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 		_lowerCamelCase		:
    __a						=   PegasusConfig
    __a						=   {}
    __a						=   "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			,			)							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: Dict=   parent
     SCREAMING_SNAKE_CASE__: str=   batch_size
     SCREAMING_SNAKE_CASE__: str=   seq_length
     SCREAMING_SNAKE_CASE__: int=   is_training
     SCREAMING_SNAKE_CASE__: Tuple=   use_labels
     SCREAMING_SNAKE_CASE__: str=   vocab_size
     SCREAMING_SNAKE_CASE__: Any=   hidden_size
     SCREAMING_SNAKE_CASE__: List[str]=   num_hidden_layers
     SCREAMING_SNAKE_CASE__: Tuple=   num_attention_heads
     SCREAMING_SNAKE_CASE__: Tuple=   intermediate_size

     SCREAMING_SNAKE_CASE__: List[Any]=   hidden_dropout_prob
     SCREAMING_SNAKE_CASE__: Optional[int]=   attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__: Optional[int]=   max_position_embeddings
     SCREAMING_SNAKE_CASE__: int=   eos_token_id
     SCREAMING_SNAKE_CASE__: Any=   pad_token_id
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   bos_token_id
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: str=   ids_tensor([self.batch_size, self.seq_length - 1]			,			self.vocab_size )
     SCREAMING_SNAKE_CASE__: Tuple=   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )			,			1 )
     SCREAMING_SNAKE_CASE__: Dict=   tf.concat([input_ids, eos_tensor]			,			axis=1 )

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

     SCREAMING_SNAKE_CASE__: Union[str, 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			,			)
     SCREAMING_SNAKE_CASE__: Tuple=   prepare_pegasus_inputs_dict(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )
     return config, inputs_dict



    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: Any=   TFPegasusModel(config=lowerCAmelCase ).get_decoder()
     SCREAMING_SNAKE_CASE__: Dict=   inputs_dict['''input_ids''']

     SCREAMING_SNAKE_CASE__: List[Any]=   input_ids[:1, :]
     SCREAMING_SNAKE_CASE__: List[str]=   inputs_dict['''attention_mask'''][:1, :]
     SCREAMING_SNAKE_CASE__: str=   inputs_dict['''head_mask''']
     SCREAMING_SNAKE_CASE__: List[str]=   1

     # first forward pass
     SCREAMING_SNAKE_CASE__: List[Any]=   model(lowerCAmelCase			,			attention_mask=lowerCAmelCase			,			head_mask=lowerCAmelCase			,			use_cache=lowerCAmelCase )

     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Optional[Any]=   outputs.to_tuple()

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

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

     SCREAMING_SNAKE_CASE__: List[Any]=   model(lowerCAmelCase			,			attention_mask=lowerCAmelCase )[0]
     SCREAMING_SNAKE_CASE__: Optional[Any]=   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
     SCREAMING_SNAKE_CASE__: List[Any]=   int(ids_tensor((1,)			,			output_from_past.shape[-1] ) )
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   output_from_no_past[:, -3:, random_slice_idx]
     SCREAMING_SNAKE_CASE__: Tuple=   output_from_past[:, :, random_slice_idx]

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







def       A__   (						snake_case_			:		List[Any]							,					snake_case_			:		List[Any]							,					snake_case_			:		Any							,					snake_case_			:		Tuple=None							,					snake_case_			:		List[str]=None							,					snake_case_			:		int=None							,					snake_case_			:		Tuple=None							,					snake_case_			:		Optional[int]=None							,					):
 if attention_mask is None:
  SCREAMING_SNAKE_CASE__: str=   tf.cast(tf.math.not_equal(snake_case_							,					config.pad_token_id   )							,					tf.inta   )
 if decoder_attention_mask is None:
  SCREAMING_SNAKE_CASE__: 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:
  SCREAMING_SNAKE_CASE__: int=   tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
 if decoder_head_mask is None:
  SCREAMING_SNAKE_CASE__: str=   tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
 if cross_attn_head_mask is None:
  SCREAMING_SNAKE_CASE__: 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 		_lowerCamelCase		( UpperCamelCase_     ,  UpperCamelCase_     ,  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     UpperCamelCase_					(	self )							->       Dict:
     SCREAMING_SNAKE_CASE__: Tuple=   TFPegasusModelTester(self )
     SCREAMING_SNAKE_CASE__: Optional[Any]=   ConfigTester(self			,			config_class=lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     self.config_tester.run_common_tests()



