


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     _lowercase				(							lowercase__):


       """simple docstring"""




       def __init__(     self							:    Tuple      ,   __lowerCamelCase							:    TransformeraDModel      ,   __lowerCamelCase							:    AutoencoderKL      ,   __lowerCamelCase							:    KarrasDiffusionSchedulers      ,   __lowerCamelCase							:    Optional[Dict[int, str]] = None      ,   ):


             '''simple docstring'''



             super().__init__()
             self.register_modules(transformer=__lowerCamelCase      ,   vae=__lowerCamelCase      ,   scheduler=__lowerCamelCase )

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





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    Union[str, List[str]] ):


             '''simple docstring'''



             if not isinstance(__lowerCamelCase      ,   __lowerCamelCase ):
                   lowerCamelCase__	:       Optional[int]					=						list(__lowerCamelCase )

             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							:    List[Any]      ,   __lowerCamelCase							:    List[int]      ,   __lowerCamelCase							:    float = 4.0      ,   __lowerCamelCase							:    Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,   __lowerCamelCase							:    int = 50      ,   __lowerCamelCase							:    Optional[str] = "pil"      ,   __lowerCamelCase							:    bool = True      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						len(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						self.transformer.config.sample_size
             lowerCamelCase__	:       Tuple					=						self.transformer.config.in_channels

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

             lowerCamelCase__	:       List[Any]					=						torch.tensor(__lowerCamelCase      ,   device=self.device ).reshape(-1 )
             lowerCamelCase__	:       str					=						torch.tensor([1000] * batch_size      ,   device=self.device )
             lowerCamelCase__	:       List[Any]					=						torch.cat([class_labels, class_null]      ,   0 ) if guidance_scale > 1 else class_labels

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

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

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

                   # perform guidance
                   if guidance_scale > 1:
                         lowerCamelCase__					,     lowerCamelCase__	:       Optional[int]					=						noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                         lowerCamelCase__					,     lowerCamelCase__	:       Dict					=						torch.split(__lowerCamelCase      ,   len(__lowerCamelCase ) // 2      ,   dim=0 )

                         lowerCamelCase__	:       Any					=						uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                         lowerCamelCase__	:       Optional[Any]					=						torch.cat([half_eps, half_eps]      ,   dim=0 )

                         lowerCamelCase__	:       Tuple					=						torch.cat([eps, rest]      ,   dim=1 )

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

                   # compute previous image: x_t -> x_t-1
                   lowerCamelCase__	:       Dict					=						self.scheduler.step(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase ).prev_sample

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

             lowerCamelCase__	:       Optional[int]					=						1 / self.vae.config.scaling_factor * latents
             lowerCamelCase__	:       Optional[int]					=						self.vae.decode(__lowerCamelCase ).sample

             lowerCamelCase__	:       List[str]					=						(samples / 2 + 0.5).clamp(0      ,   1 )

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

             if output_type == "pil":
                   lowerCamelCase__	:       List[Any]					=						self.numpy_to_pil(__lowerCamelCase )

             if not return_dict:
                   return (samples,)

             return ImagePipelineOutput(images=__lowerCamelCase )






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


A   :					List[Any]	=      logging.get_logger(__name__)

A   :					Any	=      {
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "blenderbot-small"
       A__      		=    ["past_key_values"]
       A__      		=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





       def __init__(     self							:    Dict      ,   __lowerCamelCase							:    List[str]=50265      ,   __lowerCamelCase							:    str=512      ,   __lowerCamelCase							:    Tuple=8      ,   __lowerCamelCase							:    str=2048      ,   __lowerCamelCase							:    str=16      ,   __lowerCamelCase							:    List[Any]=8      ,   __lowerCamelCase							:    Any=2048      ,   __lowerCamelCase							:    List[str]=16      ,   __lowerCamelCase							:    Dict=0.0      ,   __lowerCamelCase							:    List[Any]=0.0      ,   __lowerCamelCase							:    Optional[int]=True      ,   __lowerCamelCase							:    Union[str, Any]=True      ,   __lowerCamelCase							:    Tuple="gelu"      ,   __lowerCamelCase							:    Tuple=512      ,   __lowerCamelCase							:    Dict=0.1      ,   __lowerCamelCase							:    int=0.0      ,   __lowerCamelCase							:    Union[str, Any]=0.0      ,   __lowerCamelCase							:    Any=0.0_2      ,   __lowerCamelCase							:    str=1      ,   __lowerCamelCase							:    Dict=False      ,   __lowerCamelCase							:    int=0      ,   __lowerCamelCase							:    Optional[Any]=1      ,   __lowerCamelCase							:    str=2      ,   __lowerCamelCase							:    Any=2      ,   **__lowerCamelCase							:    int      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						vocab_size
             lowerCamelCase__	:       Union[str, Any]					=						max_position_embeddings
             lowerCamelCase__	:       Union[str, Any]					=						d_model
             lowerCamelCase__	:       Optional[int]					=						encoder_ffn_dim
             lowerCamelCase__	:       Dict					=						encoder_layers
             lowerCamelCase__	:       Any					=						encoder_attention_heads
             lowerCamelCase__	:       Union[str, Any]					=						decoder_ffn_dim
             lowerCamelCase__	:       str					=						decoder_layers
             lowerCamelCase__	:       Optional[Any]					=						decoder_attention_heads
             lowerCamelCase__	:       List[str]					=						dropout
             lowerCamelCase__	:       List[Any]					=						attention_dropout
             lowerCamelCase__	:       Dict					=						activation_dropout
             lowerCamelCase__	:       Optional[Any]					=						activation_function
             lowerCamelCase__	:       Dict					=						init_std
             lowerCamelCase__	:       List[str]					=						encoder_layerdrop
             lowerCamelCase__	:       Dict					=						decoder_layerdrop
             lowerCamelCase__	:       int					=						use_cache
             lowerCamelCase__	:       List[Any]					=						encoder_layers
             lowerCamelCase__	:       Tuple					=						scale_embedding  # scale factor will be sqrt(d_model) if True

