






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     (			a      ,     unittest.TestCase ):





				"""simple docstring"""







				UpperCamelCase__ :      List[Any]			 =DiTPipeline
				UpperCamelCase__ :      Dict			 =CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
				UpperCamelCase__ :      Any			 =PipelineTesterMixin.required_optional_params - {
				    """latents""",
				    """num_images_per_prompt""",
				    """callback""",
				    """callback_steps""",
				}
				UpperCamelCase__ :      Any			 =CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
				UpperCamelCase__ :      Optional[Any]			 =False


				def       __lowercase							( self    ):




									"""simple docstring"""

									torch.manual_seed(0    )
									__UpperCamelCase      :					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=lowerCamelCase__							,  activation_fn='gelu-approximate'							,  num_embeds_ada_norm=1000							,  norm_type='ada_norm_zero'							,  norm_elementwise_affine=lowerCamelCase__							,  )
									__UpperCamelCase      :					Union[str, Any]       		=AutoencoderKL()
									__UpperCamelCase      :					int       		=DDIMScheduler()
									__UpperCamelCase      :					Tuple       		={'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
									return components


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__=0    ):




									"""simple docstring"""

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


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[Any]       		='cpu'

									__UpperCamelCase      :					Optional[int]       		=self.get_dummy_components()
									__UpperCamelCase      :					Optional[Any]       		=self.pipeline_class(**lowerCamelCase__    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

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

									self.assertEqual(image.shape							,  (1, 16, 16, 3)    )
									__UpperCamelCase      :					Tuple       		=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(lowerCamelCase__							,  1E-3    )


				def       __lowercase							( self    ):




									"""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       __lowercase							( self    ):




									"""simple docstring"""

									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3    )




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





				"""simple docstring"""







				def       __lowercase							( self    ):




									"""simple docstring"""

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


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					int       		=torch.manual_seed(0    )

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

									__UpperCamelCase      :					List[Any]       		=['vase', 'umbrella', 'white shark', 'white wolf']
									__UpperCamelCase      :					Dict       		=pipe.get_label_ids(lowerCamelCase__    )

									__UpperCamelCase      :					str       		=pipe(lowerCamelCase__							,  generator=lowerCamelCase__							,  num_inference_steps=40							,  output_type='np'    ).images

									for word, image in zip(lowerCamelCase__							,  lowerCamelCase__    ):
														__UpperCamelCase      :					Union[str, Any]       		=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       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[Any]       		=DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'    )
									__UpperCamelCase      :					Dict       		=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
									pipe.to('cuda'    )

									__UpperCamelCase      :					Tuple       		=['vase', 'umbrella']
									__UpperCamelCase      :					List[Any]       		=pipe.get_label_ids(lowerCamelCase__    )

									__UpperCamelCase      :					str       		=torch.manual_seed(0    )
									__UpperCamelCase      :					int       		=pipe(lowerCamelCase__							,  generator=lowerCamelCase__							,  num_inference_steps=25							,  output_type='np'    ).images

									for word, image in zip(lowerCamelCase__							,  lowerCamelCase__    ):
														__UpperCamelCase      :					Dict       		=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"""






import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 			A__   (  __SCREAMING_SNAKE_CASE  ,		unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	FunnelTokenizer
	SCREAMING_SNAKE_CASE          =	FunnelTokenizerFast
	SCREAMING_SNAKE_CASE          =	True
	SCREAMING_SNAKE_CASE          =	True

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Optional[int]:

				"""simple docstring"""

				super().setUp()

				__lowerCAmelCase      :  str												=       [
				    "<unk>",
				    "<cls>",
				    "<sep>",
				    "want",
				    "##want",
				    "##ed",
				    "wa",
				    "un",
				    "runn",
				    "##ing",
				    ",",
				    "low",
				    "lowest",
				]
				__lowerCAmelCase      :  int												=       os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES["vocab_file"])
				with open(self.vocab_file       ,			"w"       ,			encoding="utf-8") as vocab_writer:
							vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			**_SCREAMING_SNAKE_CASE:		Union[str, Any])				->       int:

				"""simple docstring"""

				return FunnelTokenizer.from_pretrained(self.tmpdirname       ,			**_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any       ,			**_SCREAMING_SNAKE_CASE:		Any)				->       str:

				"""simple docstring"""

				return FunnelTokenizerFast.from_pretrained(self.tmpdirname       ,			**_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple       ,			_SCREAMING_SNAKE_CASE:		str)				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       "UNwant\u00E9d,running"
				__lowerCAmelCase      :  str												=       "unwanted, running"
				return input_text, output_text

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

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       self.tokenizer_class(self.vocab_file)

				__lowerCAmelCase      :  Any												=       tokenizer.tokenize("UNwant\u00E9d,running")
				self.assertListEqual(_SCREAMING_SNAKE_CASE       ,			["un", "##want", "##ed", ",", "runn", "##ing"])
				self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE)       ,			[7, 4, 5, 10, 8, 9])







	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       self.get_tokenizers(do_lower_case=_SCREAMING_SNAKE_CASE)
				for tokenizer in tokenizers:
							__lowerCAmelCase      :  List[str]												=       tokenizer("UNwant\u00E9d,running")
							__lowerCAmelCase      :  Optional[int]												=       len(inputs["input_ids"]) - 1
							self.assertListEqual(inputs["token_type_ids"]       ,			[2] + [0] * sentence_len)

							__lowerCAmelCase      :  List[str]												=       tokenizer("UNwant\u00E9d,running"       ,			"UNwant\u00E9d,running")
							self.assertListEqual(inputs["token_type_ids"]       ,			[2] + [0] * sentence_len + [1] * sentence_len)