


import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							def __init__(							self	:  Tuple   ,     lowerCamelCase	:  Dict   ,     lowerCamelCase	:  int    )     ->   List[Any]:
														__snake_case		:      List[Any]										=       params

														__snake_case		:      int										=       np.array(lowerCamelCase    )
														__snake_case		:      Any										=       np.array([len(lowerCamelCase    ) for t in data]    )

														self.check()
														self.remove_long_sequences()
														self.remove_empty_sequences()
														self.remove_unknown_sequences()
														self.check()
														self.print_statistics()

							def __getitem__(							self	:  List[Any]   ,     lowerCamelCase	:  Dict    )     ->   str:
														return (self.token_ids[index], self.lengths[index])

							def __len__(							self	:  Dict    )     ->   Union[str, Any]:
														return len(self.lengths    )

							def    __snake_case (							self	:  Optional[int]    )     ->   str:
														assert len(self.token_ids    ) == len(self.lengths    )
														assert all(self.lengths[i] == len(self.token_ids[i]    ) for i in range(len(self.lengths    )    )    )

							def    __snake_case (							self	:  Union[str, Any]    )     ->   List[str]:
														__snake_case		:      Optional[int]										=       self.params.max_model_input_size
														__snake_case		:      Union[str, Any]										=       self.lengths > max_len
														logger.info(F'Splitting {sum(lowerCamelCase    )} too long sequences.'    )

														def divide_chunks(lowerCamelCase	:  str   ,     lowerCamelCase	:  Any    ):
																					return [l[i : i + n] for i in range(0   ,     len(lowerCamelCase    )   ,     lowerCamelCase    )]

														__snake_case		:      str										=       []
														__snake_case		:      int										=       []
														if self.params.mlm:
																					__snake_case			,      __snake_case		:      Optional[int]										=       self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
														else:
																					__snake_case			,      __snake_case		:      List[Any]										=       self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]

														for seq_, len_ in zip(self.token_ids   ,     self.lengths    ):
																					assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
																					if len_ <= max_len:
																												new_tok_ids.append(seq_    )
																												new_lengths.append(len_    )
																					else:
																												__snake_case		:      str										=       []
																												for sub_s in divide_chunks(seq_   ,     max_len - 2    ):
																																			if sub_s[0] != cls_id:
																																										__snake_case		:      List[Any]										=       np.insert(lowerCamelCase   ,     0   ,     lowerCamelCase    )
																																			if sub_s[-1] != sep_id:
																																										__snake_case		:      Optional[Any]										=       np.insert(lowerCamelCase   ,     len(lowerCamelCase    )   ,     lowerCamelCase    )
																																			assert len(lowerCamelCase    ) <= max_len
																																			assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																																			sub_seqs.append(lowerCamelCase    )

																												new_tok_ids.extend(lowerCamelCase    )
																												new_lengths.extend([len(lowerCamelCase    ) for l in sub_seqs]    )

														__snake_case		:      str										=       np.array(lowerCamelCase    )
														__snake_case		:      Optional[Any]										=       np.array(lowerCamelCase    )

							def    __snake_case (							self	:  Any    )     ->   List[Any]:
														__snake_case		:      List[Any]										=       len(self    )
														__snake_case		:      int										=       self.lengths > 11
														__snake_case		:      str										=       self.token_ids[indices]
														__snake_case		:      Optional[int]										=       self.lengths[indices]
														__snake_case		:      Tuple										=       len(self    )
														logger.info(F'Remove {init_size - new_size} too short (<=11 tokens) sequences.'    )

							def    __snake_case (							self	:  int    )     ->   Optional[Any]:
														if "unk_token" not in self.params.special_tok_ids:
																					return
														else:
																					__snake_case		:      str										=       self.params.special_tok_ids["unk_token"]
														__snake_case		:      Optional[Any]										=       len(self    )
														__snake_case		:      str										=       np.array([np.count_nonzero(a == unk_token_id    ) for a in self.token_ids]    )
														__snake_case		:      int										=       (unk_occs / self.lengths) < 0.5
														__snake_case		:      Tuple										=       self.token_ids[indices]
														__snake_case		:      List[str]										=       self.lengths[indices]
														__snake_case		:      List[Any]										=       len(self    )
														logger.info(F'Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'    )

							def    __snake_case (							self	:  str    )     ->   List[str]:
														if not self.params.is_master:
																					return
														logger.info(F'{len(self    )} sequences'    )
														# data_len = sum(self.lengths)
														# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
														# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