             super().__init__(
                 pad_token_id=__lowerCamelCase      ,   bos_token_id=__lowerCamelCase      ,   eos_token_id=__lowerCamelCase      ,   is_encoder_decoder=__lowerCamelCase      ,   decoder_start_token_id=__lowerCamelCase      ,   forced_eos_token_id=__lowerCamelCase      ,   **__lowerCamelCase      ,   )







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             if self.task in ["default", "seq2seq-lm"]:
                   lowerCamelCase__	:       int					=						OrderedDict(
                       [
                           ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                           ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                       ] )

                   if self.use_past:
                         lowerCamelCase__	:       Union[str, Any]					=						{0: "batch"}
                         lowerCamelCase__	:       int					=						{0: "batch", 1: "past_decoder_sequence + sequence"}
                   else:
                         lowerCamelCase__	:       Tuple					=						{0: "batch", 1: "decoder_sequence"}
                         lowerCamelCase__	:       str					=						{0: "batch", 1: "decoder_sequence"}

                   if self.use_past:
                         self.fill_with_past_key_values_(__lowerCamelCase      ,   direction="inputs" )
             elif self.task == "causal-lm":
                   # TODO: figure this case out.
                   lowerCamelCase__	:       Tuple					=						OrderedDict(
                       [
                           ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                           ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                       ] )
                   if self.use_past:
                         lowerCamelCase__					,     lowerCamelCase__	:       Tuple					=						self.num_layers
                         for i in range(__lowerCamelCase ):
                               lowerCamelCase__	:       Union[str, Any]					=						{0: "batch", 2: "past_sequence + sequence"}
                               lowerCamelCase__	:       Optional[int]					=						{0: "batch", 2: "past_sequence + sequence"}
             else:
                   lowerCamelCase__	:       Any					=						OrderedDict(
                       [
                           ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                           ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                           ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                           ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
                       ] )

             return common_inputs





       @property
       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             if self.task in ["default", "seq2seq-lm"]:
                   lowerCamelCase__	:       Union[str, Any]					=						super().outputs
             else:
                   lowerCamelCase__	:       int					=						super(__lowerCamelCase      ,   self ).outputs
                   if self.use_past:
                         lowerCamelCase__					,     lowerCamelCase__	:       Tuple					=						self.num_layers
                         for i in range(__lowerCamelCase ):
                               lowerCamelCase__	:       Tuple					=						{0: "batch", 2: "past_sequence + sequence"}
                               lowerCamelCase__	:       Any					=						{0: "batch", 2: "past_sequence + sequence"}
             return common_outputs





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional[TensorType] = None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )

             # Generate decoder inputs
             lowerCamelCase__	:       List[str]					=						seq_length if not self.use_past else 1
             lowerCamelCase__	:       List[Any]					=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )
             lowerCamelCase__	:       Optional[Any]					=						{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
             lowerCamelCase__	:       Optional[Any]					=						dict(**__lowerCamelCase      ,   **__lowerCamelCase )

             if self.use_past:
                   if not is_torch_available():
                         raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
                   else:
                         import torch
                   lowerCamelCase__					,     lowerCamelCase__	:       Tuple					=						common_inputs["input_ids"].shape
                   lowerCamelCase__	:       int					=						common_inputs["decoder_input_ids"].shape[1]
                   lowerCamelCase__					,     lowerCamelCase__	:       Optional[Any]					=						self.num_attention_heads
                   lowerCamelCase__	:       str					=						(
                       batch,
                       num_encoder_attention_heads,
                       encoder_seq_length,
                       self._config.hidden_size // num_encoder_attention_heads,
                   )
                   lowerCamelCase__	:       Optional[int]					=						decoder_seq_length + 3
                   lowerCamelCase__	:       Dict					=						(
                       batch,
                       num_decoder_attention_heads,
                       decoder_past_length,
                       self._config.hidden_size // num_decoder_attention_heads,
                   )

