





"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import 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 TFLEDForConditionalGeneration, TFLEDModel



@require_tf
class 	lowerCAmelCase					:
       lowerCAmelCase__						 =  LEDConfig
       lowerCAmelCase__						 =  {}
       lowerCAmelCase__						 =  """gelu"""






       def __init__(   self    ,  a__    ,  a__=13    ,  a__=7    ,  a__=True    ,  a__=False    ,  a__=99    ,  a__=32    ,  a__=2    ,  a__=4    ,  a__=37    ,  a__=0.1    ,  a__=0.1    ,  a__=20    ,  a__=2    ,  a__=1    ,  a__=0    ,  a__=4    ,  ):
              _UpperCAmelCase     =     parent
              _UpperCAmelCase     =     batch_size
              _UpperCAmelCase     =     seq_length
              _UpperCAmelCase     =     is_training
              _UpperCAmelCase     =     use_labels
              _UpperCAmelCase     =     vocab_size
              _UpperCAmelCase     =     hidden_size
              _UpperCAmelCase     =     num_hidden_layers
              _UpperCAmelCase     =     num_attention_heads
              _UpperCAmelCase     =     intermediate_size

              _UpperCAmelCase     =     hidden_dropout_prob
              _UpperCAmelCase     =     attention_probs_dropout_prob
              _UpperCAmelCase     =     max_position_embeddings
              _UpperCAmelCase     =     eos_token_id
              _UpperCAmelCase     =     pad_token_id
              _UpperCAmelCase     =     bos_token_id
              _UpperCAmelCase     =     attention_window

              # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
              # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
              # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
              # because its local attention only attends to `self.attention_window` and one before and one after
              _UpperCAmelCase     =     self.attention_window + 2

              # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
              # the `test_attention_outputs` and `test_hidden_states_output` tests
              _UpperCAmelCase     =     (
                  self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
              )






       def 							__A (   self     ):
              _UpperCAmelCase     =     ids_tensor([self.batch_size, self.seq_length - 1]    ,  self.vocab_size     )
              _UpperCAmelCase     =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size     )    ,  1     )
              _UpperCAmelCase     =     tf.concat([input_ids, eos_tensor]    ,  axis=1     )

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

              _UpperCAmelCase     =     self.config_cls(
                  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    ,  max_position_embeddings=self.max_position_embeddings    ,  eos_token_ids=[2]    ,  bos_token_id=self.bos_token_id    ,  pad_token_id=self.pad_token_id    ,  decoder_start_token_id=self.pad_token_id    ,  attention_window=self.attention_window    ,  **self.config_updates    ,  )
              _UpperCAmelCase     =     prepare_led_inputs_dict(a__    ,  a__    ,  a__     )
              _UpperCAmelCase     =     tf.concat(
                  [tf.zeros_like(a__     )[:, :-1], tf.ones_like(a__     )[:, -1:]]    ,  axis=-1    ,  )
              _UpperCAmelCase     =     global_attention_mask
              return config, inputs_dict






       def 							__A (   self    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     TFLEDModel(config=a__     ).get_decoder()
              _UpperCAmelCase     =     inputs_dict['input_ids']

              _UpperCAmelCase     =     input_ids[:1, :]
              _UpperCAmelCase     =     inputs_dict['attention_mask'][:1, :]
              _UpperCAmelCase     =     1

              # first forward pass
              _UpperCAmelCase     =     model(a__    ,  attention_mask=a__    ,  use_cache=a__     )

              _UpperCAmelCase    ,							_UpperCAmelCase     =     outputs.to_tuple()

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

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

              _UpperCAmelCase     =     model(a__    ,  attention_mask=a__     )[0]
              _UpperCAmelCase     =     model(a__    ,  attention_mask=a__    ,  past_key_values=a__     )[0]

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

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

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


def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=None,SCREAMING_SNAKE_CASE=None,SCREAMING_SNAKE_CASE=None,SCREAMING_SNAKE_CASE=None,)		->       Union[str, Any]:






