'''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 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   int       ,      __a   :   TransformeraDModel       ,      __a   :   AutoencoderKL       ,      __a   :   KarrasDiffusionSchedulers       ,      __a   :   Optional[Dict[int, str]] = None       ,      ):
         super().__init__()
         self.register_modules(transformer=__a       ,      vae=__a       ,      scheduler=__a			)

         # create a imagenet -> id dictionary for easier use
         UpperCAmelCase_	   =       {}
         if idalabel is not None:
              for key, value in idalabel.items():
                   for label in value.split(","			):
                        UpperCAmelCase_	   =       int(__a			)
              UpperCAmelCase_	   =       dict(sorted(self.labels.items()			)			)



    def  _lowercase						(self   :   Dict       ,      __a   :   Union[str, List[str]]			):

         if not isinstance(__a       ,      __a			):
              UpperCAmelCase_	   =       list(__a			)

         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   :   Tuple       ,      __a   :   List[int]       ,      __a   :   float = 4.0       ,      __a   :   Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,      __a   :   int = 50       ,      __a   :   Optional[str] = "pil"       ,      __a   :   bool = True       ,      ):
         UpperCAmelCase_	   =       len(__a			)
         UpperCAmelCase_	   =       self.transformer.config.sample_size
         UpperCAmelCase_	   =       self.transformer.config.in_channels

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

         UpperCAmelCase_	   =       torch.tensor(__a       ,      device=self.device			).reshape(-1			)
         UpperCAmelCase_	   =       torch.tensor([1000] * batch_size       ,      device=self.device			)
         UpperCAmelCase_	   =       torch.cat([class_labels, class_null]       ,      0			) if guidance_scale > 1 else class_labels

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

         for t in self.progress_bar(self.scheduler.timesteps			):
              if guidance_scale > 1:
                   UpperCAmelCase_	   =       latent_model_input[: len(__a			) // 2]
                   UpperCAmelCase_	   =       torch.cat([half, half]       ,      dim=0			)
              UpperCAmelCase_	   =       self.scheduler.scale_model_input(__a       ,      __a			)

              UpperCAmelCase_	   =       t
              if not torch.is_tensor(__a			):
                   # 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+)
                   UpperCAmelCase_	   =       latent_model_input.device.type == "mps"
                   if isinstance(__a       ,      __a			):
                        UpperCAmelCase_	   =       torch.floataa if is_mps else torch.floataa
                   else:
                        UpperCAmelCase_	   =       torch.intaa if is_mps else torch.intaa
                   UpperCAmelCase_	   =       torch.tensor([timesteps]       ,      dtype=__a       ,      device=latent_model_input.device			)
              elif len(timesteps.shape			) == 0:
                   UpperCAmelCase_	   =       timesteps[None].to(latent_model_input.device			)
              # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
              UpperCAmelCase_	   =       timesteps.expand(latent_model_input.shape[0]			)
              # predict noise model_output
              UpperCAmelCase_	   =       self.transformer(
                  __a       ,      timestep=__a       ,      class_labels=__a			).sample

              # perform guidance
              if guidance_scale > 1:
                   UpperCAmelCase_   ,			UpperCAmelCase_	   =       noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                   UpperCAmelCase_   ,			UpperCAmelCase_	   =       torch.split(__a       ,      len(__a			) // 2       ,      dim=0			)

                   UpperCAmelCase_	   =       uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                   UpperCAmelCase_	   =       torch.cat([half_eps, half_eps]       ,      dim=0			)

                   UpperCAmelCase_	   =       torch.cat([eps, rest]       ,      dim=1			)

              # learned sigma
              if self.transformer.config.out_channels // 2 == latent_channels:
                   UpperCAmelCase_   ,			UpperCAmelCase_	   =       torch.split(__a       ,      __a       ,      dim=1			)
              else:
                   UpperCAmelCase_	   =       noise_pred

              # compute previous image: x_t -> x_t-1
              UpperCAmelCase_	   =       self.scheduler.step(__a       ,      __a       ,      __a			).prev_sample

