
"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


__UpperCAmelCase 					=    logging.get_logger(__name__)

__UpperCAmelCase 					=    {
    'layoutlmv2-base-uncased': 'https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json',
    'layoutlmv2-large-uncased': 'https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
				import detectrona




class      _SCREAMING_SNAKE_CASE   (     A__				):
			UpperCAmelCase_    :Optional[int]												=		"layoutlmv2"






			def __init__(   self			,  __A=3_0522			,  __A=768			,  __A=12			,  __A=12			,  __A=3072			,  __A="gelu"			,  __A=0.1			,  __A=0.1			,  __A=512			,  __A=2			,  __A=0.0_2			,  __A=1E-12			,  __A=0			,  __A=1024			,  __A=128			,  __A=32			,  __A=True			,  __A=256			,  __A=64			,  __A=True			,  __A=[7, 7, 256]			,  __A=128			,  __A=128			,  __A=True			,  __A=True			,  __A=False			,  __A=None			,  **__A			,  )     ->	List[str]:
						super().__init__(
						    vocab_size=__A			,  hidden_size=__A			,  num_hidden_layers=__A			,  num_attention_heads=__A			,  intermediate_size=__A			,  hidden_act=__A			,  hidden_dropout_prob=__A			,  attention_probs_dropout_prob=__A			,  max_position_embeddings=__A			,  type_vocab_size=__A			,  initializer_range=__A			,  layer_norm_eps=__A			,  pad_token_id=__A			,  **__A			,  )
						lowerCAmelCase_       :Optional[Any]     	=    max_ad_position_embeddings
						lowerCAmelCase_       :Dict     	=    max_rel_pos
						lowerCAmelCase_       :str     	=    rel_pos_bins
						lowerCAmelCase_       :List[str]     	=    fast_qkv
						lowerCAmelCase_       :Union[str, Any]     	=    max_rel_ad_pos
						lowerCAmelCase_       :List[str]     	=    rel_ad_pos_bins
						lowerCAmelCase_       :Any     	=    convert_sync_batchnorm
						lowerCAmelCase_       :Dict     	=    image_feature_pool_shape
						lowerCAmelCase_       :str     	=    coordinate_size
						lowerCAmelCase_       :Tuple     	=    shape_size
						lowerCAmelCase_       :Tuple     	=    has_relative_attention_bias
						lowerCAmelCase_       :Union[str, Any]     	=    has_spatial_attention_bias
						lowerCAmelCase_       :int     	=    has_visual_segment_embedding
						lowerCAmelCase_       :Any     	=    (
						    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
						)






			@classmethod
			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						return {
						    "MODEL.MASK_ON": True,
						    "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
						    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
						    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
						    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
						    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
						    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
						    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
						    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
						    "MODEL.POST_NMS_TOPK_TEST": 1000,
						    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
						    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
						    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
						    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
						    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
						    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
						    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
						    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
						    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
						    "MODEL.RESNETS.DEPTH": 101,
						    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
						    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
						    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
						    "MODEL.RESNETS.NUM_GROUPS": 32,
						    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
						    "MODEL.RESNETS.STRIDE_IN_1X1": False,
						}






			def 					__lowerCAmelCase					(   self			)     ->	List[Any]:
						lowerCAmelCase_       :Any     	=    detectrona.config.get_cfg()
						for k, v in self.detectrona_config_args.items():
									lowerCAmelCase_       :int     	=    k.split("""."""			)
									lowerCAmelCase_       :List[Any]     	=    detectrona_config
									for attribute in attributes[:-1]:
												lowerCAmelCase_       :List[Any]     	=    getattr(__A			,  __A			)
									setattr(__A			,  attributes[-1]			,  __A			)

						return detectrona_config








"""simple docstring"""






import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class      _SCREAMING_SNAKE_CASE   (     A__      ,					unittest.TestCase				):
			UpperCAmelCase_    :List[str]												=		GPTNeoXJapaneseTokenizer
			UpperCAmelCase_    :List[str]												=		False
			UpperCAmelCase_    :int												=		{"do_clean_text": False, "add_prefix_space": False}






