



'''simple docstring'''






import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY

@is_pipeline_test
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       MODEL_FOR_MASKED_LM_MAPPING
	lowerCamelCase__			 =       TF_MODEL_FOR_MASKED_LM_MAPPING







	def      A	(						self			:     List[str]		)			->		List[Any]:
								super().tearDown()
								# clean-up as much as possible GPU memory occupied by PyTorch
								gc.collect()
								if is_torch_available():
															import torch

															torch.cuda.empty_cache()







	@require_tf
	def      A	(						self			:     List[str]		)			->		List[Any]:
								UpperCAmelCase :       Tuple 							=		pipeline(task='''fill-mask'''		,			model='''sshleifer/tiny-distilroberta-base'''		,			top_k=2		,			framework='''tf'''		)
								UpperCAmelCase :       str 							=		unmasker('''My name is <mask>'''		)
								self.assertEqual(
								    nested_simplify(__snake_case		,			decimals=6		)		,			[
								        {'''sequence''': '''My name is grouped''', '''score''': 2.1E-05, '''token''': 38015, '''token_str''': ''' grouped'''},
								        {'''sequence''': '''My name is accuser''', '''score''': 2.1E-05, '''token''': 25506, '''token_str''': ''' accuser'''},
								    ]		,			)

								UpperCAmelCase :       Optional[Any] 							=		unmasker('''The largest city in France is <mask>'''		)
								self.assertEqual(
								    nested_simplify(__snake_case		,			decimals=6		)		,			[
								        {
								            '''sequence''': '''The largest city in France is grouped''',
								            '''score''': 2.1E-05,
								            '''token''': 38015,
								            '''token_str''': ''' grouped''',
								        },
								        {
								            '''sequence''': '''The largest city in France is accuser''',
								            '''score''': 2.1E-05,
								            '''token''': 25506,
								            '''token_str''': ''' accuser''',
								        },
								    ]		,			)

								UpperCAmelCase :       Any 							=		unmasker('''My name is <mask>'''		,			targets=[''' Patrick''', ''' Clara''', ''' Teven''']		,			top_k=3		)
								self.assertEqual(
								    nested_simplify(__snake_case		,			decimals=6		)		,			[
								        {'''sequence''': '''My name is Clara''', '''score''': 2E-05, '''token''': 13606, '''token_str''': ''' Clara'''},
								        {'''sequence''': '''My name is Patrick''', '''score''': 2E-05, '''token''': 3499, '''token_str''': ''' Patrick'''},
								        {'''sequence''': '''My name is Te''', '''score''': 1.9E-05, '''token''': 2941, '''token_str''': ''' Te'''},
								    ]		,			)







	@require_torch
	def      A	(						self			:     Optional[Any]		)			->		List[Any]:
								UpperCAmelCase :       Union[str, Any] 							=		pipeline(task='''fill-mask'''		,			model='''sshleifer/tiny-distilroberta-base'''		,			top_k=2		,			framework='''pt'''		)

								UpperCAmelCase :       List[Any] 							=		unmasker('''My name is <mask>'''		)
								self.assertEqual(
								    nested_simplify(__snake_case		,			decimals=6		)		,			[
								        {'''sequence''': '''My name is Maul''', '''score''': 2.2E-05, '''token''': 35676, '''token_str''': ''' Maul'''},
								        {'''sequence''': '''My name isELS''', '''score''': 2.2E-05, '''token''': 16416, '''token_str''': '''ELS'''},
								    ]		,			)

								UpperCAmelCase :       int 							=		unmasker('''The largest city in France is <mask>'''		)
								self.assertEqual(
								    nested_simplify(__snake_case		,			decimals=6		)		,			[
								        {
								            '''sequence''': '''The largest city in France is Maul''',
								            '''score''': 2.2E-05,
								            '''token''': 35676,
								            '''token_str''': ''' Maul''',
								        },
								        {'''sequence''': '''The largest city in France isELS''', '''score''': 2.2E-05, '''token''': 16416, '''token_str''': '''ELS'''},
								    ]		,			)

