



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

from utils import logger
class 				_snake_case			(					lowercase__):




    def __init__(						self					:						List[str],					__lowercase					:						Union[str, Any],					__lowercase					:						Dict						):
       lowercase__					  =				params

       lowercase__					  =				np.array(__lowercase						)
       lowercase__					  =				np.array([len(__lowercase						) 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					:						str,					__lowercase					:						List[str]						):
       return (self.token_ids[index], self.lengths[index])




    def __len__(						self					:						str						):
       return len(self.lengths						)




    def 							A__      (						self					:						Optional[Any]						):
       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 							A__      (						self					:						Union[str, Any]						):
       lowercase__					  =				self.params.max_model_input_size
       lowercase__					  =				self.lengths > max_len
       logger.info(F'''Splitting {sum(__lowercase						)} too long sequences.'''						)

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

       lowercase__					  =				[]
       lowercase__					  =				[]
       if self.params.mlm:
          lowercase__  ,       lowercase__					  =				self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
       else:
          lowercase__  ,       lowercase__					  =				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:
             lowercase__					  =				[]
             for sub_s in divide_chunks(seq_,					max_len - 2						):
                if sub_s[0] != cls_id:
                   lowercase__					  =				np.insert(__lowercase,					0,					__lowercase						)
                if sub_s[-1] != sep_id:
                   lowercase__					  =				np.insert(__lowercase,					len(__lowercase						),					__lowercase						)
                assert len(__lowercase						) <= max_len
                assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                sub_seqs.append(__lowercase						)

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

       lowercase__					  =				np.array(__lowercase						)
       lowercase__					  =				np.array(__lowercase						)




    def 							A__      (						self					:						Tuple						):
       lowercase__					  =				len(self						)
       lowercase__					  =				self.lengths > 11
       lowercase__					  =				self.token_ids[indices]
       lowercase__					  =				self.lengths[indices]
       lowercase__					  =				len(self						)
       logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''						)




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




    def 							A__      (						self					:						Tuple						):
       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 							A__      (						self					:						List[Any],					__lowercase					:						Optional[int]						):
       lowercase__					  =				[t[0] for t in batch]
       lowercase__					  =				[t[1] for t in batch]
       assert len(__lowercase						) == len(__lowercase						)

       # Max for paddings
       lowercase__					  =				max(__lowercase						)

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

       lowercase__					  =				torch.tensor(tk_						)  # (bs, max_seq_len_)
       lowercase__					  =				torch.tensor(__lowercase						)  # (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 argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser



def __lowerCAmelCase (     ):
			lowercase__					  =				ArgumentParser("Accelerate CLI tool"						,  usage="accelerate <command> [<args>]"						,  allow_abbrev=SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				parser.add_subparsers(help="accelerate command helpers"  )

			# Register commands
			get_config_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			env_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			launch_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			tpu_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			test_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )

			# Let's go
			lowercase__					  =				parser.parse_args()

			if not hasattr(SCREAMING_SNAKE_CASE_						,  "func"  ):
						parser.print_help()
						exit(1  )

			# Run
			args.func(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
							main()




