


'''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  UpperCamelCase__:
				__magic_name__       :   Optional[Any]							= PegasusConfig
				__magic_name__       :   Union[str, Any]							= {}
				__magic_name__       :   List[Any]							= "gelu"




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

										"""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    :				Optional[Any]   )->  List[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(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )
										return config, inputs_dict





				def   a__(	self    :				Tuple   ,					lowerCAmelCase    :				int   ,					lowerCAmelCase    :				str   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			TFPegasusModel(config=lowerCAmelCase   ).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(lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					head_mask=lowerCAmelCase   ,					use_cache=lowerCAmelCase   )

										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(lowerCAmelCase   ,					attention_mask=lowerCAmelCase   )[0]
										UpperCAmelCase         =			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
										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(lowerCAmelCase   ,					lowerCAmelCase   ,					rtol=1E-3   )







def    lowerCamelCase__   ( A     :    List[Any]   ,     A     :    Union[str, Any]   ,     A     :    int   ,     A     :    str=None   ,     A     :    List[Any]=None   ,     A     :    List[Any]=None   ,     A     :    Union[str, Any]=None   ,     A     :    Optional[int]=None   ,     ):




						'''simple docstring'''






						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  UpperCamelCase__(     lowerCAmelCase		,       lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   List[Any]							= (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
				__magic_name__       :   List[str]							= (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
				__magic_name__       :   str							= (
				    {
				        "conversational": TFPegasusForConditionalGeneration,
				        "feature-extraction": TFPegasusModel,
				        "summarization": TFPegasusForConditionalGeneration,
				        "text2text-generation": TFPegasusForConditionalGeneration,
				        "translation": TFPegasusForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				__magic_name__       :   List[str]							= True
				__magic_name__       :   List[str]							= False
				__magic_name__       :   Optional[int]							= False




				def   a__(	self    :				Tuple   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			TFPegasusModelTester(self   )
										UpperCAmelCase         =			ConfigTester(self   ,					config_class=lowerCAmelCase   )




				def   a__(	self    :				str   )->  Optional[int]:

										"""simple docstring"""




										self.config_tester.run_common_tests()





				def   a__(	self    :				Optional[int]   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			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  UpperCamelCase__(     unittest.TestCase ):
				__magic_name__       :   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!\" ",
				]
				__magic_name__       :   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
				__magic_name__       :   Optional[int]							= "google/pegasus-xsum"




				@cached_property
				def   a__(	self    :				Optional[Any]   )->  Union[str, Any]:

										"""simple docstring"""




										return AutoTokenizer.from_pretrained(self.model_name   )




				@cached_property
				def   a__(	self    :				Any   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
										return model




				def   a__(	self    :				str   ,					**lowerCAmelCase    :				Any   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			self.translate_src_text(**lowerCAmelCase   )
										assert self.expected_text == generated_words




				def   a__(	self    :				List[Any]   ,					**lowerCAmelCase    :				Tuple   )->  Any:

										"""simple docstring"""




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





				@slow
				def   a__(	self    :				List[str]   )->  List[str]:

										"""simple docstring"""




										self._assert_generated_batch_equal_expected()









'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Tuple							= ["image_processor", "tokenizer"]
				__magic_name__       :   Any							= "ViTImageProcessor"
				__magic_name__       :   str							= ("CLIPTokenizer", "CLIPTokenizerFast")




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				List[Any]=None   ,					lowerCAmelCase    :				List[str]=None   ,					**lowerCAmelCase    :				Optional[int]   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.'''   ,					lowerCAmelCase   ,					)
																UpperCAmelCase         =			kwargs.pop('''feature_extractor'''   )

										UpperCAmelCase         =			image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
																raise ValueError('''You need to specify an `image_processor`.'''   )
										if tokenizer is None:
																raise ValueError('''You need to specify a `tokenizer`.'''   )

										super().__init__(lowerCAmelCase   ,					lowerCAmelCase   )




				def __call__(	self    :				str   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				str=None   ,					**lowerCAmelCase    :				List[str]   )->  List[Any]:

										"""simple docstring"""




										if text is None and visual_prompt is None and images is None:
																raise ValueError('''You have to specify either text, visual prompt or images.'''   )

										if text is not None and visual_prompt is not None:
																raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''   )

										if text is not None:
																UpperCAmelCase         =			self.tokenizer(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if images is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None and images is not None:
																UpperCAmelCase         =			{
																    '''pixel_values''': image_features.pixel_values,
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										elif text is not None and images is not None:
																UpperCAmelCase         =			image_features.pixel_values
																return encoding
										elif text is not None:
																return encoding
										elif visual_prompt is not None:
																UpperCAmelCase         =			{
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										else:
																return BatchEncoding(data=dict(**lowerCAmelCase   )   ,					tensor_type=lowerCAmelCase   )




				def   a__(	self    :				List[str]   ,					*lowerCAmelCase    :				str   ,					**lowerCAmelCase    :				Optional[int]   )->  Union[str, Any]:

										"""simple docstring"""




										return self.tokenizer.batch_decode(*lowerCAmelCase   ,					**lowerCAmelCase   )




				def   a__(	self    :				Dict   ,					*lowerCAmelCase    :				Tuple   ,					**lowerCAmelCase    :				List[str]   )->  Optional[Any]:

										"""simple docstring"""




										return self.tokenizer.decode(*lowerCAmelCase   ,					**lowerCAmelCase   )




				@property
				def   a__(	self    :				List[Any]   )->  Optional[Any]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,					lowerCAmelCase   ,					)
										return self.image_processor_class





				@property
				def   a__(	self    :				Any   )->  List[Any]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   ,					lowerCAmelCase   ,					)
										return self.image_processor





