






"""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    __UpperCAmelCase(       unittest.TestCase							):




						"""simple docstring"""




						@slow
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"      )
												lowercase__    :				int=	AutoTokenizer.from_pretrained("google/mt5-small"      )

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

												lowercase__    :				Union[str, Any]=	shift_tokens_right(snake_case__	,  model.config.pad_token_id	,  model.config.decoder_start_token_id      )

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

												lowercase__    :				Optional[int]=	-(labels.shape[-1] * loss.item())

												lowercase__    :				List[Any]=	-84.91_27
												self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )














"""simple docstring"""







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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__ = None	,  ):
												'''simple docstring'''
												super().__init__()
												self.register_modules(transformer=snake_case__	,  vae=snake_case__	,  scheduler=snake_case__      )

												# create a imagenet -> id dictionary for easier use
												lowercase__    :				int=	{}
												if idalabel is not None:
																		for key, value in idalabel.items():
																								for label in value.split(","      ):
																														lowercase__    :				Tuple=	int(snake_case__      )
																		lowercase__    :				Union[str, Any]=	dict(sorted(self.labels.items()      )      )




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''

												if not isinstance(snake_case__	,  snake_case__      ):
																		lowercase__    :				List[Any]=	list(snake_case__      )

												for l in label:
																		if l not in self.labels:
																								raise ValueError(
																								    F'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''      )

												return [self.labels[l] for l in label]




						@torch.no_grad()
						def __call__(       self	,  snake_case__	,  snake_case__ = 4.0	,  snake_case__ = None	,  snake_case__ = 50	,  snake_case__ = "pil"	,  snake_case__ = True	,  ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	len(snake_case__      )
												lowercase__    :				Optional[int]=	self.transformer.config.sample_size
												lowercase__    :				List[str]=	self.transformer.config.in_channels

												lowercase__    :				Any=	randn_tensor(
												    shape=(batch_size, latent_channels, latent_size, latent_size)	,  generator=snake_case__	,  device=self.device	,  dtype=self.transformer.dtype	,  )
												lowercase__    :				Any=	torch.cat([latents] * 2      ) if guidance_scale > 1 else latents

												lowercase__    :				Tuple=	torch.tensor(snake_case__	,  device=self.device      ).reshape(-1      )
												lowercase__    :				Any=	torch.tensor([1000] * batch_size	,  device=self.device      )
												lowercase__    :				Tuple=	torch.cat([class_labels, class_null]	,  0      ) if guidance_scale > 1 else class_labels

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

												for t in self.progress_bar(self.scheduler.timesteps      ):
																		if guidance_scale > 1:
																								lowercase__    :				List[str]=	latent_model_input[: len(snake_case__      ) // 2]
																								lowercase__    :				int=	torch.cat([half, half]	,  dim=0      )
																		lowercase__    :				Union[str, Any]=	self.scheduler.scale_model_input(snake_case__	,  snake_case__      )

																		lowercase__    :				Optional[int]=	t
																		if not torch.is_tensor(snake_case__      ):
																								# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																								# This would be a good case for the `match` statement (Python 3.10+)
																								lowercase__    :				List[str]=	latent_model_input.device.type == "mps"
																								if isinstance(snake_case__	,  snake_case__      ):
																														lowercase__    :				int=	torch.floataa if is_mps else torch.floataa
																								else:
																														lowercase__    :				Dict=	torch.intaa if is_mps else torch.intaa
																								lowercase__    :				Tuple=	torch.tensor([timesteps]	,  dtype=snake_case__	,  device=latent_model_input.device      )
																		elif len(timesteps.shape      ) == 0:
																								lowercase__    :				Dict=	timesteps[None].to(latent_model_input.device      )
																		# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																		lowercase__    :				int=	timesteps.expand(latent_model_input.shape[0]      )
																		# predict noise model_output
																		lowercase__    :				Union[str, Any]=	self.transformer(
																		    snake_case__	,  timestep=snake_case__	,  class_labels=snake_case__      ).sample

																		# perform guidance
																		if guidance_scale > 1:
																								lowercase__,     lowercase__    :				Tuple=	noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																								lowercase__,     lowercase__    :				Union[str, Any]=	torch.split(snake_case__	,  len(snake_case__      ) // 2	,  dim=0      )

																								lowercase__    :				str=	uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																								lowercase__    :				Dict=	torch.cat([half_eps, half_eps]	,  dim=0      )

																								lowercase__    :				Optional[int]=	torch.cat([eps, rest]	,  dim=1      )

																		# learned sigma
																		if self.transformer.config.out_channels // 2 == latent_channels:
																								lowercase__,     lowercase__    :				Union[str, Any]=	torch.split(snake_case__	,  snake_case__	,  dim=1      )
																		else:
																								lowercase__    :				int=	noise_pred

																		# compute previous image: x_t -> x_t-1
																		lowercase__    :				List[Any]=	self.scheduler.step(snake_case__	,  snake_case__	,  snake_case__      ).prev_sample

												if guidance_scale > 1:
																		lowercase__,     lowercase__    :				Any=	latent_model_input.chunk(2	,  dim=0      )
												else:
																		lowercase__    :				str=	latent_model_input

												lowercase__    :				Dict=	1 / self.vae.config.scaling_factor * latents
												lowercase__    :				Any=	self.vae.decode(snake_case__      ).sample

												lowercase__    :				Tuple=	(samples / 2 + 0.5).clamp(0	,  1      )

												# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
												lowercase__    :				List[Any]=	samples.cpu().permute(0	,  2	,  3	,  1      ).float().numpy()

												if output_type == "pil":
																		lowercase__    :				Optional[Any]=	self.numpy_to_pil(snake_case__      )

												if not return_dict:
																		return (samples,)

												return ImagePipelineOutput(images=snake_case__      )






