





'''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       a_   :
     __A      =			PegasusConfig
     __A      =			{}
     __A      =			"gelu"





     def __init__( self		:					Any	,      lowercase		:					Optional[Any]	,      lowercase		:					Union[str, Any]=13	,      lowercase		:					Union[str, Any]=7	,      lowercase		:					List[str]=True	,      lowercase		:					int=False	,      lowercase		:					Tuple=99	,      lowercase		:					List[Any]=32	,      lowercase		:					int=2	,      lowercase		:					Optional[Any]=4	,      lowercase		:					List[str]=37	,      lowercase		:					List[Any]=0.1	,      lowercase		:					List[str]=0.1	,      lowercase		:					List[Any]=40	,      lowercase		:					List[Any]=2	,      lowercase		:					Tuple=1	,      lowercase		:					Any=0	,      ):





        """simple docstring"""




        lowercase_			:Dict    			= parent
        lowercase_			:str    			= batch_size
        lowercase_			:Tuple    			= seq_length
        lowercase_			:str    			= is_training
        lowercase_			:Dict    			= use_labels
        lowercase_			:Dict    			= vocab_size
        lowercase_			:int    			= hidden_size
        lowercase_			:Tuple    			= num_hidden_layers
        lowercase_			:Any    			= num_attention_heads
        lowercase_			:Dict    			= intermediate_size

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





     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        lowercase_			:List[str]    			= ids_tensor([self.batch_size, self.seq_length - 1]	,      self.vocab_size						)
        lowercase_			:Tuple    			= tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)	,      1						)
        lowercase_			:Union[str, Any]    			= tf.concat([input_ids, eos_tensor]	,      axis=1						)

        lowercase_			:Optional[Any]    			= ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size						)

        lowercase_			:str    			= 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[Any]    			= prepare_pegasus_inputs_dict(lowercase	,      lowercase	,      lowercase						)
        return config, inputs_dict







     def 							lowercase__      ( self		:					Tuple	,      lowercase		:					Union[str, Any]	,      lowercase		:					List[str]						):





        """simple docstring"""




        lowercase_			:int    			= TFPegasusModel(config=lowercase						).get_decoder()
        lowercase_			:List[Any]    			= inputs_dict["input_ids"]

        lowercase_			:Dict    			= input_ids[:1, :]
        lowercase_			:Dict    			= inputs_dict["attention_mask"][:1, :]
        lowercase_			:str    			= inputs_dict["head_mask"]
        lowercase_			:Any    			= 1

        # first forward pass
        lowercase_			:int    			= model(lowercase	,      attention_mask=lowercase	,      head_mask=lowercase	,      use_cache=lowercase						)

        lowercase_			,							lowercase_			:str    			= outputs.to_tuple()

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

        # append to next input_ids and
        lowercase_			:List[str]    			= tf.concat([input_ids, next_tokens]	,      axis=-1						)
        lowercase_			:List[Any]    			= tf.concat([attention_mask, next_attn_mask]	,      axis=-1						)

        lowercase_			:List[Any]    			= model(lowercase	,      attention_mask=lowercase						)[0]
        lowercase_			:Optional[Any]    			= model(lowercase	,      attention_mask=lowercase	,      past_key_values=lowercase						)[0]

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

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

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







def 							UpperCAmelCase_  (						__lowerCamelCase					:					Optional[int]	,__lowerCamelCase					:					List[Any]	,__lowerCamelCase					:					Tuple	,__lowerCamelCase					:					Tuple=None	,__lowerCamelCase					:					List[str]=None	,__lowerCamelCase					:					List[str]=None	,__lowerCamelCase					:					Optional[Any]=None	,__lowerCamelCase					:					int=None	,):
   if attention_mask is None:
      lowercase_			:Optional[int]    			= tf.cast(tf.math.not_equal(__lowerCamelCase	,config.pad_token_id )	,tf.inta )
   if decoder_attention_mask is None:
      lowercase_			:List[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_			:Optional[int]    			= tf.ones((config.encoder_layers, config.encoder_attention_heads) )
   if decoder_head_mask is None:
      lowercase_			:Union[str, Any]    			= tf.ones((config.decoder_layers, config.decoder_attention_heads) )
   if cross_attn_head_mask is None:
      lowercase_			:int    			= 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_   (				_lowerCAmelCase	,						_lowerCAmelCase	,						unittest.TestCase							):
     __A      =			(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
     __A      =			(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
     __A      =			(
         {
             "conversational": TFPegasusForConditionalGeneration,
             "feature-extraction": TFPegasusModel,
             "summarization": TFPegasusForConditionalGeneration,
             "text2text-generation": TFPegasusForConditionalGeneration,
             "translation": TFPegasusForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     __A      =			True
     __A      =			False
     __A      =			False





     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= TFPegasusModelTester(self						)
        lowercase_			:List[str]    			= ConfigTester(self	,      config_class=lowercase						)





     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        self.config_tester.run_common_tests()







     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        lowercase_			:str    			= self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*lowercase						)







@require_sentencepiece
@require_tokenizers
@require_tf
class       a_   (				unittest.TestCase							):
     __A      =			[
         " 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      =			[
         "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      =			"google/pegasus-xsum"





     @cached_property
     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        return AutoTokenizer.from_pretrained(self.model_name						)





     @cached_property
     def 							lowercase__      ( self		:					int						):





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
        return model





     def 							lowercase__      ( self		:					Union[str, Any]	,      **lowercase		:					int						):





        """simple docstring"""




        lowercase_			:str    			= self.translate_src_text(**lowercase						)
        assert self.expected_text == generated_words





     def 							lowercase__      ( self		:					Tuple	,      **lowercase		:					str						):





