


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_    :					str             ={
    """configuration_fsmt""": ["""FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FSMTConfig"""],
    """tokenization_fsmt""": ["""FSMTTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_    :					Optional[int]             =["""FSMTForConditionalGeneration""", """FSMTModel""", """PretrainedFSMTModel"""]


if TYPE_CHECKING:
	from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
	from .tokenization_fsmt import FSMTTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
	import sys

	A_    :					Union[str, Any]             =_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        lowercase_				( UpperCamelCase__			,unittest.TestCase):






							"""simple docstring"""







							snake_case_        =		BartTokenizer
							snake_case_        =		BartTokenizerFast
							snake_case_        =		True
							snake_case_        =		filter_roberta_detectors


							def 						lowercase__     (   self		):







													"""simple docstring"""


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

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


							def 						lowercase__     (   self			,    **_UpperCAmelCase		):







													"""simple docstring"""


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


							def 						lowercase__     (   self			,    **_UpperCAmelCase		):







													"""simple docstring"""


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


							def 						lowercase__     (   self			,    _UpperCAmelCase		):







													"""simple docstring"""


													return "lower newer", "lower newer"


							@cached_property
							def 						lowercase__     (   self		):







													"""simple docstring"""


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


							@cached_property
							def 						lowercase__     (   self		):







													"""simple docstring"""


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


							@require_torch
							def 						lowercase__     (   self		):







													"""simple docstring"""


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

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

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


							@require_torch
							def 						lowercase__     (   self		):







													"""simple docstring"""


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


							@require_torch
							def 						lowercase__     (   self		):







													"""simple docstring"""


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


							@require_torch
							def 						lowercase__     (   self		):







													"""simple docstring"""


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


							@require_torch
							def 						lowercase__     (   self		):







													"""simple docstring"""


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







													"""simple docstring"""


													pass






							def 						lowercase__     (   self		):







													"""simple docstring"""


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

																									# 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_					=		tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]		)
																									a_					=		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(
																									    _UpperCAmelCase			,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)
																									self.assertSequenceEqual(
																									    _UpperCAmelCase			,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)