



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



					@require_multi_gpu
					def    A__		(			self     ):
							print(f'''Found {torch.cuda.device_count()} devices.'''     )
							UpperCAmelCase_						=				["torchrun", f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
							with patch_environment(omp_num_threads=1     ):
									execute_subprocess_async(lowerCAmelCase    ,	env=os.environ.copy()     )



					@require_multi_gpu
					def    A__		(			self     ):
							print(f'''Found {torch.cuda.device_count()} devices.'''     )
							UpperCAmelCase_						=				["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(lowerCAmelCase    ,	env=os.environ.copy()     )



					@require_multi_gpu
					def    A__		(			self     ):
							UpperCAmelCase_						=				["torchrun", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__     )]
							with patch_environment(omp_num_threads=1     ):
									execute_subprocess_async(lowerCAmelCase    ,	env=os.environ.copy()     )



					@require_multi_gpu
					def    A__		(			self     ):
							print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''     )
							UpperCAmelCase_						=				["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(lowerCAmelCase    ,	env=os.environ.copy()     )


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

		SCREAMING_SNAKE_CASE     				=						""

		SCREAMING_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)."

		SCREAMING_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."
		SCREAMING_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)






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE     				=						{
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

SCREAMING_SNAKE_CASE     				=						{
    "yjernite/retribert-base-uncased": 512,
}


SCREAMING_SNAKE_CASE     				=						{
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    List[str]									=  VOCAB_FILES_NAMES
					lowerCAmelCase_				:    int									=  PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_				:    Any									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_				:    Dict									=  PRETRAINED_INIT_CONFIGURATION
					lowerCAmelCase_				:    List[str]									=  RetriBertTokenizer
					lowerCAmelCase_				:    Union[str, Any]									=  ['input_ids', 'attention_mask']



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

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

							UpperCAmelCase_						=				do_lower_case



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=None     ):
							UpperCAmelCase_						=				[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    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				[self.sep_token_id]
							UpperCAmelCase_						=				[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    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				self._tokenizer.model.save(lowerCAmelCase    ,	name=lowerCAmelCase     )
							return tuple(lowerCAmelCase     )

