
'''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      A							(    __UpperCAmelCase						,  unittest.TestCase			):
			lowerCamelCase							:							Optional[Any]    						=					GPTNeoXJapaneseTokenizer
			lowerCamelCase							:							Tuple    						=					False
			lowerCamelCase							:							str    						=					{"""do_clean_text""": False, """add_prefix_space""": False}



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									super().setUp()

									lowercase__								=    [
									    """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|>""",
									]
									lowercase__								=    {"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
									lowercase__								=    {"""unk_token""": """<unk>"""}

									lowercase__								=    os.path.join(self.tmpdirname    ,       VOCAB_FILES_NAMES["""vocab_file"""]    )
									lowercase__								=    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(lowerCamelCase__    )    )



			def A__	(   self    ,       **lowerCamelCase__    )     ->						Optional[Any]:




									'''simple docstring'''



									kwargs.update(self.special_tokens_map    )
									return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname    ,       **lowerCamelCase__    )



			def A__	(   self    ,       lowerCamelCase__    )     ->						Any:




									'''simple docstring'''



									lowercase__								=    """こんにちは、世界。 \nこんばんは、㔺界。😀"""
									lowercase__								=    """こんにちは、世界。 \nこんばんは、世界。😀"""
									return input_text, output_text



			def A__	(   self    ,       lowerCamelCase__    )     ->						str:




									'''simple docstring'''



									lowercase__	,  lowercase__								=    self.get_input_output_texts(lowerCamelCase__    )
									lowercase__								=    tokenizer.encode(lowerCamelCase__    ,       add_special_tokens=lowerCamelCase__    )
									lowercase__								=    tokenizer.decode(lowerCamelCase__    ,       clean_up_tokenization_spaces=lowerCamelCase__    )
									return text, ids



			def A__	(   self    )     ->						int:




									'''simple docstring'''



									pass  # TODO add if relevant



			def A__	(   self    )     ->						Union[str, Any]:




									'''simple docstring'''



									pass  # TODO add if relevant



			def A__	(   self    )     ->						Union[str, Any]:




									'''simple docstring'''



									pass  # TODO add if relevant



			def A__	(   self    )     ->						Optional[int]:




									'''simple docstring'''



									lowercase__								=    self.get_tokenizer()

									# Testing tokenization
									lowercase__								=    """こんにちは、世界。　こんばんは、㔺界。"""
									lowercase__								=    ["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
									lowercase__								=    tokenizer.tokenize(lowerCamelCase__    )
									self.assertListEqual(lowerCamelCase__    ,       lowerCamelCase__    )

									# Testing conversion to ids without special tokens
									lowercase__								=    [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
									lowercase__								=    tokenizer.convert_tokens_to_ids(lowerCamelCase__    )
									self.assertListEqual(lowerCamelCase__    ,       lowerCamelCase__    )

									# Testing conversion to ids with special tokens
									lowercase__								=    tokens + [tokenizer.unk_token]
									lowercase__								=    [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
									lowercase__								=    tokenizer.convert_tokens_to_ids(lowerCamelCase__    )
									self.assertListEqual(lowerCamelCase__    ,       lowerCamelCase__    )



			@slow
			def A__	(   self    )     ->						Any:




									'''simple docstring'''



									lowercase__								=    self.tokenizer_class.from_pretrained("""abeja/gpt-neox-japanese-2.7b"""    )

									lowercase__								=    tokenizer.encode("""ありがとう。"""    ,       add_special_tokens=lowerCamelCase__    )
									lowercase__								=    tokenizer.encode("""どういたしまして。"""    ,       add_special_tokens=lowerCamelCase__    )

									lowercase__								=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase__    )
									lowercase__								=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase__    ,       lowerCamelCase__    )

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



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




									'''simple docstring'''



									pass



			def A__	(   self    )     ->						Any:




									'''simple docstring'''



									pass








'''simple docstring'''



import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


__A       =      logging.get_logger(__name__)

__A       =      {
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}





class      A							(    __UpperCAmelCase			):
			lowerCamelCase							:							List[str]    						=					"""vit-hybrid"""



			def __init__(   self    ,       lowerCamelCase__=None    ,       lowerCamelCase__=768    ,       lowerCamelCase__=12    ,       lowerCamelCase__=12    ,       lowerCamelCase__=3_072    ,       lowerCamelCase__="gelu"    ,       lowerCamelCase__=0.0    ,       lowerCamelCase__=0.0    ,       lowerCamelCase__=0.02    ,       lowerCamelCase__=1e-12    ,       lowerCamelCase__=224    ,       lowerCamelCase__=1    ,       lowerCamelCase__=3    ,       lowerCamelCase__=[1, 1_024, 24, 24]    ,       lowerCamelCase__=True    ,       **lowerCamelCase__    ,       )     ->						Optional[Any]:




									'''simple docstring'''



									super().__init__(**lowerCamelCase__    )

									if backbone_config is None:
															logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone."""    )
															lowercase__								=    {
															    """global_padding""": """same""",
															    """layer_type""": """bottleneck""",
															    """depths""": [3, 4, 9],
															    """out_features""": ["""stage3"""],
															    """embedding_dynamic_padding""": True,
															}

									if isinstance(lowerCamelCase__    ,       lowerCamelCase__    ):
															if "model_type" in backbone_config:
																					lowercase__								=    CONFIG_MAPPING[backbone_config["""model_type"""]]
															else:
																					logger.info(
																					    """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."""    )
																					lowercase__								=    BitConfig
															lowercase__								=    backbone_config_class(**lowerCamelCase__    )

									lowercase__								=    backbone_featmap_shape
									lowercase__								=    backbone_config
									lowercase__								=    hidden_size
									lowercase__								=    num_hidden_layers
									lowercase__								=    num_attention_heads
									lowercase__								=    intermediate_size
									lowercase__								=    hidden_act
									lowercase__								=    hidden_dropout_prob
									lowercase__								=    attention_probs_dropout_prob
									lowercase__								=    initializer_range
									lowercase__								=    layer_norm_eps
									lowercase__								=    image_size
									lowercase__								=    patch_size
									lowercase__								=    num_channels
									lowercase__								=    qkv_bias



			def A__	(   self    )     ->						Dict[str, any]:




									'''simple docstring'''



									lowercase__								=    copy.deepcopy(self.__dict__    )
									lowercase__								=    self.backbone_config.to_dict()
									lowercase__								=    self.__class__.model_type
									return output






