

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


_a:			Union[str, Any]       							=	logging.get_logger(__name__)

_a:			Union[str, Any]       							=	{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_a:			Optional[Any]       							=	{
    """vocab_file""": {
        """microsoft/layoutlm-base-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt"""
        ),
        """microsoft/layoutlm-large-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """microsoft/layoutlm-base-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json"""
        ),
        """microsoft/layoutlm-large-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

_a:			int       							=	{
    """microsoft/layoutlm-base-uncased""": 512,
    """microsoft/layoutlm-large-uncased""": 512,
}

_a:			Optional[int]       							=	{
    """microsoft/layoutlm-base-uncased""": {"""do_lower_case""": True},
    """microsoft/layoutlm-large-uncased""": {"""do_lower_case""": True},
}

class   __UpperCamelCase			(    __lowercase  ):
	SCREAMING_SNAKE_CASE__			 =   VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE__			 =   PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE__			 =   PRETRAINED_INIT_CONFIGURATION
	SCREAMING_SNAKE_CASE__			 =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE__			 =   LayoutLMTokenizer




	def __init__(						self		:  str						,		lowerCAmelCase		:  Optional[int]=None						,		lowerCAmelCase		:  Any=None						,		lowerCAmelCase		:  List[Any]=True						,		lowerCAmelCase		:  Union[str, Any]="[UNK]"						,		lowerCAmelCase		:  Union[str, Any]="[SEP]"						,		lowerCAmelCase		:  Tuple="[PAD]"						,		lowerCAmelCase		:  str="[CLS]"						,		lowerCAmelCase		:  Dict="[MASK]"						,		lowerCAmelCase		:  Union[str, Any]=True						,		lowerCAmelCase		:  Tuple=None						,		**lowerCAmelCase		:  str						,		):


		'''simple docstring'''







		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		:  Optional[Any]						,		lowerCAmelCase		:  List[str]						,		lowerCAmelCase		:  Tuple=None							):


		'''simple docstring'''







		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		:  int						,		lowerCAmelCase		:  Any						,		lowerCAmelCase		:  Optional[Any] = None							):


		'''simple docstring'''







		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		:  Optional[Any]						,		lowerCAmelCase		:  Optional[Any]						,		lowerCAmelCase		:  Union[str, Any] = None							):


		'''simple docstring'''







		UpperCAmelCase_				 =	self._tokenizer.model.save(__lowerCamelCase						,		name=__lowerCamelCase							)
		return tuple(__lowerCamelCase							)






# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json





class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       @classmethod
       def 			UpperCamelCase_	(					cls							)	->   Tuple:
            return cls







       @slow
       @require_torch_gpu
       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.get_auto_remove_tmp_dir()
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							F"""
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        """.split()

            # we want this test to run even if there is only one GPU, but if there are more we use them all
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							get_gpu_count()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							F"""
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        """.split()
            _SCREAMING_SNAKE_CASE				:		int  			=							[sys.executable] + distributed_args + args
            execute_subprocess_async(__lowerCamelCase			,  env=self.get_env()							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							os.path.join(__lowerCamelCase			,  "test_bleu.json"							)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							load_json(__lowerCamelCase							)
            # print(metrics)
            self.assertGreaterEqual(metrics["bleu"]			,  2_5							)