





'''simple docstring'''







import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
   from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder

def    __UpperCamelCase	(	a    : Dict					,   a    : Optional[Any]					,   a    : Any					,   a    : Optional[Any]=None					,   a    : Union[str, Any]=None					,   a    : Any=None					,   a    : Tuple=None					,   a    : Dict=None					,   ) ->Optional[int]:
 if attention_mask is None:
  snake_case				=			input_ids.ne(config.pad_token_id     )
 if decoder_attention_mask is None:
  snake_case				=			decoder_input_ids.ne(config.pad_token_id     )
 if head_mask is None:
  snake_case				=			torch.ones(config.encoder_layers					,   config.encoder_attention_heads					,   device=a     )
 if decoder_head_mask is None:
  snake_case				=			torch.ones(config.decoder_layers					,   config.decoder_attention_heads					,   device=a     )
 if cross_attn_head_mask is None:
  snake_case				=			torch.ones(config.decoder_layers					,   config.decoder_attention_heads					,   device=a     )
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": attention_mask,
     "head_mask": head_mask,
     "decoder_head_mask": decoder_head_mask,
     "cross_attn_head_mask": cross_attn_head_mask,
 }
class 			_lowercase						:







     def __init__( self    ,   A__    ,   A__=13    ,   A__=7    ,   A__=True    ,   A__=False    ,   A__=99    ,   A__=16    ,   A__=2    ,   A__=4    ,   A__=4    ,   A__="relu"    ,   A__=0.1    ,   A__=0.1    ,   A__=0.0    ,   A__=0.0    ,   A__=20    ,   A__=2    ,   A__=1    ,   A__=0    ,   ) ->	List[str]:
      snake_case				=			parent
      snake_case				=			batch_size
      snake_case				=			seq_length
      snake_case				=			is_training
      snake_case				=			use_labels
      snake_case				=			vocab_size
      snake_case				=			hidden_size
      snake_case				=			num_hidden_layers
      snake_case				=			num_attention_heads
      snake_case				=			intermediate_size
      snake_case				=			hidden_act
      snake_case				=			hidden_dropout_prob
      snake_case				=			attention_probs_dropout_prob
      snake_case				=			encoder_layerdrop
      snake_case				=			decoder_layerdrop
      snake_case				=			max_position_embeddings
      snake_case				=			eos_token_id
      snake_case				=			pad_token_id
      snake_case				=			bos_token_id







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size    )
      snake_case				=			self.eos_token_id  # Eos Token
      snake_case				=			ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size    )

      # we need to clamp the input ids here to avoid having pad token in between
      # this is because for M2M100 the position_ids are prepared such that
      # all pad tokens have pos id = 2 and rest are between 2..seq_length
      # and the seq_length here is seq_length - num_pad_tokens
      # but when using past, there is no way of knowing if the past input ids had
      # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
      # position_ids being off by num_pad_tokens in past input
      snake_case				=			input_ids.clamp(self.pad_token_id + 1    )
      snake_case				=			decoder_input_ids.clamp(self.pad_token_id + 1    )

      snake_case				=			self.get_config()
      snake_case				=			prepare_mam_aaa_inputs_dict(A__    ,   A__    ,   A__    )
      return config, inputs_dict







     def     UpperCamelCase			( self    ) ->	int:
      return MaMaaaConfig(
          vocab_size=self.vocab_size    ,   d_model=self.hidden_size    ,   encoder_layers=self.num_hidden_layers    ,   decoder_layers=self.num_hidden_layers    ,   encoder_attention_heads=self.num_attention_heads    ,   decoder_attention_heads=self.num_attention_heads    ,   encoder_ffn_dim=self.intermediate_size    ,   decoder_ffn_dim=self.intermediate_size    ,   dropout=self.hidden_dropout_prob    ,   attention_dropout=self.attention_probs_dropout_prob    ,   encoder_layerdrop=self.encoder_layerdrop    ,   decoder_layerdrop=self.decoder_layerdrop    ,   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    ,   )