       """simple docstring"""




       if attention_mask is None:
              _UpperCAmelCase     =     tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE,config.pad_token_id							),tf.inta							)
       if decoder_attention_mask is None:
              _UpperCAmelCase     =     tf.concat(
                  [
                      tf.ones(decoder_input_ids[:, :1].shape,dtype=tf.inta							),
                      tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:],config.pad_token_id							),tf.inta							),
                  ],axis=-1,)
       if head_mask is None:
              _UpperCAmelCase     =     tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
       if decoder_head_mask is None:
              _UpperCAmelCase     =     tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
       return {
           "input_ids": input_ids,
           "attention_mask": attention_mask,
           "decoder_input_ids": decoder_input_ids,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
       }



@require_tf
class 	lowerCAmelCase					(      snake_case			,	snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
       lowerCAmelCase__						 =  (TFLEDForConditionalGeneration,) if is_tf_available() else ()
       lowerCAmelCase__						 =  (
           {
               """conversational""": TFLEDForConditionalGeneration,
               """feature-extraction""": TFLEDModel,
               """summarization""": TFLEDForConditionalGeneration,
               """text2text-generation""": TFLEDForConditionalGeneration,
               """translation""": TFLEDForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       lowerCAmelCase__						 =  True
       lowerCAmelCase__						 =  False
       lowerCAmelCase__						 =  False
       lowerCAmelCase__						 =  False






       def 							__A (   self     ):
              _UpperCAmelCase     =     TFLEDModelTester(self     )
              _UpperCAmelCase     =     ConfigTester(self    ,  config_class=a__     )






       def 							__A (   self     ):
              self.config_tester.run_common_tests()






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()
              self.model_tester.check_decoder_model_past_large_inputs(*a__     )






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()
              _UpperCAmelCase     =     tf.zeros_like(inputs_dict['attention_mask']     )
              _UpperCAmelCase     =     2
              _UpperCAmelCase     =     tf.where(
                  tf.range(self.model_tester.seq_length     )[None, :] < num_global_attn_indices    ,  1    ,  inputs_dict['global_attention_mask']    ,  )

              _UpperCAmelCase     =     True
              _UpperCAmelCase     =     self.model_tester.seq_length
              _UpperCAmelCase     =     self.model_tester.encoder_seq_length

              def check_decoder_attentions_output(a__     ):
                     _UpperCAmelCase     =     outputs.decoder_attentions
                     self.assertEqual(len(a__     )    ,  self.model_tester.num_hidden_layers     )
                     self.assertListEqual(
                         list(decoder_attentions[0].shape[-3:]     )    ,  [self.model_tester.num_attention_heads, seq_length, seq_length]    ,  )

              def check_encoder_attentions_output(a__     ):
                     _UpperCAmelCase     =     [t.numpy() for t in outputs.encoder_attentions]
                     _UpperCAmelCase     =     [t.numpy() for t in outputs.encoder_global_attentions]
                     self.assertEqual(len(a__     )    ,  self.model_tester.num_hidden_layers     )
                     self.assertEqual(len(a__     )    ,  self.model_tester.num_hidden_layers     )
                     self.assertListEqual(
                         list(attentions[0].shape[-3:]     )    ,  [self.model_tester.num_attention_heads, seq_length, seq_length]    ,  )
                     self.assertListEqual(
                         list(global_attentions[0].shape[-3:]     )    ,  [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]    ,  )

              for model_class in self.all_model_classes:
                     _UpperCAmelCase     =     True
                     _UpperCAmelCase     =     False
                     _UpperCAmelCase     =     False
                     _UpperCAmelCase     =     model_class(a__     )
                     _UpperCAmelCase     =     model(self._prepare_for_class(a__    ,  a__     )     )
                     _UpperCAmelCase     =     len(a__     )
                     self.assertEqual(config.output_hidden_states    ,  a__     )
                     check_encoder_attentions_output(a__     )

                     if self.is_encoder_decoder:
                            _UpperCAmelCase     =     model_class(a__     )
                            _UpperCAmelCase     =     model(self._prepare_for_class(a__    ,  a__     )     )
                            self.assertEqual(config.output_hidden_states    ,  a__     )
                            check_decoder_attentions_output(a__     )

                     # Check that output attentions can also be changed via the config
                     del inputs_dict["output_attentions"]
                     _UpperCAmelCase     =     True
                     _UpperCAmelCase     =     model_class(a__     )
                     _UpperCAmelCase     =     model(self._prepare_for_class(a__    ,  a__     )     )
                     self.assertEqual(config.output_hidden_states    ,  a__     )
                     check_encoder_attentions_output(a__     )

