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


_lowerCamelCase   :				int     			=						logging.get_logger(__name__)


@dataclass
class   lowercase      (       a       ):
							lowercase__  :    Tuple									=							[
							    """no_inference""",
							    """no_cuda""",
							    """no_tpu""",
							    """no_speed""",
							    """no_memory""",
							    """no_env_print""",
							    """no_multi_process""",
							]




							def __init__(  self				:   str		,							**_UpperCamelCase				:   Dict )   ->			Tuple:



												'''simple docstring'''

												for deprecated_arg in self.deprecated_args:
																	if deprecated_arg in kwargs:
																						SCREAMING_SNAKE_CASE           =    deprecated_arg[3:]
																						SCREAMING_SNAKE_CASE           =    not kwargs.pop(_UpperCamelCase )
																						logger.warning(
																						    F"{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"
																						    F" {positive_arg}={kwargs[positive_arg]}" )
												SCREAMING_SNAKE_CASE           =    kwargs.pop("tpu_name"		,							self.tpu_name )
												SCREAMING_SNAKE_CASE           =    kwargs.pop("device_idx"		,							self.device_idx )
												SCREAMING_SNAKE_CASE           =    kwargs.pop("eager_mode"		,							self.eager_mode )
												SCREAMING_SNAKE_CASE           =    kwargs.pop("use_xla"		,							self.use_xla )
												super().__init__(**_UpperCamelCase )

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




							@cached_property
							def 		__snake_case(  self				:   Optional[Any] )   ->			Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:



												'''simple docstring'''

												requires_backends(self		,							["tf"] )
												SCREAMING_SNAKE_CASE           =    None
												if self.tpu:
																	try:
																						if self.tpu_name:
																											SCREAMING_SNAKE_CASE           =    tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
																						else:
																											SCREAMING_SNAKE_CASE           =    tf.distribute.cluster_resolver.TPUClusterResolver()
																	except ValueError:
																						SCREAMING_SNAKE_CASE           =    None
												return tpu




							@cached_property
							def 		__snake_case(  self				:   List[Any] )   ->			Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:



												'''simple docstring'''

												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 )

																	SCREAMING_SNAKE_CASE           =    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" )
																						SCREAMING_SNAKE_CASE           =    tf.distribute.OneDeviceStrategy(device=F"/gpu:{self.device_idx}" )
																	else:
																						tf.config.set_visible_devices([]		,							"GPU" )  # disable GPU
																						SCREAMING_SNAKE_CASE           =    tf.distribute.OneDeviceStrategy(device=F"/cpu:{self.device_idx}" )

												return strategy




							@property
							def 		__snake_case(  self				:   Union[str, Any] )   ->			bool:



												'''simple docstring'''

												requires_backends(self		,							["tf"] )
												return self._setup_tpu is not None




							@property
							def 		__snake_case(  self				:   Optional[int] )   ->			"tf.distribute.Strategy":



												'''simple docstring'''

												requires_backends(self		,							["tf"] )
												return self._setup_strategy




							@property
							def 		__snake_case(  self				:   Dict )   ->			Union[str, Any]:



												'''simple docstring'''

												requires_backends(self		,							["tf"] )
												return tf.config.list_physical_devices("GPU" )




							@property
							def 		__snake_case(  self				:   Union[str, Any] )   ->			int:



												'''simple docstring'''

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





							@property
							def 		__snake_case(  self				:   Optional[int] )   ->			bool:



												'''simple docstring'''

												return self.n_gpu > 0





from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


_lowerCamelCase   :				Optional[Any]     			=						logging.get_logger(__name__)

_lowerCamelCase   :				Any     			=						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase   :				int     			=						{
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

_lowerCamelCase   :				Tuple     			=						{'''allegro/herbert-base-cased''': 5_14}
_lowerCamelCase   :				Optional[int]     			=						{}


class   lowercase      (       a       ):
							lowercase__  :    List[str]									=							VOCAB_FILES_NAMES
							lowercase__  :    str									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Tuple									=							PRETRAINED_INIT_CONFIGURATION
							lowercase__  :    Optional[int]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    str									=							HerbertTokenizer




							def __init__(  self				:   Dict		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Optional[int]=None		,							_UpperCamelCase				:   Optional[int]="<s>"		,							_UpperCamelCase				:   Union[str, Any]="<unk>"		,							_UpperCamelCase				:   List[str]="<pad>"		,							_UpperCamelCase				:   List[str]="<mask>"		,							_UpperCamelCase				:   Tuple="</s>"		,							**_UpperCamelCase				:   Any		,							)   ->			str:



												'''simple docstring'''

												super().__init__(
												    _UpperCamelCase		,							_UpperCamelCase		,							tokenizer_file=_UpperCamelCase		,							cls_token=_UpperCamelCase		,							unk_token=_UpperCamelCase		,							pad_token=_UpperCamelCase		,							mask_token=_UpperCamelCase		,							sep_token=_UpperCamelCase		,							**_UpperCamelCase		,							)




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.cls_token_id]
												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												if token_ids_a is None:
																	return cls + token_ids_a + sep

												return cls + token_ids_a + sep + token_ids_a + sep




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None		,							_UpperCamelCase				:   bool = False )   ->			List[int]:



												'''simple docstring'''

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=_UpperCamelCase		,							token_ids_a=_UpperCamelCase		,							already_has_special_tokens=_UpperCamelCase )

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




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_SNAKE_CASE           =    [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 ) * [0] + len(token_ids_a + sep ) * [1]





							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self._tokenizer.model.save(_UpperCamelCase		,							name=_UpperCamelCase )
												return tuple(_UpperCamelCase )




