






"""simple docstring"""




from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


_snake_case					   =     {
    'configuration_utils': ['GenerationConfig'],
    'streamers': ['TextIteratorStreamer', 'TextStreamer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case					   =     [
	    'Constraint',
	    'ConstraintListState',
	    'DisjunctiveConstraint',
	    'PhrasalConstraint',
	]
	_snake_case					   =     [
	    'BeamHypotheses',
	    'BeamScorer',
	    'BeamSearchScorer',
	    'ConstrainedBeamSearchScorer',
	]
	_snake_case					   =     [
	    'EpsilonLogitsWarper',
	    'EtaLogitsWarper',
	    'ForcedBOSTokenLogitsProcessor',
	    'ForcedEOSTokenLogitsProcessor',
	    'HammingDiversityLogitsProcessor',
	    'InfNanRemoveLogitsProcessor',
	    'LogitsProcessor',
	    'LogitsProcessorList',
	    'LogitsWarper',
	    'MinLengthLogitsProcessor',
	    'MinNewTokensLengthLogitsProcessor',
	    'NoBadWordsLogitsProcessor',
	    'NoRepeatNGramLogitsProcessor',
	    'PrefixConstrainedLogitsProcessor',
	    'RepetitionPenaltyLogitsProcessor',
	    'SequenceBiasLogitsProcessor',
	    'EncoderRepetitionPenaltyLogitsProcessor',
	    'TemperatureLogitsWarper',
	    'TopKLogitsWarper',
	    'TopPLogitsWarper',
	    'TypicalLogitsWarper',
	    'EncoderNoRepeatNGramLogitsProcessor',
	    'ExponentialDecayLengthPenalty',
	    'LogitNormalization',
	]
	_snake_case					   =     [
	    'MaxNewTokensCriteria',
	    'MaxLengthCriteria',
	    'MaxTimeCriteria',
	    'StoppingCriteria',
	    'StoppingCriteriaList',
	    'validate_stopping_criteria',
	]
	_snake_case					   =     [
	    'GenerationMixin',
	    'top_k_top_p_filtering',
	    'GreedySearchEncoderDecoderOutput',
	    'GreedySearchDecoderOnlyOutput',
	    'SampleEncoderDecoderOutput',
	    'SampleDecoderOnlyOutput',
	    'BeamSearchEncoderDecoderOutput',
	    'BeamSearchDecoderOnlyOutput',
	    'BeamSampleEncoderDecoderOutput',
	    'BeamSampleDecoderOnlyOutput',
	    'ContrastiveSearchEncoderDecoderOutput',
	    'ContrastiveSearchDecoderOnlyOutput',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case					   =     [
	    'TFForcedBOSTokenLogitsProcessor',
	    'TFForcedEOSTokenLogitsProcessor',
	    'TFLogitsProcessor',
	    'TFLogitsProcessorList',
	    'TFLogitsWarper',
	    'TFMinLengthLogitsProcessor',
	    'TFNoBadWordsLogitsProcessor',
	    'TFNoRepeatNGramLogitsProcessor',
	    'TFRepetitionPenaltyLogitsProcessor',
	    'TFTemperatureLogitsWarper',
	    'TFTopKLogitsWarper',
	    'TFTopPLogitsWarper',
	    'TFForceTokensLogitsProcessor',
	    'TFSuppressTokensAtBeginLogitsProcessor',
	    'TFSuppressTokensLogitsProcessor',
	]
	_snake_case					   =     [
	    'TFGenerationMixin',
	    'tf_top_k_top_p_filtering',
	    'TFGreedySearchDecoderOnlyOutput',
	    'TFGreedySearchEncoderDecoderOutput',
	    'TFSampleEncoderDecoderOutput',
	    'TFSampleDecoderOnlyOutput',
	    'TFBeamSearchEncoderDecoderOutput',
	    'TFBeamSearchDecoderOnlyOutput',
	    'TFBeamSampleEncoderDecoderOutput',
	    'TFBeamSampleDecoderOnlyOutput',
	    'TFContrastiveSearchEncoderDecoderOutput',
	    'TFContrastiveSearchDecoderOnlyOutput',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case					   =     [
	    'FlaxForcedBOSTokenLogitsProcessor',
	    'FlaxForcedEOSTokenLogitsProcessor',
	    'FlaxLogitsProcessor',
	    'FlaxLogitsProcessorList',
	    'FlaxLogitsWarper',
	    'FlaxMinLengthLogitsProcessor',
	    'FlaxTemperatureLogitsWarper',
	    'FlaxTopKLogitsWarper',
	    'FlaxTopPLogitsWarper',
	]
	_snake_case					   =     [
	    'FlaxGenerationMixin',
	    'FlaxGreedySearchOutput',
	    'FlaxSampleOutput',
	    'FlaxBeamSearchOutput',
	]