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

         UpperCAmelCase_	   =       1 / self.vae.config.scaling_factor * latents
         UpperCAmelCase_	   =       self.vae.decode(__a			).sample

         UpperCAmelCase_	   =       (samples / 2 + 0.5).clamp(0       ,      1			)

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

         if output_type == "pil":
              UpperCAmelCase_	   =       self.numpy_to_pil(__a			)

         if not return_dict:
              return (samples,)

         return ImagePipelineOutput(images=__a			)





'''simple docstring'''





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE_:					List[str]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					List[Any]  ={
    't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
    't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
    't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
    't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
    't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}




class 	__A				(			UpperCamelCase__     ):
    a__	:						Dict	       =  """t5"""
    a__	:						List[str]	       =  ["""past_key_values"""]
    a__	:						Union[str, Any]	       =  {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



    def __init__(self   :   List[str]       ,      __a   :   Any=32128       ,      __a   :   Optional[int]=512       ,      __a   :   Union[str, Any]=64       ,      __a   :   List[str]=2048       ,      __a   :   Optional[int]=6       ,      __a   :   Union[str, Any]=None       ,      __a   :   Union[str, Any]=8       ,      __a   :   Dict=32       ,      __a   :   List[Any]=128       ,      __a   :   Optional[Any]=0.1       ,      __a   :   List[Any]=1E-6       ,      __a   :   str=1.0       ,      __a   :   Dict="relu"       ,      __a   :   int=True       ,      __a   :   int=True       ,      __a   :   Optional[Any]=0       ,      __a   :   Dict=1       ,      **__a   :   List[Any]       ,      ):
         UpperCAmelCase_	   =       vocab_size
         UpperCAmelCase_	   =       d_model
         UpperCAmelCase_	   =       d_kv
         UpperCAmelCase_	   =       d_ff
         UpperCAmelCase_	   =       num_layers
         UpperCAmelCase_	   =       (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         UpperCAmelCase_	   =       num_heads
         UpperCAmelCase_	   =       relative_attention_num_buckets
         UpperCAmelCase_	   =       relative_attention_max_distance
         UpperCAmelCase_	   =       dropout_rate
         UpperCAmelCase_	   =       layer_norm_epsilon
         UpperCAmelCase_	   =       initializer_factor
         UpperCAmelCase_	   =       feed_forward_proj
         UpperCAmelCase_	   =       use_cache

         UpperCAmelCase_	   =       self.feed_forward_proj.split("-"			)
         UpperCAmelCase_	   =       act_info[-1]
         UpperCAmelCase_	   =       act_info[0] == "gated"

         if len(__a			) > 1 and act_info[0] != "gated" or len(__a			) > 2:
              raise ValueError(
                  f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                  "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                  "'gated-gelu' or 'relu'"			)

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
              UpperCAmelCase_	   =       "gelu_new"

         super().__init__(
             pad_token_id=__a       ,      eos_token_id=__a       ,      is_encoder_decoder=__a       ,      **__a       ,      )




class 	__A				(			UpperCamelCase__     ):



    @property
    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       {
             "input_ids": {0: "batch", 1: "encoder_sequence"},
             "attention_mask": {0: "batch", 1: "encoder_sequence"},
         }
         if self.use_past:
              UpperCAmelCase_	   =       "past_encoder_sequence + sequence"
              UpperCAmelCase_	   =       {0: "batch"}
              UpperCAmelCase_	   =       {0: "batch", 1: "past_decoder_sequence + sequence"}
         else:
              UpperCAmelCase_	   =       {0: "batch", 1: "decoder_sequence"}
              UpperCAmelCase_	   =       {0: "batch", 1: "decoder_sequence"}

         if self.use_past:
              self.fill_with_past_key_values_(__a       ,      direction="inputs"			)

         return common_inputs






    @property
    def  _lowercase						(self   :   Optional[int]			):
         return 13




