



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case     :    Optional[int]						=    {
    'configuration_retribert': ['RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RetriBertConfig'],
    'tokenization_retribert': ['RetriBertTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case     :    int						=    ['RetriBertTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case     :    int						=    [
		    'RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RetriBertModel',
		    'RetriBertPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
		from .tokenization_retribert import RetriBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_retribert_fast import RetriBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_retribert import (
				    RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RetriBertModel,
				    RetriBertPreTrainedModel,
				)

else:
		import sys

		snake_case     :    Optional[int]						=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class 				lowerCAmelCase__	(						UpperCamelCase       ,				unittest.TestCase    ):
					__A  :     Any     =    BartTokenizer
					__A  :     str     =    BartTokenizerFast
					__A  :     Optional[int]     =    True
					__A  :     Optional[int]     =    filter_roberta_detectors







					def 			_lowercase      (							self   :  Dict):
											super().setUp()
											A__						:				Union[str, Any]         =			[
											    "l",
											    "o",
											    "w",
											    "e",
											    "r",
											    "s",
											    "t",
											    "i",
											    "d",
											    "n",
											    "\u0120",
											    "\u0120l",
											    "\u0120n",
											    "\u0120lo",
											    "\u0120low",
											    "er",
											    "\u0120lowest",
											    "\u0120newer",
											    "\u0120wider",
											    "<unk>",
											]
											A__						:				List[str]         =			dict(zip(_A      ,		range(len(_A))))
											A__						:				List[str]         =			["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
											A__						:				str         =			{"unk_token": "<unk>"}

											A__						:				Dict         =			os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["vocab_file"])
											A__						:				Optional[Any]         =			os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["merges_file"])
											with open(self.vocab_file      ,		"w"      ,		encoding="utf-8") as fp:
																	fp.write(json.dumps(_A) + "\n")
											with open(self.merges_file      ,		"w"      ,		encoding="utf-8") as fp:
																	fp.write("\n".join(_A))







					def 			_lowercase      (							self   :  Union[str, Any]      ,		**_A   :  str):
											kwargs.update(self.special_tokens_map)
											return self.tokenizer_class.from_pretrained(self.tmpdirname      ,		**_A)







					def 			_lowercase      (							self   :  Union[str, Any]      ,		**_A   :  List[Any]):
											kwargs.update(self.special_tokens_map)
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,		**_A)







					def 			_lowercase      (							self   :  Tuple      ,		_A   :  Union[str, Any]):
											return "lower newer", "lower newer"







					@cached_property
					def 			_lowercase      (							self   :  Optional[int]):
											return BartTokenizer.from_pretrained("facebook/bart-large")







					@cached_property
					def 			_lowercase      (							self   :  Optional[int]):
											return BartTokenizerFast.from_pretrained("facebook/bart-large")







					@require_torch
					def 			_lowercase      (							self   :  Dict):
											A__						:				Optional[int]         =			["A long paragraph for summarization.", "Another paragraph for summarization."]
											A__						:				List[Any]         =			[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				Tuple         =			tokenizer(_A      ,		max_length=len(_A)      ,		padding=_A      ,		return_tensors="pt")
																	self.assertIsInstance(_A      ,		_A)

																	self.assertEqual((2, 9)      ,		batch.input_ids.shape)
																	self.assertEqual((2, 9)      ,		batch.attention_mask.shape)
																	A__						:				Dict         =			batch.input_ids.tolist()[0]
																	self.assertListEqual(_A      ,		_A)
																	# Test that special tokens are reset







					@require_torch
					def 			_lowercase      (							self   :  List[Any]):
											A__						:				Optional[Any]         =			["A long paragraph for summarization.", "Another paragraph for summarization."]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				Tuple         =			tokenizer(_A      ,		padding=_A      ,		return_tensors="pt")
																	# check if input_ids are returned and no labels
																	self.assertIn("input_ids"      ,		_A)
																	self.assertIn("attention_mask"      ,		_A)
																	self.assertNotIn("labels"      ,		_A)
																	self.assertNotIn("decoder_attention_mask"      ,		_A)







					@require_torch
					def 			_lowercase      (							self   :  int):
											A__						:				Optional[Any]         =			[
											    "Summary of the text.",
											    "Another summary.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				int         =			tokenizer(text_target=_A      ,		max_length=32      ,		padding="max_length"      ,		return_tensors="pt")
																	self.assertEqual(32      ,		targets["input_ids"].shape[1])







					@require_torch
					def 			_lowercase      (							self   :  List[str]):
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				List[str]         =			tokenizer(
																	    ["I am a small frog" * 1024, "I am a small frog"]      ,		padding=_A      ,		truncation=_A      ,		return_tensors="pt")
																	self.assertIsInstance(_A      ,		_A)
																	self.assertEqual(batch.input_ids.shape      ,		(2, 1024))







					@require_torch
					def 			_lowercase      (							self   :  List[str]):
											A__						:				int         =			["A long paragraph for summarization."]
											A__						:				List[str]         =			[
											    "Summary of the text.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				Optional[Any]         =			tokenizer(_A      ,		return_tensors="pt")
																	A__						:				Union[str, Any]         =			tokenizer(text_target=_A      ,		return_tensors="pt")
																	A__						:				Tuple         =			inputs["input_ids"]
																	A__						:				str         =			targets["input_ids"]
																	self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
																	self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
																	self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
																	self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())







					def 			_lowercase      (							self   :  Optional[int]):
											pass







					def 			_lowercase      (							self   :  Any):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
																							A__						:				Union[str, Any]         =			self.rust_tokenizer_class.from_pretrained(_A      ,		**_A)
																							A__						:				int         =			self.tokenizer_class.from_pretrained(_A      ,		**_A)
																							A__						:				Union[str, Any]         =			"A, <mask> AllenNLP sentence."
																							A__						:				Dict         =			tokenizer_r.encode_plus(_A      ,		add_special_tokens=_A      ,		return_token_type_ids=_A)
																							A__						:				Optional[Any]         =			tokenizer_p.encode_plus(_A      ,		add_special_tokens=_A      ,		return_token_type_ids=_A)

																							# token_type_ids should put 0 everywhere
																							self.assertEqual(sum(tokens_r["token_type_ids"])      ,		sum(tokens_p["token_type_ids"]))

																							# attention_mask should put 1 everywhere, so sum over length should be 1
																							self.assertEqual(
																							    sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"])      ,		sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"])      ,		)

																							A__						:				Optional[int]         =			tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
																							A__						:				Dict         =			tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])

																							# Rust correctly handles the space before the mask while python doesnt
																							self.assertSequenceEqual(tokens_p["input_ids"]      ,		[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])
																							self.assertSequenceEqual(tokens_r["input_ids"]      ,		[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])

																							self.assertSequenceEqual(
																							    _A      ,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])
																							self.assertSequenceEqual(
																							    _A      ,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])