if TYPE_CHECKING:
	from .configuration_utils import GenerationConfig
	from .streamers import TextIteratorStreamer, TextStreamer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
		from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
		from .logits_process import (
		    EncoderNoRepeatNGramLogitsProcessor,
		    EncoderRepetitionPenaltyLogitsProcessor,
		    EpsilonLogitsWarper,
		    EtaLogitsWarper,
		    ExponentialDecayLengthPenalty,
		    ForcedBOSTokenLogitsProcessor,
		    ForcedEOSTokenLogitsProcessor,
		    HammingDiversityLogitsProcessor,
		    InfNanRemoveLogitsProcessor,
		    LogitNormalization,
		    LogitsProcessor,
		    LogitsProcessorList,
		    LogitsWarper,
		    MinLengthLogitsProcessor,
		    MinNewTokensLengthLogitsProcessor,
		    NoBadWordsLogitsProcessor,
		    NoRepeatNGramLogitsProcessor,
		    PrefixConstrainedLogitsProcessor,
		    RepetitionPenaltyLogitsProcessor,
		    SequenceBiasLogitsProcessor,
		    TemperatureLogitsWarper,
		    TopKLogitsWarper,
		    TopPLogitsWarper,
		    TypicalLogitsWarper,
		)
		from .stopping_criteria import (
		    MaxLengthCriteria,
		    MaxNewTokensCriteria,
		    MaxTimeCriteria,
		    StoppingCriteria,
		    StoppingCriteriaList,
		    validate_stopping_criteria,
		)
		from .utils import (
		    BeamSampleDecoderOnlyOutput,
		    BeamSampleEncoderDecoderOutput,
		    BeamSearchDecoderOnlyOutput,
		    BeamSearchEncoderDecoderOutput,
		    ContrastiveSearchDecoderOnlyOutput,
		    ContrastiveSearchEncoderDecoderOutput,
		    GenerationMixin,
		    GreedySearchDecoderOnlyOutput,
		    GreedySearchEncoderDecoderOutput,
		    SampleDecoderOnlyOutput,
		    SampleEncoderDecoderOutput,
		    top_k_top_p_filtering,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tf_logits_process import (
		    TFForcedBOSTokenLogitsProcessor,
		    TFForcedEOSTokenLogitsProcessor,
		    TFForceTokensLogitsProcessor,
		    TFLogitsProcessor,
		    TFLogitsProcessorList,
		    TFLogitsWarper,
		    TFMinLengthLogitsProcessor,
		    TFNoBadWordsLogitsProcessor,
		    TFNoRepeatNGramLogitsProcessor,
		    TFRepetitionPenaltyLogitsProcessor,
		    TFSuppressTokensAtBeginLogitsProcessor,
		    TFSuppressTokensLogitsProcessor,
		    TFTemperatureLogitsWarper,
		    TFTopKLogitsWarper,
		    TFTopPLogitsWarper,
		)
		from .tf_utils import (
		    TFBeamSampleDecoderOnlyOutput,
		    TFBeamSampleEncoderDecoderOutput,
		    TFBeamSearchDecoderOnlyOutput,
		    TFBeamSearchEncoderDecoderOutput,
		    TFContrastiveSearchDecoderOnlyOutput,
		    TFContrastiveSearchEncoderDecoderOutput,
		    TFGenerationMixin,
		    TFGreedySearchDecoderOnlyOutput,
		    TFGreedySearchEncoderDecoderOutput,
		    TFSampleDecoderOnlyOutput,
		    TFSampleEncoderDecoderOutput,
		    tf_top_k_top_p_filtering,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .flax_logits_process import (
		    FlaxForcedBOSTokenLogitsProcessor,
		    FlaxForcedEOSTokenLogitsProcessor,
		    FlaxLogitsProcessor,
		    FlaxLogitsProcessorList,
		    FlaxLogitsWarper,
		    FlaxMinLengthLogitsProcessor,
		    FlaxTemperatureLogitsWarper,
		    FlaxTopKLogitsWarper,
		    FlaxTopPLogitsWarper,
		)
		from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
	import sys

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









"""simple docstring"""




import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class  UpperCamelCase    (			snake_case_	,  unittest.TestCase   ):
 UpperCamelCase      : Union[str, Any]    =      BertweetTokenizer
 UpperCamelCase      : Optional[Any]    =      False


 def _lowercase			(  self	:		Dict							)      -> List[Any]:
   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   _a						: Tuple    =			["""I""", """m""", """V@@""", """R@@""", """r""", """e@@"""]
   _a						: str    =			dict(zip(UpperCAmelCase__							,		range(len(UpperCAmelCase__							)							)							)							)
   _a						: Dict    =			["""#version: 0.2""", """a m</w>"""]
   _a						: int    =			{"""unk_token""": """<unk>"""}

   _a						: Union[str, Any]    =			os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["""vocab_file"""]							)
   _a						: List[Any]    =			os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["""merges_file"""]							)
   with open(self.vocab_file							,		"""w"""							,		encoding="""utf-8"""							) as fp:
     for token in vocab_tokens:
       fp.write(f"""{token} {vocab_tokens[token]}\n"""							)
   with open(self.merges_file							,		"""w"""							,		encoding="""utf-8"""							) as fp:
     fp.write("""\n""".join(UpperCAmelCase__							)							)


 def _lowercase			(  self	:		Any							,		**UpperCAmelCase__	:		Optional[Any]							)      -> Dict:
   kwargs.update(self.special_tokens_map							)
   return BertweetTokenizer.from_pretrained(self.tmpdirname							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		Dict							,		UpperCAmelCase__	:		Optional[int]							)      -> Dict:
   _a						: List[str]    =			"""I am VinAI Research"""
   _a						: Optional[int]    =			"""I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
   return input_text, output_text







 def _lowercase			(  self	:		Tuple							)      -> Optional[int]:
   _a						: Optional[int]    =			BertweetTokenizer(self.vocab_file							,		self.merges_file							,		**self.special_tokens_map							)
   _a						: Tuple    =			"""I am VinAI Research"""
   _a						: Dict    =			"""I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
   _a						: str    =			tokenizer.tokenize(UpperCAmelCase__							)
   self.assertListEqual(UpperCAmelCase__							,		UpperCAmelCase__							)

   _a						: Tuple    =			tokens + [tokenizer.unk_token]

   _a						: Tuple    =			[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__							)							,		UpperCAmelCase__							)

