




"""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 __lowercase:




					'''simple docstring'''


					__a						:						List[str]     		= PegasusConfig
					__a						:						Any     		= {}
					__a						:						Dict     		= 'gelu'
					def __init__(    self						,	__a						,	__a=13						,	__a=7						,	__a=True						,	__a=False						,	__a=99						,	__a=32						,	__a=2						,	__a=4						,	__a=37						,	__a=0.1						,	__a=0.1						,	__a=40						,	__a=2						,	__a=1						,	__a=0						,	):
								__lowerCamelCase					:	Optional[Any]	       =				parent
								__lowerCamelCase					:	Any	       =				batch_size
								__lowerCamelCase					:	Optional[int]	       =				seq_length
								__lowerCamelCase					:	str	       =				is_training
								__lowerCamelCase					:	Optional[int]	       =				use_labels
								__lowerCamelCase					:	Optional[int]	       =				vocab_size
								__lowerCamelCase					:	str	       =				hidden_size
								__lowerCamelCase					:	Union[str, Any]	       =				num_hidden_layers
								__lowerCamelCase					:	Tuple	       =				num_attention_heads
								__lowerCamelCase					:	int	       =				intermediate_size

								__lowerCamelCase					:	Dict	       =				hidden_dropout_prob
								__lowerCamelCase					:	Optional[int]	       =				attention_probs_dropout_prob
								__lowerCamelCase					:	Optional[int]	       =				max_position_embeddings
								__lowerCamelCase					:	int	       =				eos_token_id
								__lowerCamelCase					:	Union[str, Any]	       =				pad_token_id
								__lowerCamelCase					:	Union[str, Any]	       =				bos_token_id
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	str	       =				ids_tensor([self.batch_size, self.seq_length - 1]						,	self.vocab_size     )
								__lowerCamelCase					:	Union[str, Any]	       =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size     )						,	1     )
								__lowerCamelCase					:	Union[str, Any]	       =				tf.concat([input_ids, eos_tensor]						,	axis=1     )

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

								__lowerCamelCase					:	Optional[int]	       =				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					:	Union[str, Any]	       =				prepare_pegasus_inputs_dict(__a						,	__a						,	__a     )
								return config, inputs_dict





					def     snake_case_				(    self						,	__a						,	__a     ):
								__lowerCamelCase					:	Union[str, Any]	       =				TFPegasusModel(config=__a     ).get_decoder()
								__lowerCamelCase					:	Optional[Any]	       =				inputs_dict['input_ids']

								__lowerCamelCase					:	str	       =				input_ids[:1, :]
								__lowerCamelCase					:	str	       =				inputs_dict['attention_mask'][:1, :]
								__lowerCamelCase					:	Optional[int]	       =				inputs_dict['head_mask']
								__lowerCamelCase					:	Optional[Any]	       =				1

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

								__lowerCamelCase       ,      __lowerCamelCase					:	Union[str, Any]	       =				outputs.to_tuple()

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

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

								__lowerCamelCase					:	str	       =				model(__a						,	attention_mask=__a     )[0]
								__lowerCamelCase					:	str	       =				model(__a						,	attention_mask=__a						,	past_key_values=__a     )[0]

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

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

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





def 						UpperCAmelCase				(			A__:    List[str]							,				A__:    int							,				A__:    int							,				A__:    Dict=None							,				A__:    str=None							,				A__:    List[str]=None							,				A__:    Optional[Any]=None							,				A__:    List[str]=None							,				)  ->     Tuple:
			if attention_mask is None:
						__lowerCamelCase					:	Dict	       =				tf.cast(tf.math.not_equal(A__							,				config.pad_token_id     )							,				tf.inta     )
			if decoder_attention_mask is None:
						__lowerCamelCase					:	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					:	str	       =				tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
			if decoder_head_mask is None:
						__lowerCamelCase					:	Dict	       =				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 __lowercase(      lowercase__	,	lowercase__	,	unittest.TestCase				):




					'''simple docstring'''


					__a						:						Optional[Any]     		= (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
					__a						:						int     		= (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
					__a						:						Dict     		= (
					    {
					        'conversational': TFPegasusForConditionalGeneration,
					        'feature-extraction': TFPegasusModel,
					        'summarization': TFPegasusForConditionalGeneration,
					        'text2text-generation': TFPegasusForConditionalGeneration,
					        'translation': TFPegasusForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					__a						:						Dict     		= True
					__a						:						Tuple     		= False
					__a						:						List[Any]     		= False
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				TFPegasusModelTester(self     )
								__lowerCamelCase					:	List[Any]	       =				ConfigTester(self						,	config_class=__a     )
					def     snake_case_				(    self     ):
								self.config_tester.run_common_tests()





					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*__a     )



@require_sentencepiece
@require_tokenizers
@require_tf
class __lowercase(      unittest.TestCase				):




					'''simple docstring'''


					__a						:						List[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!" ',
					]
					__a						:						Dict     		= [
					    '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						:						List[str]     		= 'google/pegasus-xsum'
					@cached_property
					def     snake_case_				(    self     ):
								return AutoTokenizer.from_pretrained(self.model_name     )
					@cached_property
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
								return model
					def     snake_case_				(    self						,	**__a     ):
								__lowerCamelCase					:	Optional[Any]	       =				self.translate_src_text(**__a     )
								assert self.expected_text == generated_words
					def     snake_case_				(    self						,	**__a     ):
								__lowerCamelCase					:	int	       =				self.tokenizer(self.src_text						,	**__a						,	padding=__a						,	return_tensors='tf'     )
								__lowerCamelCase					:	str	       =				self.model.generate(
								    model_inputs.input_ids						,	attention_mask=model_inputs.attention_mask						,	num_beams=2						,	use_cache=__a						,	)
								__lowerCamelCase					:	List[Any]	       =				self.tokenizer.batch_decode(generated_ids.numpy()						,	skip_special_tokens=__a     )
								return generated_words





