




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

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
				if is_torch_available():
								import torch
				if is_tf_available():
								import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


lowercase_			:       List[str]						       =				logging.get_logger(__name__)

lowercase_			:       List[str]						       =				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

lowercase_			:       int						       =				{
    'vocab_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json',
    },
    'merges_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt',
    },
}

lowercase_			:       Any						       =				{
    'Salesforce/codegen-350M-mono': 20_48,
}


@lru_cache()
def 							__SCREAMING_SNAKE_CASE						(    ):

						'''simple docstring'''


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


def 							__SCREAMING_SNAKE_CASE						(    snake_case_		):

						'''simple docstring'''


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






class 					__lowerCAmelCase				( UpperCAmelCase__				):
			snake_case_   :				int				  =  VOCAB_FILES_NAMES
			snake_case_   :				str				  =  PRETRAINED_VOCAB_FILES_MAP
			snake_case_   :				List[str]				  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case_   :				Tuple				  =  ["input_ids", "attention_mask"]







			def __init__( self					:    List[str]					,					snake_case__					:    Any					,					snake_case__					:    Union[str, Any]					,					snake_case__					:    str="replace"					,					snake_case__					:    List[str]="<|endoftext|>"					,					snake_case__					:    Optional[int]="<|endoftext|>"					,					snake_case__					:    Tuple="<|endoftext|>"					,					snake_case__					:    Any=None					,					snake_case__					:    Tuple=False					,					snake_case__					:    List[Any]=False					,					**snake_case__					:    Dict					,					):




									"""simple docstring"""


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

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

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







			@property
			def UpperCamelCase    ( self					:    Any   ):




									"""simple docstring"""


									return len(self.encoder   )







			def UpperCamelCase    ( self					:    Any   ):




									"""simple docstring"""


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







			def UpperCamelCase    ( self					:    Any					,					snake_case__					:    Union[str, Any]   ):




									"""simple docstring"""


									if token in self.cache:
															return self.cache[token]
									_UpperCAmelCase									=  tuple(snake_case__   )
									_UpperCAmelCase									=  get_pairs(snake_case__   )

									if not pairs:
															return token

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

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







			def UpperCamelCase    ( self					:    Optional[Any]					,					snake_case__					:    int					,					snake_case__					:    Any=None   ):




									"""simple docstring"""


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

									_UpperCAmelCase									=  bos_token_ids + token_ids_a

									if token_ids_a is None:
															return output

									return output + bos_token_ids + token_ids_a







			def UpperCamelCase    ( self					:    List[Any]					,					snake_case__					:    List[str]   ):




									"""simple docstring"""


									_UpperCAmelCase									=  []
									for token in re.findall(self.pat					,					snake_case__   ):
															_UpperCAmelCase									=  "".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(snake_case__   ).split(" "   )   )
									return bpe_tokens







			def UpperCamelCase    ( self					:    Optional[Any]					,					snake_case__					:    List[str]   ):




									"""simple docstring"""


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







			def UpperCamelCase    ( self					:    List[str]					,					snake_case__					:    List[str]   ):




									"""simple docstring"""


									return self.decoder.get(snake_case__   )







			def UpperCamelCase    ( self					:    Tuple					,					snake_case__					:    int   ):




									"""simple docstring"""


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







			def UpperCamelCase    ( self					:    int					,					snake_case__					:    str					,					snake_case__					:    Optional[str] = None   ):




									"""simple docstring"""


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

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

									_UpperCAmelCase									=  0
									with open(snake_case__					,					"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 snake_case__   : 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!"   )
																											_UpperCAmelCase									=  token_index
																					writer.write(" ".join(snake_case__   ) + "\n"   )
																					index += 1

									return vocab_file, merge_file







			def UpperCamelCase    ( self					:    Tuple					,					snake_case__					:    Tuple					,					snake_case__					:    List[Any]=False					,					**snake_case__					:    Union[str, Any]   ):




									"""simple docstring"""


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







			def UpperCamelCase    ( self					:    List[str]					,					snake_case__					:    Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]					,					snake_case__					:    bool = False					,					snake_case__					:    bool = None					,					snake_case__					:    Optional[List[str]] = None					,					**snake_case__					:    str					,					):




									"""simple docstring"""


									_UpperCAmelCase									=  super()._decode(
									    token_ids=snake_case__					,					skip_special_tokens=snake_case__					,					clean_up_tokenization_spaces=snake_case__					,					**snake_case__					,					)

									if truncate_before_pattern is not None and len(snake_case__   ) > 0:
															_UpperCAmelCase									=  self.truncate(snake_case__					,					snake_case__   )

									return decoded_text







			def UpperCamelCase    ( self					:    Optional[Any]					,					snake_case__					:    Any					,					snake_case__					:    Dict   ):




									"""simple docstring"""


									def find_re(snake_case__					:    Union[str, Any]					,					snake_case__					:    Optional[int]					,					snake_case__					:    Any   ):
															_UpperCAmelCase									=  pattern.search(snake_case__					,					snake_case__   )
															return m.start() if m else -1

									_UpperCAmelCase									=  [re.compile(snake_case__					,					re.MULTILINE   ) for pattern in truncate_before_pattern]

									_UpperCAmelCase									=  list(re.finditer("^print"					,					snake_case__					,					re.MULTILINE   )   )

									if len(snake_case__   ) > 1:
															_UpperCAmelCase									=  completion[: prints[1].start()]

									_UpperCAmelCase									=  list(re.finditer("^def"					,					snake_case__					,					re.MULTILINE   )   )

									if len(snake_case__   ) > 1:
															_UpperCAmelCase									=  completion[: defs[1].start()]

