
'''simple docstring'''






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 							_snake_case     (  lowerCAmelCase_						,    unittest.TestCase ):
	"""simple docstring"""





	_UpperCamelCase							:  str								=		BartTokenizer
	_UpperCamelCase							:  List[Any]								=		BartTokenizerFast
	_UpperCamelCase							:  List[str]								=		True
	_UpperCamelCase							:  str								=		filter_roberta_detectors




	def 	__A      (						self				: List[str]      ):
			super().setUp()
			lowerCAmelCase_   :       Optional[Any]    							=[
			    '''l''',
			    '''o''',
			    '''w''',
			    '''e''',
			    '''r''',
			    '''s''',
			    '''t''',
			    '''i''',
			    '''d''',
			    '''n''',
			    '''\u0120''',
			    '''\u0120l''',
			    '''\u0120n''',
			    '''\u0120lo''',
			    '''\u0120low''',
			    '''er''',
			    '''\u0120lowest''',
			    '''\u0120newer''',
			    '''\u0120wider''',
			    '''<unk>''',
			]
			lowerCAmelCase_   :       int    							=dict(zip(UpperCamelCase_					,						range(len(UpperCamelCase_      )      )      )      )
			lowerCAmelCase_   :       Union[str, Any]    							=['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
			lowerCAmelCase_   :       List[Any]    							={'''unk_token''': '''<unk>'''}

			lowerCAmelCase_   :       Optional[Any]    							=os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES['''vocab_file''']      )
			lowerCAmelCase_   :       Tuple    							=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 	__A      (						self				: Tuple					,						**UpperCamelCase_				: str      ):
			kwargs.update(self.special_tokens_map      )
			return self.tokenizer_class.from_pretrained(self.tmpdirname					,						**UpperCamelCase_      )




	def 	__A      (						self				: Optional[Any]					,						**UpperCamelCase_				: Dict      ):
			kwargs.update(self.special_tokens_map      )
			return self.rust_tokenizer_class.from_pretrained(self.tmpdirname					,						**UpperCamelCase_      )




	def 	__A      (						self				: Optional[Any]					,						UpperCamelCase_				: Dict      ):
			return "lower newer", "lower newer"




	@cached_property
	def 	__A      (						self				: List[Any]      ):
			return BartTokenizer.from_pretrained('''facebook/bart-large'''      )




	@cached_property
	def 	__A      (						self				: List[Any]      ):
			return BartTokenizerFast.from_pretrained('''facebook/bart-large'''      )




	@require_torch
	def 	__A      (						self				: Optional[int]      ):
			lowerCAmelCase_   :       Any    							=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
			lowerCAmelCase_   :       Optional[int]    							=[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					lowerCAmelCase_   :       Tuple    							=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      )
					lowerCAmelCase_   :       Optional[int]    							=batch.input_ids.tolist()[0]
					self.assertListEqual(UpperCamelCase_					,						UpperCamelCase_      )
					# Test that special tokens are reset




	@require_torch
	def 	__A      (						self				: Tuple      ):
			lowerCAmelCase_   :       Optional[int]    							=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					lowerCAmelCase_   :       str    							=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 	__A      (						self				: Union[str, Any]      ):
			lowerCAmelCase_   :       List[str]    							=[
			    '''Summary of the text.''',
			    '''Another summary.''',
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					lowerCAmelCase_   :       Any    							=tokenizer(text_target=UpperCamelCase_					,						max_length=32					,						padding='''max_length'''					,						return_tensors='''pt'''      )
					self.assertEqual(32					,						targets['''input_ids'''].shape[1]      )




	@require_torch
	def 	__A      (						self				: List[Any]      ):
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					lowerCAmelCase_   :       Any    							=tokenizer(
					    ['''I am a small frog''' * 1024, '''I am a small frog''']					,						padding=UpperCamelCase_					,						truncation=UpperCamelCase_					,						return_tensors='''pt'''      )
					self.assertIsInstance(UpperCamelCase_					,						UpperCamelCase_      )
					self.assertEqual(batch.input_ids.shape					,						(2, 1024)      )




	@require_torch
	def 	__A      (						self				: Tuple      ):
			lowerCAmelCase_   :       Union[str, Any]    							=['''A long paragraph for summarization.''']
			lowerCAmelCase_   :       Any    							=[
			    '''Summary of the text.''',
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					lowerCAmelCase_   :       Optional[Any]    							=tokenizer(UpperCamelCase_					,						return_tensors='''pt'''      )
					lowerCAmelCase_   :       int    							=tokenizer(text_target=UpperCamelCase_					,						return_tensors='''pt'''      )
					lowerCAmelCase_   :       Any    							=inputs['''input_ids''']
					lowerCAmelCase_   :       Optional[Any]    							=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 	__A      (						self				: List[Any]      ):
			pass





	def 	__A      (						self				: Optional[Any]      ):
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
							lowerCAmelCase_   :       Tuple    							=self.rust_tokenizer_class.from_pretrained(UpperCamelCase_					,						**UpperCamelCase_      )
							lowerCAmelCase_   :       Dict    							=self.tokenizer_class.from_pretrained(UpperCamelCase_					,						**UpperCamelCase_      )
							lowerCAmelCase_   :       List[str]    							='''A, <mask> AllenNLP sentence.'''
							lowerCAmelCase_   :       Union[str, Any]    							=tokenizer_r.encode_plus(UpperCamelCase_					,						add_special_tokens=UpperCamelCase_					,						return_token_type_ids=UpperCamelCase_      )
							lowerCAmelCase_   :       str    							=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''']      )					,						)

							lowerCAmelCase_   :       Union[str, Any]    							=tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']      )
							lowerCAmelCase_   :       Tuple    							=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(
							    UpperCamelCase_					,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']      )
							self.assertSequenceEqual(
							    UpperCamelCase_					,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']      )








'''simple docstring'''






from typing import TYPE_CHECKING

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


__lowercase	=    {
    '''configuration_xlm_prophetnet''': ['''XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMProphetNetConfig'''],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    ['''XLMProphetNetTokenizer''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XLMProphetNetDecoder''',
							    '''XLMProphetNetEncoder''',
							    '''XLMProphetNetForCausalLM''',
							    '''XLMProphetNetForConditionalGeneration''',
							    '''XLMProphetNetModel''',
							    '''XLMProphetNetPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xlm_prophetnet import (
														    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XLMProphetNetDecoder,
														    XLMProphetNetEncoder,
														    XLMProphetNetForCausalLM,
														    XLMProphetNetForConditionalGeneration,
														    XLMProphetNetModel,
														    XLMProphetNetPreTrainedModel,
														)

else:
							import sys

							__lowercase	=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