                     # Check attention is always last and order is fine
                     _UpperCAmelCase     =     True
                     _UpperCAmelCase     =     True
                     _UpperCAmelCase     =     model_class(a__     )
                     _UpperCAmelCase     =     model(self._prepare_for_class(a__    ,  a__     )     )

                     self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)    ,  len(a__     )     )
                     self.assertEqual(model.config.output_hidden_states    ,  a__     )
                     check_encoder_attentions_output(a__     )






       @unittest.skip('LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'     )
       def 							__A (   self     ):
              pass






       def 							__A (   self     ):
              # TODO: Head-masking not yet implement
              pass


def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       Union[str, Any]:






       """simple docstring"""




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


lowerCAmelCase_			=							1E-4



@slow
@require_tf
class 	lowerCAmelCase					(      unittest.TestCase							):






       def 							__A (   self     ):
              _UpperCAmelCase     =     TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'     ).led

              # change to intended input here
              _UpperCAmelCase     =     _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]     )
              _UpperCAmelCase     =     _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]     )
              _UpperCAmelCase     =     prepare_led_inputs_dict(model.config    ,  a__    ,  a__     )
              _UpperCAmelCase     =     model(**a__     )[0]
              _UpperCAmelCase     =     (1, 10_24, 7_68)
              self.assertEqual(output.shape    ,  a__     )
              # change to expected output here
              _UpperCAmelCase     =     tf.convert_to_tensor(
                  [[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]]    ,  )
              tf.debugging.assert_near(output[:, :3, :3]    ,  a__    ,  atol=1E-3     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'     )

              # change to intended input here
              _UpperCAmelCase     =     _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]     )
              _UpperCAmelCase     =     _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]     )
              _UpperCAmelCase     =     prepare_led_inputs_dict(model.config    ,  a__    ,  a__     )
              _UpperCAmelCase     =     model(**a__     )[0]
              _UpperCAmelCase     =     (1, 10_24, model.config.vocab_size)
              self.assertEqual(output.shape    ,  a__     )
              # change to expected output here
              _UpperCAmelCase     =     tf.convert_to_tensor(
                  [[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]]    ,  )
              tf.debugging.assert_near(output[:, :3, :3]    ,  a__    ,  atol=1E-3    ,  rtol=1E-3     )













"""simple docstring"""






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_			=							logging.get_logger(__name__)

lowerCAmelCase_			=							'''▁'''

lowerCAmelCase_			=							{'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

lowerCAmelCase_			=							{
    '''vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
    },
    '''monolingual_vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
    },
}

lowerCAmelCase_			=							{'''vinai/bartpho-syllable''': 1_024}



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  VOCAB_FILES_NAMES
       lowerCAmelCase__						 =  PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__						 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__						 =  ["""input_ids""", """attention_mask"""]






       def __init__(   self    ,  a__    ,  a__    ,  a__="<s>"    ,  a__="</s>"    ,  a__="</s>"    ,  a__="<s>"    ,  a__="<unk>"    ,  a__="<pad>"    ,  a__="<mask>"    ,  a__ = None    ,  **a__    ,  ):
              # Mask token behave like a normal word, i.e. include the space before it
              _UpperCAmelCase     =     AddedToken(a__    ,  lstrip=a__    ,  rstrip=a__     ) if isinstance(a__    ,  a__     ) else mask_token

              _UpperCAmelCase     =     {} if sp_model_kwargs is None else sp_model_kwargs

              super().__init__(
                  bos_token=a__    ,  eos_token=a__    ,  unk_token=a__    ,  sep_token=a__    ,  cls_token=a__    ,  pad_token=a__    ,  mask_token=a__    ,  sp_model_kwargs=self.sp_model_kwargs    ,  **a__    ,  )

              _UpperCAmelCase     =     vocab_file
              _UpperCAmelCase     =     monolingual_vocab_file
              _UpperCAmelCase     =     spm.SentencePieceProcessor(**self.sp_model_kwargs     )
              self.sp_model.Load(str(a__     )     )