    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: 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 		_lowerCamelCase		( 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     UpperCamelCase_					(	self )							->       Dict:
     return AutoTokenizer.from_pretrained(self.model_name )
    @cached_property
    def     UpperCamelCase_					(	self )							->       Tuple:
     SCREAMING_SNAKE_CASE__: List[Any]=   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
     return model
    def     UpperCamelCase_					(	self			,			**lowerCAmelCase )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: str=   self.translate_src_text(**lowerCAmelCase )
     assert self.expected_text == generated_words
    def     UpperCamelCase_					(	self			,			**lowerCAmelCase )							->       str:
     SCREAMING_SNAKE_CASE__: Tuple=   self.tokenizer(self.src_text			,			**lowerCAmelCase			,			padding=lowerCAmelCase			,			return_tensors='''tf''' )
     SCREAMING_SNAKE_CASE__: Tuple=   self.model.generate(
         model_inputs.input_ids			,			attention_mask=model_inputs.attention_mask			,			num_beams=2			,			use_cache=lowerCAmelCase			,			)
     SCREAMING_SNAKE_CASE__: Optional[Any]=   self.tokenizer.batch_decode(generated_ids.numpy()			,			skip_special_tokens=lowerCAmelCase )
     return generated_words



    @slow
    def     UpperCamelCase_					(	self )							->       str:
     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 PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_			:  Any										=   logging.get_logger(__name__)

UpperCAmelCase_			:  Tuple										=   {'vocab_file': 'spm_char.model'}

UpperCAmelCase_			:  int										=   {
    'vocab_file': {
        'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
        'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
        'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
    }
}

UpperCAmelCase_			:  List[Any]										=   {
    'microsoft/speecht5_asr': 1_0_2_4,
    'microsoft/speecht5_tts': 1_0_2_4,
    'microsoft/speecht5_vc': 1_0_2_4,
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Optional[Any]     				=    VOCAB_FILES_NAMES
 __lowercase					:		int     				=    PRETRAINED_VOCAB_FILES_MAP
 __lowercase					:		int     				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase					:		Optional[int]     				=    ['''input_ids''', '''attention_mask''']


 def __init__(   self   ,   __lowercase   ,   __lowercase="<s>"   ,   __lowercase="</s>"   ,   __lowercase="<unk>"   ,   __lowercase="<pad>"   ,   __lowercase = None   ,   **__lowercase   ,   ):

  """simple docstring"""







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

  super().__init__(
      bos_token=__lowercase   ,   eos_token=__lowercase   ,   unk_token=__lowercase   ,   pad_token=__lowercase   ,   sp_model_kwargs=self.sp_model_kwargs   ,   **__lowercase   ,   )

  __A	:		Optional[Any]	  =      vocab_file

  __A	:		Any	  =      spm.SentencePieceProcessor(**self.sp_model_kwargs    )
  self.sp_model.Load(__lowercase    )


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.sp_model.get_piece_size()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Tuple	  =      {self.convert_ids_to_tokens(__lowercase    ): i for i in range(self.vocab_size    )}
  vocab.update(self.added_tokens_encoder    )
  return vocab


 def __getstate__(   self    ):

  """simple docstring"""







  __A	:		List[Any]	  =      self.__dict__.copy()
  __A	:		str	  =      None
  return state


 def __setstate__(   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[str]	  =      d

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

  __A	:		List[str]	  =      spm.SentencePieceProcessor(**self.sp_model_kwargs    )
  self.sp_model.Load(self.vocab_file    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return self.sp_model.encode(__lowercase   ,   out_type=__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return self.sp_model.piece_to_id(__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      self.sp_model.IdToPiece(__lowercase    )
  return token


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		str	  =      []
  __A	:		str	  =      ''
  for token in tokens:
   # make sure that special tokens are not decoded using sentencepiece model
   if token in self.all_special_tokens:
    out_string += self.sp_model.decode(__lowercase    ) + token
    __A	:		Any	  =      []
   else:
    current_sub_tokens.append(__lowercase    )
  out_string += self.sp_model.decode(__lowercase    )
  return out_string.strip()


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase=None    ):

  """simple docstring"""







  if token_ids_a is None:
   return token_ids_a + [self.eos_token_id]
  # We don't expect to process pairs, but leave the pair logic for API consistency
  return token_ids_a + token_ids_a + [self.eos_token_id]


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None   ,   __lowercase = False    ):

  """simple docstring"""







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

  __A	:		Dict	  =      [1]
  if token_ids_a is None:
   return ([0] * len(__lowercase    )) + suffix_ones
  return ([0] * len(__lowercase    )) + ([0] * len(__lowercase    )) + suffix_ones







 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None    ):

  """simple docstring"""







  if not os.path.isdir(__lowercase    ):
   logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
   return
  __A	:		Optional[int]	  =      os.path.join(
      __lowercase   ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

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

  return (out_vocab_file,)






