
'''simple docstring'''



import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation

__A          =    logging.get_logger(__name__)

__A          =    {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

__A          =    {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
}

__A          =    {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}





@lru_cache()
def  _A		(							):
						lowercase__							 =       (
						    list(range(ord("""!"""	)		,		ord("""~"""	) + 1	)	) + list(range(ord("""¡"""	)		,		ord("""¬"""	) + 1	)	) + list(range(ord("""®"""	)		,		ord("""ÿ"""	) + 1	)	)
						)
						lowercase__							 =       bs[:]
						lowercase__							 =       0
						for b in range(2**8	):
												if b not in bs:
																		bs.append(lowercase__	)
																		cs.append(2**8 + n	)
																		n += 1
						lowercase__							 =       [chr(lowercase__	) for n in cs]
						return dict(zip(lowercase__		,		lowercase__	)	)





def  _A		(							lowercase__	):
						lowercase__							 =       set()
						lowercase__							 =       word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)	)
												lowercase__							 =       char
						return pairs







class 			A							(       __UpperCAmelCase				):
			lowerCamelCase							:							List[str]       		=  VOCAB_FILES_NAMES
			lowerCamelCase							:							Any       		=  PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase							:							int       		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase							:							List[str]       		=  ["""input_ids""", """attention_mask"""]



			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__="replace"       ,     lowerCamelCase__="<|endoftext|>"       ,     lowerCamelCase__="<|endoftext|>"       ,     lowerCamelCase__="<|endoftext|>"       ,     lowerCamelCase__=None       ,     lowerCamelCase__=False       ,     lowerCamelCase__=False       ,     **lowerCamelCase__       ,     )			->		Tuple:




									'''simple docstring'''



									lowercase__							 =       AddedToken(lowerCamelCase__       ,     lstrip=lowerCamelCase__       ,     rstrip=lowerCamelCase__       ) if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ) else bos_token
									lowercase__							 =       AddedToken(lowerCamelCase__       ,     lstrip=lowerCamelCase__       ,     rstrip=lowerCamelCase__       ) if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ) else eos_token
									lowercase__							 =       AddedToken(lowerCamelCase__       ,     lstrip=lowerCamelCase__       ,     rstrip=lowerCamelCase__       ) if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ) else unk_token
									lowercase__							 =       AddedToken(lowerCamelCase__       ,     lstrip=lowerCamelCase__       ,     rstrip=lowerCamelCase__       ) if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ) else pad_token
									super().__init__(
									    errors=lowerCamelCase__       ,     unk_token=lowerCamelCase__       ,     bos_token=lowerCamelCase__       ,     eos_token=lowerCamelCase__       ,     pad_token=lowerCamelCase__       ,     add_prefix_space=lowerCamelCase__       ,     add_bos_token=lowerCamelCase__       ,     **lowerCamelCase__       ,     )
									lowercase__							 =       add_bos_token

									with open(lowerCamelCase__       ,     encoding="""utf-8"""       ) as vocab_handle:
															lowercase__							 =       json.load(lowerCamelCase__       )
									lowercase__							 =       {v: k for k, v in self.encoder.items()}
									lowercase__							 =       errors  # how to handle errors in decoding
									lowercase__							 =       bytes_to_unicode()
									lowercase__							 =       {v: k for k, v in self.byte_encoder.items()}
									with open(lowerCamelCase__       ,     encoding="""utf-8"""       ) as merges_handle:
															lowercase__							 =       merges_handle.read().split("""\n"""       )[1:-1]
									lowercase__							 =       [tuple(merge.split()       ) for merge in bpe_merges]
									lowercase__							 =       dict(zip(lowerCamelCase__       ,     range(len(lowerCamelCase__       )       )       )       )
									lowercase__							 =       {}
									lowercase__							 =       add_prefix_space

									# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
									lowercase__							 =       re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""       )



			@property
			def A__ (					self       )			->		str:




									'''simple docstring'''



									return len(self.encoder       )



			def A__ (					self       )			->		str:




									'''simple docstring'''



									return dict(self.encoder       ,     **self.added_tokens_encoder       )



			def A__ (					self       ,     lowerCamelCase__       )			->		Optional[int]:




									'''simple docstring'''



									if token in self.cache:
															return self.cache[token]
									lowercase__							 =       tuple(lowerCamelCase__       )
									lowercase__							 =       get_pairs(lowerCamelCase__       )

									if not pairs:
															return token

									while True:
															lowercase__							 =       min(lowerCamelCase__       ,     key=lambda lowerCamelCase__       : self.bpe_ranks.get(lowerCamelCase__       ,     float("""inf"""       )       )       )
															if bigram not in self.bpe_ranks:
																					break
															lowercase__ ,						lowercase__							 =       bigram
															lowercase__							 =       []
															lowercase__							 =       0
															while i < len(lowerCamelCase__       ):
																					try:
																											lowercase__							 =       word.index(lowerCamelCase__       ,     lowerCamelCase__       )
																					except ValueError:
																											new_word.extend(word[i:]       )
																											break
																					else:
																											new_word.extend(word[i:j]       )
																											lowercase__							 =       j

																					if word[i] == first and i < len(lowerCamelCase__       ) - 1 and word[i + 1] == second:
																											new_word.append(first + second       )
																											i += 2
																					else:
																											new_word.append(word[i]       )
																											i += 1
															lowercase__							 =       tuple(lowerCamelCase__       )
															lowercase__							 =       new_word
															if len(lowerCamelCase__       ) == 1:
																					break
															else:
																					lowercase__							 =       get_pairs(lowerCamelCase__       )
									lowercase__							 =       """ """.join(lowerCamelCase__       )
									lowercase__							 =       word
									return word



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__=None       )			->		Optional[int]:




									'''simple docstring'''



									if self.add_bos_token:
															lowercase__							 =       [self.bos_token_id]
									else:
															lowercase__							 =       []

									lowercase__							 =       bos_token_ids + token_ids_a

									if token_ids_a is None:
															return output

									return output + bos_token_ids + token_ids_a



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = False       )			->		List[int]:




									'''simple docstring'''



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

									if not self.add_bos_token:
															return super().get_special_tokens_mask(
															    token_ids_a=lowerCamelCase__       ,     token_ids_a=lowerCamelCase__       ,     already_has_special_tokens=lowerCamelCase__       )

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



			def A__ (					self       ,     lowerCamelCase__       )			->		Any:




									'''simple docstring'''



									lowercase__							 =       []
									for token in re.findall(self.pat       ,     lowerCamelCase__       ):
															lowercase__							 =       """""".join(
															    self.byte_encoder[b] for b in token.encode("""utf-8"""       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
															bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase__       ).split(""" """       )       )
									return bpe_tokens



			def A__ (					self       ,     lowerCamelCase__       )			->		Optional[Any]:




									'''simple docstring'''



									return self.encoder.get(lowerCamelCase__       ,     self.encoder.get(self.unk_token       )       )



			def A__ (					self       ,     lowerCamelCase__       )			->		Union[str, Any]:




									'''simple docstring'''



									return self.decoder.get(lowerCamelCase__       )



			def A__ (					self       ,     lowerCamelCase__       )			->		Any:




									'''simple docstring'''



									lowercase__							 =       """""".join(lowerCamelCase__       )
									lowercase__							 =       bytearray([self.byte_decoder[c] for c in text]       ).decode("""utf-8"""       ,     errors=self.errors       )
									return text



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = None       )			->		Tuple[str]:




									'''simple docstring'''



									if not os.path.isdir(lowerCamelCase__       ):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
															return
									lowercase__							 =       os.path.join(
									    lowerCamelCase__       ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )
									lowercase__							 =       os.path.join(
									    lowerCamelCase__       ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]       )

									with open(lowerCamelCase__       ,     """w"""       ,     encoding="""utf-8"""       ) as f:
															f.write(json.dumps(self.encoder       ,     indent=2       ,     sort_keys=lowerCamelCase__       ,     ensure_ascii=lowerCamelCase__       ) + """\n"""       )

									lowercase__							 =       0
									with open(lowerCamelCase__       ,     """w"""       ,     encoding="""utf-8"""       ) as writer:
															writer.write("""#version: 0.2\n"""       )
															for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,     key=lambda lowerCamelCase__       : kv[1]       ):
																					if index != token_index:
																											logger.warning(
																											    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																											    """ Please check that the tokenizer is not corrupted!"""       )
																											lowercase__							 =       token_index
																					writer.write(""" """.join(lowerCamelCase__       ) + """\n"""       )
																					index += 1

									return vocab_file, merge_file



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__=False       ,     **lowerCamelCase__       )			->		Optional[Any]:




									'''simple docstring'''



									lowercase__							 =       kwargs.pop("""add_prefix_space"""       ,     self.add_prefix_space       )
									if is_split_into_words or add_prefix_space:
															lowercase__							 =       """ """ + text
									return (text, kwargs)



			def A__ (					self       ,     lowerCamelCase__       )			->		List[int]:




									'''simple docstring'''



									lowercase__							 =       []
									for is_user, text in conversation.iter_texts():
															input_ids.extend(self.encode(lowerCamelCase__       ,     add_special_tokens=lowerCamelCase__       ) + [self.eos_token_id]       )
									if len(lowerCamelCase__       ) > self.model_max_length:
															lowercase__							 =       input_ids[-self.model_max_length :]
									return input_ids








'''simple docstring'''



import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
				import torch

				from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
				import pyanvml.pyanvml as nvml


__A          =    logging.get_logger(__name__)





class 			A							(       __UpperCAmelCase				):
			lowerCamelCase							:							PyTorchBenchmarkArguments
			lowerCamelCase							:							PretrainedConfig
			lowerCamelCase							:							str       		=  "PyTorch"



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




									'''simple docstring'''



									return torch.__version__



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:




									'''simple docstring'''



									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
									return self._measure_speed(_inference       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:




									'''simple docstring'''



									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
									return self._measure_memory(_inference       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:




									'''simple docstring'''



									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
									return self._measure_speed(_train       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:




									'''simple docstring'''



									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
									return self._measure_memory(_train       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Callable[[], None]:




									'''simple docstring'''



									lowercase__							 =       self.config_dict[model_name]

									if self.args.torchscript:
															lowercase__							 =       True

									lowercase__							 =       (
									    hasattr(lowerCamelCase__       ,     """architectures"""       )
									    and isinstance(config.architectures       ,     lowerCamelCase__       )
									    and len(config.architectures       ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																					lowercase__							 =       config.architectures[0]
																					lowercase__							 =       __import__("""transformers"""       ,     fromlist=[model_class]       )
																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
																					lowercase__							 =       model_cls(lowerCamelCase__       )
															except ImportError:
																					raise ImportError(
																					    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																					    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""       )
									else:
															lowercase__							 =       MODEL_MAPPING[config.__class__](lowerCamelCase__       )

									model.eval()
									model.to(self.args.device       )

									# encoder-decoder has vocab size saved differently
									lowercase__							 =       config.vocab_size if hasattr(lowerCamelCase__       ,     """vocab_size"""       ) else config.encoder.vocab_size
									lowercase__							 =       torch.randint(lowerCamelCase__       ,     (batch_size, sequence_length)       ,     dtype=torch.long       ,     device=self.args.device       )

									if self.args.fpaa:
															logger.info("""Running training in Mixed Precision..."""       )
															if not self.args.is_gpu:
																					raise ValueError("""Mixed precision is possible only for GPU."""       )
															# amp seems to have memory leaks so that memory usage
															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
															model.half()

									if self.args.torchscript:
															with torch.no_grad():
																					lowercase__							 =       torch.jit.trace(lowerCamelCase__       ,     lowerCamelCase__       )
									else:
															lowercase__							 =       model

									def encoder_decoder_forward():
															with torch.no_grad():
																					lowercase__							 =       inference_model(lowerCamelCase__       ,     decoder_input_ids=lowerCamelCase__       )
															return outputs

									def encoder_forward():
															with torch.no_grad():
																					lowercase__							 =       inference_model(lowerCamelCase__       )
															return outputs

									lowercase__							 =       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
									return _forward



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Callable[[], None]:




									'''simple docstring'''



									lowercase__							 =       self.config_dict[model_name]

									lowercase__							 =       (
									    hasattr(lowerCamelCase__       ,     """architectures"""       )
									    and isinstance(config.architectures       ,     lowerCamelCase__       )
									    and len(config.architectures       ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																					lowercase__							 =       config.architectures[0]
																					lowercase__							 =       __import__("""transformers"""       ,     fromlist=[model_class]       )
																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
																					lowercase__							 =       model_cls(lowerCamelCase__       )
															except ImportError:
																					raise ImportError(
																					    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																					    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""       )
									else:
															lowercase__							 =       MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase__       )

									if self.args.torchscript:
															raise NotImplementedError("""Training for torchscript is currently not implemented"""       )
									else:
															lowercase__							 =       model

									model.train()
									model.to(self.args.device       )

									# encoder-decoder has vocab size saved differently
									lowercase__							 =       config.vocab_size if hasattr(lowerCamelCase__       ,     """vocab_size"""       ) else config.encoder.vocab_size
									lowercase__							 =       torch.randint(lowerCamelCase__       ,     (batch_size, sequence_length)       ,     dtype=torch.long       ,     device=self.args.device       )

									if self.args.fpaa:
															logger.info("""Running training in Mixed Precision..."""       )
															if not self.args.is_gpu:
																					raise ValueError("""Mixed precision is possible only for GPU."""       )

															# amp seems to have memory leaks so that memory usage
															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
															model.half()

									def compute_loss_and_backprob_encoder():
															lowercase__							 =       train_model(lowerCamelCase__       ,     labels=lowerCamelCase__       )[0]
															loss.backward()
															return loss

									def compute_loss_and_backprob_encoder_decoder():
															lowercase__							 =       train_model(lowerCamelCase__       ,     decoder_input_ids=lowerCamelCase__       ,     labels=lowerCamelCase__       )[0]
															loss.backward()
															return loss

									lowercase__							 =       (
									    compute_loss_and_backprob_encoder_decoder
									    if config.is_encoder_decoder
									    else compute_loss_and_backprob_encoder
									)
									return _train



			def A__ (					self       ,     lowerCamelCase__       )			->		float:




									'''simple docstring'''



									try:
															if self.args.is_tpu or self.args.torchscript:
																					# run additional 10 times to stabilize compilation for tpu and torchscript
																					logger.info("""Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"""       )
																					timeit.repeat(
																					    lowerCamelCase__       ,     repeat=1       ,     number=5       ,     )

															# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
															lowercase__							 =       timeit.repeat(
															    lowerCamelCase__       ,     repeat=self.args.repeat       ,     number=10       ,     )

															if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
																					import torch_xla.debug.metrics as met

																					self.print_fn(met.metrics_report()       )

															return min(lowerCamelCase__       ) / 10.0
									except RuntimeError as e:
															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''       )
															return "N/A"



			def A__ (					self       ,     lowerCamelCase__       )			->		[Memory, MemorySummary]:




									'''simple docstring'''



									try:
															if self.args.trace_memory_line_by_line:
																					lowercase__							 =       start_memory_tracing("""transformers"""       )

															if self.args.is_tpu:
																					# tpu
																					raise NotImplementedError(
																					    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"""
																					    """ `--no-memory` or `args.memory=False`"""       )
															elif self.args.is_gpu:
																					if not is_pyanvml_available():
																											logger.warning(
																											    """py3nvml not installed, we won't log GPU memory usage. """
																											    """Install py3nvml (pip install py3nvml) to log information about GPU."""       )
																											lowercase__							 =       """N/A"""
																					else:
																											logger.info(
																											    """Measuring total GPU usage on GPU device. Make sure to not have additional processes running"""
																											    """ on the same GPU."""       )
																											# init nvml
																											nvml.nvmlInit()
																											func()
																											lowercase__							 =       nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx       )
																											lowercase__							 =       nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase__       )
																											lowercase__							 =       meminfo.used
																											lowercase__							 =       Memory(lowerCamelCase__       )
																											# shutdown nvml
																											nvml.nvmlShutdown()
															else:
																					# cpu
																					lowercase__							 =       measure_peak_memory_cpu(lowerCamelCase__       )
																					lowercase__							 =       Memory(lowerCamelCase__       ) if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ) else memory_bytes

															if self.args.trace_memory_line_by_line:
																					lowercase__							 =       stop_memory_tracing(lowerCamelCase__       )
															else:
																					lowercase__							 =       None

															return memory, summary
									except RuntimeError as e:
															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''       )
															return "N/A", None






