



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 			lowerCamelCase__     (				unittest.TestCase):




		'''simple docstring'''



		def 						lowerCAmelCase__ (self					)	->     List[Any]:

						"""simple docstring"""






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



		@require_multi_gpu
		def 						lowerCAmelCase__ (self					)	->     List[Any]:

						"""simple docstring"""






						print(f"""Found {torch.cuda.device_count()} devices."""					)
						lowerCAmelCase__	:							Any				=	['''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 						lowerCAmelCase__ (self					)	->     Any:

						"""simple docstring"""






						print(f"""Found {torch.cuda.device_count()} devices."""					)
						lowerCAmelCase__	:							Any				=	['''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 						lowerCAmelCase__ (self					)	->     List[str]:

						"""simple docstring"""






						lowerCAmelCase__	:							Dict				=	['''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 						lowerCAmelCase__ (self					)	->     str:

						"""simple docstring"""






						print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""					)
						lowerCAmelCase__	:							Any				=	['''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							:						Optional[Any]      =Accelerator()
							__snake_case							:						List[str]      =(accelerator.state.process_index + 2, 1_0)
							__snake_case							:						List[Any]      =torch.randint(0, 1_0, shape).to(accelerator.device)

							__snake_case							:						Tuple      =''

							__snake_case							:						Dict      =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							:						int      =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							:						str      =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)





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE							    =							{
    'vocab_file': {'mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt'},
    'tokenizer_file': {
        'mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json'
    },
}

SCREAMING_SNAKE_CASE							    =							{'mobilebert-uncased': 5_1_2}


SCREAMING_SNAKE_CASE							    =							{}


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      VOCAB_FILES_NAMES
     _lowerCamelCase   =      PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase   =      PRETRAINED_INIT_CONFIGURATION
     _lowerCamelCase   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase   =      MobileBertTokenizer


     def __init__(	self  ,       __A=None  ,       __A=None  ,       __A=True  ,       __A="[UNK]"  ,       __A="[SEP]"  ,       __A="[PAD]"  ,       __A="[CLS]"  ,       __A="[MASK]"  ,       __A=True  ,       __A=None  ,       **__A  ,       ):
        super().__init__(
            __A  ,       tokenizer_file=__A  ,       do_lower_case=__A  ,       unk_token=__A  ,       sep_token=__A  ,       pad_token=__A  ,       cls_token=__A  ,       mask_token=__A  ,       tokenize_chinese_chars=__A  ,       strip_accents=__A  ,       **__A  ,       )

        __a					=  json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
        if (
            normalizer_state.get("""lowercase"""  ,       __A    ) != do_lower_case
            or normalizer_state.get("""strip_accents"""  ,       __A    ) != strip_accents
            or normalizer_state.get("""handle_chinese_chars"""  ,       __A    ) != tokenize_chinese_chars
        ):
           __a					=  getattr(__A  ,       normalizer_state.pop("""type"""    )    )
           __a					=  do_lower_case
           __a					=  strip_accents
           __a					=  tokenize_chinese_chars
           __a					=  normalizer_class(**__A    )

        __a					=  do_lower_case


     def 	snake_case_  (	self  ,       __A  ,       __A=None    ):
        __a					=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
           output += token_ids_a + [self.sep_token_id]

        return output


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  [self.sep_token_id]
        __a					=  [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 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  self._tokenizer.model.save(__A  ,       name=__A    )
        return tuple(__A    )



