



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__			:




		'''simple docstring'''



		snake_case_	=PegasusConfig
		snake_case_	={}
		snake_case_	='''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]:

						"""simple docstring"""






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

						lowerCAmelCase__     :				Union[str, Any]												=     hidden_dropout_prob
						lowerCAmelCase__     :				Optional[int]												=     attention_probs_dropout_prob
						lowerCAmelCase__     :				List[Any]												=     max_position_embeddings
						lowerCAmelCase__     :				List[Any]												=     eos_token_id
						lowerCAmelCase__     :				List[Any]												=     pad_token_id
						lowerCAmelCase__     :				Tuple												=     bos_token_id



		def 		lowerCAmelCase__      (self )     ->			List[Any]:

						"""simple docstring"""






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

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

						lowerCAmelCase__     :				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		,)
						lowerCAmelCase__     :				Dict												=     prepare_pegasus_inputs_dict(lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__ )
						return config, inputs_dict



		def 		lowerCAmelCase__      (self		,__lowerCamelCase		,__lowerCamelCase )     ->			Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__     :				Optional[int]												=     TFPegasusModel(config=lowerCAmelCase__ ).get_decoder()
						lowerCAmelCase__     :				Dict												=     inputs_dict['''input_ids''']

						lowerCAmelCase__     :				Optional[Any]												=     input_ids[:1, :]
						lowerCAmelCase__     :				Union[str, Any]												=     inputs_dict['''attention_mask'''][:1, :]
						lowerCAmelCase__     :				List[Any]												=     inputs_dict['''head_mask''']
						lowerCAmelCase__     :				Tuple												=     1

						# first forward pass
						lowerCAmelCase__     :				str												=     model(lowerCAmelCase__		,attention_mask=lowerCAmelCase__		,head_mask=lowerCAmelCase__		,use_cache=lowerCAmelCase__ )

						lowerCAmelCase__						,		lowerCAmelCase__     :				Tuple												=     outputs.to_tuple()

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

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

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

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






def 	lowerCAmelCase__		(    lowerCamelCase_		:		Any       ,lowerCamelCase_		:		Union[str, Any]       ,lowerCamelCase_		:		List[str]       ,lowerCamelCase_		:		List[Any]=None       ,lowerCamelCase_		:		Optional[int]=None       ,lowerCamelCase_		:		Dict=None       ,lowerCamelCase_		:		List[str]=None       ,lowerCamelCase_		:		List[Any]=None       ,):





				'''simple docstring'''

				if attention_mask is None:
								lowerCAmelCase__     :				Optional[Any]												=     tf.cast(tf.math.not_equal(lowerCamelCase_       ,config.pad_token_id)       ,tf.inta)
				if decoder_attention_mask is None:
								lowerCAmelCase__     :				List[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:
								lowerCAmelCase__     :				Any												=     tf.ones((config.encoder_layers, config.encoder_attention_heads))
				if decoder_head_mask is None:
								lowerCAmelCase__     :				str												=     tf.ones((config.decoder_layers, config.decoder_attention_heads))
				if cross_attn_head_mask is None:
								lowerCAmelCase__     :				int												=     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):




		'''simple docstring'''



		snake_case_	=(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
		snake_case_	=(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
		snake_case_	=(
		    {
		        '''conversational''': TFPegasusForConditionalGeneration,
		        '''feature-extraction''': TFPegasusModel,
		        '''summarization''': TFPegasusForConditionalGeneration,
		        '''text2text-generation''': TFPegasusForConditionalGeneration,
		        '''translation''': TFPegasusForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		snake_case_	=True
		snake_case_	=False
		snake_case_	=False



		def 		lowerCAmelCase__      (self )     ->			Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__     :				str												=     TFPegasusModelTester(self )
						lowerCAmelCase__     :				str												=     ConfigTester(self		,config_class=lowerCAmelCase__ )



		def 		lowerCAmelCase__      (self )     ->			str:

						"""simple docstring"""






						self.config_tester.run_common_tests()



		def 		lowerCAmelCase__      (self )     ->			Any:

						"""simple docstring"""






						lowerCAmelCase__     :				int												=     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):




		'''simple docstring'''



		snake_case_	=[
		    ''' 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!" ''',
		]
		snake_case_	=[
		    '''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
		snake_case_	='''google/pegasus-xsum'''



		@cached_property
		def 		lowerCAmelCase__      (self )     ->			str:

						"""simple docstring"""






						return AutoTokenizer.from_pretrained(self.model_name )



