




from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel




def 					lowerCamelCase__       (						A__       :					List[Any]  ,			A__       :					Union[str, Any]  ,			A__       :					Any=None  ,			A__       :					List[str]=None	):





       '''simple docstring'''



       if attention_mask is None:
              __lowerCamelCase   =						tf.cast(tf.math.not_equal(A__  ,			config.pad_token_id	)  ,			tf.inta	)
       return {"input_ids": input_ids, "attention_mask": attention_mask}





@require_tf
class     lowerCamelCase__:
      UpperCAmelCase__      :	Tuple    		=   OPTConfig
      UpperCAmelCase__      :	Optional[Any]    		=   {}
      UpperCAmelCase__      :	int    		=   'gelu'
      def __init__(				self: Optional[int]    ,       UpperCamelCase_: Dict    ,       UpperCamelCase_: Tuple=13    ,       UpperCamelCase_: str=7    ,       UpperCamelCase_: Tuple=True    ,       UpperCamelCase_: int=False    ,       UpperCamelCase_: str=99    ,       UpperCamelCase_: List[Any]=16    ,       UpperCamelCase_: int=2    ,       UpperCamelCase_: Tuple=4    ,       UpperCamelCase_: Dict=4    ,       UpperCamelCase_: List[str]="gelu"    ,       UpperCamelCase_: List[str]=0.1    ,       UpperCamelCase_: Optional[int]=0.1    ,       UpperCamelCase_: List[Any]=20    ,       UpperCamelCase_: str=2    ,       UpperCamelCase_: Tuple=1    ,       UpperCamelCase_: Optional[Any]=0    ,       UpperCamelCase_: Union[str, Any]=16    ,       UpperCamelCase_: List[str]=16    ,       ):
             __lowerCamelCase   =						parent
             __lowerCamelCase   =						batch_size
             __lowerCamelCase   =						seq_length
             __lowerCamelCase   =						is_training
             __lowerCamelCase   =						use_labels
             __lowerCamelCase   =						vocab_size
             __lowerCamelCase   =						hidden_size
             __lowerCamelCase   =						num_hidden_layers
             __lowerCamelCase   =						num_attention_heads
             __lowerCamelCase   =						intermediate_size
             __lowerCamelCase   =						hidden_act
             __lowerCamelCase   =						hidden_dropout_prob
             __lowerCamelCase   =						attention_probs_dropout_prob
             __lowerCamelCase   =						max_position_embeddings
             __lowerCamelCase   =						eos_token_id
             __lowerCamelCase   =						pad_token_id
             __lowerCamelCase   =						bos_token_id
             __lowerCamelCase   =						embed_dim
             __lowerCamelCase   =						word_embed_proj_dim
             __lowerCamelCase   =						False
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						ids_tensor([self.batch_size, self.seq_length - 1]    ,       self.vocab_size   )
             __lowerCamelCase   =						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )    ,       1   )
             __lowerCamelCase   =						tf.concat([input_ids, eos_tensor]    ,       axis=1   )

             __lowerCamelCase   =						self.config_cls(
                 vocab_size=self.vocab_size    ,       hidden_size=self.hidden_size    ,       num_hidden_layers=self.num_hidden_layers    ,       num_attention_heads=self.num_attention_heads    ,       ffn_dim=self.intermediate_size    ,       dropout=self.hidden_dropout_prob    ,       attention_dropout=self.attention_probs_dropout_prob    ,       max_position_embeddings=self.max_position_embeddings    ,       eos_token_id=self.eos_token_id    ,       bos_token_id=self.bos_token_id    ,       pad_token_id=self.pad_token_id    ,       embed_dim=self.embed_dim    ,       word_embed_proj_dim=self.word_embed_proj_dim    ,       is_encoder_decoder=UpperCamelCase_    ,       **self.config_updates    ,       )
             __lowerCamelCase   =						prepare_opt_inputs_dict(UpperCamelCase_    ,       UpperCamelCase_   )
             return config, inputs_dict




      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: str   ):
             __lowerCamelCase   =						TFOPTModel(config=UpperCamelCase_   )
             __lowerCamelCase   =						inputs_dict["""input_ids"""]

             __lowerCamelCase   =						input_ids[:1, :]
             __lowerCamelCase   =						inputs_dict["""attention_mask"""][:1, :]
             __lowerCamelCase   =						1

