


"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel

@require_tf
class 				_SCREAMING_SNAKE_CASE				:
   """simple docstring"""



   _a   :       Optional[int]									=    PegasusConfig
   _a   :       Any									=    {}
   _a   :       Union[str, Any]									=    '''gelu'''


   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=13 ,     lowerCamelCase__=7 ,     lowerCamelCase__=True ,     lowerCamelCase__=False ,     lowerCamelCase__=99 ,     lowerCamelCase__=32 ,     lowerCamelCase__=2 ,     lowerCamelCase__=4 ,     lowerCamelCase__=37 ,     lowerCamelCase__=0.1 ,     lowerCamelCase__=0.1 ,     lowerCamelCase__=40 ,     lowerCamelCase__=2 ,     lowerCamelCase__=1 ,     lowerCamelCase__=0 ,     )      ->							Dict:
    lowercase__       :       Tuple				=						parent
    lowercase__       :       Tuple				=						batch_size
    lowercase__       :       List[str]				=						seq_length
    lowercase__       :       Optional[int]				=						is_training
    lowercase__       :       Dict				=						use_labels
    lowercase__       :       Dict				=						vocab_size
    lowercase__       :       Optional[int]				=						hidden_size
    lowercase__       :       int				=						num_hidden_layers
    lowercase__       :       str				=						num_attention_heads
    lowercase__       :       List[Any]				=						intermediate_size

    lowercase__       :       Union[str, Any]				=						hidden_dropout_prob
    lowercase__       :       List[str]				=						attention_probs_dropout_prob
    lowercase__       :       Any				=						max_position_embeddings
    lowercase__       :       Tuple				=						eos_token_id
    lowercase__       :       Tuple				=						pad_token_id
    lowercase__       :       Optional[Any]				=						bos_token_id


   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       int				=						ids_tensor([self.batch_size, self.seq_length - 1] ,     self.vocab_size						)
    lowercase__       :       int				=						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						) ,     1						)
    lowercase__       :       int				=						tf.concat([input_ids, eos_tensor] ,     axis=1						)

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

    lowercase__       :       Any				=						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 ,     **self.config_updates ,     )
    lowercase__       :       List[str]				=						prepare_pegasus_inputs_dict(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)
    return config, inputs_dict







   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							int:
    lowercase__       :       Union[str, Any]				=						TFPegasusModel(config=lowerCamelCase__						).get_decoder()
    lowercase__       :       Tuple				=						inputs_dict["""input_ids"""]

    lowercase__       :       Optional[Any]				=						input_ids[:1, :]
    lowercase__       :       Dict				=						inputs_dict["""attention_mask"""][:1, :]
    lowercase__       :       List[str]				=						inputs_dict["""head_mask"""]
    lowercase__       :       List[Any]				=						1

    # first forward pass
    lowercase__       :       Optional[Any]				=						model(lowerCamelCase__ ,     attention_mask=lowerCamelCase__ ,     head_mask=lowerCamelCase__ ,     use_cache=lowerCamelCase__						)

    lowercase__	,       lowercase__       :       Union[str, Any]				=						outputs.to_tuple()

    # create hypothetical next token and extent to next_input_ids
    lowercase__       :       List[Any]				=						ids_tensor((self.batch_size, 3) ,     config.vocab_size						)
    lowercase__       :       List[str]				=						tf.cast(ids_tensor((self.batch_size, 3) ,     2						) ,     tf.inta						)

    # append to next input_ids and
    lowercase__       :       Optional[int]				=						tf.concat([input_ids, next_tokens] ,     axis=-1						)
    lowercase__       :       str				=						tf.concat([attention_mask, next_attn_mask] ,     axis=-1						)

    lowercase__       :       int				=						model(lowerCamelCase__ ,     attention_mask=lowerCamelCase__						)[0]
    lowercase__       :       Optional[int]				=						model(lowerCamelCase__ ,     attention_mask=lowerCamelCase__ ,     past_key_values=lowerCamelCase__						)[0]