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				,		snake_case				=			self.prepare_config_and_inputs()
      return config, inputs_dict







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	List[Any]:
      snake_case				=			MaMaaaModel(config=A__    ).get_decoder().to(A__    ).eval()
      snake_case				=			inputs_dict['''input_ids''']
      snake_case				=			inputs_dict['''attention_mask''']
      snake_case				=			inputs_dict['''head_mask''']

      # first forward pass
      snake_case				=			model(A__    ,   attention_mask=A__    ,   head_mask=A__    ,   use_cache=A__    )

      snake_case				,		snake_case				=			outputs.to_tuple()

      # create hypothetical multiple next token and extent to next_input_ids
      snake_case				=			ids_tensor((self.batch_size, 3)    ,   config.vocab_size    )
      snake_case				=			ids_tensor((self.batch_size, 3)    ,   2    )

      # append to next input_ids and
      snake_case				=			torch.cat([input_ids, next_tokens]    ,   dim=-1    )
      snake_case				=			torch.cat([attention_mask, next_attn_mask]    ,   dim=-1    )

      snake_case				=			model(A__    ,   attention_mask=A__    )['''last_hidden_state''']
      snake_case				=			model(A__    ,   attention_mask=A__    ,   past_key_values=A__    )[
          '''last_hidden_state'''
      ]

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

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(A__    ,   A__    ,   atol=1e-2    )    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	str:
      snake_case				=			MaMaaaModel(config=A__    ).to(A__    ).eval()
      snake_case				=			model(**A__    )

      snake_case				=			outputs.encoder_last_hidden_state
      snake_case				=			outputs.last_hidden_state

      with tempfile.TemporaryDirectory() as tmpdirname:
       snake_case				=			model.get_encoder()
       encoder.save_pretrained(A__    )
       snake_case				=			MaMaaaEncoder.from_pretrained(A__    ).to(A__    )

      snake_case				=			encoder(inputs_dict['''input_ids''']    ,   attention_mask=inputs_dict['''attention_mask''']    )[
          0
      ]

      self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3    )

      with tempfile.TemporaryDirectory() as tmpdirname:
       snake_case				=			model.get_decoder()
       decoder.save_pretrained(A__    )
       snake_case				=			MaMaaaDecoder.from_pretrained(A__    ).to(A__    )

      snake_case				=			decoder(
          input_ids=inputs_dict['''decoder_input_ids''']    ,   attention_mask=inputs_dict['''decoder_attention_mask''']    ,   encoder_hidden_states=A__    ,   encoder_attention_mask=inputs_dict['''attention_mask''']    ,   )[0]

      self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3    )




@require_torch
class 			_lowercase						(       __a				,		__a				,		__a				,		unittest.TestCase					):
     _UpperCAmelCase										=					(
         (
             MaMaaaModel,
             MaMaaaForConditionalGeneration,
         )
         if is_torch_available()
         else ()
     )
     _UpperCAmelCase										=					(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
     _UpperCAmelCase										=					(
         {
             '''conversational''': MaMaaaForConditionalGeneration,
             '''feature-extraction''': MaMaaaModel,
             '''summarization''': MaMaaaForConditionalGeneration,
             '''text2text-generation''': MaMaaaForConditionalGeneration,
             '''translation''': MaMaaaForConditionalGeneration,
         }
         if is_torch_available()
         else {}
     )
     _UpperCAmelCase										=					True
     _UpperCAmelCase										=					True
     _UpperCAmelCase										=					False
     _UpperCAmelCase										=					False







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ) ->	Any:
      if pipeline_test_casse_name == "TranslationPipelineTests":
       # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
       # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
       return True

      return False







     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			MaMaaaModelTester(self    )
      snake_case				=			ConfigTester(self    ,   config_class=A__    )







     def     UpperCamelCase			( self    ) ->	Tuple:
      self.config_tester.run_common_tests()







     def     UpperCamelCase			( self    ) ->	str:
      snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
       snake_case				=			model_class(A__    )

       with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_pretrained(A__    )
        snake_case				,		snake_case				=			model_class.from_pretrained(A__    ,   output_loading_info=A__    )
       self.assertEqual(info['''missing_keys''']    ,   []    )







     def     UpperCamelCase			( self    ) ->	str:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(*A__    )







