




import torch

from diffusers import DiffusionPipeline




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    def __init__(    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Optional[Any]:
           super().__init__()

           self.register_modules(unet=UpperCamelCase__     ,  scheduler=UpperCamelCase__     )







    def __call__(    self     )     ->      List[Any]:
           lowerCamelCase   :      Dict			 =					torch.randn(
               (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,  )
           lowerCamelCase   :      Optional[Any]			 =					1

           lowerCamelCase   :      Optional[Any]			 =					self.unet(UpperCamelCase__     ,  UpperCamelCase__     ).sample
           lowerCamelCase   :      int			 =					self.scheduler.step(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ).prev_sample

           lowerCamelCase   :      Optional[Any]			 =					scheduler_output - scheduler_output + torch.ones_like(UpperCamelCase__     )

           return result








'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
							from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






@require_torch
@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase__   (  unittest.TestCase     ):






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''				,							return_dict=__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       AutoTokenizer.from_pretrained('''google/mt5-small'''		)

											lowercase_	:     int													=       tokenizer('''Hello there'''				,							return_tensors='''pt'''		).input_ids
											lowercase_	:     Union[str, Any]													=       tokenizer('''Hi I am'''				,							return_tensors='''pt'''		).input_ids

											lowercase_	:     Union[str, Any]													=       model(input_ids.to(__SCREAMING_SNAKE_CASE		)				,							labels=labels.to(__SCREAMING_SNAKE_CASE		)		).loss
											lowercase_	:     int													=       -(labels.shape[-1] * loss.item())

											lowercase_	:     Any													=       -84.9_127
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)



