




'''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       :  Tuple            =      PegasusConfig
       __a       :  List[Any]            =      {}
       __a       :  Dict            =      """gelu"""
       def __init__(			self		:			Dict     ,       lowercase		:			int     ,       lowercase		:			Any=13     ,       lowercase		:			Any=7     ,       lowercase		:			Dict=True     ,       lowercase		:			int=False     ,       lowercase		:			str=99     ,       lowercase		:			Dict=32     ,       lowercase		:			List[Any]=2     ,       lowercase		:			Optional[int]=4     ,       lowercase		:			int=37     ,       lowercase		:			List[str]=0.1     ,       lowercase		:			List[Any]=0.1     ,       lowercase		:			List[str]=40     ,       lowercase		:			List[Any]=2     ,       lowercase		:			List[Any]=1     ,       lowercase		:			Union[str, Any]=0     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	parent
           UpperCAmelCase     =	batch_size
           UpperCAmelCase     =	seq_length
           UpperCAmelCase     =	is_training
           UpperCAmelCase     =	use_labels
           UpperCAmelCase     =	vocab_size
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	num_hidden_layers
           UpperCAmelCase     =	num_attention_heads
           UpperCAmelCase     =	intermediate_size

           UpperCAmelCase     =	hidden_dropout_prob
           UpperCAmelCase     =	attention_probs_dropout_prob
           UpperCAmelCase     =	max_position_embeddings
           UpperCAmelCase     =	eos_token_id
           UpperCAmelCase     =	pad_token_id
           UpperCAmelCase     =	bos_token_id
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length - 1]     ,       self.vocab_size       )
           UpperCAmelCase     =	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )     ,       1       )
           UpperCAmelCase     =	tf.concat([input_ids, eos_tensor]     ,       axis=1       )

           UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size       )

           UpperCAmelCase     =	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     =	prepare_pegasus_inputs_dict(lowercase     ,       lowercase     ,       lowercase       )
           return config, inputs_dict







       def 					A   (			self		:			int     ,       lowercase		:			int     ,       lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	TFPegasusModel(config=lowercase       ).get_decoder()
           UpperCAmelCase     =	inputs_dict['''input_ids''']

           UpperCAmelCase     =	input_ids[:1, :]
           UpperCAmelCase     =	inputs_dict['''attention_mask'''][:1, :]
           UpperCAmelCase     =	inputs_dict['''head_mask''']
           UpperCAmelCase     =	1

           # first forward pass
           UpperCAmelCase     =	model(lowercase     ,       attention_mask=lowercase     ,       head_mask=lowercase     ,       use_cache=lowercase       )

           UpperCAmelCase ,  UpperCAmelCase     =	outputs.to_tuple()

           # create hypothetical next token and extent to next_input_ids
           UpperCAmelCase     =	ids_tensor((self.batch_size, 3)     ,       config.vocab_size       )
           UpperCAmelCase     =	tf.cast(ids_tensor((self.batch_size, 3)     ,       2       )     ,       tf.inta       )

           # append to next input_ids and
           UpperCAmelCase     =	tf.concat([input_ids, next_tokens]     ,       axis=-1       )
           UpperCAmelCase     =	tf.concat([attention_mask, next_attn_mask]     ,       axis=-1       )

           UpperCAmelCase     =	model(lowercase     ,       attention_mask=lowercase       )[0]
           UpperCAmelCase     =	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
           UpperCAmelCase     =	int(ids_tensor((1,)     ,       output_from_past.shape[-1]       )       )
           UpperCAmelCase     =	output_from_no_past[:, -3:, random_slice_idx]
           UpperCAmelCase     =	output_from_past[:, :, random_slice_idx]

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






def 		snake_case_							(_a			:							Optional[Any]					,     _a			:							int					,     _a			:							Tuple					,     _a			:							Optional[int]=None					,     _a			:							Optional[int]=None					,     _a			:							int=None					,     _a			:							Dict=None					,     _a			:							int=None					,     ):
    if attention_mask is None:
        UpperCAmelCase     =	tf.cast(tf.math.not_equal(_a					,     config.pad_token_id  )					,     tf.inta  )
    if decoder_attention_mask is None:
        UpperCAmelCase     =	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     =	tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
    if decoder_head_mask is None:
        UpperCAmelCase     =	tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
    if cross_attn_head_mask is None:
        UpperCAmelCase     =	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				):
       __a       :  Any            =      (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
       __a       :  int            =      (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
       __a       :  Tuple            =      (
           {
               """conversational""": TFPegasusForConditionalGeneration,
               """feature-extraction""": TFPegasusModel,
               """summarization""": TFPegasusForConditionalGeneration,
               """text2text-generation""": TFPegasusForConditionalGeneration,
               """translation""": TFPegasusForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       __a       :  List[Any]            =      True
       __a       :  Dict            =      False
       __a       :  Optional[int]            =      False
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