								UpperCAmelCase :       Tuple 							=		unmasker('''My name is <mask>'''		,			targets=[''' Patrick''', ''' Clara''', ''' Teven''']		,			top_k=3		)
								self.assertEqual(
								    nested_simplify(__snake_case		,			decimals=6		)		,			[
								        {'''sequence''': '''My name is Patrick''', '''score''': 2.1E-05, '''token''': 3499, '''token_str''': ''' Patrick'''},
								        {'''sequence''': '''My name is Te''', '''score''': 2E-05, '''token''': 2941, '''token_str''': ''' Te'''},
								        {'''sequence''': '''My name is Clara''', '''score''': 2E-05, '''token''': 13606, '''token_str''': ''' Clara'''},
								    ]		,			)

								UpperCAmelCase :       int 							=		unmasker('''My name is <mask> <mask>'''		,			top_k=2		)

								self.assertEqual(
								    nested_simplify(__snake_case		,			decimals=6		)		,			[
								        [
								            {
								                '''score''': 2.2E-05,
								                '''token''': 35676,
								                '''token_str''': ''' Maul''',
								                '''sequence''': '''<s>My name is Maul<mask></s>''',
								            },
								            {'''score''': 2.2E-05, '''token''': 16416, '''token_str''': '''ELS''', '''sequence''': '''<s>My name isELS<mask></s>'''},
								        ],
								        [
								            {
								                '''score''': 2.2E-05,
								                '''token''': 35676,
								                '''token_str''': ''' Maul''',
								                '''sequence''': '''<s>My name is<mask> Maul</s>''',
								            },
								            {'''score''': 2.2E-05, '''token''': 16416, '''token_str''': '''ELS''', '''sequence''': '''<s>My name is<mask>ELS</s>'''},
								        ],
								    ]		,			)







	@require_torch_gpu
	def      A	(						self			:     Optional[Any]		)			->		Optional[Any]:
								UpperCAmelCase :       List[Any] 							=		pipeline('''fill-mask'''		,			model='''hf-internal-testing/tiny-random-distilbert'''		,			device=0		,			framework='''pt'''		)

								# convert model to fp16
								pipe.model.half()

								UpperCAmelCase :       List[Any] 							=		pipe('''Paris is the [MASK] of France.'''		)
								# We actually don't care about the result, we just want to make sure
								# it works, meaning the float16 tensor got casted back to float32
								# for postprocessing.
								self.assertIsInstance(__snake_case		,			__snake_case		)







	@slow
	@require_torch
	def      A	(						self			:     Optional[Any]		)			->		int:
								UpperCAmelCase :       Tuple 							=		pipeline(task='''fill-mask'''		,			model='''distilroberta-base'''		,			top_k=2		,			framework='''pt'''		)
								self.run_large_test(__snake_case		)







	@slow
	@require_tf
	def      A	(						self			:     Dict		)			->		Optional[int]:
								UpperCAmelCase :       Tuple 							=		pipeline(task='''fill-mask'''		,			model='''distilroberta-base'''		,			top_k=2		,			framework='''tf'''		)
								self.run_large_test(__snake_case		)







	def      A	(						self			:     Dict		,			__snake_case			:     Any		)			->		str:
								UpperCAmelCase :       List[str] 							=		unmasker('''My name is <mask>'''		)
								self.assertEqual(
								    nested_simplify(__snake_case		)		,			[
								        {'''sequence''': '''My name is John''', '''score''': 0.0_08, '''token''': 610, '''token_str''': ''' John'''},
								        {'''sequence''': '''My name is Chris''', '''score''': 0.0_07, '''token''': 1573, '''token_str''': ''' Chris'''},
								    ]		,			)
								UpperCAmelCase :       Optional[int] 							=		unmasker('''The largest city in France is <mask>'''		)
								self.assertEqual(
								    nested_simplify(__snake_case		)		,			[
								        {
								            '''sequence''': '''The largest city in France is Paris''',
								            '''score''': 0.2_51,
								            '''token''': 2201,
								            '''token_str''': ''' Paris''',
								        },
								        {
								            '''sequence''': '''The largest city in France is Lyon''',
								            '''score''': 0.2_14,
								            '''token''': 12790,
								            '''token_str''': ''' Lyon''',
								        },
								    ]		,			)

