





"""simple docstring"""



from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 def __init__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''simple docstring'''




     super().__init__()
     self.register_modules(unet=snake_case__   ,		scheduler=snake_case__    )







 @torch.no_grad()
 def __call__(       self   ,		__lowerCamelCase = 1   ,		__lowerCamelCase = None   ,		__lowerCamelCase = 50   ,		__lowerCamelCase = "pil"   ,		__lowerCamelCase = True   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		List[str]							=	torch.randn(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,		generator=snake_case__   ,		)
     __A       :		List[Any]							=	image.to(self.device    )

     # set step values
     self.scheduler.set_timesteps(snake_case__    )

     for t in self.progress_bar(self.scheduler.timesteps    ):
         # 1. predict noise model_output
         __A       :		str							=	self.unet(snake_case__   ,		snake_case__    ).sample

         # 2. predict previous mean of image x_t-1 and add variance depending on eta
         # eta corresponds to η in paper and should be between [0, 1]
         # do x_t -> x_t-1
         __A       :		int							=	self.scheduler.step(snake_case__   ,		snake_case__   ,		snake_case__    ).prev_sample

     __A       :		Optional[Any]							=	(image / 2 + 0.5).clamp(0   ,		1    )
     __A       :		Any							=	image.cpu().permute(0   ,		2   ,		3   ,		1    ).numpy()
     if output_type == "pil":
         __A       :		int							=	self.numpy_to_pil(snake_case__    )

     if not return_dict:
         return (image,), "This is a local test"

     return ImagePipelineOutput(images=snake_case__    ), "This is a local test"












"""simple docstring"""



import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
 import optax
 from flax.training.common_utils import onehot

 from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
 from transformers.models.ta.modeling_flax_ta import shift_tokens_right




@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   @slow
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"							)
        UpperCAmelCase__						:    str							=							AutoTokenizer.from_pretrained("google/mt5-small"							)

        UpperCAmelCase__						:    str							=							tokenizer("Hello there"							,			return_tensors="np"							).input_ids
        UpperCAmelCase__						:    Optional[Any]							=							tokenizer("Hi I am"							,			return_tensors="np"							).input_ids

        UpperCAmelCase__						:    Any							=							shift_tokens_right(snake_case__							,			model.config.pad_token_id							,			model.config.decoder_start_token_id							)

        UpperCAmelCase__						:    Any							=							model(snake_case__							,			decoder_input_ids=snake_case__							).logits
        UpperCAmelCase__						:    Any							=							optax.softmax_cross_entropy(snake_case__							,			onehot(snake_case__							,			logits.shape[-1]							)							).mean()

        UpperCAmelCase__						:    List[str]							=							-(labels.shape[-1] * loss.item())

        UpperCAmelCase__						:    Dict							=							-84.9127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1e-4							)


