import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 	snake_case__ (							_lowerCAmelCase				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Any:
							super().__init__()
							self.register_modules(vqvae=lowerCAmelCase__      ,       unet=lowerCAmelCase__      ,       scheduler=lowerCAmelCase__			)







	@torch.no_grad()
	def __call__(    self      ,       lowerCAmelCase__ = 1      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = 0.0      ,       lowerCAmelCase__ = 50      ,       lowerCAmelCase__ = "pil"      ,       lowerCAmelCase__ = True      ,       **lowerCAmelCase__      ,       )							->				Union[Tuple, ImagePipelineOutput]:
							__magic_name__      :       Dict       						= randn_tensor(
							    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)      ,       generator=lowerCAmelCase__      ,       )
							__magic_name__      :       Any       						= latents.to(self.device			)

							# scale the initial noise by the standard deviation required by the scheduler
							__magic_name__      :       Tuple       						= latents * self.scheduler.init_noise_sigma

							self.scheduler.set_timesteps(lowerCAmelCase__			)

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							__magic_name__      :       Dict       						= """eta""" in set(inspect.signature(self.scheduler.step			).parameters.keys()			)

							__magic_name__      :       List[Any]       						= {}
							if accepts_eta:
													__magic_name__      :       Tuple       						= eta

							for t in self.progress_bar(self.scheduler.timesteps			):
													__magic_name__      :       Union[str, Any]       						= self.scheduler.scale_model_input(lowerCAmelCase__      ,       lowerCAmelCase__			)
													# predict the noise residual
													__magic_name__      :       Union[str, Any]       						= self.unet(lowerCAmelCase__      ,       lowerCAmelCase__			).sample
													# compute the previous noisy sample x_t -> x_t-1
													__magic_name__      :       List[Any]       						= self.scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample

							# decode the image latents with the VAE
							__magic_name__      :       int       						= self.vqvae.decode(lowerCAmelCase__			).sample

							__magic_name__      :       Union[str, Any]       						= (image / 2 + 0.5).clamp(0      ,       1			)
							__magic_name__      :       Dict       						= image.cpu().permute(0      ,       2      ,       3      ,       1			).numpy()
							if output_type == "pil":
													__magic_name__      :       Optional[Any]       						= self.numpy_to_pil(lowerCAmelCase__			)

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=lowerCAmelCase__			)








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 	snake_case__ (							unittest.TestCase				):
	@slow
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       Any       						= FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""			)
							__magic_name__      :       Dict       						= AutoTokenizer.from_pretrained("""google/mt5-small"""			)

							__magic_name__      :       Tuple       						= tokenizer("""Hello there"""      ,       return_tensors="""np"""			).input_ids
							__magic_name__      :       List[Any]       						= tokenizer("""Hi I am"""      ,       return_tensors="""np"""			).input_ids

							__magic_name__      :       Any       						= shift_tokens_right(lowerCAmelCase__      ,       model.config.pad_token_id      ,       model.config.decoder_start_token_id			)

							__magic_name__      :       List[Any]       						= model(lowerCAmelCase__      ,       decoder_input_ids=lowerCAmelCase__			).logits
							__magic_name__      :       Tuple       						= optax.softmax_cross_entropy(lowerCAmelCase__      ,       onehot(lowerCAmelCase__      ,       logits.shape[-1]			)			).mean()

							__magic_name__      :       List[Any]       						= -(labels.shape[-1] * loss.item())

							__magic_name__      :       List[Any]       						= -8_4.9_1_2_7
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 1e-4			)
