import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class        lowercase_   (					__lowercase						,			unittest.TestCase		):
							_lowerCamelCase						=		PhobertTokenizer
							_lowerCamelCase						=		False



							def 	UpperCamelCase	(						self    ):
													super().setUp()

													# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
													_snake_case						:					Optional[int]    =   ['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
													_snake_case						:					Tuple    =   dict(zip(snake_case_				,    range(len(snake_case_    )    )    )    )
													_snake_case						:					int    =   ['''#version: 0.2''', '''l à</w>''']
													_snake_case						:					List[Any]    =   {'''unk_token''': '''<unk>'''}

													_snake_case						:					Any    =   os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES["vocab_file"]    )
													_snake_case						:					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(snake_case_    )    )



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													kwargs.update(self.special_tokens_map    )
													return PhobertTokenizer.from_pretrained(self.tmpdirname				,    **snake_case_    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					Union[str, Any]    =   '''Tôi là VinAI Research'''
													_snake_case						:					int    =   '''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
													return input_text, output_text



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   PhobertTokenizer(self.vocab_file				,    self.merges_file				,    **self.special_tokens_map    )
													_snake_case						:					Any    =   '''Tôi là VinAI Research'''
													_snake_case						:					Union[str, Any]    =   '''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
													_snake_case						:					List[str]    =   tokenizer.tokenize(snake_case_    )
													print(snake_case_    )
													self.assertListEqual(snake_case_				,    snake_case_    )

													_snake_case						:					str    =   tokens + [tokenizer.unk_token]

													_snake_case						:					str    =   [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_    )				,    snake_case_    )
from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
				import tensorflow as tf


__SCREAMING_SNAKE_CASE						:   str         =			logging.get_logger(__name__)



@dataclass
class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		[
							    'no_inference',
							    'no_cuda',
							    'no_tpu',
							    'no_speed',
							    'no_memory',
							    'no_env_print',
							    'no_multi_process',
							]



							def __init__(						self				,    **lowercase_    ):
													for deprecated_arg in self.deprecated_args:
																			if deprecated_arg in kwargs:
																									_snake_case						:					List[str]    =   deprecated_arg[3:]
																									_snake_case						:					Optional[int]    =   not kwargs.pop(lowercase_    )
																									logger.warning(
																									    f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
																									    f""" {positive_arg}={kwargs[positive_arg]}"""    )
													_snake_case						:					Tuple    =   kwargs.pop("tpu_name"				,    self.tpu_name    )
													_snake_case						:					Any    =   kwargs.pop("device_idx"				,    self.device_idx    )
													_snake_case						:					List[str]    =   kwargs.pop("eager_mode"				,    self.eager_mode    )
													_snake_case						:					List[str]    =   kwargs.pop("use_xla"				,    self.use_xla    )
													super().__init__(**lowercase_    )

							_lowerCamelCase						=		field(
							    default=__snake_case						,			metadata={'help': 'Name of TPU'}						,			)
							_lowerCamelCase						=		field(
							    default=0						,			metadata={'help': 'CPU / GPU device index. Defaults to 0.'}						,			)
							_lowerCamelCase						=		field(default=__snake_case						,			metadata={'help': 'Benchmark models in eager model.'}		)
							_lowerCamelCase						=		field(
							    default=__snake_case						,			metadata={
							        'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
							    }						,			)



							@cached_property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													_snake_case						:					str    =   None
													if self.tpu:
																			try:
																									if self.tpu_name:
																															_snake_case						:					Optional[Any]    =   tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name    )
																									else:
																															_snake_case						:					List[str]    =   tf.distribute.cluster_resolver.TPUClusterResolver()
																			except ValueError:
																									_snake_case						:					Union[str, Any]    =   None
													return tpu



							@cached_property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													if self.is_tpu:
																			tf.config.experimental_connect_to_cluster(self._setup_tpu    )
																			tf.tpu.experimental.initialize_tpu_system(self._setup_tpu    )

																			_snake_case						:					List[str]    =   tf.distribute.TPUStrategy(self._setup_tpu    )
													else:
																			# currently no multi gpu is allowed
																			if self.is_gpu:
																									# TODO: Currently only single GPU is supported
																									tf.config.set_visible_devices(self.gpu_list[self.device_idx]				,    "GPU"    )
																									_snake_case						:					Any    =   tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}"""    )
																			else:
																									tf.config.set_visible_devices([]				,    "GPU"    )  # disable GPU
																									_snake_case						:					Any    =   tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}"""    )

													return strategy



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													return self._setup_tpu is not None



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													return self._setup_strategy



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													return tf.config.list_physical_devices("GPU"    )



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													if self.cuda:
																			return len(self.gpu_list    )
													return 0



							@property
							def 	UpperCamelCase	(						self    ):
													return self.n_gpu > 0