

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

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


SCREAMING_SNAKE_CASE		:Optional[Any]				  =    logging.get_logger(__name__)  # pylint: disable=invalid-name




def  lowerCAmelCase(			self ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	Optional[Any]:



     """simple docstring"""






     UpperCamelCase_      =	image.to(device=SCREAMING_SNAKE_CASE_ )
     UpperCamelCase_      =	image  # take image as image_embeddings
     UpperCamelCase_      =	image_embeddings.unsqueeze(1 )

     # duplicate image embeddings for each generation per prompt, using mps friendly method
     UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_      =	image_embeddings.shape
     UpperCamelCase_      =	image_embeddings.repeat(1 ,	SCREAMING_SNAKE_CASE_ ,	1 )
     UpperCamelCase_      =	image_embeddings.view(bs_embed * num_images_per_prompt ,	SCREAMING_SNAKE_CASE_ ,	-1 )

     if do_classifier_free_guidance:
          UpperCamelCase_      =	torch.zeros_like(SCREAMING_SNAKE_CASE_ )

          # For classifier free guidance, we need to do two forward passes.
          # Here we concatenate the unconditional and text embeddings into a single batch
          # to avoid doing two forward passes
          UpperCamelCase_      =	torch.cat([uncond_embeddings, image_embeddings] )

     return image_embeddings






class 	__magic_name__				(       snake_case							):






     def __init__(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase = None		,	)->    int:
          super().__init__()

          UpperCamelCase_      =	{"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

          UpperCamelCase_      =	decoder_pipe_kwargs.get("torch_dtype"		,	_lowercase  ) or prior.dtype

          UpperCamelCase_      =	StableDiffusionImageVariationPipeline.from_pretrained(
              "lambdalabs/sd-image-variations-diffusers"		,	**_lowercase  )

          # replace `_encode_image` method
          UpperCamelCase_      =	types.MethodType(_encode_image		,	self.decoder_pipe  )

          self.register_modules(
              prior=_lowercase		,	tokenizer=_lowercase		,	text_encoder=_lowercase		,	prior_scheduler=_lowercase		,	)






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase = None		,	_lowercase = None		,	)->    List[str]:
          if text_model_output is None:
               UpperCamelCase_      =	len(_lowercase  ) if isinstance(_lowercase		,	_lowercase  ) else 1
               # get prompt text embeddings
               UpperCamelCase_      =	self.tokenizer(
                   _lowercase		,	padding="max_length"		,	max_length=self.tokenizer.model_max_length		,	return_tensors="pt"		,	)
               UpperCamelCase_      =	text_inputs.input_ids
               UpperCamelCase_      =	text_inputs.attention_mask.bool().to(_lowercase  )

               if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                    UpperCamelCase_      =	self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]  )
                    logger.warning(
                        "The following part of your input was truncated because CLIP can only handle sequences up to"
                        F" {self.tokenizer.model_max_length} tokens: {removed_text}"  )
                    UpperCamelCase_      =	text_input_ids[:, : self.tokenizer.model_max_length]

               UpperCamelCase_      =	self.text_encoder(text_input_ids.to(_lowercase  )  )

               UpperCamelCase_      =	text_encoder_output.text_embeds
               UpperCamelCase_      =	text_encoder_output.last_hidden_state

          else:
               UpperCamelCase_      =	text_model_output[0].shape[0]
               UpperCamelCase_     ,					UpperCamelCase_      =	text_model_output[0], text_model_output[1]
               UpperCamelCase_      =	text_attention_mask

          UpperCamelCase_      =	text_embeddings.repeat_interleave(_lowercase		,	dim=0  )
          UpperCamelCase_      =	text_encoder_hidden_states.repeat_interleave(_lowercase		,	dim=0  )
          UpperCamelCase_      =	text_mask.repeat_interleave(_lowercase		,	dim=0  )

          if do_classifier_free_guidance:
               UpperCamelCase_      =	[""] * batch_size

               UpperCamelCase_      =	self.tokenizer(
                   _lowercase		,	padding="max_length"		,	max_length=self.tokenizer.model_max_length		,	truncation=_lowercase		,	return_tensors="pt"		,	)
               UpperCamelCase_      =	uncond_input.attention_mask.bool().to(_lowercase  )
               UpperCamelCase_      =	self.text_encoder(uncond_input.input_ids.to(_lowercase  )  )

