
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 __snake_case		:
    _a		:						Optional[Any]=				PegasusConfig
    _a		:						Any=				{}
    _a		:						List[Any]=				"gelu"
    def __init__(  self		,snake_case		,snake_case=13		,snake_case=7		,snake_case=True		,snake_case=False		,snake_case=99		,snake_case=32		,snake_case=2		,snake_case=4		,snake_case=37		,snake_case=0.1		,snake_case=0.1		,snake_case=40		,snake_case=2		,snake_case=1		,snake_case=0		,):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      parent
           lowercase							:     Tuple       	=      batch_size
           lowercase							:     str       	=      seq_length
           lowercase							:     Optional[Any]       	=      is_training
           lowercase							:     Optional[Any]       	=      use_labels
           lowercase							:     List[Any]       	=      vocab_size
           lowercase							:     Any       	=      hidden_size
           lowercase							:     List[Any]       	=      num_hidden_layers
           lowercase							:     Optional[Any]       	=      num_attention_heads
           lowercase							:     List[Any]       	=      intermediate_size

           lowercase							:     List[Any]       	=      hidden_dropout_prob
           lowercase							:     str       	=      attention_probs_dropout_prob
           lowercase							:     List[Any]       	=      max_position_embeddings
           lowercase							:     List[str]       	=      eos_token_id
           lowercase							:     Optional[Any]       	=      pad_token_id
           lowercase							:     Tuple       	=      bos_token_id
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







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

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

           lowercase							:     Dict       	=      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							:     Union[str, Any]       	=      prepare_pegasus_inputs_dict(snake_case		,snake_case		,snake_case      )
           return config, inputs_dict
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







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

           lowercase							:     List[Any]       	=      input_ids[:1, :]
           lowercase							:     Tuple       	=      inputs_dict["""attention_mask"""][:1, :]
           lowercase							:     Optional[int]       	=      inputs_dict["""head_mask"""]
           lowercase							:     List[str]       	=      1

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

           lowercase	,			lowercase							:     Dict       	=      outputs.to_tuple()

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

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

           lowercase							:     str       	=      model(snake_case		,attention_mask=snake_case      )[0]
           lowercase							:     List[str]       	=      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
           lowercase							:     int       	=      int(ids_tensor((1,)		,output_from_past.shape[-1]      )      )
           lowercase							:     str       	=      output_from_no_past[:, -3:, random_slice_idx]
           lowercase							:     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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=None					,	) ->    Optional[Any]:
       if attention_mask is None:
              lowercase							:     int       	=      tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE__					,	config.pad_token_id				)					,	tf.inta				)
       if decoder_attention_mask is None:
              lowercase							:     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:
              lowercase							:     int       	=      tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
       if decoder_head_mask is None:
              lowercase							:     int       	=      tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
       if cross_attn_head_mask is None:
              lowercase							:     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 __snake_case		(      lowerCAmelCase					,	lowerCAmelCase					,	unittest.TestCase     ):
    _a		:						Dict=				(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
    _a		:						Dict=				(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
    _a		:						str=				(
        {
            "conversational": TFPegasusForConditionalGeneration,
            "feature-extraction": TFPegasusModel,
            "summarization": TFPegasusForConditionalGeneration,
            "text2text-generation": TFPegasusForConditionalGeneration,
            "translation": TFPegasusForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    _a		:						int=				True
    _a		:						Dict=				False
    _a		:						Optional[int]=				False
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      TFPegasusModelTester(self      )
           lowercase							:     Any       	=      ConfigTester(self		,config_class=snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self.config_tester.run_common_tests()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      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 __snake_case		(      unittest.TestCase     ):
    _a		:						Optional[int]=				[
        " 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		:						Optional[Any]=				[
        "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		:						Tuple=				"google/pegasus-xsum"
    @cached_property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return AutoTokenizer.from_pretrained(self.model_name      )
    @cached_property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
           return model
    def       _SCREAMING_SNAKE_CASE       (  self		,**snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.translate_src_text(**snake_case      )
           assert self.expected_text == generated_words
    def       _SCREAMING_SNAKE_CASE       (  self		,**snake_case      ):



           '''simple docstring'''







           lowercase							:     Any       	=      self.tokenizer(self.src_text		,**snake_case		,padding=snake_case		,return_tensors="""tf"""      )
           lowercase							:     Tuple       	=      self.model.generate(
               model_inputs.input_ids		,attention_mask=model_inputs.attention_mask		,num_beams=2		,use_cache=snake_case		,)
           lowercase							:     Union[str, Any]       	=      self.tokenizer.batch_decode(generated_ids.numpy()		,skip_special_tokens=snake_case      )
           return generated_words
    @slow
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self._assert_generated_batch_equal_expected()


import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Any:
       assert isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       assert dataset.num_rows == 4
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_1", "col_2", "col_3"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype







@require_sqlalchemy
@pytest.mark.parametrize("""keep_in_memory"""					,	[False, True]				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       lowercase							:     Union[str, Any]       	=      tmp_path / """cache"""
       lowercase							:     Optional[Any]       	=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              lowercase							:     Any       	=      SqlDatasetReader(
                  """dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__					,	keep_in_memory=SCREAMING_SNAKE_CASE__				).read()
       _check_sql_dataset(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







@require_sqlalchemy
@pytest.mark.parametrize(
    """features"""					,	[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]					,	)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Tuple:
       lowercase							:     Union[str, Any]       	=      tmp_path / """cache"""
       lowercase							:     int       	=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       lowercase							:     str       	=      features.copy() if features else default_expected_features
       lowercase							:     Optional[Any]       	=      (
           Features({feature: Value(SCREAMING_SNAKE_CASE__				) for feature, dtype in features.items()}				) if features is not None else None
       )
       lowercase							:     Optional[int]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	features=SCREAMING_SNAKE_CASE__					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       _check_sql_dataset(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       with contextlib.closing(sqlitea.connect(SCREAMING_SNAKE_CASE__				)				) as con:
              lowercase							:     Optional[int]       	=      con.cursor()
              cur.execute("""SELECT * FROM dataset"""				)
              for row in cur:
                     yield row







@require_sqlalchemy
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    int:
       lowercase							:     Any       	=      tmp_path / """cache"""
       lowercase							:     int       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	"""tmp.sql"""				)
       lowercase							:     List[str]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       SqlDatasetWriter(SCREAMING_SNAKE_CASE__					,	"""dataset"""					,	"""sqlite:///""" + output_sqlite_path					,	num_proc=1				).write()

       lowercase							:     List[str]       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Tuple       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)

       for rowa, rowa in zip(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
              assert rowa == rowa







@require_sqlalchemy
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Dict:
       lowercase							:     Dict       	=      tmp_path / """cache"""
       lowercase							:     List[str]       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	"""tmp.sql"""				)
       lowercase							:     List[str]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       SqlDatasetWriter(SCREAMING_SNAKE_CASE__					,	"""dataset"""					,	"""sqlite:///""" + output_sqlite_path					,	num_proc=2				).write()

       lowercase							:     Optional[int]       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[Any]       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)

       for rowa, rowa in zip(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
              assert rowa == rowa







@require_sqlalchemy
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     str       	=      tmp_path / """cache"""
       lowercase							:     Optional[int]       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	"""tmp.sql"""				)
       lowercase							:     Optional[Any]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       with pytest.raises(SCREAMING_SNAKE_CASE__				):
              SqlDatasetWriter(SCREAMING_SNAKE_CASE__					,	"""dataset"""					,	"""sqlite:///""" + output_sqlite_path					,	num_proc=0				).write()
