"""simple docstring"""







import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int      ):
     lowercase_	:				Union[str, Any]													=	[]
     embed.append(
         (
             F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
             F'''stage{idx}.patch_embed.proj.weight''',
         )      )
     embed.append(
         (
             F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
             F'''stage{idx}.patch_embed.proj.bias''',
         )      )
     embed.append(
         (
             F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
             F'''stage{idx}.patch_embed.norm.weight''',
         )      )
     embed.append(
         (
             F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
             F'''stage{idx}.patch_embed.norm.bias''',
         )      )
     return embed



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Dict      ):
     lowercase_	:				Union[str, Any]													=	[]
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
         )      )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
         )      )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', F'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''')      )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', F'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''')      )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', F'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''')      )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', F'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''')      )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', F'''stage{idx}.blocks.{cnt}.norm1.weight''')      )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', F'''stage{idx}.blocks.{cnt}.norm1.bias''')      )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', F'''stage{idx}.blocks.{cnt}.norm2.weight''')      )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', F'''stage{idx}.blocks.{cnt}.norm2.bias''')      )
     return attention_weights



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Tuple      ):
     lowercase_	:				Optional[int]													=	[]
     token.append((F'''cvt.encoder.stages.{idx}.cls_token''', 'stage2.cls_token')      )
     return token



def 			lowercase__(       ):
     lowercase_	:				Tuple													=	[]
     head.append(('layernorm.weight', 'norm.weight')      )
     head.append(('layernorm.bias', 'norm.bias')      )
     head.append(('classifier.weight', 'head.weight')      )
     head.append(('classifier.bias', 'head.bias')      )
     return head



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     Tuple      ):
     lowercase_	:				Union[str, Any]													=	'imagenet-1k-id2label.json'
     lowercase_	:				int													=	10_00

     lowercase_	:				List[str]													=	'huggingface/label-files'
     lowercase_	:				Dict													=	num_labels
     lowercase_	:				Tuple													=	json.load(open(cached_download(hf_hub_url(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			repo_type='dataset'      )      )     ,			'r'      )      )
     lowercase_	:				Dict													=	{int(__SCREAMING_SNAKE_CASE      ): v for k, v in idalabel.items()}

     lowercase_	:				Optional[int]													=	idalabel
     lowercase_	:				List[Any]													=	{v: k for k, v in idalabel.items()}

     lowercase_	:				Any													=	CvtConfig(num_labels=__SCREAMING_SNAKE_CASE     ,			idalabel=__SCREAMING_SNAKE_CASE     ,			labelaid=__SCREAMING_SNAKE_CASE      )

     # For depth size 13 (13 = 1+2+10)
     if cvt_model.rsplit('/'     ,			1      )[-1][4:6] == "13":
          lowercase_	:				Optional[Any]													=	[1, 2, 10]

     # For depth size 21 (21 = 1+4+16)
     elif cvt_model.rsplit('/'     ,			1      )[-1][4:6] == "21":
          lowercase_	:				Dict													=	[1, 4, 16]

     # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
     else:
          lowercase_	:				Optional[int]													=	[2, 2, 20]
          lowercase_	:				List[str]													=	[3, 12, 16]
          lowercase_	:				Optional[int]													=	[1_92, 7_68, 10_24]

     lowercase_	:				Any													=	CvtForImageClassification(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Union[str, Any]													=	AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'      )
     lowercase_	:				Any													=	image_size
     lowercase_	:				Dict													=	torch.load(__SCREAMING_SNAKE_CASE     ,			map_location=torch.device('cpu'      )      )

     lowercase_	:				Optional[Any]													=	OrderedDict()
     lowercase_	:				Tuple													=	[]

     for idx in range(len(config.depth      )      ):
          if config.cls_token[idx]:
               lowercase_	:				List[Any]													=	list_of_state_dict + cls_token(__SCREAMING_SNAKE_CASE      )
          lowercase_	:				Dict													=	list_of_state_dict + embeddings(__SCREAMING_SNAKE_CASE      )
          for cnt in range(config.depth[idx]      ):
               lowercase_	:				int													=	list_of_state_dict + attention(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     lowercase_	:				Dict													=	list_of_state_dict + final()
     for gg in list_of_state_dict:
          print(__SCREAMING_SNAKE_CASE      )
     for i in range(len(__SCREAMING_SNAKE_CASE      )      ):
          lowercase_	:				Any													=	original_weights[list_of_state_dict[i][1]]

