
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A   =     logging.get_logger(__name__)

_A   =     {
    "google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}





class 	_lowerCAmelCase					(	__a       ):
				_lowercase					='''pegasus'''
				_lowercase					=['''past_key_values''']
				_lowercase					={'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




				def __init__(					self				,				_UpperCamelCase=50_265				,				_UpperCamelCase=1_024				,				_UpperCamelCase=12				,				_UpperCamelCase=4_096				,				_UpperCamelCase=16				,				_UpperCamelCase=12				,				_UpperCamelCase=4_096				,				_UpperCamelCase=16				,				_UpperCamelCase=0.0				,				_UpperCamelCase=0.0				,				_UpperCamelCase=True				,				_UpperCamelCase=True				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=1_024				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.0				,				_UpperCamelCase=0.0				,				_UpperCamelCase=0.02				,				_UpperCamelCase=0				,				_UpperCamelCase=False				,				_UpperCamelCase=0				,				_UpperCamelCase=1				,				_UpperCamelCase=1				,				**_UpperCamelCase				,				)				->      Tuple:
							lowerCAmelCase_						=	vocab_size
							lowerCAmelCase_						=	max_position_embeddings
							lowerCAmelCase_						=	d_model
							lowerCAmelCase_						=	encoder_ffn_dim
							lowerCAmelCase_						=	encoder_layers
							lowerCAmelCase_						=	encoder_attention_heads
							lowerCAmelCase_						=	decoder_ffn_dim
							lowerCAmelCase_						=	decoder_layers
							lowerCAmelCase_						=	decoder_attention_heads
							lowerCAmelCase_						=	dropout
							lowerCAmelCase_						=	attention_dropout
							lowerCAmelCase_						=	activation_dropout
							lowerCAmelCase_						=	activation_function
							lowerCAmelCase_						=	init_std
							lowerCAmelCase_						=	encoder_layerdrop
							lowerCAmelCase_						=	decoder_layerdrop
							lowerCAmelCase_						=	use_cache
							lowerCAmelCase_						=	encoder_layers
							lowerCAmelCase_						=	scale_embedding  # scale factor will be sqrt(d_model) if True
							super().__init__(
							    pad_token_id=_UpperCamelCase				,				eos_token_id=_UpperCamelCase				,				is_encoder_decoder=_UpperCamelCase				,				decoder_start_token_id=_UpperCamelCase				,				forced_eos_token_id=_UpperCamelCase				,				**_UpperCamelCase				,				)




				@property
				def      __a						(					self     )				->      int:
							return self.encoder_attention_heads




				@property
				def      __a						(					self     )				->      int:
							return self.d_model





import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 	_lowerCAmelCase					(	__a					,      unittest.TestCase       ):
				_lowercase					=None
				_lowercase					=BloomTokenizerFast
				_lowercase					=BloomTokenizerFast
				_lowercase					=True
				_lowercase					=False
				_lowercase					='''tokenizer_file'''
				_lowercase					={'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}




				def      __a						(					self     )				->      Dict:
							super().setUp()
							lowerCAmelCase_						=	BloomTokenizerFast.from_pretrained("bigscience/tokenizer"     )
							tokenizer.save_pretrained(self.tmpdirname     )




				def      __a						(					self				,				**_UpperCamelCase     )				->      Tuple:
							kwargs.update(self.special_tokens_map     )
							return BloomTokenizerFast.from_pretrained(self.tmpdirname				,				**_UpperCamelCase     )




				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	self.get_rust_tokenizer()

							lowerCAmelCase_						=	["The quick brown fox</s>", "jumps over the lazy dog</s>"]
							lowerCAmelCase_						=	[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

							lowerCAmelCase_						=	tokenizer.batch_encode_plus(_UpperCamelCase     )["input_ids"]
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )

							lowerCAmelCase_						=	tokenizer.batch_decode(_UpperCamelCase     )
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase=6     )				->      Tuple:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
													lowerCAmelCase_						=	self.rust_tokenizer_class.from_pretrained(_UpperCamelCase				,				**_UpperCamelCase     )
													# tokenizer_r.pad_token = None # Hotfixing padding = None
													# Simple input
													lowerCAmelCase_						=	"This is a simple input"
													lowerCAmelCase_						=	["This is a simple input 1", "This is a simple input 2"]
													lowerCAmelCase_						=	("This is a simple input", "This is a pair")
													lowerCAmelCase_						=	[
													    ("This is a simple input 1", "This is a simple input 2"),
													    ("This is a simple pair 1", "This is a simple pair 2"),
													]

													# Simple input tests
													try:
																tokenizer_r.encode(_UpperCamelCase				,				max_length=_UpperCamelCase     )
																tokenizer_r.encode_plus(_UpperCamelCase				,				max_length=_UpperCamelCase     )

																tokenizer_r.batch_encode_plus(_UpperCamelCase				,				max_length=_UpperCamelCase     )
																tokenizer_r.encode(_UpperCamelCase				,				max_length=_UpperCamelCase     )
																tokenizer_r.batch_encode_plus(_UpperCamelCase				,				max_length=_UpperCamelCase     )
													except ValueError:
																self.fail("Bloom Tokenizer should be able to deal with padding"     )

													lowerCAmelCase_						=	None  # Hotfixing padding = None
													self.assertRaises(_UpperCamelCase				,				tokenizer_r.encode				,				_UpperCamelCase				,				max_length=_UpperCamelCase				,				padding="max_length"     )

													# Simple input
													self.assertRaises(_UpperCamelCase				,				tokenizer_r.encode_plus				,				_UpperCamelCase				,				max_length=_UpperCamelCase				,				padding="max_length"     )

													# Simple input
													self.assertRaises(
													    _UpperCamelCase				,				tokenizer_r.batch_encode_plus				,				_UpperCamelCase				,				max_length=_UpperCamelCase				,				padding="max_length"				,				)

													# Pair input
													self.assertRaises(_UpperCamelCase				,				tokenizer_r.encode				,				_UpperCamelCase				,				max_length=_UpperCamelCase				,				padding="max_length"     )

													# Pair input
													self.assertRaises(_UpperCamelCase				,				tokenizer_r.encode_plus				,				_UpperCamelCase				,				max_length=_UpperCamelCase				,				padding="max_length"     )

													# Pair input
													self.assertRaises(
													    _UpperCamelCase				,				tokenizer_r.batch_encode_plus				,				_UpperCamelCase				,				max_length=_UpperCamelCase				,				padding="max_length"				,				)




				def      __a						(					self     )				->      Any:
							lowerCAmelCase_						=	self.get_rust_tokenizer()
							lowerCAmelCase_						=	load_dataset("xnli"				,				"all_languages"				,				split="test"				,				streaming=_UpperCamelCase     )

							lowerCAmelCase_						=	next(iter(_UpperCamelCase     )     )["premise"]  # pick up one data
							lowerCAmelCase_						=	list(sample_data.values()     )

							lowerCAmelCase_						=	list(map(tokenizer.encode				,				_UpperCamelCase     )     )
							lowerCAmelCase_						=	[tokenizer.decode(_UpperCamelCase				,				clean_up_tokenization_spaces=_UpperCamelCase     ) for x in output_tokens]
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )




				def      __a						(					self     )				->      List[Any]:
							# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
							# any sequence length constraints. This test of the parent class will fail since it relies on the
							# maximum sequence length of the positoonal embeddings.
							self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map     )				,				1     )
							self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()     )[0]     )				,				1     )



