





from collections import OrderedDict
from typing import Mapping

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


__A             =       logging.get_logger(__name__)

__A             =       {
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}



class      _SCREAMING_SNAKE_CASE		(			UpperCamelCase__					):







	'''simple docstring'''
	lowercase_	=    "xmod"

	def __init__(self       :     Optional[int] ,				UpperCAmelCase_       :     Optional[int]=30_522 ,				UpperCAmelCase_       :     Tuple=768 ,				UpperCAmelCase_       :     int=12 ,				UpperCAmelCase_       :     Optional[int]=12 ,				UpperCAmelCase_       :     Tuple=3_072 ,				UpperCAmelCase_       :     Dict="gelu" ,				UpperCAmelCase_       :     Dict=0.1 ,				UpperCAmelCase_       :     List[str]=0.1 ,				UpperCAmelCase_       :     Any=512 ,				UpperCAmelCase_       :     List[str]=2 ,				UpperCAmelCase_       :     str=0.02 ,				UpperCAmelCase_       :     Optional[int]=1E-1_2 ,				UpperCAmelCase_       :     Optional[Any]=1 ,				UpperCAmelCase_       :     Optional[Any]=0 ,				UpperCAmelCase_       :     Any=2 ,				UpperCAmelCase_       :     Union[str, Any]="absolute" ,				UpperCAmelCase_       :     Any=True ,				UpperCAmelCase_       :     Union[str, Any]=None ,				UpperCAmelCase_       :     List[Any]=False ,				UpperCAmelCase_       :     int=2 ,				UpperCAmelCase_       :     str=False ,				UpperCAmelCase_       :     List[str]=True ,				UpperCAmelCase_       :     Tuple=True ,				UpperCAmelCase_       :     str=("en_XX",) ,				UpperCAmelCase_       :     List[str]=None ,				**UpperCAmelCase_       :     Tuple ,				) ->Optional[Any]:



			'''simple docstring'''



			super().__init__(pad_token_id=_a ,				bos_token_id=_a ,				eos_token_id=_a ,				**_a)

			lowerCamelCase__:   List[Any]    						=vocab_size
			lowerCamelCase__:   Union[str, Any]    						=hidden_size
			lowerCamelCase__:   Optional[Any]    						=num_hidden_layers
			lowerCamelCase__:   List[str]    						=num_attention_heads
			lowerCamelCase__:   str    						=hidden_act
			lowerCamelCase__:   Any    						=intermediate_size
			lowerCamelCase__:   Any    						=hidden_dropout_prob
			lowerCamelCase__:   Optional[int]    						=attention_probs_dropout_prob
			lowerCamelCase__:   Any    						=max_position_embeddings
			lowerCamelCase__:   int    						=type_vocab_size
			lowerCamelCase__:   Tuple    						=initializer_range
			lowerCamelCase__:   Dict    						=layer_norm_eps
			lowerCamelCase__:   List[Any]    						=position_embedding_type
			lowerCamelCase__:   Union[str, Any]    						=use_cache
			lowerCamelCase__:   List[Any]    						=classifier_dropout
			lowerCamelCase__:   Union[str, Any]    						=pre_norm
			lowerCamelCase__:   Union[str, Any]    						=adapter_reduction_factor
			lowerCamelCase__:   int    						=adapter_layer_norm
			lowerCamelCase__:   List[Any]    						=adapter_reuse_layer_norm
			lowerCamelCase__:   str    						=ln_before_adapter
			lowerCamelCase__:   Optional[Any]    						=list(_a)
			lowerCamelCase__:   Optional[int]    						=default_language



class      _SCREAMING_SNAKE_CASE		(			UpperCamelCase__					):







	'''simple docstring'''

	@property
	def 							SCREAMING_SNAKE_CASE_      (self       :     int) ->Mapping[str, Mapping[int, str]]:



			'''simple docstring'''



			if self.task == "multiple-choice":
					lowerCamelCase__:   int    						={0: """batch""", 1: """choice""", 2: """sequence"""}
			else:
					lowerCamelCase__:   Dict    						={0: """batch""", 1: """sequence"""}
			return OrderedDict(
			    [
			        ("input_ids", dynamic_axis),
			        ("attention_mask", dynamic_axis),
			    ])








