
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 		UpperCamelCase__  :
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				PegasusConfig
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				{}
   _SCREAMING_SNAKE_CASE				:							List[Any]     	=				"gelu"
   def __init__(self			: int ,    snake_case_			: List[Any] ,    snake_case_			: Union[str, Any]=1_3 ,    snake_case_			: List[str]=7 ,    snake_case_			: Optional[int]=True ,    snake_case_			: Optional[Any]=False ,    snake_case_			: int=9_9 ,    snake_case_			: Union[str, Any]=3_2 ,    snake_case_			: Optional[Any]=2 ,    snake_case_			: Optional[Any]=4 ,    snake_case_			: List[str]=3_7 ,    snake_case_			: Dict=0.1 ,    snake_case_			: Union[str, Any]=0.1 ,    snake_case_			: Union[str, Any]=4_0 ,    snake_case_			: Tuple=2 ,    snake_case_			: Dict=1 ,    snake_case_			: Union[str, Any]=0 ,    ):
    __a			:  List[Any]		    =  parent
    __a			:  str		    =  batch_size
    __a			:  int		    =  seq_length
    __a			:  List[Any]		    =  is_training
    __a			:  List[Any]		    =  use_labels
    __a			:  List[str]		    =  vocab_size
    __a			:  str		    =  hidden_size
    __a			:  Dict		    =  num_hidden_layers
    __a			:  Tuple		    =  num_attention_heads
    __a			:  Any		    =  intermediate_size

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

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

    __a			:  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 ,    )
    __a			:  Tuple		    =  prepare_pegasus_inputs_dict(snake_case_ ,    snake_case_ ,    snake_case_       )
    return config, inputs_dict







   def 				lowerCAmelCase      (self			: str ,    snake_case_			: str ,    snake_case_			: List[str]       ):
    __a			:  Tuple		    =  TFPegasusModel(config=snake_case_       ).get_decoder()
    __a			:  Any		    =  inputs_dict['''input_ids''']

    __a			:  int		    =  input_ids[:1, :]
    __a			:  Dict		    =  inputs_dict['''attention_mask'''][:1, :]
    __a			:  Tuple		    =  inputs_dict['''head_mask''']
    __a			:  List[str]		    =  1

    # first forward pass
    __a			:  Tuple		    =  model(snake_case_ ,    attention_mask=snake_case_ ,    head_mask=snake_case_ ,    use_cache=snake_case_       )

    __a			:  Dict		    =  outputs.to_tuple()

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

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

    __a			:  int		    =  model(snake_case_ ,    attention_mask=snake_case_       )[0]
    __a			:  Optional[Any]		    =  model(snake_case_ ,    attention_mask=snake_case_ ,    past_key_values=snake_case_       )[0]

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

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

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





def        __UpperCamelCase					(    lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    Any=None  ,       lowerCAmelCase__							:    Union[str, Any]=None  ,       lowerCAmelCase__							:    Dict=None  ,       lowerCAmelCase__							:    Tuple=None  ,       lowerCAmelCase__							:    Any=None  ,       ):
 if attention_mask is None:
  __a			:  Dict		    =  tf.cast(tf.math.not_equal(lowerCAmelCase__  ,       config.pad_token_id  )  ,       tf.inta  )
 if decoder_attention_mask is None:
  __a			:  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:
  __a			:  List[str]		    =  tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
 if decoder_head_mask is None:
  __a			:  List[str]		    =  tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
 if cross_attn_head_mask is None:
  __a			:  Any		    =  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 		UpperCamelCase__  (	__lowercase   ,__lowercase   ,unittest.TestCase   ):
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
   _SCREAMING_SNAKE_CASE				:							int     	=				(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				(
       {
           "conversational": TFPegasusForConditionalGeneration,
           "feature-extraction": TFPegasusModel,
           "summarization": TFPegasusForConditionalGeneration,
           "text2text-generation": TFPegasusForConditionalGeneration,
           "translation": TFPegasusForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   _SCREAMING_SNAKE_CASE				:							str     	=				True
   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				False
   _SCREAMING_SNAKE_CASE				:							int     	=				False
   def 				lowerCAmelCase      (self			: Optional[int]       ):
    __a			:  Any		    =  TFPegasusModelTester(self       )
    __a			:  List[str]		    =  ConfigTester(self ,    config_class=snake_case_       )
   def 				lowerCAmelCase      (self			: Optional[int]       ):
    self.config_tester.run_common_tests()







   def 				lowerCAmelCase      (self			: List[Any]       ):
    __a			:  str		    =  self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*snake_case_       )





