




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					):
			snake_case_ :					Union[str, Any]						=	BartTokenizer
			snake_case_ :					Union[str, Any]						=	BartTokenizerFast
			snake_case_ :					int						=	True
			snake_case_ :					Union[str, Any]						=	filter_roberta_detectors







			def UpperCamelCase     (			self       :     List[Any] ):




									"""simple docstring"""


									super().setUp()
									_UpperCAmelCase						=	[
									    "l",
									    "o",
									    "w",
									    "e",
									    "r",
									    "s",
									    "t",
									    "i",
									    "d",
									    "n",
									    "\u0120",
									    "\u0120l",
									    "\u0120n",
									    "\u0120lo",
									    "\u0120low",
									    "er",
									    "\u0120lowest",
									    "\u0120newer",
									    "\u0120wider",
									    "<unk>",
									]
									_UpperCAmelCase						=	dict(zip(__snake_case       ,       range(len(__snake_case ) ) ) )
									_UpperCAmelCase						=	["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
									_UpperCAmelCase						=	{"unk_token": "<unk>"}

									_UpperCAmelCase						=	os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["vocab_file"] )
									_UpperCAmelCase						=	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(__snake_case ) + "\n" )
									with open(self.merges_file       ,       "w"       ,       encoding="utf-8" ) as fp:
															fp.write("\n".join(__snake_case ) )







			def UpperCamelCase     (			self       :     Dict       ,       **snake_case__       :     Union[str, Any] ):




									"""simple docstring"""


									kwargs.update(self.special_tokens_map )
									return self.tokenizer_class.from_pretrained(self.tmpdirname       ,       **__snake_case )







			def UpperCamelCase     (			self       :     Dict       ,       **snake_case__       :     Dict ):




									"""simple docstring"""


									kwargs.update(self.special_tokens_map )
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,       **__snake_case )







			def UpperCamelCase     (			self       :     List[Any]       ,       snake_case__       :     str ):




									"""simple docstring"""


									return "lower newer", "lower newer"







			@cached_property
			def UpperCamelCase     (			self       :     Union[str, Any] ):




									"""simple docstring"""


									return BartTokenizer.from_pretrained("facebook/bart-large" )







			@cached_property
			def UpperCamelCase     (			self       :     Tuple ):




									"""simple docstring"""


									return BartTokenizerFast.from_pretrained("facebook/bart-large" )







			@require_torch
			def UpperCamelCase     (			self       :     Optional[Any] ):




									"""simple docstring"""


									_UpperCAmelCase						=	["A long paragraph for summarization.", "Another paragraph for summarization."]
									_UpperCAmelCase						=	[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

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







			@require_torch
			def UpperCamelCase     (			self       :     int ):




									"""simple docstring"""


									_UpperCAmelCase						=	["A long paragraph for summarization.", "Another paragraph for summarization."]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_UpperCAmelCase						=	tokenizer(__snake_case       ,       padding=__snake_case       ,       return_tensors="pt" )
															# check if input_ids are returned and no labels
															self.assertIn("input_ids"       ,       __snake_case )
															self.assertIn("attention_mask"       ,       __snake_case )
															self.assertNotIn("labels"       ,       __snake_case )
															self.assertNotIn("decoder_attention_mask"       ,       __snake_case )







			@require_torch
			def UpperCamelCase     (			self       :     Any ):




									"""simple docstring"""


									_UpperCAmelCase						=	[
									    "Summary of the text.",
									    "Another summary.",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_UpperCAmelCase						=	tokenizer(text_target=__snake_case       ,       max_length=32       ,       padding="max_length"       ,       return_tensors="pt" )
															self.assertEqual(32       ,       targets["input_ids"].shape[1] )







			@require_torch
			def UpperCamelCase     (			self       :     Optional[Any] ):




									"""simple docstring"""


									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_UpperCAmelCase						=	tokenizer(
															    ["I am a small frog" * 1_024, "I am a small frog"]       ,       padding=__snake_case       ,       truncation=__snake_case       ,       return_tensors="pt" )
															self.assertIsInstance(__snake_case       ,       __snake_case )
															self.assertEqual(batch.input_ids.shape       ,       (2, 1_024) )