								UpperCAmelCase :       Tuple 							=		unmasker('''My name is <mask>'''		,			targets=[''' Patrick''', ''' Clara''', ''' Teven''']		,			top_k=3		)
								self.assertEqual(
								    nested_simplify(__snake_case		)		,			[
								        {'''sequence''': '''My name is Patrick''', '''score''': 0.0_05, '''token''': 3499, '''token_str''': ''' Patrick'''},
								        {'''sequence''': '''My name is Clara''', '''score''': 0.0_00, '''token''': 13606, '''token_str''': ''' Clara'''},
								        {'''sequence''': '''My name is Te''', '''score''': 0.0_00, '''token''': 2941, '''token_str''': ''' Te'''},
								    ]		,			)







	@require_torch
	def      A	(						self			:     Union[str, Any]		)			->		Dict:
								UpperCAmelCase :       List[str] 							=		pipeline(task='''fill-mask'''		,			model='''sshleifer/tiny-distilroberta-base'''		,			framework='''pt'''		)
								UpperCAmelCase :       Optional[Any] 							=		None
								UpperCAmelCase :       Union[str, Any] 							=		None
								self.run_pipeline_test(__snake_case		,			[]		)







	@require_tf
	def      A	(						self			:     Dict		)			->		List[Any]:
								UpperCAmelCase :       List[str] 							=		pipeline(task='''fill-mask'''		,			model='''sshleifer/tiny-distilroberta-base'''		,			framework='''tf'''		)
								UpperCAmelCase :       Dict 							=		None
								UpperCAmelCase :       Union[str, Any] 							=		None
								self.run_pipeline_test(__snake_case		,			[]		)







	def      A	(						self			:     Union[str, Any]		,			__snake_case			:     Optional[int]		,			__snake_case			:     int		,			__snake_case			:     Optional[int]		)			->		Union[str, Any]:
								if tokenizer is None or tokenizer.mask_token_id is None:
															self.skipTest('''The provided tokenizer has no mask token, (probably reformer or wav2vec2)'''		)

								UpperCAmelCase :       Union[str, Any] 							=		FillMaskPipeline(model=__snake_case		,			tokenizer=__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		[
								    F"""This is another {tokenizer.mask_token} test""",
								]
								return fill_masker, examples







	def      A	(						self			:     List[str]		,			__snake_case			:     Any		,			__snake_case			:     str		)			->		str:
								UpperCAmelCase :       int 							=		fill_masker.tokenizer
								UpperCAmelCase :       Tuple 							=		fill_masker.model

								UpperCAmelCase :       Optional[Any] 							=		fill_masker(
								    F"""This is a {tokenizer.mask_token}"""		,			)
								self.assertEqual(
								    __snake_case		,			[
								        {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								        {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								        {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								        {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								        {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								    ]		,			)

								UpperCAmelCase :       int 							=		fill_masker([F"""This is a {tokenizer.mask_token}"""]		)
								self.assertEqual(
								    __snake_case		,			[
								        {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								        {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								        {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								        {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								        {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								    ]		,			)

								UpperCAmelCase :       Union[str, Any] 							=		fill_masker([F"""This is a {tokenizer.mask_token}""", F"""Another {tokenizer.mask_token} great test."""]		)
								self.assertEqual(
								    __snake_case		,			[
								        [
								            {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								            {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								            {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								            {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								            {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								        ],
								        [
								            {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								            {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								            {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								            {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								            {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								        ],
								    ]		,			)

								with self.assertRaises(__snake_case		):
															fill_masker([None]		)
								# No mask_token is not supported
								with self.assertRaises(__snake_case		):
															fill_masker('''This is'''		)

								self.run_test_top_k(__snake_case		,			__snake_case		)
								self.run_test_targets(__snake_case		,			__snake_case		)
								self.run_test_top_k_targets(__snake_case		,			__snake_case		)
								self.fill_mask_with_duplicate_targets_and_top_k(__snake_case		,			__snake_case		)
								self.fill_mask_with_multiple_masks(__snake_case		,			__snake_case		)







	def      A	(						self			:     Any		,			__snake_case			:     Any		,			__snake_case			:     List[Any]		)			->		List[Any]:
								UpperCAmelCase :       Tuple 							=		tokenizer.get_vocab()
								UpperCAmelCase :       List[str] 							=		sorted(vocab.keys()		)[:2]
								# Pipeline argument
								UpperCAmelCase :       Any 							=		FillMaskPipeline(model=__snake_case		,			tokenizer=__snake_case		,			targets=__snake_case		)
								UpperCAmelCase :       Dict 							=		fill_masker(F"""This is a {tokenizer.mask_token}"""		)
								self.assertEqual(
								    __snake_case		,			[
								        {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								        {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								    ]		,			)
								UpperCAmelCase :       Any 							=		{vocab[el] for el in targets}
								self.assertEqual({el['''token'''] for el in outputs}		,			__snake_case		)
								UpperCAmelCase :       int 							=		[tokenizer.decode([x]		) for x in target_ids]
								self.assertEqual({el['''token_str'''] for el in outputs}		,			set(__snake_case		)		)