             # first forward pass
             __lowerCamelCase   =						model(UpperCamelCase_    ,       attention_mask=UpperCamelCase_    ,       use_cache=UpperCamelCase_   )

             __lowerCamelCase,   __lowerCamelCase   =						outputs.to_tuple()

             # create hypothetical next token and extent to next_input_ids
             __lowerCamelCase   =						ids_tensor((self.batch_size, 3)    ,       config.vocab_size   )
             __lowerCamelCase   =						tf.cast(ids_tensor((self.batch_size, 3)    ,       2   )    ,       tf.inta   )

             # append to next input_ids and
             __lowerCamelCase   =						tf.concat([input_ids, next_tokens]    ,       axis=-1   )
             __lowerCamelCase   =						tf.concat([attention_mask, next_attn_mask]    ,       axis=-1   )

             __lowerCamelCase   =						model(UpperCamelCase_    ,       attention_mask=UpperCamelCase_   )[0]
             __lowerCamelCase   =						model(UpperCamelCase_    ,       attention_mask=UpperCamelCase_    ,       past_key_values=UpperCamelCase_   )[0]

             self.parent.assertEqual(next_tokens.shape[1]    ,       output_from_past.shape[1]   )

             # select random slice
             __lowerCamelCase   =						int(ids_tensor((1,)    ,       output_from_past.shape[-1]   )   )
             __lowerCamelCase   =						output_from_no_past[:, -3:, random_slice_idx]
             __lowerCamelCase   =						output_from_past[:, :, random_slice_idx]

             # test that outputs are equal for slice
             tf.debugging.assert_near(UpperCamelCase_    ,       UpperCamelCase_    ,       rtol=1E-3   )





@require_tf
class     lowerCamelCase__(					__lowerCamelCase ,     __lowerCamelCase ,     unittest.TestCase):
      UpperCAmelCase__      :	List[Any]    		=   (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
      UpperCAmelCase__      :	Tuple    		=   (TFOPTForCausalLM,) if is_tf_available() else ()
      UpperCAmelCase__      :	str    		=   (
          {'feature-extraction': TFOPTModel, 'text-generation': TFOPTForCausalLM} if is_tf_available() else {}
      )
      UpperCAmelCase__      :	List[Any]    		=   False
      UpperCAmelCase__      :	Optional[int]    		=   False
      UpperCAmelCase__      :	int    		=   False
      UpperCAmelCase__      :	Any    		=   10
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						TFOPTModelTester(self   )
             __lowerCamelCase   =						ConfigTester(self    ,       config_class=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: str   ):
             self.config_tester.run_common_tests()
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase,   __lowerCamelCase   =						self.model_tester.prepare_config_and_inputs_for_common()

             def _get_word_embedding_weight(UpperCamelCase_: Tuple    ,       UpperCamelCase_: Tuple   ):
                    if hasattr(UpperCamelCase_    ,       """weight"""   ):
                           return embedding_layer.weight
                    else:
                           # Here we build the word embeddings weights if not exists.
                           # And then we retry to get the attribute once built.
                           model.build()
                           if hasattr(UpperCamelCase_    ,       """weight"""   ):
                                  return embedding_layer.weight
                           else:
                                  return None

             for model_class in self.all_model_classes:
                    for size in [config.vocab_size - 10, config.vocab_size + 10]:
                           # build the embeddings
                           __lowerCamelCase   =						model_class(config=UpperCamelCase_   )
                           __lowerCamelCase   =						_get_word_embedding_weight(UpperCamelCase_    ,       model.get_input_embeddings()   )
                           __lowerCamelCase   =						_get_word_embedding_weight(UpperCamelCase_    ,       model.get_output_embeddings()   )

                           # reshape the embeddings
                           model.resize_token_embeddings(UpperCamelCase_   )
                           __lowerCamelCase   =						_get_word_embedding_weight(UpperCamelCase_    ,       model.get_input_embeddings()   )
                           __lowerCamelCase   =						_get_word_embedding_weight(UpperCamelCase_    ,       model.get_output_embeddings()   )

                           # check that the resized embeddings size matches the desired size.
                           __lowerCamelCase   =						size if size is not None else config.vocab_size