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



           '''simple docstring'''

           self.config_tester.run_common_tests()







       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	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       :  str            =      [
           """ 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       :  Dict            =      """google/pegasus-xsum"""
       @cached_property
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

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



           '''simple docstring'''

           UpperCAmelCase     =	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
           return model
       def 					A   (			self		:			str     ,       **lowercase		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.translate_src_text(**lowercase       )
           assert self.expected_text == generated_words
       def 					A   (			self		:			List[str]     ,       **lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.tokenizer(self.src_text     ,       **lowercase     ,       padding=lowercase     ,       return_tensors='''tf'''       )
           UpperCAmelCase     =	self.model.generate(
               model_inputs.input_ids     ,       attention_mask=model_inputs.attention_mask     ,       num_beams=2     ,       use_cache=lowercase     ,       )
           UpperCAmelCase     =	self.tokenizer.batch_decode(generated_ids.numpy()     ,       skip_special_tokens=lowercase       )
           return generated_words







       @slow
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           self._assert_generated_batch_equal_expected()










'''simple docstring'''







from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

A									=logging.get_logger(__name__)


@add_end_docstrings(__a				)
class     _a						(			__a				):
       def __init__(			self		:			Optional[int]     ,       *lowercase		:			Any     ,       **lowercase		:			Optional[int]       ):



           '''simple docstring'''

           super().__init__(*lowercase     ,       **lowercase       )
           self.check_model_type(lowercase       )
       def 					A   (			self		:			List[str]     ,       lowercase		:			str=None     ,       lowercase		:			List[str]=None     ,       lowercase		:			List[str]=None     ,       **lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase ,  UpperCAmelCase     =	{}, {}
           if padding is not None:
               UpperCAmelCase     =	padding
           if truncation is not None:
               UpperCAmelCase     =	truncation
           if top_k is not None:
               UpperCAmelCase     =	top_k
           return preprocess_params, {}, postprocess_params
       def __call__(			self		:			Optional[Any]     ,       lowercase		:			Union["Image.Image", str]     ,       lowercase		:			str = None     ,       **lowercase		:			Optional[int]       ):



           '''simple docstring'''

           if isinstance(lowercase     ,       (Image.Image, str)       ) and isinstance(lowercase     ,       lowercase       ):
               UpperCAmelCase     =	{'''image''': image, '''question''': question}
           else:
               UpperCAmelCase     =	image
           UpperCAmelCase     =	super().__call__(lowercase     ,       **lowercase       )
           return results
       def 					A   (			self		:			List[Any]     ,       lowercase		:			List[str]     ,       lowercase		:			Any=False     ,       lowercase		:			Any=False       ):



           '''simple docstring'''

           UpperCAmelCase     =	load_image(inputs['''image''']       )
           UpperCAmelCase     =	self.tokenizer(
               inputs['''question''']     ,       return_tensors=self.framework     ,       padding=lowercase     ,       truncation=lowercase       )
           UpperCAmelCase     =	self.image_processor(images=lowercase     ,       return_tensors=self.framework       )
           model_inputs.update(lowercase       )
           return model_inputs
       def 					A   (			self		:			Dict     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model(**lowercase       )
           return model_outputs







       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			int     ,       lowercase		:			Optional[Any]=5       ):



           '''simple docstring'''

           if top_k > self.model.config.num_labels:
               UpperCAmelCase     =	self.model.config.num_labels

           if self.framework == "pt":
               UpperCAmelCase     =	model_outputs.logits.sigmoid()[0]
               UpperCAmelCase ,  UpperCAmelCase     =	probs.topk(lowercase       )
           else:
               raise ValueError(f"Unsupported framework: {self.framework}"       )

           UpperCAmelCase     =	scores.tolist()
           UpperCAmelCase     =	ids.tolist()
           return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowercase     ,       lowercase       )]