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_encoder_decoder_model_standalone(*A__    )







     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
       snake_case				=			model_class(A__    )
       model.to(A__    )
       model.eval()

       snake_case				=			copy.deepcopy(self._prepare_for_class(A__    ,   A__    )    )

       if not self.is_encoder_decoder:
        snake_case				=			inputs['''input_ids''']
        del inputs["input_ids"]
       else:
        snake_case				=			inputs['''input_ids''']
        snake_case				=			inputs.get('''decoder_input_ids'''    ,   A__    )
        del inputs["input_ids"]
        inputs.pop('''decoder_input_ids'''    ,   A__    )

       snake_case				=			model.get_input_embeddings()
       if not self.is_encoder_decoder:
        snake_case				=			wte(A__    )
       else:
        snake_case				=			wte(A__    )
        snake_case				=			wte(A__    )

       with torch.no_grad():
        model(**A__    )[0]







     def     UpperCamelCase			( self    ) ->	int:
      snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs()
      snake_case				=			input_dict['''input_ids''']
      snake_case				=			input_ids.ne(1    ).to(A__    )
      snake_case				=			MaMaaaForConditionalGeneration(A__    ).eval().to(A__    )
      if torch_device == "cuda":
       model.half()
      model.generate(A__    ,   attention_mask=A__    )
      model.generate(num_beams=4    ,   do_sample=A__    ,   early_stopping=A__    ,   num_return_sequences=3    )




def    __UpperCamelCase	(	a    : List[str]     ) ->Any:
 return torch.tensor(a					,   dtype=torch.long					,   device=a     )


_lowercase       						=			1E-4






@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 			_lowercase						(       unittest.TestCase					):







     @cached_property
     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      return MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      snake_case				=			MaMaaaModel.from_pretrained('''facebook/m2m100_418M'''    ).to(A__    )
      snake_case				=			_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]    )
      snake_case				=			_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]    )
      snake_case				=			prepare_mam_aaa_inputs_dict(model.config    ,   A__    ,   A__    )
      with torch.no_grad():
       snake_case				=			model(**A__    )[0]
      snake_case				=			torch.Size((1, 11, 10_24)    )
      self.assertEqual(output.shape    ,   A__    )
      # change to expected output here
      snake_case				=			torch.tensor(
          [[-0.7_7_8_0, -0.1_6_7_6, 0.1_0_3_8], [-6.7_5_5_6, -1.3_9_9_2, 0.0_5_6_7], [-7.5_3_8_3, -0.5_9_2_0, -0.2_7_7_9]]    ,   device=A__    )
      self.assertTrue(torch.allclose(output[:, :3, :3]    ,   A__    ,   atol=A__    )    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      snake_case				=			MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''    ).to(A__    )

      # change to intended input
      snake_case				=			_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]    )
      snake_case				=			_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]    )
      snake_case				=			prepare_mam_aaa_inputs_dict(model.config    ,   A__    ,   A__    )
      with torch.no_grad():
       snake_case				=			model(**A__    )[0]
      snake_case				=			torch.Size((1, 11, model.config.vocab_size)    )
      self.assertEqual(output.shape    ,   A__    )
      # change to expected output here
      snake_case				=			torch.tensor(
          [[-1.0_4_4_8, -1.0_4_1_1, 3.7_9_9_2], [-3.2_1_9_1, -3.2_3_8_6, -1.3_4_5_1], [-3.6_2_1_0, -3.5_9_9_3, 0.4_9_2_5]]    ,   device=A__    )
      self.assertTrue(torch.allclose(output[:, :3, :3]    ,   A__    ,   atol=A__    )    )







     def     UpperCamelCase			( self    ) ->	Tuple:
      snake_case				=			MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''    ).to(A__    )
      snake_case				=			MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''    ,   src_lang='''fr'''    ,   tgt_lang='''en'''    )

      snake_case				=			[
          '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
          '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
          '''Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'''
          ''' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'''
          ''' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.''',
      ]