                           self.assertEqual(new_input_embeddings.shape[0]    ,       UpperCamelCase_   )

                           # check that weights remain the same after resizing
                           __lowerCamelCase   =						True
                           for pa, pa in zip(old_input_embeddings.value()    ,       new_input_embeddings.value()   ):
                                  if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
                                         __lowerCamelCase   =						False
                           self.assertTrue(UpperCamelCase_   )

                           if old_output_embeddings is not None and new_output_embeddings is not None:
                                  self.assertEqual(new_output_embeddings.shape[0]    ,       UpperCamelCase_   )

                                  __lowerCamelCase   =						True
                                  for pa, pa in zip(old_output_embeddings.value()    ,       new_output_embeddings.value()   ):
                                         if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
                                                __lowerCamelCase   =						False
                                  self.assertTrue(UpperCamelCase_   )




def 					lowerCamelCase__       (						A__       :					Union[str, Any]	):





       '''simple docstring'''



       return tf.constant(A__  ,			dtype=tf.intaa	)





@require_tf
class     lowerCamelCase__(					unittest.TestCase):
      UpperCAmelCase__      :	Dict    		=   99
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						tf.ones((4, 1)    ,       dtype=tf.intaa   ) * 2
             __lowerCamelCase   =						tf.concat([ids_tensor((4, 6)    ,       self.vocab_size - 3   ) + 3, eos_column_vector]    ,       axis=1   )
             __lowerCamelCase   =						input_ids.shape[0]
             __lowerCamelCase   =						OPTConfig(
                 vocab_size=self.vocab_size    ,       hidden_size=24    ,       num_hidden_layers=2    ,       num_attention_heads=2    ,       ffn_dim=32    ,       max_position_embeddings=48    ,       eos_token_id=2    ,       pad_token_id=1    ,       bos_token_id=0    ,       )
             return config, input_ids, batch_size





@require_sentencepiece
@require_tf
class     lowerCamelCase__(					unittest.TestCase):
      @slow
      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase   =						TFOPTModel.from_pretrained("""facebook/opt-350m"""   )
             __lowerCamelCase   =						_long_tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]   )
             __lowerCamelCase   =						tf.not_equal(UpperCamelCase_    ,       model.config.pad_token_id   )
             with tf.GradientTape():
                    __lowerCamelCase   =						model(input_ids=UpperCamelCase_    ,       attention_mask=UpperCamelCase_   ).last_hidden_state
             __lowerCamelCase   =						(1, 11, 5_12)
             self.assertEqual(output.shape    ,       UpperCamelCase_   )
             __lowerCamelCase   =						tf.constant(
                 [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]   )
             self.assertTrue(np.allclose(output[:, :3, :3]    ,       UpperCamelCase_    ,       atol=4E-3   )   )

             __lowerCamelCase   =						tf.function(UpperCamelCase_    ,       jit_compile=UpperCamelCase_   )
             __lowerCamelCase   =						xla_generate(UpperCamelCase_    ,       UpperCamelCase_   )[0]
             self.assertTrue(np.allclose(output[:, :3, :3]    ,       UpperCamelCase_    ,       atol=4E-2   )   )





@require_tf
@slow
class     lowerCamelCase__(					unittest.TestCase):
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             super().setUp()
             __lowerCamelCase   =						"""facebook/opt-350m"""




      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						TFOPTForCausalLM.from_pretrained(self.path_model   )
             __lowerCamelCase   =						GPTaTokenizer.from_pretrained(self.path_model   )

             __lowerCamelCase   =						[
                 """Today is a beautiful day and I want to""",
                 """In the city of""",
                 """Paris is the capital of France and""",
                 """Computers and mobile phones have taken""",
             ]
             # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
             __lowerCamelCase   =						tokenizer(UpperCamelCase_    ,       return_tensors="""tf"""    ,       padding=UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_   )
             __lowerCamelCase   =						tf.math.reduce_mean(model(inputs.input_ids    ,       attention_mask=inputs.attention_mask   )[0]    ,       axis=-1   )
             __lowerCamelCase   =						tf.constant(
                 [
                     [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
                     [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
                     [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
                     [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
                 ]   )
             self.assertTrue(np.allclose(UpperCamelCase_    ,       UpperCamelCase_    ,       atol=1E-4   )   )

