



import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 	lowerCamelCase      (						lowercase__,     unittest.TestCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Optional[int]									=  XLMTokenizer
					lowerCAmelCase_				:    List[Any]									=  False



					def    A__		(			self     ):
						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						UpperCAmelCase_						=				[
						    "l",
						    "o",
						    "w",
						    "e",
						    "r",
						    "s",
						    "t",
						    "i",
						    "d",
						    "n",
						    "w</w>",
						    "r</w>",
						    "t</w>",
						    "lo",
						    "low",
						    "er</w>",
						    "low</w>",
						    "lowest</w>",
						    "newer</w>",
						    "wider</w>",
						    "<unk>",
						]
						UpperCAmelCase_						=				dict(zip(lowerCAmelCase    ,	range(len(lowerCAmelCase     )     )     )     )
						UpperCAmelCase_						=				["l o 123", "lo w 1456", "e r</w> 1789", ""]

						UpperCAmelCase_						=				os.path.join(self.tmpdirname    ,	VOCAB_FILES_NAMES["vocab_file"]     )
						UpperCAmelCase_						=				os.path.join(self.tmpdirname    ,	VOCAB_FILES_NAMES["merges_file"]     )
						with open(self.vocab_file    ,	"w"     ) as fp:
							fp.write(json.dumps(lowerCAmelCase     )     )
						with open(self.merges_file    ,	"w"     ) as fp:
							fp.write("\n".join(lowerCAmelCase     )     )



					def    A__		(			self    ,	lowerCAmelCase     ):
						UpperCAmelCase_						=				"lower newer"
						UpperCAmelCase_						=				"lower newer"
						return input_text, output_text



					def    A__		(			self     ):
						UpperCAmelCase_						=				XLMTokenizer(self.vocab_file    ,	self.merges_file     )

						UpperCAmelCase_						=				"lower"
						UpperCAmelCase_						=				["low", "er</w>"]
						UpperCAmelCase_						=				tokenizer.tokenize(lowerCAmelCase     )
						self.assertListEqual(lowerCAmelCase    ,	lowerCAmelCase     )

						UpperCAmelCase_						=				tokens + ["<unk>"]
						UpperCAmelCase_						=				[14, 15, 20]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase     )    ,	lowerCAmelCase     )



					@slow
					def    A__		(			self     ):
						UpperCAmelCase_						=				XLMTokenizer.from_pretrained("xlm-mlm-en-2048"     )

						UpperCAmelCase_						=				tokenizer.encode("sequence builders"    ,	add_special_tokens=lowerCAmelCase     )
						UpperCAmelCase_						=				tokenizer.encode("multi-sequence build"    ,	add_special_tokens=lowerCAmelCase     )

						UpperCAmelCase_						=				tokenizer.build_inputs_with_special_tokens(lowerCAmelCase     )
						UpperCAmelCase_						=				tokenizer.build_inputs_with_special_tokens(lowerCAmelCase    ,	lowerCAmelCase     )

						assert encoded_sentence == [0] + text + [1]
						assert encoded_pair == [0] + text + [1] + text_a + [1]






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     				=						{
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    List[str]									=  'xlm-roberta'



					def __init__(			self    ,	lowerCAmelCase=3_0522    ,	lowerCAmelCase=768    ,	lowerCAmelCase=12    ,	lowerCAmelCase=12    ,	lowerCAmelCase=3072    ,	lowerCAmelCase="gelu"    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=512    ,	lowerCAmelCase=2    ,	lowerCAmelCase=0.02    ,	lowerCAmelCase=1e-1_2    ,	lowerCAmelCase=1    ,	lowerCAmelCase=0    ,	lowerCAmelCase=2    ,	lowerCAmelCase="absolute"    ,	lowerCAmelCase=True    ,	lowerCAmelCase=None    ,	**lowerCAmelCase    ,	):
							super().__init__(pad_token_id=lowerCAmelCase    ,	bos_token_id=lowerCAmelCase    ,	eos_token_id=lowerCAmelCase    ,	**lowerCAmelCase     )

							UpperCAmelCase_						=				vocab_size
							UpperCAmelCase_						=				hidden_size
							UpperCAmelCase_						=				num_hidden_layers
							UpperCAmelCase_						=				num_attention_heads
							UpperCAmelCase_						=				hidden_act
							UpperCAmelCase_						=				intermediate_size
							UpperCAmelCase_						=				hidden_dropout_prob
							UpperCAmelCase_						=				attention_probs_dropout_prob
							UpperCAmelCase_						=				max_position_embeddings
							UpperCAmelCase_						=				type_vocab_size
							UpperCAmelCase_						=				initializer_range
							UpperCAmelCase_						=				layer_norm_eps
							UpperCAmelCase_						=				position_embedding_type
							UpperCAmelCase_						=				use_cache
							UpperCAmelCase_						=				classifier_dropout





class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					@property
					def    A__		(			self     ):
							if self.task == "multiple-choice":
									UpperCAmelCase_						=				{0: "batch", 1: "choice", 2: "sequence"}
							else:
									UpperCAmelCase_						=				{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]     )