								# Call argument
								UpperCAmelCase :       List[Any] 							=		FillMaskPipeline(model=__snake_case		,			tokenizer=__snake_case		)
								UpperCAmelCase :       Union[str, Any] 							=		fill_masker(F"""This is a {tokenizer.mask_token}"""		,			targets=__snake_case		)
								self.assertEqual(
								    __snake_case		,			[
								        {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								        {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								    ]		,			)
								UpperCAmelCase :       Tuple 							=		{vocab[el] for el in targets}
								self.assertEqual({el['''token'''] for el in outputs}		,			__snake_case		)
								UpperCAmelCase :       Union[str, Any] 							=		[tokenizer.decode([x]		) for x in target_ids]
								self.assertEqual({el['''token_str'''] for el in outputs}		,			set(__snake_case		)		)

								# Score equivalence
								UpperCAmelCase :       int 							=		fill_masker(F"""This is a {tokenizer.mask_token}"""		,			targets=__snake_case		)
								UpperCAmelCase :       int 							=		[top_mask['''token_str'''] for top_mask in outputs]
								UpperCAmelCase :       Optional[int] 							=		[top_mask['''score'''] for top_mask in outputs]

								# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
								if set(__snake_case		) == set(__snake_case		):
															UpperCAmelCase :       List[str] 							=		fill_masker(F"""This is a {tokenizer.mask_token}"""		,			targets=__snake_case		)
															UpperCAmelCase :       str 							=		[top_mask['''score'''] for top_mask in unmasked_targets]
															self.assertEqual(nested_simplify(__snake_case		)		,			nested_simplify(__snake_case		)		)

								# Raises with invalid
								with self.assertRaises(__snake_case		):
															UpperCAmelCase :       Any 							=		fill_masker(F"""This is a {tokenizer.mask_token}"""		,			targets=[]		)
								# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
								if "" not in tokenizer.get_vocab():
															with self.assertRaises(__snake_case		):
																						UpperCAmelCase :       Union[str, Any] 							=		fill_masker(F"""This is a {tokenizer.mask_token}"""		,			targets=['''''']		)
															with self.assertRaises(__snake_case		):
																						UpperCAmelCase :       Any 							=		fill_masker(F"""This is a {tokenizer.mask_token}"""		,			targets=''''''		)







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     Any		,			__snake_case			:     Optional[int]		)			->		Optional[Any]:
								UpperCAmelCase :       List[Any] 							=		FillMaskPipeline(model=__snake_case		,			tokenizer=__snake_case		,			top_k=2		)
								UpperCAmelCase :       Optional[int] 							=		fill_masker(F"""This is a {tokenizer.mask_token}"""		)
								self.assertEqual(
								    __snake_case		,			[
								        {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								        {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								    ]		,			)

								UpperCAmelCase :       Union[str, Any] 							=		FillMaskPipeline(model=__snake_case		,			tokenizer=__snake_case		)
								UpperCAmelCase :       Tuple 							=		fill_masker(F"""This is a {tokenizer.mask_token}"""		,			top_k=2		)
								self.assertEqual(
								    __snake_case		,			[
								        {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								        {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								    ]		,			)
								self.assertEqual(nested_simplify(__snake_case		)		,			nested_simplify(__snake_case		)		)







	def      A	(						self			:     Dict		,			__snake_case			:     List[Any]		,			__snake_case			:     Tuple		)			->		int:
								UpperCAmelCase :       List[str] 							=		tokenizer.get_vocab()
								UpperCAmelCase :       Optional[int] 							=		FillMaskPipeline(model=__snake_case		,			tokenizer=__snake_case		)

								# top_k=2, ntargets=3
								UpperCAmelCase :       Union[str, Any] 							=		sorted(vocab.keys()		)[:3]
								UpperCAmelCase :       Any 							=		fill_masker(F"""This is a {tokenizer.mask_token}"""		,			top_k=2		,			targets=__snake_case		)

