


import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
	    SqueezeBertForMaskedLM,
	    SqueezeBertForMultipleChoice,
	    SqueezeBertForQuestionAnswering,
	    SqueezeBertForSequenceClassification,
	    SqueezeBertForTokenClassification,
	    SqueezeBertModel,
	)



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					def __init__(self       :		Any ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Tuple=13 ,   _UpperCAmelCase       :		str=7 ,   _UpperCAmelCase       :		Optional[int]=True ,   _UpperCAmelCase       :		Union[str, Any]=True ,   _UpperCAmelCase       :		Union[str, Any]=False ,   _UpperCAmelCase       :		Optional[int]=True ,   _UpperCAmelCase       :		Any=99 ,   _UpperCAmelCase       :		Optional[Any]=32 ,   _UpperCAmelCase       :		Optional[Any]=5 ,   _UpperCAmelCase       :		Optional[Any]=4 ,   _UpperCAmelCase       :		Optional[Any]=64 ,   _UpperCAmelCase       :		Any="gelu" ,   _UpperCAmelCase       :		Optional[int]=0.1 ,   _UpperCAmelCase       :		Optional[int]=0.1 ,   _UpperCAmelCase       :		Optional[Any]=512 ,   _UpperCAmelCase       :		Any=16 ,   _UpperCAmelCase       :		int=2 ,   _UpperCAmelCase       :		Union[str, Any]=0.02 ,   _UpperCAmelCase       :		Tuple=3 ,   _UpperCAmelCase       :		Optional[int]=4 ,   _UpperCAmelCase       :		int=None ,   _UpperCAmelCase       :		Tuple=2 ,   _UpperCAmelCase       :		Tuple=2 ,   _UpperCAmelCase       :		str=2 ,   _UpperCAmelCase       :		Dict=2 ,   _UpperCAmelCase       :		str=4 ,   _UpperCAmelCase       :		Tuple=1 ,   )		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						parent
							lowercase__	     =						batch_size
							lowercase__	     =						seq_length
							lowercase__	     =						is_training
							lowercase__	     =						use_input_mask
							lowercase__	     =						use_token_type_ids
							lowercase__	     =						use_labels
							lowercase__	     =						vocab_size
							lowercase__	     =						hidden_size
							lowercase__	     =						num_hidden_layers
							lowercase__	     =						num_attention_heads
							lowercase__	     =						intermediate_size
							lowercase__	     =						hidden_act
							lowercase__	     =						hidden_dropout_prob
							lowercase__	     =						attention_probs_dropout_prob
							lowercase__	     =						max_position_embeddings
							lowercase__	     =						type_vocab_size
							lowercase__	     =						type_sequence_label_size
							lowercase__	     =						initializer_range
							lowercase__	     =						num_labels
							lowercase__	     =						num_choices
							lowercase__	     =						scope
							lowercase__	     =						q_groups
							lowercase__	     =						k_groups
							lowercase__	     =						v_groups
							lowercase__	     =						post_attention_groups
							lowercase__	     =						intermediate_groups
							lowercase__	     =						output_groups

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size						)

							lowercase__	     =						None
							if self.use_input_mask:
									lowercase__	     =						random_attention_mask([self.batch_size, self.seq_length]						)

							lowercase__	     =						None
							lowercase__	     =						None
							lowercase__	     =						None
							if self.use_labels:
									lowercase__	     =						ids_tensor([self.batch_size] ,   self.type_sequence_label_size						)
									lowercase__	     =						ids_tensor([self.batch_size, self.seq_length] ,   self.num_labels						)
									lowercase__	     =						ids_tensor([self.batch_size] ,   self.num_choices						)

							lowercase__	     =						self.get_config()

							return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

					def     lowerCamelCase__	(self       :		List[str]						)		->  Optional[int]:




							"""simple docstring"""


							return SqueezeBertConfig(
							    embedding_size=self.hidden_size ,   vocab_size=self.vocab_size ,   hidden_size=self.hidden_size ,   num_hidden_layers=self.num_hidden_layers ,   num_attention_heads=self.num_attention_heads ,   intermediate_size=self.intermediate_size ,   hidden_act=self.hidden_act ,   attention_probs_dropout_prob=self.hidden_dropout_prob ,   attention_dropout=self.attention_probs_dropout_prob ,   max_position_embeddings=self.max_position_embeddings ,   initializer_range=self.initializer_range ,   q_groups=self.q_groups ,   k_groups=self.k_groups ,   v_groups=self.v_groups ,   post_attention_groups=self.post_attention_groups ,   intermediate_groups=self.intermediate_groups ,   output_groups=self.output_groups ,   )

