



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       UpperCAmelCase__(							_UpperCamelCase     ,							unittest.TestCase	):

   '''simple docstring'''




   A    :  Optional[int]       				=    DiTPipeline
   A    :  Tuple       				=    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
   A    :  Union[str, Any]       				=    PipelineTesterMixin.required_optional_params - {
       "latents",
       "num_images_per_prompt",
       "callback",
       "callback_steps",
   }
   A    :  Union[str, Any]       				=    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
   A    :  Optional[Any]       				=    False


   def 			UpperCAmelCase		(						self  :			int)	->				List[Any]:




         """simple docstring"""

         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=lowerCAmelCase			,				activation_fn='gelu-approximate'			,				num_embeds_ada_norm=10_00			,				norm_type='ada_norm_zero'			,				norm_elementwise_affine=lowerCAmelCase			,				)
         lowercase__     =  AutoencoderKL()
         lowercase__     =  DDIMScheduler()
         lowercase__     =  {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
         return components


   def 			UpperCAmelCase		(						self  :			List[str]			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			int=0)	->				str:




         """simple docstring"""

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


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




         """simple docstring"""

         lowercase__     =  "cpu"

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

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

         self.assertEqual(image.shape			,				(1, 16, 16, 3))
         lowercase__     =  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])
         lowercase__     =  np.abs(image_slice.flatten() - expected_slice).max()
         self.assertLessEqual(lowerCAmelCase			,				1E-3)


   def 			UpperCAmelCase		(						self  :			str)	->				Optional[Any]:




         """simple docstring"""

         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 			UpperCAmelCase		(						self  :			Tuple)	->				Tuple:




         """simple docstring"""

         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)



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

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			int)	->				Dict:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			str)	->				Optional[int]:




         """simple docstring"""

         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(lowerCAmelCase)

         lowercase__     =  pipe(lowerCAmelCase			,				generator=lowerCAmelCase			,				num_inference_steps=40			,				output_type='np').images

         for word, image in zip(lowerCAmelCase			,				lowerCAmelCase):
               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 			UpperCAmelCase		(						self  :			Union[str, Any])	->				str:




         """simple docstring"""

         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(lowerCAmelCase)

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

         for word, image in zip(lowerCAmelCase			,				lowerCAmelCase):
               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





from typing import Mapping

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


a__      :				Union[str, Any]   	=			logging.get_logger(__name__)

a__      :				Optional[Any]   	=			{
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  Union[str, Any]       				=    "umt5"
   A    :  List[str]       				=    ["past_key_values"]


   def __init__(						self  :			List[Any]			,				lowerCAmelCase  :			Optional[int]=25_01_12			,				lowerCAmelCase  :			str=5_12			,				lowerCAmelCase  :			List[Any]=64			,				lowerCAmelCase  :			Optional[int]=10_24			,				lowerCAmelCase  :			Union[str, Any]=8			,				lowerCAmelCase  :			Tuple=None			,				lowerCAmelCase  :			Optional[Any]=6			,				lowerCAmelCase  :			int=32			,				lowerCAmelCase  :			int=1_28			,				lowerCAmelCase  :			List[str]=0.1			,				lowerCAmelCase  :			List[str]=1E-6			,				lowerCAmelCase  :			Optional[int]=1.0			,				lowerCAmelCase  :			Optional[Any]="gated-gelu"			,				lowerCAmelCase  :			List[Any]=True			,				lowerCAmelCase  :			List[str]=True			,				lowerCAmelCase  :			List[Any]="T5Tokenizer"			,				lowerCAmelCase  :			str=True			,				lowerCAmelCase  :			Optional[int]=0			,				lowerCAmelCase  :			Tuple=1			,				lowerCAmelCase  :			Any=0			,				**lowerCAmelCase  :			int			,				)	->				str:




         """simple docstring"""

         super().__init__(
             is_encoder_decoder=lowerCAmelCase			,				tokenizer_class=lowerCAmelCase			,				tie_word_embeddings=lowerCAmelCase			,				pad_token_id=lowerCAmelCase			,				eos_token_id=lowerCAmelCase			,				decoder_start_token_id=lowerCAmelCase			,				**lowerCAmelCase			,				)
         lowercase__     =  vocab_size
         lowercase__     =  d_model
         lowercase__     =  d_kv
         lowercase__     =  d_ff
         lowercase__     =  num_layers
         lowercase__     =  (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         lowercase__     =  num_heads
         lowercase__     =  relative_attention_num_buckets
         lowercase__     =  relative_attention_max_distance
         lowercase__     =  dropout_rate
         lowercase__     =  layer_norm_epsilon
         lowercase__     =  initializer_factor
         lowercase__     =  feed_forward_proj
         lowercase__     =  use_cache

         lowercase__     =  self.feed_forward_proj.split('-')
         lowercase__     =  act_info[-1]
         lowercase__     =  act_info[0] == 'gated'

         if len(lowerCAmelCase) > 1 and act_info[0] != "gated" or len(lowerCAmelCase) > 2:
               raise ValueError(
                   f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                   'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                   '\'gated-gelu\' or \'relu\'')

         if feed_forward_proj == "gated-gelu":
               lowercase__     =  'gelu_new'


   @property
   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Dict:




         """simple docstring"""

         return self.d_model


   @property
   def 			UpperCAmelCase		(						self  :			List[str])	->				Union[str, Any]:




         """simple docstring"""

         return self.num_heads



   @property
   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Optional[Any]:




         """simple docstring"""

         return self.num_layers



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Mapping[str, Mapping[int, str]]:




         """simple docstring"""

         lowercase__     =  {
             'input_ids': {0: 'batch', 1: 'encoder_sequence'},
             'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
         }
         if self.use_past:
               lowercase__     =  'past_encoder_sequence + sequence'
               lowercase__     =  {0: 'batch'}
               lowercase__     =  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
         else:
               lowercase__     =  {0: 'batch', 1: 'decoder_sequence'}
               lowercase__     =  {0: 'batch', 1: 'decoder_sequence'}

         if self.use_past:
               self.fill_with_past_key_values_(lowerCAmelCase			,				direction='inputs')

         return common_inputs


   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
   def 			UpperCAmelCase		(						self  :			int)	->				int:




         """simple docstring"""

         return 13



   @property
   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				float:




         """simple docstring"""

         return 5E-4
