




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     __a:






    """simple docstring"""





    lowerCAmelCase         = PegasusConfig
    lowerCAmelCase         = {}
    lowerCAmelCase         = '''gelu'''





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

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





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

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

         UpperCAmelCase_     :				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  ,)
         UpperCAmelCase_     :				Optional[Any]  =     prepare_pegasus_inputs_dict(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
         return config, inputs_dict





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Union[str, Any]:
         UpperCAmelCase_     :				Tuple  =     TFPegasusModel(config=_SCREAMING_SNAKE_CASE    ).get_decoder()
         UpperCAmelCase_     :				Optional[Any]  =     inputs_dict['''input_ids''']

         UpperCAmelCase_     :				Dict  =     input_ids[:1, :]
         UpperCAmelCase_     :				List[Any]  =     inputs_dict['''attention_mask'''][:1, :]
         UpperCAmelCase_     :				Optional[Any]  =     inputs_dict['''head_mask''']
         UpperCAmelCase_     :				Any  =     1

         # first forward pass
         UpperCAmelCase_     :				Tuple  =     model(_SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE  ,head_mask=_SCREAMING_SNAKE_CASE  ,use_cache=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_,       UpperCAmelCase_     :				int  =     outputs.to_tuple()

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

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

         UpperCAmelCase_     :				Optional[Any]  =     model(_SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE    )[0]
         UpperCAmelCase_     :				Tuple  =     model(_SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE  ,past_key_values=_SCREAMING_SNAKE_CASE    )[0]

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

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

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







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase=None					,		_lowercase=None					,		_lowercase=None					,		_lowercase=None					,		_lowercase=None					,		):

     '''simple docstring'''


     if attention_mask is None:
          UpperCAmelCase_     :				Optional[int]  =     tf.cast(tf.math.not_equal(_lowercase					,		config.pad_token_id )					,		tf.inta )
     if decoder_attention_mask is None:
          UpperCAmelCase_     :				str  =     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_     :				Any  =     tf.ones((config.encoder_layers, config.encoder_attention_heads) )
     if decoder_head_mask is None:
          UpperCAmelCase_     :				List[str]  =     tf.ones((config.decoder_layers, config.decoder_attention_heads) )
     if cross_attn_head_mask is None:
          UpperCAmelCase_     :				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     __a(	_a							,		_a							,		unittest.TestCase  ):






    """simple docstring"""





    lowerCAmelCase         = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
    lowerCAmelCase         = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
    lowerCAmelCase         = (
        {
            '''conversational''': TFPegasusForConditionalGeneration,
            '''feature-extraction''': TFPegasusModel,
            '''summarization''': TFPegasusForConditionalGeneration,
            '''text2text-generation''': TFPegasusForConditionalGeneration,
            '''translation''': TFPegasusForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    lowerCAmelCase         = True
    lowerCAmelCase         = False
    lowerCAmelCase         = False





    def 				a__ ( self    )					->						int:
         UpperCAmelCase_     :				Union[str, Any]  =     TFPegasusModelTester(self    )
         UpperCAmelCase_     :				str  =     ConfigTester(self  ,config_class=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Optional[int]:
         self.config_tester.run_common_tests()





    def 				a__ ( self    )					->						Dict:
         UpperCAmelCase_     :				Any  =     self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE    )


@require_sentencepiece
@require_tokenizers
@require_tf
class     __a(	unittest.TestCase  ):






    """simple docstring"""





    lowerCAmelCase         = [
        ''' 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!" ''',
    ]
    lowerCAmelCase         = [
        '''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
    lowerCAmelCase         = '''google/pegasus-xsum'''





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





    @cached_property
    def 				a__ ( self    )					->						Optional[Any]:
         UpperCAmelCase_     :				str  =     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
         return model





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						Tuple:
         UpperCAmelCase_     :				Optional[Any]  =     self.translate_src_text(**_SCREAMING_SNAKE_CASE    )
         assert self.expected_text == generated_words





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						Optional[int]:
         UpperCAmelCase_     :				Any  =     self.tokenizer(self.src_text  ,**_SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,return_tensors='''tf'''    )
         UpperCAmelCase_     :				Union[str, Any]  =     self.model.generate(
             model_inputs.input_ids  ,attention_mask=model_inputs.attention_mask  ,num_beams=2  ,use_cache=_SCREAMING_SNAKE_CASE  ,)
         UpperCAmelCase_     :				Dict  =     self.tokenizer.batch_decode(generated_ids.numpy()  ,skip_special_tokens=_SCREAMING_SNAKE_CASE    )
         return generated_words





    @slow
    def 				a__ ( self    )					->						List[Any]:
         self._assert_generated_batch_equal_expected()






'''simple docstring'''


import os
import unicodedata
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 SPIECE_UNDERLINE, logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{"vocab_file": "spiece.model"}

_SCREAMING_SNAKE_CASE									=				{
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    }
}

class  lowerCAmelCase_		(		__magic_name__		):




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase="<s>"      ,  _lowerCAmelCase="</s>"      ,  _lowerCAmelCase="<unk>"      ,  _lowerCAmelCase="<sep>"      ,  _lowerCAmelCase="<pad>"      ,  _lowerCAmelCase="<cls>"      ,  _lowerCAmelCase="<mask>"      ,  _lowerCAmelCase=["<eop>", "<eod>"]      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				None:
     _lowerCAmelCase						      =   AddedToken(_lowerCAmelCase      ,  lstrip=_lowerCAmelCase      ,  rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase      ,  _lowerCAmelCase		) else mask_token