      # The below article tests that we don't add any hypotheses outside of the top n_beams
      snake_case				=			tokenizer(A__    ,   padding=A__    ,   return_tensors='''pt'''    )

      snake_case				=			model.generate(
          input_ids=dct['''input_ids'''].to(A__    )    ,   attention_mask=dct['''attention_mask'''].to(A__    )    ,   num_beams=5    ,   forced_bos_token_id=tokenizer.get_lang_id('''en'''    )    ,   )

      snake_case				=			[
          '''The NSA case highlights the total absence of intelligence debate''',
          '''I think there are two levels of response from the French government.''',
          '''When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'''
          ''' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'''
          ''' communications in France.''',
      ]

      snake_case				=			tokenizer.batch_decode(
          hypotheses_batch.tolist()    ,   clean_up_tokenization_spaces=A__    ,   skip_special_tokens=A__    )
      assert generated == expected_en








'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_lowercase       						=			logging.get_logger(__name__)
class 			_lowercase						(       __a					):







     def __init__( self    ,   A__    ,   A__    ,   A__    ,   **A__    ) ->	Union[str, Any]:
      snake_case				=			feature_size
      snake_case				=			sampling_rate
      snake_case				=			padding_value

      snake_case				=			kwargs.pop('''padding_side'''    ,   '''right'''    )
      snake_case				=			kwargs.pop('''return_attention_mask'''    ,   A__    )

      super().__init__(**A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = True    ,   A__ = None    ,   A__ = False    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   ) ->	BatchFeature:
      # If we have a list of dicts, let's convert it in a dict of lists
      # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
      if isinstance(A__    ,   (list, tuple)    ) and isinstance(processed_features[0]    ,   (dict, BatchFeature)    ):
       snake_case				=			{
           key: [example[key] for example in processed_features] for key in processed_features[0].keys()
       }

      # The model's main input name, usually `input_values`, has be passed for padding
      if self.model_input_names[0] not in processed_features:
       raise ValueError(
           '''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
           F""" to this method that includes {self.model_input_names[0]}, but you provided"""
           F""" {list(processed_features.keys()    )}"""    )

      snake_case				=			processed_features[self.model_input_names[0]]
      snake_case				=			(
          return_attention_mask if return_attention_mask is not None else self.return_attention_mask
      )

      if len(A__    ) == 0:
       if return_attention_mask:
        snake_case				=			[]
       return processed_features

      # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
      # and rebuild them afterwards if no return_tensors is specified
      # Note that we lose the specific device the tensor may be on for PyTorch

      snake_case				=			required_input[0]
      if isinstance(A__    ,   (list, tuple)    ):
       # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
       snake_case				=			0
       while len(required_input[index]    ) == 0:
        index += 1
       if index < len(A__    ):
        snake_case				=			required_input[index][0]

      if return_tensors is None:
       if is_tf_tensor(A__    ):
        snake_case				=			'''tf'''
       elif is_torch_tensor(A__    ):
        snake_case				=			'''pt'''
       elif isinstance(A__    ,   (int, float, list, tuple, np.ndarray)    ):
        snake_case				=			'''np'''
       else:
        raise ValueError(
            F"""type of {first_element} unknown: {type(A__    )}. """
            '''Should be one of a python, numpy, pytorch or tensorflow object.'''    )

      for key, value in processed_features.items():
       if isinstance(value[0]    ,   (int, float)    ):
        snake_case				=			to_numpy(A__    )
       else:
        snake_case				=			[to_numpy(A__    ) for v in value]