             __lowerCamelCase   =						tf.function(UpperCamelCase_    ,       jit_compile=UpperCamelCase_   )
             __lowerCamelCase   =						tf.math.reduce_mean(xla_generate(inputs.input_ids    ,       attention_mask=inputs.attention_mask   )[0]    ,       axis=-1   )
             self.assertTrue(np.allclose(UpperCamelCase_    ,       UpperCamelCase_    ,       atol=1E-4   )   )





@require_tf
@slow
class     lowerCamelCase__(					unittest.TestCase):
      @property
      def 			lowerCAmelCase__						(				self: int   ):
             return [
                 "Today is a beautiful day and I want",
                 "In the city of",
                 "Paris is the capital of France and",
                 "Computers and mobile phones have taken",
             ]
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						"""facebook/opt-125m"""

             __lowerCamelCase   =						[
                 """Today is a beautiful day and I want to""",
                 """In the city of New York, the city""",
                 """Paris is the capital of France and the capital""",
                 """Computers and mobile phones have taken over the""",
             ]

             __lowerCamelCase   =						[]
             __lowerCamelCase   =						GPTaTokenizer.from_pretrained(UpperCamelCase_   )
             __lowerCamelCase   =						TFOPTForCausalLM.from_pretrained(UpperCamelCase_   )

             for prompt in self.prompts:
                    __lowerCamelCase   =						tokenizer(UpperCamelCase_    ,       return_tensors="""tf"""   ).input_ids

                    __lowerCamelCase   =						model.generate(UpperCamelCase_    ,       max_length=10   )

                    __lowerCamelCase   =						tokenizer.batch_decode(UpperCamelCase_    ,       skip_special_tokens=UpperCamelCase_   )
                    predicted_outputs += generated_string

             self.assertListEqual(UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             __lowerCamelCase   =						"""facebook/opt-350m"""

             __lowerCamelCase   =						GPTaTokenizer.from_pretrained(UpperCamelCase_   )
             __lowerCamelCase   =						TFOPTForCausalLM.from_pretrained(UpperCamelCase_   )

             __lowerCamelCase   =						"""left"""

             # use different length sentences to test batching
             __lowerCamelCase   =						[
                 """Hello, my dog is a little""",
                 """Today, I""",
             ]

             __lowerCamelCase   =						tokenizer(UpperCamelCase_    ,       return_tensors="""tf"""    ,       padding=UpperCamelCase_   )
             __lowerCamelCase   =						inputs["""input_ids"""]

             __lowerCamelCase   =						model.generate(input_ids=UpperCamelCase_    ,       attention_mask=inputs["""attention_mask"""]   )

             __lowerCamelCase   =						tokenizer(sentences[0]    ,       return_tensors="""tf"""   ).input_ids
             __lowerCamelCase   =						model.generate(input_ids=UpperCamelCase_   )

             __lowerCamelCase   =						inputs_non_padded.shape[-1] - tf.math.reduce_sum(
                 tf.cast(inputs["""attention_mask"""][-1]    ,       tf.intaa   )   )
             __lowerCamelCase   =						tokenizer(sentences[1]    ,       return_tensors="""tf"""   ).input_ids
             __lowerCamelCase   =						model.generate(input_ids=UpperCamelCase_    ,       max_length=model.config.max_length - num_paddings   )

             __lowerCamelCase   =						tokenizer.batch_decode(UpperCamelCase_    ,       skip_special_tokens=UpperCamelCase_   )
             __lowerCamelCase   =						tokenizer.decode(output_non_padded[0]    ,       skip_special_tokens=UpperCamelCase_   )
             __lowerCamelCase   =						tokenizer.decode(output_padded[0]    ,       skip_special_tokens=UpperCamelCase_   )

             __lowerCamelCase   =						[
                 """Hello, my dog is a little bit of a dork.\nI'm a little bit""",
                 """Today, I was in the middle of a conversation with a friend about the""",
             ]
             self.assertListEqual(UpperCamelCase_    ,       UpperCamelCase_   )
             self.assertListEqual(UpperCamelCase_    ,       [non_padded_sentence, padded_sentence]   )




      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						"""facebook/opt-350m"""

             __lowerCamelCase   =						[
                 """Today is a beautiful day and I want to""",
                 """In the city of San Francisco, the city""",
                 """Paris is the capital of France and the capital""",
                 """Computers and mobile phones have taken over the""",
             ]

