
'''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  :
	snake_case__   :Dict   =  PegasusConfig
	snake_case__   :str   =  {}
	snake_case__   :Tuple   =  'gelu'





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


				"""simple docstring"""

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

				lowerCAmelCase__										=				hidden_dropout_prob
				lowerCAmelCase__										=				attention_probs_dropout_prob
				lowerCAmelCase__										=				max_position_embeddings
				lowerCAmelCase__										=				eos_token_id
				lowerCAmelCase__										=				pad_token_id
				lowerCAmelCase__										=				bos_token_id





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

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

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

				lowerCAmelCase__										=				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__										=				prepare_pegasus_inputs_dict(__magic_name__   ,				__magic_name__   ,				__magic_name__				)
				return config, inputs_dict







	def 				__SCREAMING_SNAKE_CASE	(       self      :							str   ,				__magic_name__      :							Any   ,				__magic_name__      :							int				):


				"""simple docstring"""

				lowerCAmelCase__										=				TFPegasusModel(config=__magic_name__				).get_decoder()
				lowerCAmelCase__										=				inputs_dict["input_ids"]

				lowerCAmelCase__										=				input_ids[:1, :]
				lowerCAmelCase__										=				inputs_dict["attention_mask"][:1, :]
				lowerCAmelCase__										=				inputs_dict["head_mask"]
				lowerCAmelCase__										=				1

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

				lowerCAmelCase__						,lowerCAmelCase__										=				outputs.to_tuple()

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

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

				lowerCAmelCase__										=				model(__magic_name__   ,				attention_mask=__magic_name__				)[0]
				lowerCAmelCase__										=				model(__magic_name__   ,				attention_mask=__magic_name__   ,				past_key_values=__magic_name__				)[0]

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

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

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







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







			'''simple docstring'''



			if attention_mask is None:
						lowerCAmelCase__										=				tf.cast(tf.math.not_equal(UpperCamelCase_    ,						config.pad_token_id   )    ,						tf.inta   )
			if decoder_attention_mask is None:
						lowerCAmelCase__										=				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__										=				tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
			if decoder_head_mask is None:
						lowerCAmelCase__										=				tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
			if cross_attn_head_mask is None:
						lowerCAmelCase__										=				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  (						SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   unittest.TestCase     ):
	snake_case__   :Dict   =  (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
	snake_case__   :Optional[Any]   =  (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
	snake_case__   :List[str]   =  (
	    {
	        'conversational': TFPegasusForConditionalGeneration,
	        'feature-extraction': TFPegasusModel,
	        'summarization': TFPegasusForConditionalGeneration,
	        'text2text-generation': TFPegasusForConditionalGeneration,
	        'translation': TFPegasusForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	snake_case__   :Tuple   =  True
	snake_case__   :Any   =  False
	snake_case__   :Union[str, Any]   =  False





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				lowerCAmelCase__										=				TFPegasusModelTester(self				)
				lowerCAmelCase__										=				ConfigTester(self   ,				config_class=__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				self.config_tester.run_common_tests()







	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*__magic_name__				)






@require_sentencepiece
@require_tokenizers
@require_tf
class  A  (						unittest.TestCase     ):
	snake_case__   :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!" ',
	]
	snake_case__   :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
	snake_case__   :Optional[int]   =  'google/pegasus-xsum'





	@cached_property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							int				):


				"""simple docstring"""

				return AutoTokenizer.from_pretrained(self.model_name				)





	@cached_property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int   ,				**__magic_name__      :							Optional[int]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.translate_src_text(**__magic_name__				)
				assert self.expected_text == generated_words





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]   ,				**__magic_name__      :							int				):


				"""simple docstring"""

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







	@slow
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				self._assert_generated_batch_equal_expected()










import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def        __lowerCAmelCase		(	__lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Any						)	->    Dict:
					assert isinstance(__lowerCamelCase					, __lowerCamelCase						)
					assert dataset.num_rows == 4
					assert dataset.num_columns == 1
					assert dataset.column_names == ["text"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("""keep_in_memory"""					, [False, True]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     str					, __lowerCamelCase						:     Optional[Any]						)	->    List[Any]:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase					, keep_in_memory=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize(
    """features"""					, [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]					, )
def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     Any					, __lowerCamelCase						:     Tuple						)	->    List[Any]:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =features.copy() if features else default_expected_features
					__lowerCAmelCase						 =(
					    Features({feature: Value(__lowerCamelCase						) for feature, dtype in features.items()}						) if features is not None else None
					)
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, features=__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize("""split"""					, [None, NamedSplit("""train"""						), """train""", """test"""]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     Union[str, Any]					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Union[str, Any]						)	->    Any:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase					, split=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)
					assert dataset.split == split if split else "train"






@pytest.mark.parametrize("""path_type"""					, [str, list]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     int					, __lowerCamelCase						:     List[str]						)	->    Any:
					if issubclass(__lowerCamelCase					, __lowerCamelCase						):
										__lowerCAmelCase						 =text_path
					elif issubclass(__lowerCamelCase					, __lowerCamelCase						):
										__lowerCAmelCase						 =[text_path]
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     str=("train",)						)	->    Dict:
					assert isinstance(__lowerCamelCase					, __lowerCamelCase						)
					for split in splits:
										__lowerCAmelCase						 =dataset_dict[split]
										assert dataset.num_rows == 4
										assert dataset.num_columns == 1
										assert dataset.column_names == ["text"]
										for feature, expected_dtype in expected_features.items():
															assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("""keep_in_memory"""					, [False, True]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     List[Any]						)	->    Dict:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__lowerCAmelCase						 =TextDatasetReader({"""train""": text_path}					, cache_dir=__lowerCamelCase					, keep_in_memory=__lowerCamelCase						).read()
					_check_text_datasetdict(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize(
    """features"""					, [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]					, )
def        __lowerCAmelCase		(	__lowerCamelCase						:     Any					, __lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     Union[str, Any]						)	->    Tuple:
					__lowerCAmelCase						 =tmp_path / """cache"""
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =features.copy() if features else default_expected_features
					__lowerCAmelCase						 =(
					    Features({feature: Value(__lowerCamelCase						) for feature, dtype in features.items()}						) if features is not None else None
					)
					__lowerCAmelCase						 =TextDatasetReader({"""train""": text_path}					, features=__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_datasetdict(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize("""split"""					, [None, NamedSplit("""train"""						), """train""", """test"""]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     int					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Any						)	->    int:
					if split:
										__lowerCAmelCase						 ={split: text_path}
					else:
										__lowerCAmelCase						 ="""train"""
										__lowerCAmelCase						 ={"""train""": text_path, """test""": text_path}
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_datasetdict(__lowerCamelCase					, __lowerCamelCase					, splits=list(path.keys()						)						)
					assert all(dataset[split].split == split for split in path.keys()						)