               UpperCamelCase_      =	uncond_embeddings_text_encoder_output.text_embeds
               UpperCamelCase_      =	uncond_embeddings_text_encoder_output.last_hidden_state

               # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

               UpperCamelCase_      =	uncond_embeddings.shape[1]
               UpperCamelCase_      =	uncond_embeddings.repeat(1		,	_lowercase  )
               UpperCamelCase_      =	uncond_embeddings.view(batch_size * num_images_per_prompt		,	_lowercase  )

               UpperCamelCase_      =	uncond_text_encoder_hidden_states.shape[1]
               UpperCamelCase_      =	uncond_text_encoder_hidden_states.repeat(1		,	_lowercase		,	1  )
               UpperCamelCase_      =	uncond_text_encoder_hidden_states.view(
                   batch_size * num_images_per_prompt		,	_lowercase		,	-1  )
               UpperCamelCase_      =	uncond_text_mask.repeat_interleave(_lowercase		,	dim=0  )

               # done duplicates

               # For classifier free guidance, we need to do two forward passes.
               # Here we concatenate the unconditional and text embeddings into a single batch
               # to avoid doing two forward passes
               UpperCamelCase_      =	torch.cat([uncond_embeddings, text_embeddings]  )
               UpperCamelCase_      =	torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]  )

               UpperCamelCase_      =	torch.cat([uncond_text_mask, text_mask]  )

          return text_embeddings, text_encoder_hidden_states, text_mask






     @property
     def   UpperCAmelCase_							(							self  )->    List[Any]:
          if self.device != torch.device("meta"  ) or not hasattr(self.prior		,	"_hf_hook"  ):
               return self.device
          for module in self.prior.modules():
               if (
                   hasattr(_lowercase		,	"_hf_hook"  )
                   and hasattr(module._hf_hook		,	"execution_device"  )
                   and module._hf_hook.execution_device is not None
               ):
                    return torch.device(module._hf_hook.execution_device  )
          return self.device






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )->    Optional[int]:
          if latents is None:
               UpperCamelCase_      =	randn_tensor(_lowercase		,	generator=_lowercase		,	device=_lowercase		,	dtype=_lowercase  )
          else:
               if latents.shape != shape:
                    raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"  )
               UpperCamelCase_      =	latents.to(_lowercase  )

          UpperCamelCase_      =	latents * scheduler.init_noise_sigma
          return latents






     def   UpperCAmelCase_							(							self		,	_lowercase = None  )->    Union[str, Any]:
          self.decoder_pipe.to(_lowercase  )
          super().to(_lowercase  )






     @torch.no_grad()
     def __call__(							self		,	_lowercase = None		,	_lowercase = None		,	_lowercase = None		,	_lowercase = 1		,	_lowercase = 25		,	_lowercase = None		,	_lowercase = None		,	_lowercase = None		,	_lowercase = None		,	_lowercase = 4.0		,	_lowercase = 8.0		,	_lowercase = 50		,	_lowercase = 1		,	_lowercase = 0.0		,	_lowercase = "pil"		,	_lowercase = True		,	)->    List[Any]:
          if prompt is not None:
               if isinstance(_lowercase		,	_lowercase  ):
                    UpperCamelCase_      =	1
               elif isinstance(_lowercase		,	_lowercase  ):
                    UpperCamelCase_      =	len(_lowercase  )
               else:
                    raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(_lowercase  )}"  )
          else:
               UpperCamelCase_      =	text_model_output[0].shape[0]

          UpperCamelCase_      =	self._execution_device

          UpperCamelCase_      =	batch_size * num_images_per_prompt

          UpperCamelCase_      =	prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

          UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_      =	self._encode_prompt(
              _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )

          # prior

          self.prior_scheduler.set_timesteps(_lowercase		,	device=_lowercase  )
          UpperCamelCase_      =	self.prior_scheduler.timesteps

          UpperCamelCase_      =	self.prior.config.embedding_dim

          UpperCamelCase_      =	self.prepare_latents(
              (batch_size, embedding_dim)		,	text_embeddings.dtype		,	_lowercase		,	_lowercase		,	_lowercase		,	self.prior_scheduler		,	)

          for i, t in enumerate(self.progress_bar(_lowercase  )  ):
               # expand the latents if we are doing classifier free guidance
               UpperCamelCase_      =	torch.cat([prior_latents] * 2  ) if do_classifier_free_guidance else prior_latents

               UpperCamelCase_      =	self.prior(
                   _lowercase		,	timestep=_lowercase		,	proj_embedding=_lowercase		,	encoder_hidden_states=_lowercase		,	attention_mask=_lowercase		,	).predicted_image_embedding

               if do_classifier_free_guidance:
                    UpperCamelCase_     ,					UpperCamelCase_      =	predicted_image_embedding.chunk(2  )
                    UpperCamelCase_      =	predicted_image_embedding_uncond + prior_guidance_scale * (
                        predicted_image_embedding_text - predicted_image_embedding_uncond
                    )

               if i + 1 == prior_timesteps_tensor.shape[0]:
                    UpperCamelCase_      =	None
               else:
                    UpperCamelCase_      =	prior_timesteps_tensor[i + 1]

               UpperCamelCase_      =	self.prior_scheduler.step(
                   _lowercase		,	timestep=_lowercase		,	sample=_lowercase		,	generator=_lowercase		,	prev_timestep=_lowercase		,	).prev_sample

          UpperCamelCase_      =	self.prior.post_process_latents(_lowercase  )

          UpperCamelCase_      =	prior_latents

          UpperCamelCase_      =	self.decoder_pipe(
              image=_lowercase		,	height=_lowercase		,	width=_lowercase		,	num_inference_steps=_lowercase		,	guidance_scale=_lowercase		,	generator=_lowercase		,	output_type=_lowercase		,	return_dict=_lowercase		,	num_images_per_prompt=_lowercase		,	eta=_lowercase		,	)
          return output



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


SCREAMING_SNAKE_CASE		:Any				  =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:Dict				  =    {
    """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json""",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}






class 	__magic_name__				(       snake_case							):
     UpperCamelCase_  :str								=			"""blenderbot"""
     UpperCamelCase_  :Union[str, Any]								=			["""past_key_values"""]
     UpperCamelCase_  :Any								=			{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






     def __init__(							self		,	_lowercase=8_008		,	_lowercase=128		,	_lowercase=2		,	_lowercase=10_240		,	_lowercase=32		,	_lowercase=24		,	_lowercase=10_240		,	_lowercase=32		,	_lowercase=0.0		,	_lowercase=0.0		,	_lowercase=True		,	_lowercase=True		,	_lowercase="gelu"		,	_lowercase=2_560		,	_lowercase=0.1		,	_lowercase=0.0		,	_lowercase=0.0		,	_lowercase=0.02		,	_lowercase=1		,	_lowercase=False		,	_lowercase=0		,	_lowercase=1		,	_lowercase=2		,	_lowercase=3		,	_lowercase=2		,	**_lowercase		,	)->    List[str]:
         UpperCamelCase_      =	vocab_size
         UpperCamelCase_      =	max_position_embeddings
         UpperCamelCase_      =	d_model
         UpperCamelCase_      =	encoder_ffn_dim
         UpperCamelCase_      =	encoder_layers
         UpperCamelCase_      =	encoder_attention_heads
         UpperCamelCase_      =	decoder_ffn_dim
         UpperCamelCase_      =	decoder_layers
         UpperCamelCase_      =	decoder_attention_heads
         UpperCamelCase_      =	dropout
         UpperCamelCase_      =	attention_dropout
         UpperCamelCase_      =	activation_dropout
         UpperCamelCase_      =	activation_function
         UpperCamelCase_      =	init_std
         UpperCamelCase_      =	encoder_layerdrop
         UpperCamelCase_      =	decoder_layerdrop
         UpperCamelCase_      =	use_cache
         UpperCamelCase_      =	encoder_layers
         UpperCamelCase_      =	scale_embedding  # scale factor will be sqrt(d_model) if True

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






class 	__magic_name__				(       snake_case							):






     @property
     def   UpperCAmelCase_							(							self  )->    Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                 ]  )
             if self.use_past:
                 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_(_lowercase		,	direction="inputs"  )
         elif self.task == "causal-lm":
             UpperCamelCase_      =	OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                 ]  )
             if self.use_past:
                 UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers
                 for i in range(_lowercase  ):
                     UpperCamelCase_      =	{0: "batch", 2: "past_sequence + sequence"}
                     UpperCamelCase_      =	{0: "batch", 2: "past_sequence + sequence"}
         else:
             UpperCamelCase_      =	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
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
     def   UpperCAmelCase_							(							self  )->    Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	super().outputs
         else:
             UpperCamelCase_      =	super(_lowercase		,	self  ).outputs
             if self.use_past:
                 UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers
                 for i in range(_lowercase  ):
                     UpperCamelCase_      =	{0: "batch", 2: "past_sequence + sequence"}
                     UpperCamelCase_      =	{0: "batch", 2: "past_sequence + sequence"}
         return common_outputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         UpperCamelCase_      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )
         # Generate decoder inputs
         UpperCamelCase_      =	seq_length if not self.use_past else 1
         UpperCamelCase_      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )
         UpperCamelCase_      =	{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
         UpperCamelCase_      =	dict(**_lowercase		,	**_lowercase  )