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

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

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


def    _lowerCamelCase   (     lowerCamelCase__	:							Any       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							Union[str, Any]       ,		lowerCamelCase__	:							List[Any]=None       ,		lowerCamelCase__	:							Dict=None       ,		lowerCamelCase__	:							Tuple=None       ,		lowerCamelCase__	:							Tuple=None       ,		lowerCamelCase__	:							Dict=None       ,		):
 if attention_mask is None:
  lowercase__       :       List[Any]				=						tf.cast(tf.math.not_equal(lowerCamelCase__       ,		config.pad_token_id  )       ,		tf.inta  )
 if decoder_attention_mask is None:
  lowercase__       :       Optional[Any]				=						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:
  lowercase__       :       List[str]				=						tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
 if decoder_head_mask is None:
  lowercase__       :       Tuple				=						tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
 if cross_attn_head_mask is None:
  lowercase__       :       List[str]				=						tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": decoder_attention_mask,
     "head_mask": head_mask,
     "decoder_head_mask": decoder_head_mask,
     "cross_attn_head_mask": cross_attn_head_mask,
 }

@require_tf
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   __UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       List[str]									=    (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
   _a   :       int									=    (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
   _a   :       Tuple									=    (
       {
           '''conversational''': TFPegasusForConditionalGeneration,
           '''feature-extraction''': TFPegasusModel,
           '''summarization''': TFPegasusForConditionalGeneration,
           '''text2text-generation''': TFPegasusForConditionalGeneration,
           '''translation''': TFPegasusForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   _a   :       int									=    True
   _a   :       List[str]									=    False
   _a   :       Any									=    False


   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    lowercase__       :       int				=						TFPegasusModelTester(self						)
    lowercase__       :       Tuple				=						ConfigTester(self ,     config_class=lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    self.config_tester.run_common_tests()







   def 					UpperCAmelCase__(						self						)      ->							Any:
    lowercase__       :       Optional[Any]				=						self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase__						)

@require_sentencepiece
@require_tokenizers
@require_tf
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   _a   :       Any									=    [
       ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
       ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
   ]
   _a   :       Dict									=    [
       '''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
       ''' reduce the risk of wildfires.''',
       '''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
   ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
   _a   :       Any									=    '''google/pegasus-xsum'''


   @cached_property
   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return AutoTokenizer.from_pretrained(self.model_name						)


   @cached_property
   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    lowercase__       :       List[Any]				=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
    return model


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							List[Any]:
    lowercase__       :       Optional[int]				=						self.translate_src_text(**lowerCamelCase__						)
    assert self.expected_text == generated_words


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							Tuple:
    lowercase__       :       int				=						self.tokenizer(self.src_text ,     **lowerCamelCase__ ,     padding=lowerCamelCase__ ,     return_tensors="""tf"""						)
    lowercase__       :       Optional[Any]				=						self.model.generate(
        model_inputs.input_ids ,     attention_mask=model_inputs.attention_mask ,     num_beams=2 ,     use_cache=lowerCamelCase__ ,     )
    lowercase__       :       List[str]				=						self.tokenizer.batch_decode(generated_ids.numpy() ,     skip_special_tokens=lowerCamelCase__						)
    return generated_words







   @slow
   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    self._assert_generated_batch_equal_expected()



"""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


__snake_case    					=					logging.get_logger(__name__)

__snake_case    					=					{'vocab_file': 'sentencepiece.bpe.model'}

__snake_case    					=					{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

__snake_case    					=					{
    'camembert-base': 512,
}

__snake_case    					=					'▁'

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    VOCAB_FILES_NAMES
   _a   :       List[Any]									=    PRETRAINED_VOCAB_FILES_MAP
   _a   :       List[str]									=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a   :       Union[str, Any]									=    ['''input_ids''', '''attention_mask''']