              # Load the reduced vocab

              # Keep order of special tokens for backward compatibility
              _UpperCAmelCase     =     {}
              _UpperCAmelCase     =     0
              for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
                     if str(a__     ) not in self.fairseq_tokens_to_ids:
                            _UpperCAmelCase     =     cnt
                            cnt += 1
              with open(a__    ,  'r'    ,  encoding='utf-8'     ) as f:
                     for line in f.readlines():
                            _UpperCAmelCase     =     line.strip().split()[0]
                            _UpperCAmelCase     =     len(self.fairseq_tokens_to_ids     )
              if str(a__     ) not in self.fairseq_tokens_to_ids:
                     _UpperCAmelCase     =     len(self.fairseq_tokens_to_ids     )

              _UpperCAmelCase     =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}






       def __getstate__(   self     ):
              _UpperCAmelCase     =     self.__dict__.copy()
              _UpperCAmelCase     =     None
              _UpperCAmelCase     =     self.sp_model.serialized_model_proto()
              return state






       def __setstate__(   self    ,  a__     ):
              _UpperCAmelCase     =     d

              # for backward compatibility
              if not hasattr(self    ,  'sp_model_kwargs'     ):
                     _UpperCAmelCase     =     {}

              _UpperCAmelCase     =     spm.SentencePieceProcessor(**self.sp_model_kwargs     )
              self.sp_model.LoadFromSerializedProto(self.sp_model_proto     )






       def 							__A (   self    ,  a__    ,  a__ = None     ):

              if token_ids_a is None:
                     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
              _UpperCAmelCase     =     [self.cls_token_id]
              _UpperCAmelCase     =     [self.sep_token_id]
              return cls + token_ids_a + sep + sep + token_ids_a + sep






       def 							__A (   self    ,  a__    ,  a__ = None    ,  a__ = False     ):

              if already_has_special_tokens:
                     return super().get_special_tokens_mask(
                         token_ids_a=a__    ,  token_ids_a=a__    ,  already_has_special_tokens=a__     )

              if token_ids_a is None:
                     return [1] + ([0] * len(a__     )) + [1]
              return [1] + ([0] * len(a__     )) + [1, 1] + ([0] * len(a__     )) + [1]






       def 							__A (   self    ,  a__    ,  a__ = None     ):
              _UpperCAmelCase     =     [self.sep_token_id]
              _UpperCAmelCase     =     [self.cls_token_id]

              if token_ids_a is None:
                     return len(cls + token_ids_a + sep     ) * [0]
              return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]






       @property
       def 							__A (   self     ):
              return len(self.fairseq_ids_to_tokens     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     {self.convert_ids_to_tokens(a__     ): i for i in range(self.vocab_size     )}
              vocab.update(self.added_tokens_encoder     )
              return vocab






       def 							__A (   self    ,  a__     ):
              return self.sp_model.encode(a__    ,  out_type=a__     )






       def 							__A (   self    ,  a__     ):
              if token in self.fairseq_tokens_to_ids:
                     return self.fairseq_tokens_to_ids[token]
              else:
                     return self.unk_token_id






       def 							__A (   self    ,  a__     ):
              return self.fairseq_ids_to_tokens[index]






       def 							__A (   self    ,  a__     ):
              _UpperCAmelCase     =     ''.join(a__     ).replace(a__    ,  ' '     ).strip()
              return out_string






       def 							__A (   self    ,  a__    ,  a__ = None     ):
              if not os.path.isdir(a__     ):
                     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""     )
                     return
              _UpperCAmelCase     =     os.path.join(
                  a__    ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
              _UpperCAmelCase     =     os.path.join(
                  a__    ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file']    ,  )

              if os.path.abspath(self.vocab_file     ) != os.path.abspath(a__     ) and os.path.isfile(self.vocab_file     ):
                     copyfile(self.vocab_file    ,  a__     )
              elif not os.path.isfile(self.vocab_file     ):
                     with open(a__    ,  'wb'     ) as fi:
                            _UpperCAmelCase     =     self.sp_model.serialized_model_proto()
                            fi.write(a__     )

              if os.path.abspath(self.monolingual_vocab_file     ) != os.path.abspath(
                  a__     ) and os.path.isfile(self.monolingual_vocab_file     ):
                     copyfile(self.monolingual_vocab_file    ,  a__     )
              elif not os.path.isfile(self.monolingual_vocab_file     ):
                     with open(a__    ,  'w'    ,  encoding='utf-8'     ) as fp:
                            for token in self.fairseq_tokens_to_ids:
                                   if token not in self.all_special_tokens:
                                          fp.write(f"""{str(a__     )} \n"""     )

              return out_vocab_file, out_monolingual_vocab_file