			@require_torch
			def UpperCamelCase     (			self       :     Dict ):




									"""simple docstring"""


									_UpperCAmelCase						=	["A long paragraph for summarization."]
									_UpperCAmelCase						=	[
									    "Summary of the text.",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_UpperCAmelCase						=	tokenizer(__snake_case       ,       return_tensors="pt" )
															_UpperCAmelCase						=	tokenizer(text_target=__snake_case       ,       return_tensors="pt" )
															_UpperCAmelCase						=	inputs["input_ids"]
															_UpperCAmelCase						=	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 UpperCamelCase     (			self       :     List[Any] ):




									"""simple docstring"""


									pass







			def UpperCamelCase     (			self       :     List[str] ):




									"""simple docstring"""


									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
																					_UpperCAmelCase						=	self.rust_tokenizer_class.from_pretrained(__snake_case       ,       **__snake_case )
																					_UpperCAmelCase						=	self.tokenizer_class.from_pretrained(__snake_case       ,       **__snake_case )
																					_UpperCAmelCase						=	"A, <mask> AllenNLP sentence."
																					_UpperCAmelCase						=	tokenizer_r.encode_plus(__snake_case       ,       add_special_tokens=__snake_case       ,       return_token_type_ids=__snake_case )
																					_UpperCAmelCase						=	tokenizer_p.encode_plus(__snake_case       ,       add_special_tokens=__snake_case       ,       return_token_type_ids=__snake_case )

																					# 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"] )       ,       )

																					_UpperCAmelCase						=	tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
																					_UpperCAmelCase						=	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, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
																					self.assertSequenceEqual(tokens_r["input_ids"]       ,       [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_      :					Dict				      =     {
    'configuration_big_bird': ['BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BigBirdConfig', 'BigBirdOnnxConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					str				      =     ['BigBirdTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Any				      =     ['BigBirdTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Optional[Any]				      =     [
	    'BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BigBirdForCausalLM',
	    'BigBirdForMaskedLM',
	    'BigBirdForMultipleChoice',
	    'BigBirdForPreTraining',
	    'BigBirdForQuestionAnswering',
	    'BigBirdForSequenceClassification',
	    'BigBirdForTokenClassification',
	    'BigBirdLayer',
	    'BigBirdModel',
	    'BigBirdPreTrainedModel',
	    'load_tf_weights_in_big_bird',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Any				      =     [
	    'FlaxBigBirdForCausalLM',
	    'FlaxBigBirdForMaskedLM',
	    'FlaxBigBirdForMultipleChoice',
	    'FlaxBigBirdForPreTraining',
	    'FlaxBigBirdForQuestionAnswering',
	    'FlaxBigBirdForSequenceClassification',
	    'FlaxBigBirdForTokenClassification',
	    'FlaxBigBirdModel',
	    'FlaxBigBirdPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_big_bird import BigBirdTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_big_bird_fast import BigBirdTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_big_bird import (
		    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BigBirdForCausalLM,
		    BigBirdForMaskedLM,
		    BigBirdForMultipleChoice,
		    BigBirdForPreTraining,
		    BigBirdForQuestionAnswering,
		    BigBirdForSequenceClassification,
		    BigBirdForTokenClassification,
		    BigBirdLayer,
		    BigBirdModel,
		    BigBirdPreTrainedModel,
		    load_tf_weights_in_big_bird,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_big_bird import (
		    FlaxBigBirdForCausalLM,
		    FlaxBigBirdForMaskedLM,
		    FlaxBigBirdForMultipleChoice,
		    FlaxBigBirdForPreTraining,
		    FlaxBigBirdForQuestionAnswering,
		    FlaxBigBirdForSequenceClassification,
		    FlaxBigBirdForTokenClassification,
		    FlaxBigBirdModel,
		    FlaxBigBirdPreTrainedModel,
		)

else:
	import sys

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