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		List[Any]						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						SqueezeBertModel(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						model(_UpperCAmelCase ,   _UpperCAmelCase						)
							lowercase__	     =						model(_UpperCAmelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size)						)

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Tuple						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						SqueezeBertForMaskedLM(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						model(_UpperCAmelCase ,   attention_mask=_UpperCAmelCase ,   labels=_UpperCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size)						)

					def     lowerCamelCase__	(self       :		str ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		int						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						SqueezeBertForQuestionAnswering(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						model(
							    _UpperCAmelCase ,   attention_mask=_UpperCAmelCase ,   start_positions=_UpperCAmelCase ,   end_positions=_UpperCAmelCase						)
							self.parent.assertEqual(result.start_logits.shape ,   (self.batch_size, self.seq_length)						)
							self.parent.assertEqual(result.end_logits.shape ,   (self.batch_size, self.seq_length)						)

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		List[Any]						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						self.num_labels
							lowercase__	     =						SqueezeBertForSequenceClassification(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						model(_UpperCAmelCase ,   attention_mask=_UpperCAmelCase ,   labels=_UpperCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_labels)						)

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		Tuple						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						self.num_labels
							lowercase__	     =						SqueezeBertForTokenClassification(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase ,   attention_mask=_UpperCAmelCase ,   labels=_UpperCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.num_labels)						)

					def     lowerCamelCase__	(self       :		Tuple ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		List[Any]						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						self.num_choices
							lowercase__	     =						SqueezeBertForMultipleChoice(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						input_ids.unsqueeze(1						).expand(-1 ,   self.num_choices ,   -1						).contiguous()
							lowercase__	     =						input_mask.unsqueeze(1						).expand(-1 ,   self.num_choices ,   -1						).contiguous()
							lowercase__	     =						model(
							    _UpperCAmelCase ,   attention_mask=_UpperCAmelCase ,   labels=_UpperCAmelCase ,   )
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_choices)						)


					def     lowerCamelCase__	(self       :		str						)		->  str:




							"""simple docstring"""


							lowercase__	     =						self.prepare_config_and_inputs()
							((lowercase__)     ,      (lowercase__)     ,      (lowercase__)     ,      (lowercase__)     ,      (lowercase__)     ,      (lowercase__))	     =						config_and_inputs
							lowercase__	     =						{"""input_ids""": input_ids, """attention_mask""": input_mask}
							return config, inputs_dict






@require_torch
class 				A       (			UpperCAmelCase__					,    UpperCAmelCase__					,    unittest.TestCase				):
					'''simple docstring'''






					A__     =					(
					    (
					        SqueezeBertModel,
					        SqueezeBertForMaskedLM,
					        SqueezeBertForMultipleChoice,
					        SqueezeBertForQuestionAnswering,
					        SqueezeBertForSequenceClassification,
					        SqueezeBertForTokenClassification,
					    )
					    if is_torch_available()
					    else None
					)
					A__     =					(
					    {
					        '''feature-extraction''': SqueezeBertModel,
					        '''fill-mask''': SqueezeBertForMaskedLM,
					        '''question-answering''': SqueezeBertForQuestionAnswering,
					        '''text-classification''': SqueezeBertForSequenceClassification,
					        '''token-classification''': SqueezeBertForTokenClassification,
					        '''zero-shot''': SqueezeBertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					A__     =					False
					A__     =					True
					A__     =					False

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						SqueezeBertModelTester(self						)
							lowercase__	     =						ConfigTester(self ,   config_class=_UpperCAmelCase ,   dim=37						)

					def     lowerCamelCase__	(self       :		Any						)		->  Any:




							"""simple docstring"""


							self.config_tester.run_common_tests()

					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_model(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  int:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_masked_lm(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		str						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_question_answering(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		str						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_token_classification(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Tuple						)		->  str:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_UpperCAmelCase						)


					@slow
					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  List[str]:




							"""simple docstring"""


							for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase__	     =						SqueezeBertModel.from_pretrained(_UpperCAmelCase						)
									self.assertIsNotNone(_UpperCAmelCase						)






@require_sentencepiece
@require_tokenizers
@require_torch
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					@slow
					def     lowerCamelCase__	(self       :		int						)		->  Dict:




							"""simple docstring"""


							lowercase__	     =						SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli"""						)

							lowercase__	     =						torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]]						)
							lowercase__	     =						model(_UpperCAmelCase						)[0]
							lowercase__	     =						torch.Size((1, 3)						)
							self.assertEqual(output.shape ,   _UpperCAmelCase						)
							lowercase__	     =						torch.tensor([[0.6_401, -0.0_349, -0.6_041]]						)
							self.assertTrue(torch.allclose(_UpperCAmelCase ,   _UpperCAmelCase ,   atol=1E-4						)						)




