


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






     def  a_					(			self		)       ->    Any:
          torch.manual_seed(0		)
          _a						=		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=_lowerCAmelCase     ,							activation_fn="gelu-approximate"     ,							num_embeds_ada_norm=1_000     ,							norm_type="ada_norm_zero"     ,							norm_elementwise_affine=_lowerCAmelCase     ,							)
          _a						=		AutoencoderKL()
          _a						=		DDIMScheduler()
          _a						=		{"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
          return components






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase=0		)       ->    Union[str, Any]:
          if str(_lowerCAmelCase		).startswith("mps"		):
               _a						=		torch.manual_seed(_lowerCAmelCase		)
          else:
               _a						=		torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)
          _a						=		{
              "class_labels": [1],
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }
          return inputs






     def  a_					(			self		)       ->    Optional[Any]:
          _a						=		"cpu"

          _a						=		self.get_dummy_components()
          _a						=		self.pipeline_class(**_lowerCAmelCase		)
          pipe.to(_lowerCAmelCase		)
          pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

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

          self.assertEqual(image.shape     ,							(1, 16, 16, 3)		)
          _a						=		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						=		np.abs(image_slice.flatten() - expected_slice		).max()
          self.assertLessEqual(_lowerCAmelCase     ,							1e-3		)






     def  a_					(			self		)       ->    Tuple:
          self._test_inference_batch_single_identical(relax_max_difference=_lowerCAmelCase     ,							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		)       ->    str:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)







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






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






     def  a_					(			self		)       ->    Any:
          _a						=		torch.manual_seed(0		)

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

          _a						=		["vase", "umbrella", "white shark", "white wolf"]
          _a						=		pipe.get_label_ids(_lowerCAmelCase		)

          _a						=		pipe(_lowerCAmelCase     ,							generator=_lowerCAmelCase     ,							num_inference_steps=40     ,							output_type="np"		).images

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

          _a						=		["vase", "umbrella"]
          _a						=		pipe.get_label_ids(_lowerCAmelCase		)

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

          for word, image in zip(_lowerCAmelCase     ,							_lowerCAmelCase		):
               _a						=		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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__				     =		logging.get_logger(__name__)

lowercase__				     =		{
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				'''convbert'''






     def __init__(			self     ,							__UpperCamelCase=30_522     ,							__UpperCamelCase=768     ,							__UpperCamelCase=12     ,							__UpperCamelCase=12     ,							__UpperCamelCase=3_072     ,							__UpperCamelCase="gelu"     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=512     ,							__UpperCamelCase=2     ,							__UpperCamelCase=0.02     ,							__UpperCamelCase=1e-12     ,							__UpperCamelCase=1     ,							__UpperCamelCase=0     ,							__UpperCamelCase=2     ,							__UpperCamelCase=768     ,							__UpperCamelCase=2     ,							__UpperCamelCase=9     ,							__UpperCamelCase=1     ,							__UpperCamelCase=None     ,							**__UpperCamelCase     ,							)       ->    Optional[int]:
          super().__init__(
              pad_token_id=__UpperCamelCase     ,							bos_token_id=__UpperCamelCase     ,							eos_token_id=__UpperCamelCase     ,							**__UpperCamelCase     ,							)

          _a						=		vocab_size
          _a						=		hidden_size
          _a						=		num_hidden_layers
          _a						=		num_attention_heads
          _a						=		intermediate_size
          _a						=		hidden_act
          _a						=		hidden_dropout_prob
          _a						=		attention_probs_dropout_prob
          _a						=		max_position_embeddings
          _a						=		type_vocab_size
          _a						=		initializer_range
          _a						=		layer_norm_eps
          _a						=		embedding_size
          _a						=		head_ratio
          _a						=		conv_kernel_size
          _a						=		num_groups
          _a						=		classifier_dropout







class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






     @property
     def  a_					(			self		)       ->    Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _a						=		{0: "batch", 1: "choice", 2: "sequence"}
          else:
               _a						=		{0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
                  ("token_type_ids", dynamic_axis),
              ]		)