        """simple docstring"""




        lowercase_			:Optional[int]    			= self.tokenizer(self.src_text	,      **lowercase	,      padding=lowercase	,      return_tensors="tf"						)
        lowercase_			:Dict    			= self.model.generate(
            model_inputs.input_ids	,      attention_mask=model_inputs.attention_mask	,      num_beams=2	,      use_cache=lowercase	,      )
        lowercase_			:List[Any]    			= self.tokenizer.batch_decode(generated_ids.numpy()	,      skip_special_tokens=lowercase						)
        return generated_words







     @slow
     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        self._assert_generated_batch_equal_expected()









'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


lowerCAmelCase					:     Tuple												=logging.get_logger(__name__)


lowerCAmelCase					:     List[str]												={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCAmelCase					:     Optional[int]												={
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
    },
}

lowerCAmelCase					:     List[Any]												={
    '''RUCAIBox/mvp''': 1_024,
}
class       a_   (				_lowerCAmelCase							):
     __A      =			VOCAB_FILES_NAMES
     __A      =			PRETRAINED_VOCAB_FILES_MAP
     __A      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A      =			["input_ids", "attention_mask"]
     __A      =			MvpTokenizer





     def __init__( self		:					Optional[Any]	,      lowercase		:					Any=None	,      lowercase		:					List[Any]=None	,      lowercase		:					Dict=None	,      lowercase		:					int="replace"	,      lowercase		:					int="<s>"	,      lowercase		:					List[str]="</s>"	,      lowercase		:					Optional[Any]="</s>"	,      lowercase		:					List[str]="<s>"	,      lowercase		:					List[str]="<unk>"	,      lowercase		:					List[str]="<pad>"	,      lowercase		:					Tuple="<mask>"	,      lowercase		:					Tuple=False	,      lowercase		:					Dict=True	,      **lowercase		:					List[str]	,      ):





        """simple docstring"""




        super().__init__(
            lowercase	,      lowercase	,      tokenizer_file=lowercase	,      errors=lowercase	,      bos_token=lowercase	,      eos_token=lowercase	,      sep_token=lowercase	,      cls_token=lowercase	,      unk_token=lowercase	,      pad_token=lowercase	,      mask_token=lowercase	,      add_prefix_space=lowercase	,      trim_offsets=lowercase	,      **lowercase	,      )

        lowercase_			:Dict    			= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
        if pre_tok_state.get("add_prefix_space"	,      lowercase						) != add_prefix_space:
           lowercase_			:List[str]    			= getattr(lowercase	,      pre_tok_state.pop("type"						)						)
           lowercase_			:int    			= add_prefix_space
           lowercase_			:Optional[int]    			= pre_tok_class(**lowercase						)

        lowercase_			:Any    			= add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        lowercase_			:List[Any]    			= "post_processor"
        lowercase_			:str    			= getattr(self.backend_tokenizer	,      lowercase	,      lowercase						)
        if tokenizer_component_instance:
           lowercase_			:Any    			= json.loads(tokenizer_component_instance.__getstate__()						)

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
              lowercase_			:int    			= tuple(state["sep"]						)
           if "cls" in state:
              lowercase_			:Any    			= tuple(state["cls"]						)

           lowercase_			:int    			= False

           if state.get("add_prefix_space"	,      lowercase						) != add_prefix_space:
              lowercase_			:Union[str, Any]    			= add_prefix_space
              lowercase_			:int    			= True

           if state.get("trim_offsets"	,      lowercase						) != trim_offsets:
              lowercase_			:Any    			= trim_offsets
              lowercase_			:int    			= True

           if changes_to_apply:
              lowercase_			:Tuple    			= getattr(lowercase	,      state.pop("type"						)						)
              lowercase_			:Any    			= component_class(**lowercase						)
              setattr(self.backend_tokenizer	,      lowercase	,      lowercase						)





     @property
     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        if self._mask_token is None:
           if self.verbose:
              logger.error("Using mask_token, but it is not set yet."						)
           return None
        return str(self._mask_token						)





     @mask_token.setter
     def 							lowercase__      ( self		:					int	,      lowercase		:					Dict						):





        """simple docstring"""




        lowercase_			:List[str]    			= AddedToken(lowercase	,      lstrip=lowercase	,      rstrip=lowercase						) if isinstance(lowercase	,      lowercase						) else value
        lowercase_			:Union[str, Any]    			= value





     def 							lowercase__      ( self		:					Optional[Any]	,      *lowercase		:					List[Any]	,      **lowercase		:					Any						):





        """simple docstring"""




        lowercase_			:Any    			= kwargs.get("is_split_into_words"	,      lowercase						)

        if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."						)

        return super()._batch_encode_plus(*lowercase	,      **lowercase						)





     def 							lowercase__      ( self		:					Optional[Any]	,      *lowercase		:					Optional[int]	,      **lowercase		:					int						):





        """simple docstring"""




        lowercase_			:Any    			= kwargs.get("is_split_into_words"	,      lowercase						)

        if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."						)

        return super()._encode_plus(*lowercase	,      **lowercase						)





     def 							lowercase__      ( self		:					Dict	,      lowercase		:					str	,      lowercase		:					Optional[str] = None						):





        """simple docstring"""




        lowercase_			:str    			= self._tokenizer.model.save(lowercase	,      name=lowercase						)
        return tuple(lowercase						)





     def 							lowercase__      ( self		:					Tuple	,      lowercase		:					Dict	,      lowercase		:					int=None						):





        """simple docstring"""




        lowercase_			:List[str]    			= [self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
           return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







     def 							lowercase__      ( self		:					int	,      lowercase		:					List[int]	,      lowercase		:					Optional[List[int]] = None						):





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= [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]


