Any]           =  pipe(__lowercase		,      generator=__lowercase		,      num_inference_steps=40		,      output_type="""np"""					).images

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

        __UpperCAmelCase							:	Any           =  ["""vase""", """umbrella"""]
        __UpperCAmelCase							:	Optional[int]           =  pipe.get_label_ids(__lowercase					)

        __UpperCAmelCase							:	Any           =  torch.manual_seed(0					)
        __UpperCAmelCase							:	Any           =  pipe(__lowercase		,      generator=__lowercase		,      num_inference_steps=25		,      output_type="""np"""					).images

        for word, image in zip(__lowercase		,      __lowercase					):
          __UpperCAmelCase							:	List[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










# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a		:       Union[str, Any]			     = {"configuration_timm_backbone": ["TimmBackboneConfig"]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a		:       Tuple			     = ["TimmBackbone"]


if TYPE_CHECKING:
       from .configuration_timm_backbone import TimmBackboneConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_timm_backbone import TimmBackbone

else:
       import sys

       a		:       List[Any]			     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


