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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE						:   List[str]         =			"""▁"""

__SCREAMING_SNAKE_CASE						:   Optional[int]         =			{"""vocab_file""": """spiece.model"""}

__SCREAMING_SNAKE_CASE						:   int         =			{
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}

__SCREAMING_SNAKE_CASE						:   int         =			{
    """google/pegasus-xsum""": 5_1_2,
}


__SCREAMING_SNAKE_CASE						:   List[Any]         =			logging.get_logger(__name__)



class        lowercase_   (					lowerCAmelCase_		):
							_lowerCamelCase						=		VOCAB_FILES_NAMES

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



							def __init__(						self				,    lowercase_				,    lowercase_="<pad>"				,    lowercase_="</s>"				,    lowercase_="<unk>"				,    lowercase_="<mask_2>"				,    lowercase_="<mask_1>"				,    lowercase_=None				,    lowercase_=103				,    lowercase_ = None				,    **lowercase_				,    ):
													_snake_case						:					Any    =   offset
													if additional_special_tokens is not None:
																			if not isinstance(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE    ):
																									raise TypeError(
																									    f"""additional_special_tokens should be of type {type(__SCREAMING_SNAKE_CASE    )}, but is"""
																									    f""" {type(__SCREAMING_SNAKE_CASE    )}"""    )

																			_snake_case						:					List[str]    =   (
																			    ([mask_token_sent] + additional_special_tokens)
																			    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																			    else additional_special_tokens
																			)
																			# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																			additional_special_tokens_extended += [
																			    f"""<unk_{i}>""" for i in range(len(__SCREAMING_SNAKE_CASE    )				,    self.offset - 1    )
																			]

																			if len(set(__SCREAMING_SNAKE_CASE    )    ) != len(__SCREAMING_SNAKE_CASE    ):
																									raise ValueError(
																									    "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
																									    f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""    )
																			_snake_case						:					Any    =   additional_special_tokens_extended
													else:
																			_snake_case						:					int    =   [mask_token_sent] if mask_token_sent is not None else []
																			additional_special_tokens += [f"""<unk_{i}>""" for i in range(2				,    self.offset    )]

													_snake_case						:					List[Any]    =   {} if sp_model_kwargs is None else sp_model_kwargs

													super().__init__(
													    eos_token=__SCREAMING_SNAKE_CASE				,    unk_token=__SCREAMING_SNAKE_CASE				,    mask_token=__SCREAMING_SNAKE_CASE				,    pad_token=__SCREAMING_SNAKE_CASE				,    mask_token_sent=__SCREAMING_SNAKE_CASE				,    offset=__SCREAMING_SNAKE_CASE				,    additional_special_tokens=__SCREAMING_SNAKE_CASE				,    sp_model_kwargs=self.sp_model_kwargs				,    **__SCREAMING_SNAKE_CASE				,    )
													_snake_case						:					int    =   mask_token_sent
													_snake_case						:					List[Any]    =   vocab_file
													_snake_case						:					Optional[Any]    =   spm.SentencePieceProcessor(**self.sp_model_kwargs    )
													self.sp_model.Load(__SCREAMING_SNAKE_CASE    )

													# add special tokens to encoder dict
													_snake_case						:					Any    =   {
													    0: self.pad_token,
													    1: self.eos_token,
													}

													if self.mask_token_sent is not None:
																			self.encoder.update(
																			    {
																			        2: self.mask_token_sent,
																			        3: self.mask_token,
																			    }    )

													if self.offset > 0:
																			# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
																			# mask_token_sent is already added to list -> so start at 1
																			self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1				,    self.offset - 1    )}    )

													_snake_case						:					Optional[Any]    =   {v: k for k, v in self.encoder.items()}



							@property
							def 	UpperCamelCase	(						self    ):
													return len(self.sp_model    ) + self.offset



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE    ): i for i in range(self.vocab_size    )}
													vocab.update(self.added_tokens_encoder    )
													return vocab



							def __getstate__(						self    ):
													_snake_case						:					Tuple    =   self.__dict__.copy()
													_snake_case						:					List[str]    =   None
													return state



							def __setstate__(						self				,    lowercase_    ):
													_snake_case						:					Optional[Any]    =   d

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

													_snake_case						:					List[str]    =   spm.SentencePieceProcessor(**self.sp_model_kwargs    )
													self.sp_model.Load(self.vocab_file    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													return self.sp_model.encode(__SCREAMING_SNAKE_CASE				,    out_type=__SCREAMING_SNAKE_CASE    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):

													if token in self.decoder:
																			return self.decoder[token]
													elif token in self.added_tokens_decoder:
																			return self.added_tokens_decoder[token]
													_snake_case						:					int    =   self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE    )
													return sp_id + self.offset



							def 	UpperCamelCase	(						self				,    lowercase_    ):

													if index in self.encoder:
																			return self.encoder[index]
													elif index in self.added_tokens_encoder:
																			return self.added_tokens_encoder[index]
													else:
																			_snake_case						:					int    =   self.sp_model.IdToPiece(index - self.offset    )
													return token



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					int    =   []
													_snake_case						:					Union[str, Any]    =   ""
													for token in tokens:
																			# make sure that special tokens are not decoded using sentencepiece model
																			if token in self.all_special_tokens:
																									out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE    ) + token
																									_snake_case						:					Tuple    =   []
																			else:
																									current_sub_tokens.append(__SCREAMING_SNAKE_CASE    )
													out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE    )
													return out_string.strip()



							def 	UpperCamelCase	(						self				,    lowercase_=False    ):
													return 1



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					List[Any]    =   set(self.all_special_ids    )  # call it once instead of inside list comp
													all_special_ids.remove(self.unk_token_id    )  # <unk> is only sometimes special

													return [1 if x in all_special_ids else 0 for x in seq]



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None				,    lowercase_ = False    ):

													if already_has_special_tokens:
																			return self._special_token_mask(__SCREAMING_SNAKE_CASE    )
													elif token_ids_a is None:
																			return self._special_token_mask(__SCREAMING_SNAKE_CASE    ) + [1]
													else:
																			return self._special_token_mask(token_ids_a + token_ids_a    ) + [1]



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_=None    ):

													if token_ids_a is None:
																			return token_ids_a + [self.eos_token_id]
													# We don't expect to process pairs, but leave the pair logic for API consistency
													return token_ids_a + token_ids_a + [self.eos_token_id]



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None    ):

													if not os.path.isdir(__SCREAMING_SNAKE_CASE    ):
																			logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""    )
																			return
													_snake_case						:					Dict    =   os.path.join(
													    __SCREAMING_SNAKE_CASE				,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

													if os.path.abspath(self.vocab_file    ) != os.path.abspath(__SCREAMING_SNAKE_CASE    ) and os.path.isfile(self.vocab_file    ):
																			copyfile(self.vocab_file				,    __SCREAMING_SNAKE_CASE    )
													elif not os.path.isfile(self.vocab_file    ):
																			with open(__SCREAMING_SNAKE_CASE				,    "wb"    ) as fi:
																									_snake_case						:					List[str]    =   self.sp_model.serialized_model_proto()
																									fi.write(__SCREAMING_SNAKE_CASE    )

													return (out_vocab_file,)
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