



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				(  lowerCamelCase__ ,		unittest.TestCase ):
				__lowerCamelCase				=       BartTokenizer
				__lowerCamelCase				=       BartTokenizerFast
				__lowerCamelCase				=       True
				__lowerCamelCase				=       filter_roberta_detectors




				def 							snake_case     (							self	):



									"""simple docstring"""





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

									_lowerCAmelCase      			=					os.path.join(self.tmpdirname		,       VOCAB_FILES_NAMES["""vocab_file"""]	)
									_lowerCAmelCase      			=					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 							snake_case     (							self		,       **_snake_case	):



									"""simple docstring"""





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




				def 							snake_case     (							self		,       **_snake_case	):



									"""simple docstring"""





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




				def 							snake_case     (							self		,       _snake_case	):



									"""simple docstring"""





									return "lower newer", "lower newer"




				@cached_property
				def 							snake_case     (							self	):



									"""simple docstring"""





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




				@cached_property
				def 							snake_case     (							self	):



									"""simple docstring"""





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




				@require_torch
				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									_lowerCAmelCase      			=					[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_lowerCAmelCase      			=					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	)
														_lowerCAmelCase      			=					batch.input_ids.tolist()[0]
														self.assertListEqual(_snake_case		,       _snake_case	)
														# Test that special tokens are reset




				@require_torch
				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_lowerCAmelCase      			=					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 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					[
									    """Summary of the text.""",
									    """Another summary.""",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_lowerCAmelCase      			=					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 							snake_case     (							self	):



									"""simple docstring"""





									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_lowerCAmelCase      			=					tokenizer(
														    ["""I am a small frog""" * 1024, """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, 1024)	)




				@require_torch
				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					["""A long paragraph for summarization."""]
									_lowerCAmelCase      			=					[
									    """Summary of the text.""",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_lowerCAmelCase      			=					tokenizer(_snake_case		,       return_tensors="""pt"""	)
														_lowerCAmelCase      			=					tokenizer(text_target=_snake_case		,       return_tensors="""pt"""	)
														_lowerCAmelCase      			=					inputs["""input_ids"""]
														_lowerCAmelCase      			=					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 							snake_case     (							self	):



									"""simple docstring"""





									pass




				def 							snake_case     (							self	):



									"""simple docstring"""





									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(_snake_case		,       **_snake_case	)
																			_lowerCAmelCase      			=					self.tokenizer_class.from_pretrained(_snake_case		,       **_snake_case	)
																			_lowerCAmelCase      			=					"""A, <mask> AllenNLP sentence."""
																			_lowerCAmelCase      			=					tokenizer_r.encode_plus(_snake_case		,       add_special_tokens=_snake_case		,       return_token_type_ids=_snake_case	)
																			_lowerCAmelCase      			=					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"""]	)		,       )

																			_lowerCAmelCase      			=					tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]	)
																			_lowerCAmelCase      			=					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, 50264, 3823, 487, 21992, 3645, 4, 2]	)
																			self.assertSequenceEqual(tokens_r["""input_ids"""]		,       [0, 250, 6, 50264, 3823, 487, 21992, 3645, 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_tf_available, is_torch_available


A__			   = {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__			   = [
	    """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """HubertForCTC""",
	    """HubertForSequenceClassification""",
	    """HubertModel""",
	    """HubertPreTrainedModel""",
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__			   = [
	    """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFHubertForCTC""",
	    """TFHubertModel""",
	    """TFHubertPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_hubert import (
		    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    HubertForCTC,
		    HubertForSequenceClassification,
		    HubertModel,
		    HubertPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_hubert import (
		    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFHubertForCTC,
		    TFHubertModel,
		    TFHubertPreTrainedModel,
		)


else:
	import sys

	A__			   = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