								# If we use the most probably targets, and filter differently, we should still
								# have the same results
								UpperCAmelCase :       Union[str, Any] 							=		[el['''token_str'''] for el in sorted(__snake_case		,			key=lambda __snake_case		: x["score"]		,			reverse=__snake_case		)]
								# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
								if set(__snake_case		).issubset(__snake_case		):
															UpperCAmelCase :       Tuple 							=		fill_masker(F"""This is a {tokenizer.mask_token}"""		,			top_k=3		,			targets=__snake_case		)
															# They should yield exactly the same result
															self.assertEqual(nested_simplify(__snake_case		)		,			nested_simplify(__snake_case		)		)







	def      A	(						self			:     Tuple		,			__snake_case			:     Dict		,			__snake_case			:     Dict		)			->		Union[str, Any]:
								UpperCAmelCase :       List[str] 							=		FillMaskPipeline(model=__snake_case		,			tokenizer=__snake_case		)
								UpperCAmelCase :       str 							=		tokenizer.get_vocab()
								# String duplicates + id duplicates
								UpperCAmelCase :       Optional[int] 							=		sorted(vocab.keys()		)[:3]
								UpperCAmelCase :       Union[str, Any] 							=		[targets[0], targets[1], targets[0], targets[2], targets[1]]
								UpperCAmelCase :       List[Any] 							=		fill_masker(F"""My name is {tokenizer.mask_token}"""		,			targets=__snake_case		,			top_k=10		)

								# The target list contains duplicates, so we can't output more
								# than them
								self.assertEqual(len(__snake_case		)		,			3		)







	def      A	(						self			:     Tuple		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Union[str, Any]		)			->		Optional[int]:
								UpperCAmelCase :       List[Any] 							=		FillMaskPipeline(model=__snake_case		,			tokenizer=__snake_case		)

								UpperCAmelCase :       Optional[Any] 							=		fill_masker(
								    F"""This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}"""		,			top_k=2		)
								self.assertEqual(
								    __snake_case		,			[
								        [
								            {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								            {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								        ],
								        [
								            {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								            {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								        ],
								        [
								            {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								            {'''sequence''': ANY(__snake_case		), '''score''': ANY(__snake_case		), '''token''': ANY(__snake_case		), '''token_str''': ANY(__snake_case		)},
								        ],
								    ]		,			)










'''simple docstring'''






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__:   Tuple								=			logging.get_logger(__name__)

UpperCamelCase__:   Optional[int]								=			{"vocab_file": "sentencepiece.bpe.model"}

UpperCamelCase__:   Optional[int]								=			{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

UpperCamelCase__:   Dict								=			{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

UpperCamelCase__:   Tuple								=			"▁"

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       VOCAB_FILES_NAMES
	lowerCamelCase__			 =       PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__			 =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCamelCase__			 =       ["""input_ids""", """attention_mask"""]







	def __init__(						self			:     List[Any]		,			__snake_case			:     List[Any]		,			__snake_case			:     Tuple="<s>"		,			__snake_case			:     List[Any]="</s>"		,			__snake_case			:     int="</s>"		,			__snake_case			:     Any="<s>"		,			__snake_case			:     Optional[int]="<unk>"		,			__snake_case			:     Union[str, Any]="<pad>"		,			__snake_case			:     Union[str, Any]="<mask>"		,			__snake_case			:     Optional[Dict[str, Any]] = None		,			**__snake_case			:     Dict		,			)			->		None:
								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase :       int 							=		AddedToken(__snake_case		,			lstrip=__snake_case		,			rstrip=__snake_case		) if isinstance(__snake_case		,			__snake_case		) else mask_token

								UpperCAmelCase :       Union[str, Any] 							=		{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=__snake_case		,			eos_token=__snake_case		,			unk_token=__snake_case		,			sep_token=__snake_case		,			cls_token=__snake_case		,			pad_token=__snake_case		,			mask_token=__snake_case		,			sp_model_kwargs=self.sp_model_kwargs		,			**__snake_case		,			)

								UpperCAmelCase :       Optional[int] 							=		vocab_file
								UpperCAmelCase :       int 							=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(str(__snake_case		)		)

