
"""simple docstring"""


import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin

class    A_     (					A__				, unittest.TestCase       ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       BertweetTokenizer
							SCREAMING_SNAKE_CASE_     				=       False





							def 				UpperCAmelCase__							(  self  :Optional[int]	):





										"""simple docstring"""

										super().setUp()

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

										lowerCamelCase__     :					str		=os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['vocab_file']	)
										lowerCamelCase__     :					Optional[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(lowerCamelCase_	)	)





							def 				UpperCAmelCase__							(  self  :str					,							**lowerCamelCase_  :int	):





										"""simple docstring"""

										kwargs.update(self.special_tokens_map	)
										return BertweetTokenizer.from_pretrained(self.tmpdirname					,							**lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :Optional[Any]					,							lowerCamelCase_  :str	):





										"""simple docstring"""

										lowerCamelCase__     :					List[Any]		='I am VinAI Research'
										lowerCamelCase__     :					Union[str, Any]		='I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
										return input_text, output_text





							def 				UpperCAmelCase__							(  self  :Any	):





										"""simple docstring"""

										lowerCamelCase__     :					Tuple		=BertweetTokenizer(self.vocab_file					,							self.merges_file					,							**self.special_tokens_map	)
										lowerCamelCase__     :					Optional[Any]		='I am VinAI Research'
										lowerCamelCase__     :					Any		='I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
										lowerCamelCase__     :					Any		=tokenizer.tokenize(lowerCamelCase_	)
										self.assertListEqual(lowerCamelCase_					,							lowerCamelCase_	)

										lowerCamelCase__     :					List[str]		=tokens + [tokenizer.unk_token]

										lowerCamelCase__     :					Optional[Any]		=[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_	)					,							lowerCamelCase_	)

"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCAmelCase								=						{
    """configuration_mobilenet_v1""": [
        """MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV1Config""",
        """MobileNetV1OnnxConfig""",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						["""MobileNetV1FeatureExtractor"""]
							lowerCAmelCase								=						["""MobileNetV1ImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MobileNetV1ForImageClassification""",
							    """MobileNetV1Model""",
							    """MobileNetV1PreTrainedModel""",
							    """load_tf_weights_in_mobilenet_v1""",
							]


if TYPE_CHECKING:
							from .configuration_mobilenet_va import (
							    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileNetVaConfig,
							    MobileNetVaOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
														from .image_processing_mobilenet_va import MobileNetVaImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilenet_va import (
														    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileNetVaForImageClassification,
														    MobileNetVaModel,
														    MobileNetVaPreTrainedModel,
														    load_tf_weights_in_mobilenet_va,
														)


else:
							import sys

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