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		(			__SCREAMING_SNAKE_CASE					,							unittest.TestCase					):







 '''simple docstring'''
 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 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any]) ->List[Any]:



   '''simple docstring'''



   torch.manual_seed(0)
   lowerCamelCase__:   Optional[int]    						=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=1_000 ,				norm_type="ada_norm_zero" ,				norm_elementwise_affine=UpperCAmelCase_ ,				)
   lowerCamelCase__:   List[str]    						=AutoencoderKL()
   lowerCamelCase__:   Tuple    						=DDIMScheduler()
   lowerCamelCase__:   Tuple    						={"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
   return components

 def 							SCREAMING_SNAKE_CASE_      (self       :     List[Any] ,				UpperCAmelCase_       :     int ,				UpperCAmelCase_       :     List[Any]=0) ->List[Any]:



   '''simple docstring'''



   if str(UpperCAmelCase_).startswith("mps"):
     lowerCamelCase__:   Any    						=torch.manual_seed(UpperCAmelCase_)
   else:
     lowerCamelCase__:   Union[str, Any]    						=torch.Generator(device=UpperCAmelCase_).manual_seed(UpperCAmelCase_)
   lowerCamelCase__:   List[Any]    						={
       "class_labels": [1],
       "generator": generator,
       "num_inference_steps": 2,
       "output_type": "numpy",
   }
   return inputs

 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple) ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:   List[str]    						="cpu"

   lowerCamelCase__:   Union[str, Any]    						=self.get_dummy_components()
   lowerCamelCase__:   List[str]    						=self.pipeline_class(**UpperCAmelCase_)
   pipe.to(UpperCAmelCase_)
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__:   List[str]    						=self.get_dummy_inputs(UpperCAmelCase_)
   lowerCamelCase__:   Any    						=pipe(**UpperCAmelCase_).images
   lowerCamelCase__:   List[str]    						=image[0, -3:, -3:, -1]

   self.assertEqual(image.shape ,				(1, 16, 16, 3))
   lowerCamelCase__:   Optional[Any]    						=np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457])
   lowerCamelCase__:   List[Any]    						=np.abs(image_slice.flatten() - expected_slice).max()
   self.assertLessEqual(UpperCAmelCase_ ,				1E-3)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple) ->str:



   '''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 							SCREAMING_SNAKE_CASE_      (self       :     Dict) ->int:



   '''simple docstring'''



   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)



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







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_      (self       :     Any) ->Optional[Any]:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_      (self       :     Any) ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:   List[Any]    						=torch.manual_seed(0)

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

   lowerCamelCase__:   Any    						=["vase", "umbrella", "white shark", "white wolf"]
   lowerCamelCase__:   Any    						=pipe.get_label_ids(UpperCAmelCase_)

   lowerCamelCase__:   Any    						=pipe(UpperCAmelCase_ ,				generator=UpperCAmelCase_ ,				num_inference_steps=40 ,				output_type="np").images

   for word, image in zip(UpperCAmelCase_ ,				UpperCAmelCase_):
     lowerCamelCase__:   Optional[int]    						=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 							SCREAMING_SNAKE_CASE_      (self       :     int) ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:   Union[str, Any]    						=DiTPipeline.from_pretrained("facebook/DiT-XL-2-512")
   lowerCamelCase__:   List[str]    						=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
   pipe.to("cuda")

   lowerCamelCase__:   List[str]    						=["vase", "umbrella"]
   lowerCamelCase__:   List[Any]    						=pipe.get_label_ids(UpperCAmelCase_)

   lowerCamelCase__:   str    						=torch.manual_seed(0)
   lowerCamelCase__:   Optional[int]    						=pipe(UpperCAmelCase_ ,				generator=UpperCAmelCase_ ,				num_inference_steps=25 ,				output_type="np").images

   for word, image in zip(UpperCAmelCase_ ,				UpperCAmelCase_):
     lowerCamelCase__:   str    						=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