             __lowerCamelCase   =						[]
             __lowerCamelCase   =						GPTaTokenizer.from_pretrained(UpperCamelCase_   )
             __lowerCamelCase   =						TFOPTForCausalLM.from_pretrained(UpperCamelCase_   )

             for prompt in self.prompts:
                    __lowerCamelCase   =						tokenizer(UpperCamelCase_    ,       return_tensors="""tf"""   ).input_ids

                    __lowerCamelCase   =						model.generate(UpperCamelCase_    ,       max_length=10   )

                    __lowerCamelCase   =						tokenizer.batch_decode(UpperCamelCase_    ,       skip_special_tokens=UpperCamelCase_   )
                    predicted_outputs += generated_string

             self.assertListEqual(UpperCamelCase_    ,       UpperCamelCase_   )







import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


UpperCAmelCase_									=	logging.get_logger(__name__)





class     lowerCamelCase__:
      def __init__(				self: Any    ,       UpperCamelCase_: str    ,       UpperCamelCase_: Dict   ):
             __lowerCamelCase   =						question_encoder
             __lowerCamelCase   =						generator
             __lowerCamelCase   =						self.question_encoder
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: Optional[Any]   ):
             if os.path.isfile(UpperCamelCase_   ):
                    raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'   )
             os.makedirs(UpperCamelCase_    ,       exist_ok=UpperCamelCase_   )
             __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       """question_encoder_tokenizer"""   )
             __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       """generator_tokenizer"""   )
             self.question_encoder.save_pretrained(UpperCamelCase_   )
             self.generator.save_pretrained(UpperCamelCase_   )
      @classmethod
      def 			lowerCAmelCase__						(				cls: List[Any]    ,       UpperCamelCase_: Dict    ,       **UpperCamelCase_: Union[str, Any]   ):
             # dynamically import AutoTokenizer
             from ..auto.tokenization_auto import AutoTokenizer

             __lowerCamelCase   =						kwargs.pop("""config"""    ,       UpperCamelCase_   )

             if config is None:
                    __lowerCamelCase   =						RagConfig.from_pretrained(UpperCamelCase_   )

             __lowerCamelCase   =						AutoTokenizer.from_pretrained(
                 UpperCamelCase_    ,       config=config.question_encoder    ,       subfolder="""question_encoder_tokenizer"""   )
             __lowerCamelCase   =						AutoTokenizer.from_pretrained(
                 UpperCamelCase_    ,       config=config.generator    ,       subfolder="""generator_tokenizer"""   )
             return cls(question_encoder=UpperCamelCase_    ,       generator=UpperCamelCase_   )
      def __call__(				self: Tuple    ,       *UpperCamelCase_: int    ,       **UpperCamelCase_: int   ):
             return self.current_tokenizer(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Tuple    ,       *UpperCamelCase_: List[Any]    ,       **UpperCamelCase_: List[Any]   ):
             return self.generator.batch_decode(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       *UpperCamelCase_: str    ,       **UpperCamelCase_: Union[str, Any]   ):
             return self.generator.decode(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						self.question_encoder
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						self.generator




      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: Optional[List[str]] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: str = "longest"    ,       UpperCamelCase_: str = None    ,       UpperCamelCase_: bool = True    ,       **UpperCamelCase_: int    ,       ):
             warnings.warn(
                 """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
                 """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
                 """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
                 """details"""    ,       UpperCamelCase_    ,       )
             if max_length is None:
                    __lowerCamelCase   =						self.current_tokenizer.model_max_length
             __lowerCamelCase   =						self(
                 UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_    ,       return_tensors=UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       padding=UpperCamelCase_    ,       truncation=UpperCamelCase_    ,       **UpperCamelCase_    ,       )
             if tgt_texts is None:
                    return model_inputs
             # Process tgt_texts
             if max_target_length is None:
                    __lowerCamelCase   =						self.current_tokenizer.model_max_length
             __lowerCamelCase   =						self(
                 text_target=UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_    ,       return_tensors=UpperCamelCase_    ,       padding=UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       truncation=UpperCamelCase_    ,       **UpperCamelCase_    ,       )
             __lowerCamelCase   =						labels["""input_ids"""]
             return model_inputs