									_UpperCAmelCase									=  0

									_UpperCAmelCase									=  [
									    pos for pos in [find_re(snake_case__					,					snake_case__					,					snake_case__   ) for terminal in terminals] if pos != -1
									]

									if len(snake_case__   ) > 0:
															return completion[: min(snake_case__   )]
									else:
															return completion










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


lowercase_			:       int						       =				logging.get_logger(__name__)






class 					__lowerCAmelCase				( UpperCAmelCase__				):
			snake_case_   :				PyTorchBenchmarkArguments
			snake_case_   :				PretrainedConfig
			snake_case_   :				str				  =  "PyTorch"







			@property
			def UpperCamelCase    ( self					:    List[Any]   ):




									"""simple docstring"""


									return torch.__version__







			def UpperCamelCase    ( self					:    str					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self._prepare_inference_func(snake_case__					,					snake_case__					,					snake_case__   )
									return self._measure_speed(_inference   )







			def UpperCamelCase    ( self					:    List[Any]					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self._prepare_inference_func(snake_case__					,					snake_case__					,					snake_case__   )
									return self._measure_memory(_inference   )







			def UpperCamelCase    ( self					:    Any					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self._prepare_train_func(snake_case__					,					snake_case__					,					snake_case__   )
									return self._measure_speed(_train   )







			def UpperCamelCase    ( self					:    List[str]					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self._prepare_train_func(snake_case__					,					snake_case__					,					snake_case__   )
									return self._measure_memory(_train   )







			def UpperCamelCase    ( self					:    Dict					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self.config_dict[model_name]

									if self.args.torchscript:
															_UpperCAmelCase									=  True

									_UpperCAmelCase									=  (
									    hasattr(snake_case__					,					"architectures"   )
									    and isinstance(config.architectures					,					snake_case__   )
									    and len(config.architectures   ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																					_UpperCAmelCase									=  config.architectures[0]
																					_UpperCAmelCase									=  __import__("transformers"					,					fromlist=[model_class]   )
																					_UpperCAmelCase									=  getattr(snake_case__					,					snake_case__   )
																					_UpperCAmelCase									=  model_cls(snake_case__   )
															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:
															_UpperCAmelCase									=  MODEL_MAPPING[config.__class__](snake_case__   )

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

									# encoder-decoder has vocab size saved differently
									_UpperCAmelCase									=  config.vocab_size if hasattr(snake_case__					,					"vocab_size"   ) else config.encoder.vocab_size
									_UpperCAmelCase									=  torch.randint(snake_case__					,					(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():
																					_UpperCAmelCase									=  torch.jit.trace(snake_case__					,					snake_case__   )
									else:
															_UpperCAmelCase									=  model

									def encoder_decoder_forward():
															with torch.no_grad():
																					_UpperCAmelCase									=  inference_model(snake_case__					,					decoder_input_ids=snake_case__   )
															return outputs

									def encoder_forward():
															with torch.no_grad():
																					_UpperCAmelCase									=  inference_model(snake_case__   )
															return outputs

									_UpperCAmelCase									=  encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
									return _forward







			def UpperCamelCase    ( self					:    str					,					snake_case__					:    str					,					snake_case__					:    int					,					snake_case__					:    int   ):




									"""simple docstring"""


									_UpperCAmelCase									=  self.config_dict[model_name]

									_UpperCAmelCase									=  (
									    hasattr(snake_case__					,					"architectures"   )
									    and isinstance(config.architectures					,					snake_case__   )
									    and len(config.architectures   ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																					_UpperCAmelCase									=  config.architectures[0]
																					_UpperCAmelCase									=  __import__("transformers"					,					fromlist=[model_class]   )
																					_UpperCAmelCase									=  getattr(snake_case__					,					snake_case__   )
																					_UpperCAmelCase									=  model_cls(snake_case__   )
															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:
															_UpperCAmelCase									=  MODEL_WITH_LM_HEAD_MAPPING[config.__class__](snake_case__   )

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

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

									# encoder-decoder has vocab size saved differently
									_UpperCAmelCase									=  config.vocab_size if hasattr(snake_case__					,					"vocab_size"   ) else config.encoder.vocab_size
									_UpperCAmelCase									=  torch.randint(snake_case__					,					(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():
															_UpperCAmelCase									=  train_model(snake_case__					,					labels=snake_case__   )[0]
															loss.backward()
															return loss

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

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







			def UpperCamelCase    ( self					:    Any					,					snake_case__					:    Tuple   ):




									"""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(
																					    snake_case__					,					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
															_UpperCAmelCase									=  timeit.repeat(
															    snake_case__					,					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(snake_case__   ) / 10.0
									except RuntimeError as e:
															self.print_fn(F"""Doesn't fit on GPU. {e}"""   )
															return "N/A"







			def UpperCamelCase    ( self					:    Tuple					,					snake_case__					:    Callable[[], None]   ):




									"""simple docstring"""


									try:
															if self.args.trace_memory_line_by_line:
																					_UpperCAmelCase									=  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."   )
																											_UpperCAmelCase									=  "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()
																											_UpperCAmelCase									=  nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx   )
																											_UpperCAmelCase									=  nvml.nvmlDeviceGetMemoryInfo(snake_case__   )
																											_UpperCAmelCase									=  meminfo.used
																											_UpperCAmelCase									=  Memory(snake_case__   )
																											# shutdown nvml
																											nvml.nvmlShutdown()
															else:
																					# cpu
																					_UpperCAmelCase									=  measure_peak_memory_cpu(snake_case__   )
																					_UpperCAmelCase									=  Memory(snake_case__   ) if isinstance(snake_case__					,					snake_case__   ) else memory_bytes

															if self.args.trace_memory_line_by_line:
																					_UpperCAmelCase									=  stop_memory_tracing(snake_case__   )
															else:
																					_UpperCAmelCase									=  None

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




