


'''simple docstring'''



import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


__A		=[
    {'dataset': 'wikipedia', 'config_name': '20220301.de'},
    {'dataset': 'wikipedia', 'config_name': '20220301.en'},
    {'dataset': 'wikipedia', 'config_name': '20220301.fr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.frr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.it'},
    {'dataset': 'wikipedia', 'config_name': '20220301.simple'},
    {'dataset': 'snli', 'config_name': 'plain_text'},
    {'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
    {'dataset': 'wiki40b', 'config_name': 'en'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
    {'dataset': 'natural_questions', 'config_name': 'default'},
]







def       _UpperCamelCase (						UpperCamelCase__=True   ):

			if with_config:
						return [
						    {
						        "testcase_name": d["dataset"] + "/" + d["config_name"],
						        "dataset": d["dataset"],
						        "config_name": d["config_name"],
						    }
						    for d in DATASETS_ON_HF_GCP
						]
			else:
						return [
						    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
						]


@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__lowercase		)		)
class 						_snake_case   (       __lowercase		):
					lowerCAmelCase :int						=   None
					lowerCAmelCase :int						=   None




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase):
								with TemporaryDirectory() as tmp_dir:
											UpperCAmelCase__					:   Union[str, Any]       				=       dataset_module_factory(_a							,							cache_dir=_a)

											UpperCAmelCase__					:   Tuple       				=       import_main_class(dataset_module.module_path							,							dataset=_a)

											UpperCAmelCase__					:   DatasetBuilder       				=       builder_cls(
											    cache_dir=_a							,							config_name=_a							,							hash=dataset_module.hash							,							)

											UpperCAmelCase__					:   Union[str, Any]       				=       '''/'''.join(
											    [
											        HF_GCP_BASE_URL,
											        builder_instance._relative_data_dir(with_hash=_a).replace(os.sep							,							"""/"""),
											        config.DATASET_INFO_FILENAME,
											    ])
											UpperCAmelCase__					:   Dict       				=       cached_path(_a							,							cache_dir=_a)
											self.assertTrue(os.path.exists(_a))







@pytest.mark.integration
def       _UpperCamelCase (						UpperCamelCase__   ):
			UpperCAmelCase__					:   Union[str, Any]       				=       tmp_path_factory.mktemp("""test_hf_gcp"""   ) / '''test_wikipedia_simple'''
			UpperCAmelCase__					:   str       				=       dataset_module_factory("""wikipedia"""	, cache_dir=__a   )
			UpperCAmelCase__					:   Dict       				=       import_main_class(dataset_module.module_path   )
			UpperCAmelCase__					:   DatasetBuilder       				=       builder_cls(
			    cache_dir=__a	, config_name="""20220301.frr"""	, hash=dataset_module.hash	, )
			# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
			UpperCAmelCase__					:   Union[str, Any]       				=       None
			builder_instance.download_and_prepare()
			UpperCAmelCase__					:   Optional[int]       				=       builder_instance.as_dataset()
			assert ds







@pytest.mark.integration
def       _UpperCamelCase (						UpperCamelCase__   ):
			UpperCAmelCase__					:   Optional[Any]       				=       dataset_module_factory("""wikipedia"""	, cache_dir=__a   )
			UpperCAmelCase__					:   Any       				=       import_main_class(dataset_module.module_path	, dataset=__a   )
			UpperCAmelCase__					:   DatasetBuilder       				=       builder_cls(
			    cache_dir=__a	, config_name="""20220301.frr"""	, hash=dataset_module.hash	, )
			UpperCAmelCase__					:   Dict       				=       builder_instance.as_streaming_dataset()
			assert ds
			assert isinstance(__a	, __a   )
			assert "train" in ds
			assert isinstance(ds["""train"""]	, __a   )
			assert next(iter(ds["""train"""]   )   )