        # Convert padding_strategy in PaddingStrategy
      snake_case				=			self._get_padding_strategies(padding=A__    ,   max_length=A__    )

      snake_case				=			processed_features[self.model_input_names[0]]

      snake_case				=			len(A__    )
      if not all(len(A__    ) == batch_size for v in processed_features.values()    ):
       raise ValueError('''Some items in the output dictionary have a different batch size than others.'''    )

      snake_case				=			[]
      for i in range(A__    ):
       snake_case				=			{k: v[i] for k, v in processed_features.items()}
       # truncation
       snake_case				=			self._truncate(
           A__    ,   max_length=A__    ,   pad_to_multiple_of=A__    ,   truncation=A__    ,   )
       truncated_inputs.append(A__    )

      if padding_strategy == PaddingStrategy.LONGEST:
       # make sure that `max_length` cannot be longer than the longest truncated length
       snake_case				=			max(len(input_slice[self.model_input_names[0]]    ) for input_slice in truncated_inputs    )
       snake_case				=			PaddingStrategy.MAX_LENGTH

      snake_case				=			{}
      for i in range(A__    ):
       # padding
       snake_case				=			self._pad(
           truncated_inputs[i]    ,   max_length=A__    ,   padding_strategy=A__    ,   pad_to_multiple_of=A__    ,   return_attention_mask=A__    ,   )

       for key, value in outputs.items():
        if key not in batch_outputs:
         snake_case				=			[]
        if value.dtype is np.dtype(np.floataa    ):
         snake_case				=			value.astype(np.floataa    )
        batch_outputs[key].append(A__    )

      return BatchFeature(A__    ,   tensor_type=A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ,   A__ = PaddingStrategy.DO_NOT_PAD    ,   A__ = None    ,   A__ = None    ,   ) ->	dict:
      snake_case				=			processed_features[self.model_input_names[0]]

      if padding_strategy == PaddingStrategy.LONGEST:
       snake_case				=			len(A__    )

      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
       snake_case				=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      snake_case				=			padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A__    ) < max_length

      if return_attention_mask and "attention_mask" not in processed_features:
       snake_case				=			np.ones(len(A__    )    ,   dtype=np.intaa    )

      if needs_to_be_padded:
       snake_case				=			max_length - len(A__    )
       if self.padding_side == "right":
        if return_attention_mask:
         snake_case				=			np.pad(
             processed_features['''attention_mask''']    ,   (0, difference)    )
        snake_case				=			((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
        snake_case				=			np.pad(
            A__    ,   A__    ,   '''constant'''    ,   constant_values=self.padding_value    )
       elif self.padding_side == "left":
        if return_attention_mask:
         snake_case				=			np.pad(
             processed_features['''attention_mask''']    ,   (difference, 0)    )
        snake_case				=			((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
        snake_case				=			np.pad(
            A__    ,   A__    ,   '''constant'''    ,   constant_values=self.padding_value    )
       else:
        raise ValueError('''Invalid padding strategy:''' + str(self.padding_side    )    )

      return processed_features







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   ) ->	Union[str, Any]:
      if not truncation:
       return processed_features
      elif truncation and max_length is None:
       raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.'''    )

      snake_case				=			processed_features[self.model_input_names[0]]

      # find `max_length` that fits `pad_to_multiple_of`
      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
       snake_case				=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      snake_case				=			len(A__    ) > max_length

      if needs_to_be_truncated:
       snake_case				=			processed_features[self.model_input_names[0]][:max_length]
       if "attention_mask" in processed_features:
        snake_case				=			processed_features['''attention_mask'''][:max_length]

      return processed_features







     def     UpperCamelCase			( self    ,   A__=False    ,   A__=None    ) ->	Union[str, Any]:

      # Get padding strategy
      if padding is not False:
       if padding is True:
        snake_case				=			PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
       elif not isinstance(A__    ,   A__    ):
        snake_case				=			PaddingStrategy(A__    )
       elif isinstance(A__    ,   A__    ):
        snake_case				=			padding
      else:
       snake_case				=			PaddingStrategy.DO_NOT_PAD

      # Set max length if needed
      if max_length is None:
       if padding_strategy == PaddingStrategy.MAX_LENGTH:
        raise ValueError(
            F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""    )

        # Test if we have a padding value
      if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
       raise ValueError(
           '''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
           ''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'''    )

      return padding_strategy