                   lowerCamelCase__	:       List[Any]					=						torch.cat(
                       [common_inputs["decoder_attention_mask"], torch.ones(__lowerCamelCase      ,   __lowerCamelCase )]      ,   dim=1 )

                   lowerCamelCase__	:       Optional[Any]					=						[]
                   # If the number of encoder and decoder layers are present in the model configuration, both are considered
                   lowerCamelCase__					,     lowerCamelCase__	:       str					=						self.num_layers
                   lowerCamelCase__	:       Union[str, Any]					=						min(__lowerCamelCase      ,   __lowerCamelCase )
                   lowerCamelCase__	:       Union[str, Any]					=						max(__lowerCamelCase      ,   __lowerCamelCase ) - min_num_layers
                   lowerCamelCase__	:       str					=						"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

                   for _ in range(__lowerCamelCase ):
                         common_inputs["past_key_values"].append(
                             (
                                 torch.zeros(__lowerCamelCase ),
                                 torch.zeros(__lowerCamelCase ),
                                 torch.zeros(__lowerCamelCase ),
                                 torch.zeros(__lowerCamelCase ),
                             ) )
                   # TODO: test this.
                   lowerCamelCase__	:       Optional[int]					=						encoder_shape if remaining_side_name == "encoder" else decoder_shape
                   for _ in range(__lowerCamelCase      ,   __lowerCamelCase ):
                         common_inputs["past_key_values"].append((torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) )
             return common_inputs





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional[TensorType] = None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )

             if self.use_past:
                   if not is_torch_available():
                         raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
                   else:
                         import torch
                   lowerCamelCase__					,     lowerCamelCase__	:       int					=						common_inputs["input_ids"].shape
                   # Not using the same length for past_key_values
                   lowerCamelCase__	:       str					=						seqlen + 2
                   lowerCamelCase__					,     lowerCamelCase__	:       Optional[int]					=						self.num_layers
                   lowerCamelCase__					,     lowerCamelCase__	:       int					=						self.num_attention_heads
                   lowerCamelCase__	:       Tuple					=						(
                       batch,
                       num_encoder_attention_heads,
                       past_key_values_length,
                       self._config.hidden_size // num_encoder_attention_heads,
                   )

                   lowerCamelCase__	:       Union[str, Any]					=						common_inputs["attention_mask"].dtype
                   lowerCamelCase__	:       List[str]					=						torch.cat(
                       [common_inputs["attention_mask"], torch.ones(__lowerCamelCase      ,   __lowerCamelCase      ,   dtype=__lowerCamelCase )]      ,   dim=1 )
                   lowerCamelCase__	:       Tuple					=						[
                       (torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) for _ in range(__lowerCamelCase )
                   ]
             return common_inputs





       def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional[TensorType] = None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						compute_effective_axis_dimension(
                 __lowerCamelCase      ,   fixed_dimension=OnnxConfig.default_fixed_batch      ,   num_token_to_add=0 )

             # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
             lowerCamelCase__	:       List[str]					=						tokenizer.num_special_tokens_to_add(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						compute_effective_axis_dimension(
                 __lowerCamelCase      ,   fixed_dimension=OnnxConfig.default_fixed_sequence      ,   num_token_to_add=__lowerCamelCase )

             # Generate dummy inputs according to compute batch and sequence
             lowerCamelCase__	:       Optional[int]					=						[" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
             lowerCamelCase__	:       Optional[Any]					=						dict(tokenizer(__lowerCamelCase      ,   return_tensors=__lowerCamelCase ) )
             return common_inputs





       def 		lowerCAmelCase  (     self							:    Any      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional[TensorType] = None      ,   ):


             '''simple docstring'''



             if self.task in ["default", "seq2seq-lm"]:
                   lowerCamelCase__	:       Optional[int]					=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                       __lowerCamelCase      ,   batch_size=__lowerCamelCase      ,   seq_length=__lowerCamelCase      ,   is_pair=__lowerCamelCase      ,   framework=__lowerCamelCase )

             elif self.task == "causal-lm":
                   lowerCamelCase__	:       Any					=						self._generate_dummy_inputs_for_causal_lm(
                       __lowerCamelCase      ,   batch_size=__lowerCamelCase      ,   seq_length=__lowerCamelCase      ,   is_pair=__lowerCamelCase      ,   framework=__lowerCamelCase )
             else:
                   lowerCamelCase__	:       Any					=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                       __lowerCamelCase      ,   batch_size=__lowerCamelCase      ,   seq_length=__lowerCamelCase      ,   is_pair=__lowerCamelCase      ,   framework=__lowerCamelCase )

             return common_inputs





       def 		lowerCAmelCase  (     self							:    Any      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



             if self.task in ["default", "seq2seq-lm"]:
                   lowerCamelCase__	:       Dict					=						super()._flatten_past_key_values_(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )
             else:
                   lowerCamelCase__	:       int					=						super(__lowerCamelCase      ,   self )._flatten_past_key_values_(
                       __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )


