
"""simple docstring"""



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

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_snake_case          =						logging.get_logger(__name__)  # pylint: disable=invalid-name
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				UpperCAmelCase							):






				'''simple docstring'''



				@register_to_config
				def __init__(					self	:			List[str]				,     UpperCAmelCase_	:			bool				,     UpperCAmelCase_	:			Optional[int] = None				,     UpperCAmelCase_	:			Optional[int] = None  )		->  List[str]:







											"""simple docstring"""




											super().__init__()

											_lowerCAmelCase  	=			learnable

											if self.learnable:
																		assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
																		assert length is not None, "learnable=True requires `length` to be set"

																		_lowerCAmelCase  	=			torch.zeros(UpperCAmelCase_				,     UpperCAmelCase_  )
											else:
																		_lowerCAmelCase  	=			None

											_lowerCAmelCase  	=			torch.nn.Parameter(UpperCAmelCase_  )






class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							VQModel
				SCREAMING_SNAKE_CASE_:							CLIPTextModel
				SCREAMING_SNAKE_CASE_:							CLIPTokenizer
				SCREAMING_SNAKE_CASE_:							TransformeraDModel
				SCREAMING_SNAKE_CASE_:							LearnedClassifierFreeSamplingEmbeddings
				SCREAMING_SNAKE_CASE_:							VQDiffusionScheduler


				def __init__(					self	:			Dict				,     UpperCAmelCase_	:			VQModel				,     UpperCAmelCase_	:			CLIPTextModel				,     UpperCAmelCase_	:			CLIPTokenizer				,     UpperCAmelCase_	:			TransformeraDModel				,     UpperCAmelCase_	:			VQDiffusionScheduler				,     UpperCAmelCase_	:			LearnedClassifierFreeSamplingEmbeddings				,     )		->  Union[str, Any]:







											"""simple docstring"""




											super().__init__()

											self.register_modules(
											    vqvae=UpperCAmelCase_				,     transformer=UpperCAmelCase_				,     text_encoder=UpperCAmelCase_				,     tokenizer=UpperCAmelCase_				,     scheduler=UpperCAmelCase_				,     learned_classifier_free_sampling_embeddings=UpperCAmelCase_				,     )


				def 				__lowerCamelCase	(					self	:			List[Any]				,     UpperCAmelCase_	:			Optional[Any]				,     UpperCAmelCase_	:			Dict				,     UpperCAmelCase_	:			List[str]  )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			len(UpperCAmelCase_  ) if isinstance(UpperCAmelCase_				,     UpperCAmelCase_  ) else 1

											# get prompt text embeddings
											_lowerCAmelCase  	=			self.tokenizer(
											    UpperCAmelCase_				,     padding='max_length'				,     max_length=self.tokenizer.model_max_length				,     return_tensors='pt'				,     )
											_lowerCAmelCase  	=			text_inputs.input_ids

											if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																		_lowerCAmelCase  	=			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}"""  )
																		_lowerCAmelCase  	=			text_input_ids[:, : self.tokenizer.model_max_length]
											_lowerCAmelCase  	=			self.text_encoder(text_input_ids.to(self.device  )  )[0]

											# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
											# While CLIP does normalize the pooled output of the text transformer when combining
											# the image and text embeddings, CLIP does not directly normalize the last hidden state.
											#
											# CLIP normalizing the pooled output.
											# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
											_lowerCAmelCase  	=			prompt_embeds / prompt_embeds.norm(dim=-1				,     keepdim=UpperCAmelCase_  )

											# duplicate text embeddings for each generation per prompt
											_lowerCAmelCase  	=			prompt_embeds.repeat_interleave(UpperCAmelCase_				,     dim=0  )

											if do_classifier_free_guidance:
																		if self.learned_classifier_free_sampling_embeddings.learnable:
																									_lowerCAmelCase  	=			self.learned_classifier_free_sampling_embeddings.embeddings
																									_lowerCAmelCase  	=			negative_prompt_embeds.unsqueeze(0  ).repeat(UpperCAmelCase_				,     1				,     1  )
																		else:
																									_lowerCAmelCase  	=			[''] * batch_size

																									_lowerCAmelCase  	=			text_input_ids.shape[-1]
																									_lowerCAmelCase  	=			self.tokenizer(
																									    UpperCAmelCase_				,     padding='max_length'				,     max_length=UpperCAmelCase_				,     truncation=UpperCAmelCase_				,     return_tensors='pt'				,     )
																									_lowerCAmelCase  	=			self.text_encoder(uncond_input.input_ids.to(self.device  )  )[0]
																									# See comment for normalizing text embeddings
																									_lowerCAmelCase  	=			negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1				,     keepdim=UpperCAmelCase_  )

																		# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																		_lowerCAmelCase  	=			negative_prompt_embeds.shape[1]
																		_lowerCAmelCase  	=			negative_prompt_embeds.repeat(1				,     UpperCAmelCase_				,     1  )
																		_lowerCAmelCase  	=			negative_prompt_embeds.view(batch_size * num_images_per_prompt				,     UpperCAmelCase_				,     -1  )

																		# 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
																		_lowerCAmelCase  	=			torch.cat([negative_prompt_embeds, prompt_embeds]  )

											return prompt_embeds


				@torch.no_grad()
				def __call__(					self	:			Optional[int]				,     UpperCAmelCase_	:			Union[str, List[str]]				,     UpperCAmelCase_	:			int = 100				,     UpperCAmelCase_	:			float = 5.0				,     UpperCAmelCase_	:			float = 1.0				,     UpperCAmelCase_	:			int = 1				,     UpperCAmelCase_	:			Optional[Union[torch.Generator, List[torch.Generator]]] = None				,     UpperCAmelCase_	:			Optional[torch.FloatTensor] = None				,     UpperCAmelCase_	:			Optional[str] = "pil"				,     UpperCAmelCase_	:			bool = True				,     UpperCAmelCase_	:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,     UpperCAmelCase_	:			int = 1				,     )		->  Union[ImagePipelineOutput, Tuple]:







											"""simple docstring"""




											if isinstance(UpperCAmelCase_				,     UpperCAmelCase_  ):
																		_lowerCAmelCase  	=			1
											elif isinstance(UpperCAmelCase_				,     UpperCAmelCase_  ):
																		_lowerCAmelCase  	=			len(UpperCAmelCase_  )
											else:
																		raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase_  )}"""  )

											_lowerCAmelCase  	=			batch_size * num_images_per_prompt

											_lowerCAmelCase  	=			guidance_scale > 1.0

											_lowerCAmelCase  	=			self._encode_prompt(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_  )

											if (callback_steps is None) or (
											    callback_steps is not None and (not isinstance(UpperCAmelCase_				,     UpperCAmelCase_  ) or callback_steps <= 0)
											):
																		raise ValueError(
																		    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
																		    F""" {type(UpperCAmelCase_  )}."""  )

											# get the initial completely masked latents unless the user supplied it

											_lowerCAmelCase  	=			(batch_size, self.transformer.num_latent_pixels)
											if latents is None:
																		_lowerCAmelCase  	=			self.transformer.num_vector_embeds - 1
																		_lowerCAmelCase  	=			torch.full(UpperCAmelCase_				,     UpperCAmelCase_  ).to(self.device  )
											else:
																		if latents.shape != latents_shape:
																									raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""  )
																		if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
																									raise ValueError(
																									    'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
																									    F""" {self.transformer.num_vector_embeds - 1} (inclusive)."""  )
																		_lowerCAmelCase  	=			latents.to(self.device  )

											# set timesteps
											self.scheduler.set_timesteps(UpperCAmelCase_				,     device=self.device  )

											_lowerCAmelCase  	=			self.scheduler.timesteps.to(self.device  )

											_lowerCAmelCase  	=			latents

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

																		# predict the un-noised image
																		# model_output == `log_p_x_0`
																		_lowerCAmelCase  	=			self.transformer(UpperCAmelCase_				,     encoder_hidden_states=UpperCAmelCase_				,     timestep=UpperCAmelCase_  ).sample

																		if do_classifier_free_guidance:
																									_lowerCAmelCase	,		_lowerCAmelCase  	=			model_output.chunk(2  )
																									_lowerCAmelCase  	=			model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																									model_output -= torch.logsumexp(UpperCAmelCase_				,     dim=1				,     keepdim=UpperCAmelCase_  )

																		_lowerCAmelCase  	=			self.truncate(UpperCAmelCase_				,     UpperCAmelCase_  )

																		# remove `log(0)`'s (`-inf`s)
																		_lowerCAmelCase  	=			model_output.clamp(-70  )

																		# compute the previous noisy sample x_t -> x_t-1
																		_lowerCAmelCase  	=			self.scheduler.step(UpperCAmelCase_				,     timestep=UpperCAmelCase_				,     sample=UpperCAmelCase_				,     generator=UpperCAmelCase_  ).prev_sample

																		# call the callback, if provided
																		if callback is not None and i % callback_steps == 0:
																									callback(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_  )

											_lowerCAmelCase  	=			self.vqvae.config.vq_embed_dim
											_lowerCAmelCase  	=			(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
											_lowerCAmelCase  	=			self.vqvae.quantize.get_codebook_entry(UpperCAmelCase_				,     shape=UpperCAmelCase_  )
											_lowerCAmelCase  	=			self.vqvae.decode(UpperCAmelCase_				,     force_not_quantize=UpperCAmelCase_  ).sample

											_lowerCAmelCase  	=			(image / 2 + 0.5).clamp(0				,     1  )
											_lowerCAmelCase  	=			image.cpu().permute(0				,     2				,     3				,     1  ).numpy()

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

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=UpperCAmelCase_  )







				def 				__lowerCamelCase	(					self	:			Tuple				,     UpperCAmelCase_	:			torch.FloatTensor				,     UpperCAmelCase_	:			float  )		->  torch.FloatTensor:







											"""simple docstring"""




											_lowerCAmelCase	,		_lowerCAmelCase  	=			torch.sort(UpperCAmelCase_				,     1				,     descending=UpperCAmelCase_  )
											_lowerCAmelCase  	=			torch.exp(UpperCAmelCase_  )
											_lowerCAmelCase  	=			sorted_p_x_0.cumsum(dim=1  ) < truncation_rate

											# Ensure that at least the largest probability is not zeroed out
											_lowerCAmelCase  	=			torch.full_like(keep_mask[:, 0:1, :]				,     UpperCAmelCase_  )
											_lowerCAmelCase  	=			torch.cat((all_true, keep_mask)				,     dim=1  )
											_lowerCAmelCase  	=			keep_mask[:, :-1, :]

											_lowerCAmelCase  	=			keep_mask.gather(1				,     indices.argsort(1  )  )

											_lowerCAmelCase  	=			log_p_x_0.clone()

											_lowerCAmelCase  	=			-torch.inf  # -inf = log(0)

											return rv








"""simple docstring"""



import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
	import tensorflow as tf

	from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
	from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

_snake_case          =						logging.get_logger(__name__)
class        _SCREAMING_SNAKE_CASE  (  enum.Enum							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Any							=					0
				SCREAMING_SNAKE_CASE_:							str							=					1






@add_end_docstrings(UpperCAmelCase							)
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							int							=					"generated"


				def __init__(					self	:			Optional[Any]				,     *UpperCAmelCase_	:			Dict				,     **UpperCAmelCase_	:			Union[str, Any]  )		->  Optional[Any]:







											"""simple docstring"""




											super().__init__(*UpperCAmelCase_				,     **UpperCAmelCase_  )

											self.check_model_type(
											    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
											    if self.framework == 'tf'
											    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING  )


				def 				__lowerCamelCase	(					self	:			List[str]				,     UpperCAmelCase_	:			Any=None				,     UpperCAmelCase_	:			List[str]=None				,     UpperCAmelCase_	:			Union[str, Any]=None				,     UpperCAmelCase_	:			List[str]=None				,     UpperCAmelCase_	:			Tuple=None				,     UpperCAmelCase_	:			Optional[Any]=None				,     **UpperCAmelCase_	:			Union[str, Any]				,     )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			{}
											if truncation is not None:
																		_lowerCAmelCase  	=			truncation

											_lowerCAmelCase  	=			generate_kwargs

											_lowerCAmelCase  	=			{}
											if return_tensors is not None and return_type is None:
																		_lowerCAmelCase  	=			ReturnType.TENSORS if return_tensors else ReturnType.TEXT
											if return_type is not None:
																		_lowerCAmelCase  	=			return_type

											if clean_up_tokenization_spaces is not None:
																		_lowerCAmelCase  	=			clean_up_tokenization_spaces

											if stop_sequence is not None:
																		_lowerCAmelCase  	=			self.tokenizer.encode(UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_  )
																		if len(UpperCAmelCase_  ) > 1:
																									warnings.warn(
																									    'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
																									    ' the stop sequence will be used as the stop sequence string in the interim.'  )
																		_lowerCAmelCase  	=			stop_sequence_ids[0]

											return preprocess_params, forward_params, postprocess_params


				def 				__lowerCamelCase	(					self	:			List[Any]				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int  )		->  Optional[Any]:







											"""simple docstring"""




											return True


				def 				__lowerCamelCase	(					self	:			Union[str, Any]				,     *UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			int  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.model.config.prefix if self.model.config.prefix is not None else ''
											if isinstance(args[0]				,     UpperCAmelCase_  ):
																		if self.tokenizer.pad_token_id is None:
																									raise ValueError('Please make sure that the tokenizer has a pad_token_id when using a batch input'  )
																		_lowerCAmelCase  	=			([prefix + arg for arg in args[0]],)
																		_lowerCAmelCase  	=			True

											elif isinstance(args[0]				,     UpperCAmelCase_  ):
																		_lowerCAmelCase  	=			(prefix + args[0],)
																		_lowerCAmelCase  	=			False
											else:
																		raise ValueError(
																		    F""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""  )
											_lowerCAmelCase  	=			self.tokenizer(*UpperCAmelCase_				,     padding=UpperCAmelCase_				,     truncation=UpperCAmelCase_				,     return_tensors=self.framework  )
											# This is produced by tokenizers but is an invalid generate kwargs
											if "token_type_ids" in inputs:
																		del inputs["token_type_ids"]
											return inputs


				def __call__(					self	:			Union[str, Any]				,     *UpperCAmelCase_	:			Optional[Any]				,     **UpperCAmelCase_	:			Tuple  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			super().__call__(*UpperCAmelCase_				,     **UpperCAmelCase_  )
											if (
											    isinstance(args[0]				,     UpperCAmelCase_  )
											    and all(isinstance(UpperCAmelCase_				,     UpperCAmelCase_  ) for el in args[0]  )
											    and all(len(UpperCAmelCase_  ) == 1 for res in result  )
											):
																		return [res[0] for res in result]
											return result


				def 				__lowerCamelCase	(					self	:			int				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			Dict=TruncationStrategy.DO_NOT_TRUNCATE				,     **UpperCAmelCase_	:			Tuple  )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase  	=			self._parse_and_tokenize(UpperCAmelCase_				,     truncation=UpperCAmelCase_				,     **UpperCAmelCase_  )
											return inputs


				def 				__lowerCamelCase	(					self	:			Optional[Any]				,     UpperCAmelCase_	:			List[Any]				,     **UpperCAmelCase_	:			List[str]  )		->  Any:







											"""simple docstring"""




											if self.framework == "pt":
																		_lowerCAmelCase	,		_lowerCAmelCase  	=			model_inputs['input_ids'].shape
											elif self.framework == "tf":
																		_lowerCAmelCase	,		_lowerCAmelCase  	=			tf.shape(model_inputs['input_ids']  ).numpy()

											_lowerCAmelCase  	=			generate_kwargs.get('min_length'				,     self.model.config.min_length  )
											_lowerCAmelCase  	=			generate_kwargs.get('max_length'				,     self.model.config.max_length  )
											self.check_inputs(UpperCAmelCase_				,     generate_kwargs['min_length']				,     generate_kwargs['max_length']  )
											_lowerCAmelCase  	=			self.model.generate(**UpperCAmelCase_				,     **UpperCAmelCase_  )
											_lowerCAmelCase  	=			output_ids.shape[0]
											if self.framework == "pt":
																		_lowerCAmelCase  	=			output_ids.reshape(UpperCAmelCase_				,     out_b // in_b				,     *output_ids.shape[1:]  )
											elif self.framework == "tf":
																		_lowerCAmelCase  	=			tf.reshape(UpperCAmelCase_				,     (in_b, out_b // in_b, *output_ids.shape[1:])  )
											return {"output_ids": output_ids}