'''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

UpperCAmelCase					 =	logging.get_logger(__name__)


class 	lowerCAmelCase  (   enum.Enum ):
       lowerCAmelCase_												=			0
       lowerCAmelCase_												=			1






@add_end_docstrings(A )
class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			"generated"







       def __init__(				self      :			Tuple    ,   *__lowercase      :			Tuple    ,   **__lowercase      :			Optional[int]   ):







            """simple docstring"""







            super().__init__(*__lowercase    ,   **__lowercase   )

            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       snake_case (				self      :			Dict    ,   __lowercase      :			Any=None    ,   __lowercase      :			int=None    ,   __lowercase      :			List[str]=None    ,   __lowercase      :			int=None    ,   __lowercase      :			str=None    ,   __lowercase      :			List[Any]=None    ,   **__lowercase      :			Union[str, Any]    ,   ):







            """simple docstring"""







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

            __lowercase      		=generate_kwargs

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

            if clean_up_tokenization_spaces is not None:
                 __lowercase      		=clean_up_tokenization_spaces

            if stop_sequence is not None:
                 __lowercase      		=self.tokenizer.encode(__lowercase    ,   add_special_tokens=__lowercase   )
                 if len(__lowercase   ) > 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      		=stop_sequence_ids[0]

            return preprocess_params, forward_params, postprocess_params







       def       snake_case (				self      :			List[Any]    ,   __lowercase      :			int    ,   __lowercase      :			int    ,   __lowercase      :			int   ):







            """simple docstring"""







            return True







       def       snake_case (				self      :			Optional[Any]    ,   *__lowercase      :			Optional[int]    ,   __lowercase      :			Union[str, Any]   ):







            """simple docstring"""







            __lowercase      		=self.model.config.prefix if self.model.config.prefix is not None else ''
            if isinstance(args[0]    ,   __lowercase   ):
                 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      		=([prefix + arg for arg in args[0]],)
                 __lowercase      		=True

            elif isinstance(args[0]    ,   __lowercase   ):
                 __lowercase      		=(prefix + args[0],)
                 __lowercase      		=False
            else:
                 raise ValueError(
                     f''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''   )
            __lowercase      		=self.tokenizer(*__lowercase    ,   padding=__lowercase    ,   truncation=__lowercase    ,   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      :			Any    ,   *__lowercase      :			int    ,   **__lowercase      :			Optional[Any]   ):







            """simple docstring"""







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







       def       snake_case (				self      :			List[str]    ,   __lowercase      :			Tuple    ,   __lowercase      :			List[Any]=TruncationStrategy.DO_NOT_TRUNCATE    ,   **__lowercase      :			Dict   ):







            """simple docstring"""







            __lowercase      		=self._parse_and_tokenize(__lowercase    ,   truncation=__lowercase    ,   **__lowercase   )
            return inputs







       def       snake_case (				self      :			Optional[int]    ,   __lowercase      :			Tuple    ,   **__lowercase      :			Tuple   ):







            """simple docstring"""







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

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







       def       snake_case (				self      :			str    ,   __lowercase      :			Optional[Any]    ,   __lowercase      :			Optional[int]=ReturnType.TEXT    ,   __lowercase      :			Union[str, Any]=False   ):







            """simple docstring"""







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






@add_end_docstrings(A )
class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			"summary"







       def __call__(				self      :			Dict    ,   *__lowercase      :			Dict    ,   **__lowercase      :			Dict   ):







            """simple docstring"""







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







       def       snake_case (				self      :			str    ,   __lowercase      :			int    ,   __lowercase      :			int    ,   __lowercase      :			int   ):







            """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(A )
class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			"translation"







       def       snake_case (				self      :			int    ,   __lowercase      :			int    ,   __lowercase      :			int    ,   __lowercase      :			int   ):







            """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       snake_case (				self      :			int    ,   *__lowercase      :			str    ,   __lowercase      :			List[str]=TruncationStrategy.DO_NOT_TRUNCATE    ,   __lowercase      :			List[Any]=None    ,   __lowercase      :			Optional[int]=None   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Optional[int]    ,   __lowercase      :			List[str]=None    ,   __lowercase      :			Optional[Any]=None    ,   **__lowercase      :			int   ):







            """simple docstring"""







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







       def __call__(				self      :			Optional[Any]    ,   *__lowercase      :			Any    ,   **__lowercase      :			List[Any]   ):







            """simple docstring"""







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


