



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			(					lowercase__    ,	unittest.TestCase):
    UpperCamelCase__				:					Dict     		=DiTPipeline
    UpperCamelCase__				:					Tuple     		=CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
    UpperCamelCase__				:					Tuple     		=PipelineTesterMixin.required_optional_params - {
        """latents""",
        """num_images_per_prompt""",
        """callback""",
        """callback_steps""",
    }
    UpperCamelCase__				:					List[str]     		=CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
    UpperCamelCase__				:					int     		=False




    def 							A__      (						self					:						int						):
       torch.manual_seed(0						)
       lowercase__					  =				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,					)
       lowercase__					  =				AutoencoderKL()
       lowercase__					  =				DDIMScheduler()
       lowercase__					  =				{"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
       return components




    def 							A__      (						self					:						Optional[int],					__lowercase					:						Dict,					__lowercase					:						Optional[int]=0						):
       if str(__lowercase						).startswith("mps"						):
          lowercase__					  =				torch.manual_seed(__lowercase						)
       else:
          lowercase__					  =				torch.Generator(device=__lowercase						).manual_seed(__lowercase						)
       lowercase__					  =				{
           "class_labels": [1],
           "generator": generator,
           "num_inference_steps": 2,
           "output_type": "numpy",
       }
       return inputs




    def 							A__      (						self					:						List[Any]						):
       lowercase__					  =				"cpu"

       lowercase__					  =				self.get_dummy_components()
       lowercase__					  =				self.pipeline_class(**__lowercase						)
       pipe.to(__lowercase						)
       pipe.set_progress_bar_config(disable=__lowercase						)

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

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




    def 							A__      (						self					:						Optional[int]						):
       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 							A__      (						self					:						List[Any]						):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)
@require_torch_gpu
@slow
class 				_snake_case			(					unittest.TestCase):




    def 							A__      (						self					:						List[Any]						):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




    def 							A__      (						self					:						Any						):
       lowercase__					  =				torch.manual_seed(0						)

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

       lowercase__					  =				["vase", "umbrella", "white shark", "white wolf"]
       lowercase__					  =				pipe.get_label_ids(__lowercase						)

       lowercase__					  =				pipe(__lowercase,					generator=__lowercase,					num_inference_steps=40,					output_type="np"						).images

       for word, image in zip(__lowercase,					__lowercase						):
          lowercase__					  =				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 							A__      (						self					:						Dict						):
       lowercase__					  =				DiTPipeline.from_pretrained("facebook/DiT-XL-2-512"						)
       lowercase__					  =				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
       pipe.to("cuda"						)

       lowercase__					  =				["vase", "umbrella"]
       lowercase__					  =				pipe.get_label_ids(__lowercase						)

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

       for word, image in zip(__lowercase,					__lowercase						):
          lowercase__					  =				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


lowercase_								=     {
    """configuration_xmod""": [
        """XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XmodConfig""",
        """XmodOnnxConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """XMOD_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XmodForCausalLM""",
							    """XmodForMaskedLM""",
							    """XmodForMultipleChoice""",
							    """XmodForQuestionAnswering""",
							    """XmodForSequenceClassification""",
							    """XmodForTokenClassification""",
							    """XmodModel""",
							    """XmodPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xmod import (
														    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XmodForCausalLM,
														    XmodForMaskedLM,
														    XmodForMultipleChoice,
														    XmodForQuestionAnswering,
														    XmodForSequenceClassification,
														    XmodForTokenClassification,
														    XmodModel,
														    XmodPreTrainedModel,
														)

else:
							import sys

							lowercase_								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




