





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment

class 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):






      """simple docstring"""




      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case								=       inspect.getfile(accelerate.test_utils				)
         __snake_case								=       os.path.sep.join(mod_file.split(os.path.sep				)[:-1] + ['''scripts''', '''test_script.py''']				)
         __snake_case								=       os.path.sep.join(
             mod_file.split(os.path.sep				)[:-1] + ['''scripts''', '''test_distributed_data_loop.py''']				)
         __snake_case								=       os.path.sep.join(mod_file.split(os.path.sep				)[:-1] + ['''scripts''', '''test_ops.py''']				)


      @require_multi_gpu
      def        a			(self	:				int				):





         """simple docstring"""



         print(f"""Found {torch.cuda.device_count()} devices."""				)
         __snake_case								=       ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
         with patch_environment(omp_num_threads=1				):
            execute_subprocess_async(a__    ,      env=os.environ.copy()				)


      @require_multi_gpu
      def        a			(self	:				List[str]				):





         """simple docstring"""



         print(f"""Found {torch.cuda.device_count()} devices."""				)
         __snake_case								=       ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
         print(f"""Command: {cmd}"""				)
         with patch_environment(omp_num_threads=1				):
            execute_subprocess_async(a__    ,      env=os.environ.copy()				)


      @require_multi_gpu
      def        a			(self	:				Any				):





         """simple docstring"""



         __snake_case								=       ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__				)]
         with patch_environment(omp_num_threads=1				):
            execute_subprocess_async(a__    ,      env=os.environ.copy()				)


      @require_multi_gpu
      def        a			(self	:				Any				):





         """simple docstring"""



         print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""				)
         __snake_case								=       ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
         with patch_environment(omp_num_threads=1    ,      cuda_visible_devices='''0,1'''				):
            execute_subprocess_async(a__    ,      env=os.environ.copy()				)


if __name__ == "__main__":
    snake_case_           =      Accelerator()
    snake_case_           =      (accelerator.state.process_index + 2, 10)
    snake_case_           =      torch.randint(0, 10, shape).to(accelerator.device)

    snake_case_           =      ''

    snake_case_           =      accelerator.pad_across_processes(tensor)
    if tensora.shape[0] != accelerator.state.num_processes + 1:
        error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
    if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
        error_msg += "Tensors have different values."
    if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
        error_msg += "Padding was not done with the right value (0)."

    snake_case_           =      accelerator.pad_across_processes(tensor, pad_first=True)
    if tensora.shape[0] != accelerator.state.num_processes + 1:
        error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
    snake_case_           =      accelerator.state.num_processes - accelerator.state.process_index - 1
    if not torch.equal(tensora[index:], tensor):
        error_msg += "Tensors have different values."
    if not torch.all(tensora[:index] == 0):
        error_msg += "Padding was not done with the right value (0)."

    # Raise error at the end to make sure we don't stop at the first failure.
    if len(error_msg) > 0:
        raise ValueError(error_msg)








from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


snake_case_           =      logging.get_logger(__name__)

snake_case_           =      {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case_           =      {
    'vocab_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
    },
    'merges_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
    },
}

snake_case_           =      {'allegro/herbert-base-cased': 514}
snake_case_           =      {}

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    Dict							=   VOCAB_FILES_NAMES
      A_						:    Optional[Any]							=   PRETRAINED_VOCAB_FILES_MAP
      A_						:    Optional[int]							=   PRETRAINED_INIT_CONFIGURATION
      A_						:    int							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A_						:    Any							=   HerbertTokenizer


      def __init__(self	:				Dict    ,      a__	:				Tuple=None    ,      a__	:				Optional[int]=None    ,      a__	:				List[str]=None    ,      a__	:				Optional[int]="<s>"    ,      a__	:				Optional[Any]="<unk>"    ,      a__	:				Any="<pad>"    ,      a__	:				List[Any]="<mask>"    ,      a__	:				Any="</s>"    ,      **a__	:				Tuple    ,      ):





         """simple docstring"""



         super().__init__(
             a__    ,      a__    ,      tokenizer_file=a__    ,      cls_token=a__    ,      unk_token=a__    ,      pad_token=a__    ,      mask_token=a__    ,      sep_token=a__    ,      **a__    ,      )


      def        a			(self	:				List[str]    ,      a__	:				List[int]    ,      a__	:				Optional[List[int]] = None				):





         """simple docstring"""



         __snake_case								=       [self.cls_token_id]
         __snake_case								=       [self.sep_token_id]
         if token_ids_a is None:
            return cls + token_ids_a + sep

         return cls + token_ids_a + sep + token_ids_a + sep


      def        a			(self	:				List[str]    ,      a__	:				List[int]    ,      a__	:				Optional[List[int]] = None    ,      a__	:				bool = False				):





         """simple docstring"""



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

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


      def        a			(self	:				Optional[int]    ,      a__	:				List[int]    ,      a__	:				Optional[List[int]] = None				):





         """simple docstring"""



         __snake_case								=       [self.sep_token_id]
         __snake_case								=       [self.cls_token_id]

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


      def        a			(self	:				int    ,      a__	:				str    ,      a__	:				Optional[str] = None				):





         """simple docstring"""



         __snake_case								=       self._tokenizer.model.save(a__    ,      name=a__				)
         return tuple(a__				)