				def 				__lowerCamelCase	(					self	:			Dict				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			Union[str, Any]=ReturnType.TEXT				,     UpperCAmelCase_	:			Optional[Any]=False  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			[]
											for output_ids in model_outputs["output_ids"][0]:
																		if return_type == ReturnType.TENSORS:
																									_lowerCAmelCase  	=			{F"""{self.return_name}_token_ids""": output_ids}
																		elif return_type == ReturnType.TEXT:
																									_lowerCAmelCase  	=			{
																									    F"""{self.return_name}_text""": self.tokenizer.decode(
																									        UpperCAmelCase_				,     skip_special_tokens=UpperCAmelCase_				,     clean_up_tokenization_spaces=UpperCAmelCase_				,     )
																									}
																		records.append(UpperCAmelCase_  )
											return records






@add_end_docstrings(UpperCAmelCase							)
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[Any]							=					"summary"


				def __call__(					self	:			Optional[int]				,     *UpperCAmelCase_	:			Optional[Any]				,     **UpperCAmelCase_	:			Dict  )		->  int:







											"""simple docstring"""




											return super().__call__(*UpperCAmelCase_				,     **UpperCAmelCase_  )







				def 				__lowerCamelCase	(					self	:			Dict				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int  )		->  bool:







											"""simple docstring"""




											if max_length < min_length:
																		logger.warning(F"""Your min_length={min_length} must be inferior than your max_length={max_length}."""  )

											if input_length < max_length:
																		logger.warning(
																		    F"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
																		    'a summarization task, where outputs shorter than the input are typically wanted, you might '
																		    F"""consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})"""  )






@add_end_docstrings(UpperCAmelCase							)
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[int]							=					"translation"


				def 				__lowerCamelCase	(					self	:			str				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int  )		->  Any:







											"""simple docstring"""




											if input_length > 0.9 * max_length:
																		logger.warning(
																		    F"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
																		    'increasing your max_length manually, e.g. translator(\'...\', max_length=400)'  )
											return True


				def 				__lowerCamelCase	(					self	:			Optional[Any]				,     *UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			Optional[int]=TruncationStrategy.DO_NOT_TRUNCATE				,     UpperCAmelCase_	:			Tuple=None				,     UpperCAmelCase_	:			Dict=None  )		->  Optional[int]:







											"""simple docstring"""




											if getattr(self.tokenizer				,     '_build_translation_inputs'				,     UpperCAmelCase_  ):
																		return self.tokenizer._build_translation_inputs(
																		    *UpperCAmelCase_				,     return_tensors=self.framework				,     truncation=UpperCAmelCase_				,     src_lang=UpperCAmelCase_				,     tgt_lang=UpperCAmelCase_  )
											else:
																		return super()._parse_and_tokenize(*UpperCAmelCase_				,     truncation=UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Union[str, Any]				,     UpperCAmelCase_	:			Tuple=None				,     UpperCAmelCase_	:			Union[str, Any]=None				,     **UpperCAmelCase_	:			Optional[int]  )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			super()._sanitize_parameters(**UpperCAmelCase_  )
											if src_lang is not None:
																		_lowerCAmelCase  	=			src_lang
											if tgt_lang is not None:
																		_lowerCAmelCase  	=			tgt_lang
											if src_lang is None and tgt_lang is None:
																		# Backward compatibility, direct arguments use is preferred.
																		_lowerCAmelCase  	=			kwargs.get('task'				,     self.task  )
																		_lowerCAmelCase  	=			task.split('_'  )
																		if task and len(UpperCAmelCase_  ) == 4:
																									# translation, XX, to YY
																									_lowerCAmelCase  	=			items[1]
																									_lowerCAmelCase  	=			items[3]
											return preprocess_params, forward_params, postprocess_params







				def __call__(					self	:			Optional[int]				,     *UpperCAmelCase_	:			int				,     **UpperCAmelCase_	:			Optional[int]  )		->  Union[str, Any]:







											"""simple docstring"""




											return super().__call__(*UpperCAmelCase_				,     **UpperCAmelCase_  )