								UpperCAmelCase :       int 							=		{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

								UpperCAmelCase :       Optional[Any] 							=		len(self.sp_model		) - 1
								UpperCAmelCase :       Optional[int] 							=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}







	def      A	(						self			:     Tuple		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		)			->		List[int]:

								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase :       Union[str, Any] 							=		[self.cls_token_id]
								UpperCAmelCase :       Optional[Any] 							=		[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep







	def      A	(						self			:     int		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		,			__snake_case			:     bool = False		)			->		List[int]:
								if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=__snake_case		,			token_ids_a=__snake_case		,			already_has_special_tokens=__snake_case		)

								if token_ids_a is None:
															return [1] + ([0] * len(__snake_case		)) + [1]
								return [1] + ([0] * len(__snake_case		)) + [1, 1] + ([0] * len(__snake_case		)) + [1]







	def      A	(						self			:     Optional[int]		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		)			->		List[int]:
								UpperCAmelCase :       Tuple 							=		[self.sep_token_id]
								UpperCAmelCase :       Tuple 							=		[self.cls_token_id]

								if token_ids_a is None:
															return len(cls + token_ids_a + sep		) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]







	@property
	def      A	(						self			:     Dict		)			->		Optional[int]:
								return len(self.sp_model		)







	def      A	(						self			:     List[str]		)			->		Dict:
								UpperCAmelCase :       Optional[Any] 							=		{self.convert_ids_to_tokens(__snake_case		): i for i in range(self.vocab_size		)}
								vocab.update(self.added_tokens_encoder		)
								return vocab







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     str		)			->		List[str]:
								return self.sp_model.encode(__snake_case		,			out_type=__snake_case		)







	def      A	(						self			:     int		,			__snake_case			:     int		)			->		int:
								if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
								UpperCAmelCase :       Optional[Any] 							=		self.sp_model.PieceToId(__snake_case		)

								return spm_id if spm_id else self.unk_token_id







	def      A	(						self			:     int		,			__snake_case			:     Any		)			->		List[Any]:
								if index in self.fairseq_ids_to_tokens:
															return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(__snake_case		)







	def      A	(						self			:     List[Any]		,			__snake_case			:     Union[str, Any]		)			->		List[str]:
								UpperCAmelCase :       Optional[Any] 							=		[]
								UpperCAmelCase :       int 							=		''''''
								UpperCAmelCase :       Union[str, Any] 							=		False
								for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																						if not prev_is_special:
																													out_string += " "
																						out_string += self.sp_model.decode(__snake_case		) + token
																						UpperCAmelCase :       str 							=		True
																						UpperCAmelCase :       List[str] 							=		[]
															else:
																						current_sub_tokens.append(__snake_case		)
																						UpperCAmelCase :       Optional[int] 							=		False
								out_string += self.sp_model.decode(__snake_case		)
								return out_string.strip()







	def __getstate__(						self			:     Union[str, Any]		)			->		Union[str, Any]:
								UpperCAmelCase :       Optional[Any] 							=		self.__dict__.copy()
								UpperCAmelCase :       Any 							=		None
								return state







	def __setstate__(						self			:     Optional[int]		,			__snake_case			:     Union[str, Any]		)			->		List[Any]:
								UpperCAmelCase :       Dict 							=		d

								# for backward compatibility
								if not hasattr(self		,			'''sp_model_kwargs'''		):
															UpperCAmelCase :       Optional[Any] 							=		{}

								UpperCAmelCase :       Any 							=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(self.vocab_file		)







	def      A	(						self			:     Optional[int]		,			__snake_case			:     str		,			__snake_case			:     Optional[str] = None		)			->		Tuple[str]:
								if not os.path.isdir(__snake_case		):
															logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
															return
								UpperCAmelCase :       Union[str, Any] 							=		os.path.join(
								    __snake_case		,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

								if os.path.abspath(self.vocab_file		) != os.path.abspath(__snake_case		) and os.path.isfile(self.vocab_file		):
															copyfile(self.vocab_file		,			__snake_case		)
								elif not os.path.isfile(self.vocab_file		):
															with open(__snake_case		,			'''wb'''		) as fi:
																						UpperCAmelCase :       Any 							=		self.sp_model.serialized_model_proto()
																						fi.write(__snake_case		)

								return (out_vocab_file,)