		@cached_property
		def 		lowerCAmelCase__      (self )     ->			int:

						"""simple docstring"""






						lowerCAmelCase__     :				List[Any]												=     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
						return model



		def 		lowerCAmelCase__      (self		,**__lowerCamelCase )     ->			Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__     :				Dict												=     self.translate_src_text(**lowerCAmelCase__ )
						assert self.expected_text == generated_words



		def 		lowerCAmelCase__      (self		,**__lowerCamelCase )     ->			Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__     :				str												=     self.tokenizer(self.src_text		,**lowerCAmelCase__		,padding=lowerCAmelCase__		,return_tensors='''tf''' )
						lowerCAmelCase__     :				List[Any]												=     self.model.generate(
						    model_inputs.input_ids		,attention_mask=model_inputs.attention_mask		,num_beams=2		,use_cache=lowerCAmelCase__		,)
						lowerCAmelCase__     :				Dict												=     self.tokenizer.batch_decode(generated_ids.numpy()		,skip_special_tokens=lowerCAmelCase__ )
						return generated_words



		@slow
		def 		lowerCAmelCase__      (self )     ->			int:

						"""simple docstring"""






						self._assert_generated_batch_equal_expected()


from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


__a  :     Optional[Any]       =  logging.get_logger(__name__)


__a  :     List[str]       =  TypeVar("""DatasetType""", Dataset, IterableDataset)





def 				UpperCAmelCase						(    lowercase							,			lowercase = None							,			lowercase = None							,			lowercase = None							,			lowercase = None							,			lowercase = "first_exhausted"							,			):







				"""simple docstring"""



				from .arrow_dataset import Dataset
				from .iterable_dataset import IterableDataset

				if not datasets:
								raise ValueError('''Unable to interleave an empty list of datasets.'''							)
				for i, dataset in enumerate(lowercase							):
								if not isinstance(lowercase							,			(Dataset, IterableDataset)							):
												if isinstance(lowercase							,			(DatasetDict, IterableDatasetDict)							):
																if not dataset:
																				raise ValueError(
																				    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
																				    '''is an empty dataset dictionary.'''							)
																raise ValueError(
																    F"Dataset at position {i} has at least one split: {list(lowercase							)}\n"
																    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowercase							)							)}']"							)
												raise ValueError(
												    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase							).__name__}."							)
								if i == 0:
												__lowercase			,     __lowercase 			=							(
												    (Dataset, IterableDataset) if isinstance(lowercase							,			lowercase							) else (IterableDataset, Dataset)
												)
								elif not isinstance(lowercase							,			lowercase							):
												raise ValueError(
												    F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."							)
				if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
								raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."							)
				if dataset_type is Dataset:
								return _interleave_map_style_datasets(
								    lowercase							,			lowercase							,			lowercase							,			info=lowercase							,			split=lowercase							,			stopping_strategy=lowercase							)
				else:
								return _interleave_iterable_datasets(
								    lowercase							,			lowercase							,			lowercase							,			info=lowercase							,			split=lowercase							,			stopping_strategy=lowercase							)





def 				UpperCAmelCase						(    lowercase							,			lowercase = None							,			lowercase = None							,			lowercase = 0							,			):







				"""simple docstring"""



				if not dsets:
								raise ValueError('''Unable to concatenate an empty list of datasets.'''							)
				for i, dataset in enumerate(lowercase							):
								if not isinstance(lowercase							,			(Dataset, IterableDataset)							):
												if isinstance(lowercase							,			(DatasetDict, IterableDatasetDict)							):
																if not dataset:
																				raise ValueError(
																				    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
																				    '''is an empty dataset dictionary.'''							)
																raise ValueError(
																    F"Dataset at position {i} has at least one split: {list(lowercase							)}\n"
																    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowercase							)							)}']"							)
												raise ValueError(
												    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase							).__name__}."							)
								if i == 0:
												__lowercase			,     __lowercase 			=							(
												    (Dataset, IterableDataset) if isinstance(lowercase							,			lowercase							) else (IterableDataset, Dataset)
												)
								elif not isinstance(lowercase							,			lowercase							):
												raise ValueError(
												    F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."							)
				if dataset_type is Dataset:
								return _concatenate_map_style_datasets(lowercase							,			info=lowercase							,			split=lowercase							,			axis=lowercase							)
				else:
								return _concatenate_iterable_datasets(lowercase							,			info=lowercase							,			split=lowercase							,			axis=lowercase							)