@require_sentencepiece
@require_tokenizers
@require_tf
class 		UpperCamelCase__  (	unittest.TestCase   ):
   _SCREAMING_SNAKE_CASE				:							Union[str, 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!\" ",
   ]
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				[
       "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
   _SCREAMING_SNAKE_CASE				:							str     	=				"google/pegasus-xsum"
   @cached_property
   def 				lowerCAmelCase      (self			: Tuple       ):
    return AutoTokenizer.from_pretrained(self.model_name       )
   @cached_property
   def 				lowerCAmelCase      (self			: Tuple       ):
    __a			:  Union[str, Any]		    =  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
    return model
   def 				lowerCAmelCase      (self			: str ,    **snake_case_			: Optional[Any]       ):
    __a			:  List[str]		    =  self.translate_src_text(**snake_case_       )
    assert self.expected_text == generated_words
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    **snake_case_			: Optional[int]       ):
    __a			:  str		    =  self.tokenizer(self.src_text ,    **snake_case_ ,    padding=snake_case_ ,    return_tensors='''tf'''       )
    __a			:  str		    =  self.model.generate(
        model_inputs.input_ids ,    attention_mask=model_inputs.attention_mask ,    num_beams=2 ,    use_cache=snake_case_ ,    )
    __a			:  List[str]		    =  self.tokenizer.batch_decode(generated_ids.numpy() ,    skip_special_tokens=snake_case_       )
    return generated_words







   @slow
   def 				lowerCAmelCase      (self			: Any       ):
    self._assert_generated_batch_equal_expected()


import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Any  ):
 if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
  __a			:  Union[str, Any]		    =  np.full((len(lowerCAmelCase__  ), sequence_length, 2)  ,       lowerCAmelCase__  )
 else:
  __a			:  str		    =  np.full((len(lowerCAmelCase__  ), sequence_length)  ,       lowerCAmelCase__  )

 for i, tensor in enumerate(lowerCAmelCase__  ):
  if padding_side == "right":
   if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
    __a			:  Any		    =  tensor[:sequence_length]
   else:
    __a			:  List[Any]		    =  tensor[:sequence_length]
  else:
   if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
    __a			:  Dict		    =  tensor[:sequence_length]
   else:
    __a			:  int		    =  tensor[:sequence_length]

 return out_tensor.tolist()




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[Any]  ):
 __a			:  str		    =  ord(lowerCAmelCase__  )
 if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
  return True
 __a			:  List[str]		    =  unicodedata.category(lowerCAmelCase__  )
 if cat.startswith('''P'''  ):
  return True
 return False



@dataclass
class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							PreTrainedTokenizerBase
   _SCREAMING_SNAKE_CASE				:							Union[bool, str, PaddingStrategy]     	=				True
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   _SCREAMING_SNAKE_CASE				:							int     	=				-100
   _SCREAMING_SNAKE_CASE				:							str     	=				"pt"
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: Tuple       ):
    import torch

    __a			:  Union[str, Any]		    =  '''label''' if '''label''' in features[0].keys() else '''labels'''
    __a			:  Tuple		    =  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    __a			:  Union[str, Any]		    =  self.tokenizer.pad(
        snake_case_ ,    padding=self.padding ,    max_length=self.max_length ,    pad_to_multiple_of=self.pad_to_multiple_of ,    return_tensors='''pt''' if labels is None else None ,    )

    if labels is None:
     return batch

    __a			:  List[str]		    =  torch.tensor(batch['''entity_ids''']       ).shape[1]
    __a			:  Tuple		    =  self.tokenizer.padding_side
    if padding_side == "right":
     __a			:  Union[str, Any]		    =  [
         list(snake_case_       ) + [self.label_pad_token_id] * (sequence_length - len(snake_case_       )) for label in labels
     ]
    else:
     __a			:  Dict		    =  [
         [self.label_pad_token_id] * (sequence_length - len(snake_case_       )) + list(snake_case_       ) for label in labels
     ]

    __a			:  Dict		    =  [feature['''ner_tags'''] for feature in features]
    __a			:  Optional[Any]		    =  padding_tensor(snake_case_ ,    -1 ,    snake_case_ ,    snake_case_       )
    __a			:  Union[str, Any]		    =  [feature['''original_entity_spans'''] for feature in features]
    __a			:  Optional[int]		    =  padding_tensor(snake_case_ ,    (-1, -1) ,    snake_case_ ,    snake_case_       )
    __a			:  List[str]		    =  {k: torch.tensor(snake_case_ ,    dtype=torch.intaa       ) for k, v in batch.items()}

    return batch