			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						super().setUp()

						lowerCAmelCase_       :int     	=    [
						    """こん""",
						    """こんに""",
						    """にちは""",
						    """ばんは""",
						    """世界,㔺界""",
						    """、""",
						    """。""",
						    """<BR>""",
						    """<SP>""",
						    """<TAB>""",
						    """<URL>""",
						    """<EMAIL>""",
						    """<TEL>""",
						    """<DATE>""",
						    """<PRICE>""",
						    """<BLOCK>""",
						    """<KIGOU>""",
						    """<U2000U2BFF>""",
						    """<|emoji1|>""",
						    """<unk>""",
						    """<|startoftext|>""",
						    """<|endoftext|>""",
						]
						lowerCAmelCase_       :str     	=    {"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
						lowerCAmelCase_       :Dict     	=    {"""unk_token""": """<unk>"""}

						lowerCAmelCase_       :Union[str, Any]     	=    os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["""vocab_file"""]			)
						lowerCAmelCase_       :Union[str, Any]     	=    os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["""emoji_file"""]			)
						with open(self.vocab_file			,  """w"""			,  encoding="""utf-8"""			) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]			)			)
						with open(self.emoji_file			,  """w"""			) as emoji_writer:
									emoji_writer.write(json.dumps(__A			)			)






			def 					__lowerCAmelCase					(   self			,  **__A			)     ->	str:
						kwargs.update(self.special_tokens_map			)
						return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname			,  **__A			)






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	List[Any]:
						lowerCAmelCase_       :Optional[Any]     	=    """こんにちは、世界。 \nこんばんは、㔺界。😀"""
						lowerCAmelCase_       :List[Any]     	=    """こんにちは、世界。 \nこんばんは、世界。😀"""
						return input_text, output_text






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	Optional[Any]:
						lowerCAmelCase_	,			lowerCAmelCase_       :Optional[int]     	=    self.get_input_output_texts(__A			)
						lowerCAmelCase_       :List[Any]     	=    tokenizer.encode(__A			,  add_special_tokens=__A			)
						lowerCAmelCase_       :Optional[Any]     	=    tokenizer.decode(__A			,  clean_up_tokenization_spaces=__A			)
						return text, ids






			def 					__lowerCAmelCase					(   self			)     ->	str:
						pass  # TODO add if relevant






			def 					__lowerCAmelCase					(   self			)     ->	Any:
						pass  # TODO add if relevant






			def 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						pass  # TODO add if relevant






			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						lowerCAmelCase_       :int     	=    self.get_tokenizer()

						# Testing tokenization
						lowerCAmelCase_       :List[str]     	=    """こんにちは、世界。　こんばんは、㔺界。"""
						lowerCAmelCase_       :int     	=    ["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
						lowerCAmelCase_       :List[Any]     	=    tokenizer.tokenize(__A			)
						self.assertListEqual(__A			,  __A			)

						# Testing conversion to ids without special tokens
						lowerCAmelCase_       :List[str]     	=    [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
						lowerCAmelCase_       :Dict     	=    tokenizer.convert_tokens_to_ids(__A			)
						self.assertListEqual(__A			,  __A			)

						# Testing conversion to ids with special tokens
						lowerCAmelCase_       :Tuple     	=    tokens + [tokenizer.unk_token]
						lowerCAmelCase_       :str     	=    [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
						lowerCAmelCase_       :Any     	=    tokenizer.convert_tokens_to_ids(__A			)
						self.assertListEqual(__A			,  __A			)






			@slow
			def 					__lowerCAmelCase					(   self			)     ->	Any:
						lowerCAmelCase_       :str     	=    self.tokenizer_class.from_pretrained("""abeja/gpt-neox-japanese-2.7b"""			)

						lowerCAmelCase_       :Union[str, Any]     	=    tokenizer.encode("""ありがとう。"""			,  add_special_tokens=__A			)
						lowerCAmelCase_       :Dict     	=    tokenizer.encode("""どういたしまして。"""			,  add_special_tokens=__A			)

						lowerCAmelCase_       :Union[str, Any]     	=    tokenizer.build_inputs_with_special_tokens(__A			)
						lowerCAmelCase_       :Optional[int]     	=    tokenizer.build_inputs_with_special_tokens(__A			,  __A			)

						assert encoded_sentence == ids_a
						assert encoded_pair == ids_a + ids_a






			def 					__lowerCAmelCase					(   self			)     ->	Dict:
						# Intentionally convert some words to accommodate character fluctuations unique to Japanese
						pass






			def 					__lowerCAmelCase					(   self			)     ->	List[str]:
						# tokenizer has no padding token
						pass