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









"""simple docstring"""
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class __lowercase(      lowercase__				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a = None						,	__a = None						,	__a = None						,	__a = False						,	__a = False						,	__a = None						,	__a = None						,	**__a						,	):
								super().__init__(
								    __a						,	split=__a						,	features=__a						,	cache_dir=__a						,	keep_in_memory=__a						,	streaming=__a						,	num_proc=__a						,	**__a						,	)
								__lowerCamelCase					:	List[Any]	       =				field
								__lowerCamelCase					:	Dict	       =				path_or_paths if isinstance(__a						,	__a     ) else {self.split: path_or_paths}
								__lowerCamelCase					:	str	       =				Json(
								    cache_dir=__a						,	data_files=__a						,	features=__a						,	field=__a						,	**__a						,	)





					def     snake_case_				(    self     ):
								# Build iterable dataset
								if self.streaming:
											__lowerCamelCase					:	str	       =				self.builder.as_streaming_dataset(split=self.split     )
								# Build regular (map-style) dataset
								else:
											__lowerCamelCase					:	Any	       =				None
											__lowerCamelCase					:	Any	       =				None
											__lowerCamelCase					:	str	       =				None
											__lowerCamelCase					:	int	       =				None

											self.builder.download_and_prepare(
											    download_config=__a						,	download_mode=__a						,	verification_mode=__a						,	base_path=__a						,	num_proc=self.num_proc						,	)
											__lowerCamelCase					:	List[Any]	       =				self.builder.as_dataset(
											    split=self.split						,	verification_mode=__a						,	in_memory=self.keep_in_memory     )
								return dataset



class __lowercase:




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a						,	__a = None						,	__a = None						,	**__a						,	):
								if num_proc is not None and num_proc <= 0:
											raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''     )

								__lowerCamelCase					:	Optional[int]	       =				dataset
								__lowerCamelCase					:	Optional[int]	       =				path_or_buf
								__lowerCamelCase					:	List[str]	       =				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
								__lowerCamelCase					:	str	       =				num_proc
								__lowerCamelCase					:	Optional[Any]	       =				'utf-8'
								__lowerCamelCase					:	Optional[int]	       =				to_json_kwargs
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				self.to_json_kwargs.pop('path_or_buf'						,	__a     )
								__lowerCamelCase					:	Any	       =				self.to_json_kwargs.pop('orient'						,	'records'     )
								__lowerCamelCase					:	List[Any]	       =				self.to_json_kwargs.pop('lines'						,	True if orient == 'records' else False     )
								__lowerCamelCase					:	Tuple	       =				self.to_json_kwargs.pop('index'						,	False if orient in ['split', 'table'] else True     )
								__lowerCamelCase					:	int	       =				self.to_json_kwargs.pop('compression'						,	__a     )

								if compression not in [None, "infer", "gzip", "bz2", "xz"]:
											raise NotImplementedError(f'''`datasets` currently does not support {compression} compression'''     )

								if isinstance(self.path_or_buf						,	(str, bytes, os.PathLike)     ):
											with fsspec.open(self.path_or_buf						,	'wb'						,	compression=__a     ) as buffer:
														__lowerCamelCase					:	int	       =				self._write(file_obj=__a						,	orient=__a						,	lines=__a						,	index=__a						,	**self.to_json_kwargs     )
								else:
											if compression:
														raise NotImplementedError(
														    f'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
														    ' was passed. Please provide a local path instead.'     )
											__lowerCamelCase					:	Dict	       =				self._write(
											    file_obj=self.path_or_buf						,	orient=__a						,	lines=__a						,	index=__a						,	**self.to_json_kwargs     )
								return written
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Union[str, Any]	       =				args

								__lowerCamelCase					:	int	       =				query_table(
								    table=self.dataset.data						,	key=slice(__a						,	offset + self.batch_size     )						,	indices=self.dataset._indices						,	)
								__lowerCamelCase					:	List[str]	       =				batch.to_pandas().to_json(
								    path_or_buf=__a						,	orient=__a						,	lines=__a						,	index=__a						,	**__a     )
								if not json_str.endswith('\n'     ):
											json_str += "\n"
								return json_str.encode(self.encoding     )





					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	**__a						,	):
								__lowerCamelCase					:	Any	       =				0

								if self.num_proc is None or self.num_proc == 1:
											for offset in logging.tqdm(
											    range(0						,	len(self.dataset     )						,	self.batch_size     )						,	unit='ba'						,	disable=not logging.is_progress_bar_enabled()						,	desc='Creating json from Arrow format'						,	):
														__lowerCamelCase					:	List[str]	       =				self._batch_json((offset, orient, lines, index, to_json_kwargs)     )
														written += file_obj.write(__a     )
								else:
											__lowerCamelCase       ,      __lowerCamelCase					:	Optional[int]	       =				len(self.dataset     ), self.batch_size
											with multiprocessing.Pool(self.num_proc     ) as pool:
														for json_str in logging.tqdm(
														    pool.imap(
														        self._batch_json						,	[(offset, orient, lines, index, to_json_kwargs) for offset in range(0						,	__a						,	__a     )]						,	)						,	total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,	unit='ba'						,	disable=not logging.is_progress_bar_enabled()						,	desc='Creating json from Arrow format'						,	):
																	written += file_obj.write(__a     )

								return written