         if self.use_past:
             if not is_torch_available():
                 raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."  )
             else:
                 import torch
             UpperCamelCase_     ,					UpperCamelCase_      =	common_inputs["input_ids"].shape
             UpperCamelCase_      =	common_inputs["decoder_input_ids"].shape[1]
             UpperCamelCase_     ,					UpperCamelCase_      =	self.num_attention_heads
             UpperCamelCase_      =	(
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             UpperCamelCase_      =	decoder_seq_length
             UpperCamelCase_      =	(
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )
             UpperCamelCase_      =	torch.cat(
                 [common_inputs["decoder_attention_mask"], torch.ones(_lowercase		,	_lowercase  )]		,	dim=1  )
             UpperCamelCase_      =	[]
             UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers

             for _ in range(_lowercase  ):
                 common_inputs["past_key_values"].append(
                     (
                         torch.zeros(_lowercase  ),
                         torch.zeros(_lowercase  ),
                         torch.zeros(_lowercase  ),
                         torch.zeros(_lowercase  ),
                     )  )
         return common_inputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         UpperCamelCase_      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )

         if self.use_past:
             if not is_torch_available():
                 raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."  )
             else:
                 import torch
             UpperCamelCase_     ,					UpperCamelCase_      =	common_inputs["input_ids"].shape
             UpperCamelCase_      =	seqlen
             UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers
             UpperCamelCase_     ,					UpperCamelCase_      =	self.num_attention_heads
             UpperCamelCase_      =	(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             UpperCamelCase_      =	common_inputs["attention_mask"].dtype
             UpperCamelCase_      =	torch.cat(
                 [common_inputs["attention_mask"], torch.ones(_lowercase		,	_lowercase		,	dtype=_lowercase  )]		,	dim=1  )
             UpperCamelCase_      =	[
                 (torch.zeros(_lowercase  ), torch.zeros(_lowercase  )) for _ in range(_lowercase  )
             ]
         return common_inputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         UpperCamelCase_      =	compute_effective_axis_dimension(
             _lowercase		,	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
         UpperCamelCase_      =	tokenizer.num_special_tokens_to_add(_lowercase  )
         UpperCamelCase_      =	compute_effective_axis_dimension(
             _lowercase		,	fixed_dimension=OnnxConfig.default_fixed_sequence		,	num_token_to_add=_lowercase  )

         # Generate dummy inputs according to compute batch and sequence
         UpperCamelCase_      =	[" ".join([tokenizer.unk_token]  ) * seq_length] * batch_size
         UpperCamelCase_      =	dict(tokenizer(_lowercase		,	return_tensors=_lowercase  )  )
         return common_inputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 _lowercase		,	batch_size=_lowercase		,	seq_length=_lowercase		,	is_pair=_lowercase		,	framework=_lowercase  )

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

         return common_inputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )->    List[Any]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	super()._flatten_past_key_values_(_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )
         else:
             UpperCamelCase_      =	super(_lowercase		,	self  )._flatten_past_key_values_(
                 _lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase  )->    Union[str, Any]:
         if direction not in ["inputs", "outputs"]:
             raise ValueError(F"direction must either be \"inputs\" or \"outputs\", but {direction} was given"  )

         UpperCamelCase_      =	"past_key_values" if direction == "inputs" else "present"
         UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers

         UpperCamelCase_      =	"past_encoder_sequence"
         UpperCamelCase_      =	"past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

         for i in range(_lowercase  ):
             UpperCamelCase_      =	{0: "batch", 2: decoder_sequence}
             UpperCamelCase_      =	{0: "batch", 2: decoder_sequence}
             UpperCamelCase_      =	{0: "batch", 2: encoder_sequence}
             UpperCamelCase_      =	{0: "batch", 2: encoder_sequence}