     model.load_state_dict(__SCREAMING_SNAKE_CASE      )
     model.save_pretrained(__SCREAMING_SNAKE_CASE      )
     image_processor.save_pretrained(__SCREAMING_SNAKE_CASE      )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE    	=argparse.ArgumentParser()
       parser.add_argument(
           "--cvt_model",
           default="cvt-w24",
           type=str,
           help="Name of the cvt model you'd like to convert.",
       )
       parser.add_argument(
           "--image_size",
           default=384,
           type=int,
           help="Input Image Size",
       )
       parser.add_argument(
           "--cvt_file_name",
           default=r"cvtmodels\CvT-w24-384x384-IN-22k.pth",
           type=str,
           help="Input Image Size",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )

       __SCREAMING_SNAKE_CASE    	=parser.parse_args()
       convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)


"""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

__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)







class        UpperCamelCase		(			enum.Enum  ):
   lowercase							=  0
   lowercase							=  1







@add_end_docstrings(lowercase_  )
class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  'generated'
   def __init__(			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      Tuple:


        '''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 			_UpperCAmelCase   (			self     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,**__UpperCamelCase     ,)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	{}
        if truncation is not None:
             lowercase_	:				int													=	truncation

        lowercase_	:				Dict													=	generate_kwargs

        lowercase_	:				List[Any]													=	{}
        if return_tensors is not None and return_type is None:
             lowercase_	:				Union[str, Any]													=	ReturnType.TENSORS if return_tensors else ReturnType.TEXT
        if return_type is not None:
             lowercase_	:				str													=	return_type

        if clean_up_tokenization_spaces is not None:
             lowercase_	:				Dict													=	clean_up_tokenization_spaces

        if stop_sequence is not None:
             lowercase_	:				Union[str, Any]													=	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.'			)
             lowercase_	:				Optional[int]													=	stop_sequence_ids[0]

        return preprocess_params, forward_params, postprocess_params
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        return True
   def 			_UpperCAmelCase   (			self     ,*__UpperCamelCase     ,__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				Dict													=	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'			)
             lowercase_	:				str													=	([prefix + arg for arg in args[0]],)
             lowercase_	:				Union[str, Any]													=	True

        elif isinstance(args[0]     ,__UpperCamelCase			):
             lowercase_	:				Union[str, Any]													=	(prefix + args[0],)
             lowercase_	:				Union[str, Any]													=	False
        else:
             raise ValueError(
                 f''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''			)
        lowercase_	:				List[Any]													=	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     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	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 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=TruncationStrategy.DO_NOT_TRUNCATE     ,**__UpperCamelCase			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				Any													=	self._parse_and_tokenize(__UpperCamelCase     ,truncation=__UpperCamelCase     ,**__UpperCamelCase			)
        return inputs
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,**__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        if self.framework == "pt":
             lowercase_							,  lowercase_	:				Optional[int]													=	model_inputs['input_ids'].shape
        elif self.framework == "tf":
             lowercase_							,  lowercase_	:				Union[str, Any]													=	tf.shape(model_inputs['input_ids']			).numpy()

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







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=ReturnType.TEXT     ,__UpperCamelCase=False			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				Dict													=	[]
        for output_ids in model_outputs["output_ids"][0]:
             if return_type == ReturnType.TENSORS:
                  lowercase_	:				List[Any]													=	{f'''{self.return_name}_token_ids''': output_ids}
             elif return_type == ReturnType.TEXT:
                  lowercase_	:				str													=	{
                      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(lowercase_  )
class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  'summary'
   def __call__(			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      Union[str, Any]:


        '''simple docstring'''


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







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      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(lowercase_  )
class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  'translation'
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Dict:


        '''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 			_UpperCAmelCase   (			self     ,*__UpperCamelCase     ,__UpperCamelCase=TruncationStrategy.DO_NOT_TRUNCATE     ,__UpperCamelCase=None     ,__UpperCamelCase=None			)	->      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 			_UpperCAmelCase   (			self     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,**__UpperCamelCase			)	->      str:


        '''simple docstring'''


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







   def __call__(			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


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