														# unk_idx = self.params.special_tok_ids['unk_token']
														# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
														# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')






							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  Any    )     ->   Union[str, Any]:
														__snake_case		:      str										=       [t[0] for t in batch]
														__snake_case		:      Union[str, Any]										=       [t[1] for t in batch]
														assert len(lowerCamelCase    ) == len(lowerCamelCase    )

														# Max for paddings
														__snake_case		:      List[str]										=       max(lowerCamelCase    )

														# Pad token ids
														if self.params.mlm:
																					__snake_case		:      Any										=       self.params.special_tok_ids["pad_token"]
														else:
																					__snake_case		:      List[str]										=       self.params.special_tok_ids["unk_token"]
														__snake_case		:      Any										=       [list(t.astype(lowerCamelCase    )    ) + [pad_idx] * (max_seq_len_ - len(lowerCamelCase    )) for t in token_ids]
														assert len(tk_    ) == len(lowerCamelCase    )
														assert all(len(lowerCamelCase    ) == max_seq_len_ for t in tk_    )

														__snake_case		:      Tuple										=       torch.tensor(tk_    )  # (bs, max_seq_len_)
														__snake_case		:      str										=       torch.tensor(lowerCamelCase    )  # (bs)
														return tk_t, lg_t





# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


_snake_case					:		Optional[Any]      =    "Create a default config file for Accelerate with only a few flags set."







def     lowerCAmelCase_					( __lowerCamelCase="no"							,     __lowerCamelCase = default_json_config_file							,     __lowerCamelCase = False     ):
							__snake_case		:      int										=       Path(__lowerCamelCase     )
							path.parent.mkdir(parents=__lowerCamelCase							,     exist_ok=__lowerCamelCase     )
							if path.exists():
														print(
														    F'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'     )
														return False
							__snake_case		:      Any										=       mixed_precision.lower()
							if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
														raise ValueError(
														    F'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'     )
							__snake_case		:      Optional[int]										=       {
							    "compute_environment": "LOCAL_MACHINE",
							    "mixed_precision": mixed_precision,
							}
							if torch.cuda.is_available():
														__snake_case		:      Dict										=       torch.cuda.device_count()
														__snake_case		:      Tuple										=       num_gpus
														__snake_case		:      List[str]										=       False
														if num_gpus > 1:
																					__snake_case		:      Optional[int]										=       "MULTI_GPU"
														else:
																					__snake_case		:      Dict										=       "NO"
							elif is_xpu_available() and use_xpu:
														__snake_case		:      List[str]										=       torch.xpu.device_count()
														__snake_case		:      str										=       num_xpus
														__snake_case		:      int										=       False
														if num_xpus > 1:
																					__snake_case		:      Optional[int]										=       "MULTI_XPU"
														else:
																					__snake_case		:      str										=       "NO"
							elif is_npu_available():
														__snake_case		:      Any										=       torch.npu.device_count()
														__snake_case		:      str										=       num_npus
														__snake_case		:      str										=       False
														if num_npus > 1:
																					__snake_case		:      Optional[int]										=       "MULTI_NPU"
														else:
																					__snake_case		:      int										=       "NO"
							else:
														__snake_case		:      List[Any]										=       0
														__snake_case		:      Dict										=       True
														__snake_case		:      Tuple										=       1
														__snake_case		:      Tuple										=       "NO"
							__snake_case		:      str										=       ClusterConfig(**__lowerCamelCase     )
							config.to_json_file(__lowerCamelCase     )
							return path







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Optional[Any]										=       parser.add_parser("default"							,     parents=__lowerCamelCase							,     help=__lowerCamelCase							,     formatter_class=__lowerCamelCase     )
							parser.add_argument(
							    "--config_file"							,     default=__lowerCamelCase							,     help=(
							        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
							        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
							        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
							        "with 'huggingface'."
							    )							,     dest="save_location"							,     )

							parser.add_argument(
							    "--mixed_precision"							,     choices=["no", "fp16", "bf16"]							,     type=__lowerCamelCase							,     help="Whether or not to use mixed precision training. "
							    "Choose between FP16 and BF16 (bfloat16) training. "
							    "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."							,     default="no"							,     )
							parser.set_defaults(func=__lowerCamelCase     )
							return parser







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      List[Any]										=       write_basic_config(args.mixed_precision							,     args.save_location     )
							if config_file:
														print(F'accelerate configuration saved at {config_file}'     )