'''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 						_snake_case   :
					lowerCAmelCase :str						=   PegasusConfig
					lowerCAmelCase :Optional[int]						=   {}
					lowerCAmelCase :Dict						=   '''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							,							):
								UpperCAmelCase__					:   Union[str, Any]       				=       parent
								UpperCAmelCase__					:   Any       				=       batch_size
								UpperCAmelCase__					:   Union[str, Any]       				=       seq_length
								UpperCAmelCase__					:   str       				=       is_training
								UpperCAmelCase__					:   Dict       				=       use_labels
								UpperCAmelCase__					:   Optional[int]       				=       vocab_size
								UpperCAmelCase__					:   int       				=       hidden_size
								UpperCAmelCase__					:   Union[str, Any]       				=       num_hidden_layers
								UpperCAmelCase__					:   str       				=       num_attention_heads
								UpperCAmelCase__					:   Any       				=       intermediate_size

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




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[Any]       				=       ids_tensor([self.batch_size, self.seq_length - 1]							,							self.vocab_size)
								UpperCAmelCase__					:   str       				=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)							,							1)
								UpperCAmelCase__					:   Union[str, Any]       				=       tf.concat([input_ids, eos_tensor]							,							axis=1)

								UpperCAmelCase__					:   int       				=       ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size)

								UpperCAmelCase__					:   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							,							)
								UpperCAmelCase__					:   Union[str, Any]       				=       prepare_pegasus_inputs_dict(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)
								return config, inputs_dict




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   Optional[int]       				=       TFPegasusModel(config=_lowerCamelCase).get_decoder()
								UpperCAmelCase__					:   Optional[int]       				=       inputs_dict["""input_ids"""]

								UpperCAmelCase__					:   Any       				=       input_ids[:1, :]
								UpperCAmelCase__					:   List[str]       				=       inputs_dict["""attention_mask"""][:1, :]
								UpperCAmelCase__					:   int       				=       inputs_dict["""head_mask"""]
								UpperCAmelCase__					:   int       				=       1

								# first forward pass
								UpperCAmelCase__					:   int       				=       model(_lowerCamelCase							,							attention_mask=_lowerCamelCase							,							head_mask=_lowerCamelCase							,							use_cache=_lowerCamelCase)

								UpperCAmelCase__				,    UpperCAmelCase__					:   List[str]       				=       outputs.to_tuple()

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

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

								UpperCAmelCase__					:   Union[str, Any]       				=       model(_lowerCamelCase							,							attention_mask=_lowerCamelCase)[0]
								UpperCAmelCase__					:   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
								UpperCAmelCase__					:   Optional[Any]       				=       int(ids_tensor((1,)							,							output_from_past.shape[-1]))
								UpperCAmelCase__					:   int       				=       output_from_no_past[:, -3:, random_slice_idx]
								UpperCAmelCase__					:   List[str]       				=       output_from_past[:, :, random_slice_idx]

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







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




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       TFPegasusModelTester(self)
								UpperCAmelCase__					:   Dict       				=       ConfigTester(self							,							config_class=_lowerCamelCase)




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




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Dict       				=       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 						_snake_case   (       unittest.TestCase		):
					lowerCAmelCase :Dict						=   [
					    ''' 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!" ''',
					]
					lowerCAmelCase :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
					lowerCAmelCase :Tuple						=   '''google/pegasus-xsum'''




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




					@cached_property
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[Any]       				=       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
								return model




					def 			snake_case__  ( self							,							**_lowerCamelCase):
								UpperCAmelCase__					:   Dict       				=       self.translate_src_text(**_lowerCamelCase)
								assert self.expected_text == generated_words




					def 			snake_case__  ( self							,							**_lowerCamelCase):
								UpperCAmelCase__					:   List[Any]       				=       self.tokenizer(self.src_text							,							**_lowerCamelCase							,							padding=_lowerCamelCase							,							return_tensors="""tf""")
								UpperCAmelCase__					:   Dict       				=       self.model.generate(
								    model_inputs.input_ids							,							attention_mask=model_inputs.attention_mask							,							num_beams=2							,							use_cache=_lowerCamelCase							,							)
								UpperCAmelCase__					:   Optional[Any]       				=       self.tokenizer.batch_decode(generated_ids.numpy()							,							skip_special_tokens=_lowerCamelCase)
								return generated_words




					@slow
					def 			snake_case__  ( self):
								self._assert_generated_batch_equal_expected()