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

    lowercase__       :       Dict				=						{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=lowerCamelCase__ ,     eos_token=lowerCamelCase__ ,     unk_token=lowerCamelCase__ ,     sep_token=lowerCamelCase__ ,     cls_token=lowerCamelCase__ ,     pad_token=lowerCamelCase__ ,     mask_token=lowerCamelCase__ ,     additional_special_tokens=lowerCamelCase__ ,     sp_model_kwargs=self.sp_model_kwargs ,     **lowerCamelCase__ ,     )
    lowercase__       :       Optional[int]				=						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(str(lowerCamelCase__						)						)
    lowercase__       :       str				=						vocab_file
    # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
    # sentencepiece vocabulary (this is the case for <s> and </s>
    lowercase__       :       Optional[int]				=						{"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
    lowercase__       :       int				=						len(self.fairseq_tokens_to_ids						)
    lowercase__       :       Dict				=						len(self.sp_model						) + len(self.fairseq_tokens_to_ids						)
    lowercase__       :       List[Any]				=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:

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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None ,     lowerCamelCase__ = False						)      ->							List[int]:
    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=lowerCamelCase__ ,     token_ids_a=lowerCamelCase__ ,     already_has_special_tokens=lowerCamelCase__						)

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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:
    lowercase__       :       int				=						[self.sep_token_id]
    lowercase__       :       Tuple				=						[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 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return len(self.fairseq_tokens_to_ids						) + len(self.sp_model						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Dict				=						{self.convert_ids_to_tokens(lowerCamelCase__						): i for i in range(self.vocab_size						)}
    vocab.update(self.added_tokens_encoder						)
    return vocab


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							List[str]:
    return self.sp_model.encode(lowerCamelCase__ ,     out_type=lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							str:
    if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
    elif self.sp_model.PieceToId(lowerCamelCase__						) == 0:
     # Convert sentence piece unk token to fairseq unk token index
     return self.unk_token_id
    return self.fairseq_offset + self.sp_model.PieceToId(lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							int:
    if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
    return self.sp_model.IdToPiece(index - self.fairseq_offset						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							str:
    lowercase__       :       Dict				=						[]
    lowercase__       :       Dict				=						""""""
    lowercase__       :       Tuple				=						False
    for token in tokens:
     # make sure that special tokens are not decoded using sentencepiece model
     if token in self.all_special_tokens:
      if not prev_is_special:
       out_string += " "
      out_string += self.sp_model.decode(lowerCamelCase__						) + token
      lowercase__       :       List[Any]				=						True
      lowercase__       :       str				=						[]
     else:
      current_sub_tokens.append(lowerCamelCase__						)
      lowercase__       :       Optional[Any]				=						False
    out_string += self.sp_model.decode(lowerCamelCase__						)
    return out_string.strip()


   def __getstate__(						self						)      ->							int:
    lowercase__       :       Dict				=						self.__dict__.copy()
    lowercase__       :       Union[str, Any]				=						None
    return state


   def __setstate__(						self ,     lowerCamelCase__						)      ->							int:
    lowercase__       :       Any				=						d

    # for backward compatibility
    if not hasattr(self ,     """sp_model_kwargs"""						):
     lowercase__       :       Tuple				=						{}

    lowercase__       :       Optional[Any]				=						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(self.vocab_file						)







   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							Tuple[str]:
    if not os.path.isdir(lowerCamelCase__						):
     logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
     return
    lowercase__       :       Any				=						os.path.join(
        lowerCamelCase__ ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

    if os.path.abspath(self.vocab_file						) != os.path.abspath(lowerCamelCase__						) and os.path.isfile(self.vocab_file						):
     copyfile(self.vocab_file ,     lowerCamelCase__						)
    elif not os.path.isfile(self.vocab_file						):
     with open(lowerCamelCase__ ,     """wb"""						) as fi:
      lowercase__       :       int				=						self.sp_model.serialized_model_proto()
      fi.write(lowerCamelCase__						)

    return (out_vocab_file,)