     _lowerCAmelCase						      =   {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         do_lower_case=_lowerCAmelCase      ,  remove_space=_lowerCAmelCase      ,  keep_accents=_lowerCAmelCase      ,  bos_token=_lowerCAmelCase      ,  eos_token=_lowerCAmelCase      ,  unk_token=_lowerCAmelCase      ,  sep_token=_lowerCAmelCase      ,  pad_token=_lowerCAmelCase      ,  cls_token=_lowerCAmelCase      ,  mask_token=_lowerCAmelCase      ,  additional_special_tokens=_lowerCAmelCase      ,  sp_model_kwargs=self.sp_model_kwargs      ,  **_lowerCAmelCase      ,  )

     _lowerCAmelCase						      =   3

     _lowerCAmelCase						      =   do_lower_case
     _lowerCAmelCase						      =   remove_space
     _lowerCAmelCase						      =   keep_accents
     _lowerCAmelCase						      =   vocab_file

     _lowerCAmelCase						      =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
     self.sp_model.Load(_lowerCAmelCase		)

     try:
         import jieba
     except ModuleNotFoundError as error:
         raise error.__class__(
             "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
             "See https://pypi.org/project/jieba/ for installation."		)
     _lowerCAmelCase						      =   jieba
     _lowerCAmelCase						      =   str.maketrans(" \n"      ,  "\u2582\u2583"		)




 @property
 # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
 def 				_snake_case (						self		)     ->				Optional[int]:
     return len(self.sp_model		)




 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   {self.convert_ids_to_tokens(_lowerCAmelCase		): i for i in range(self.vocab_size		)}
     vocab.update(self.added_tokens_encoder		)
     return vocab




 def __getstate__(						self		)     ->				Tuple:
     _lowerCAmelCase						      =   self.__dict__.copy()
     _lowerCAmelCase						      =   None
     return state




 def __setstate__(						self      ,  _lowerCAmelCase		)     ->				Dict:
     _lowerCAmelCase						      =   d

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

     _lowerCAmelCase						      =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
     self.sp_model.Load(self.vocab_file		)




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				str:
     if self.remove_space:
         _lowerCAmelCase						      =   " ".join(inputs.strip().split()		)
     else:
         _lowerCAmelCase						      =   inputs
     _lowerCAmelCase						      =   outputs.replace("``"      ,  "\""		).replace("''"      ,  "\""		)

     if not self.keep_accents:
         _lowerCAmelCase						      =   unicodedata.normalize("NFKD"      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   "".join([c for c in outputs if not unicodedata.combining(_lowerCAmelCase		)]		)
     if self.do_lower_case:
         _lowerCAmelCase						      =   outputs.lower()

     return outputs




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				List[str]:
     _lowerCAmelCase						      =   self.preprocess_text(_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.sp_model.encode(_lowerCAmelCase      ,  out_type=_lowerCAmelCase		)
     _lowerCAmelCase						      =   []
     for piece in pieces:
         if len(_lowerCAmelCase		) > 1 and piece[-1] == str(","		) and piece[-2].isdigit():
             _lowerCAmelCase						      =   self.sp_model.EncodeAsPieces(piece[:-1].replace(_lowerCAmelCase      ,  ""		)		)
             if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                 if len(cur_pieces[0]		) == 1:
                     _lowerCAmelCase						      =   cur_pieces[1:]
                 else:
                     _lowerCAmelCase						      =   cur_pieces[0][1:]
             cur_pieces.append(piece[-1]		)
             new_pieces.extend(_lowerCAmelCase		)
         else:
             new_pieces.append(_lowerCAmelCase		)

     return new_pieces




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				str:
     return self.sp_model.PieceToId(_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     return self.sp_model.IdToPiece(_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   "".join(_lowerCAmelCase		).replace(_lowerCAmelCase      ,  " "		).strip()
     return out_string




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None		)     ->				List[int]:
     _lowerCAmelCase						      =   [self.sep_token_id]
     _lowerCAmelCase						      =   [self.cls_token_id]
     if token_ids_a is None:
         return token_ids_a + sep + cls
     return token_ids_a + sep + token_ids_a + sep + cls




 def 				_snake_case (						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 not None:
         return ([0] * len(_lowerCAmelCase		)) + [1] + ([0] * len(_lowerCAmelCase		)) + [1, 1]
     return ([0] * len(_lowerCAmelCase		)) + [1, 1]




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None		)     ->				List[int]:
     _lowerCAmelCase						      =   [self.sep_token_id]
     _lowerCAmelCase						      =   [2]

     if token_ids_a is None:
         return len(token_ids_a + sep		) * [0] + cls_segment_id
     return len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1] + cls_segment_id




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None		)     ->				Tuple[str]:
     if not os.path.isdir(_lowerCAmelCase		):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
         return
     _lowerCAmelCase						      =   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:
             _lowerCAmelCase						      =   self.sp_model.serialized_model_proto()
             fi.write(_lowerCAmelCase		)

     return (out_vocab_file,)





 def 				_snake_case (						self      ,  *_lowerCAmelCase      ,  **_lowerCAmelCase		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   super()._decode(*_lowerCAmelCase      ,  **_lowerCAmelCase		)
     _lowerCAmelCase						      =   text.replace(" "      ,  ""		).replace("\u2582"      ,  " "		).replace("\u2583"      ,  "\n"		)
     return text


