


"""simple docstring"""







import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class       SCREAMING_SNAKE_CASE__				(		lowercase   ,    unittest.TestCase				):
       """simple docstring"""


       a  :				Union[str, Any]   =DebertaTokenizer
       a  :				List[str]   =True
       a  :				Optional[Any]   =DebertaTokenizerFast

       def        lowercase__							(						self						):






         """simple docstring"""





         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         lowerCAmelCase     :     List[str]								=							[
             "l",
             "o",
             "w",
             "e",
             "r",
             "s",
             "t",
             "i",
             "d",
             "n",
             "\u0120",
             "\u0120l",
             "\u0120n",
             "\u0120lo",
             "\u0120low",
             "er",
             "\u0120lowest",
             "\u0120newer",
             "\u0120wider",
             "[UNK]",
         ]
         lowerCAmelCase     :     Optional[int]								=							dict(zip(snake_case__		,     range(len(snake_case__						)						)						)						)
         lowerCAmelCase     :     List[Any]								=							["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
         lowerCAmelCase     :     List[str]								=							{"unk_token": "[UNK]"}

         lowerCAmelCase     :     List[str]								=							os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["vocab_file"]						)
         lowerCAmelCase     :     List[str]								=							os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["merges_file"]						)
         with open(self.vocab_file		,     "w"		,     encoding="utf-8"						) as fp:
           fp.write(json.dumps(snake_case__						) + "\n"						)
         with open(self.merges_file		,     "w"		,     encoding="utf-8"						) as fp:
           fp.write("\n".join(snake_case__						)						)

       def        lowercase__							(						self		,     **snake_case__						):






         """simple docstring"""





         kwargs.update(self.special_tokens_map						)
         return self.tokenizer_class.from_pretrained(self.tmpdirname		,     **snake_case__						)

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							"lower newer"
         lowerCAmelCase     :     Any								=							"lower newer"
         return input_text, output_text

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							self.get_tokenizer()
         lowerCAmelCase     :     Tuple								=							"lower newer"
         lowerCAmelCase     :     List[Any]								=							["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
         lowerCAmelCase     :     Optional[Any]								=							tokenizer.tokenize(snake_case__						)
         self.assertListEqual(snake_case__		,     snake_case__						)

         lowerCAmelCase     :     Dict								=							tokens + [tokenizer.unk_token]
         lowerCAmelCase     :     str								=							[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__						)		,     snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							self.get_tokenizer()
         lowerCAmelCase     :     Optional[int]								=							tokenizer("Hello"		,     "World"						)
         lowerCAmelCase     :     Union[str, Any]								=							[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
         self.assertListEqual(tokd["token_type_ids"]		,     snake_case__						)

       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							self.tokenizer_class.from_pretrained("microsoft/deberta-base"						)

         lowerCAmelCase     :     Tuple								=							tokenizer.encode("sequence builders"		,     add_special_tokens=snake_case__						)
         lowerCAmelCase     :     Dict								=							tokenizer.encode("multi-sequence build"		,     add_special_tokens=snake_case__						)

         lowerCAmelCase     :     List[str]								=							tokenizer.encode(
             "sequence builders"		,     add_special_tokens=snake_case__		,     add_prefix_space=snake_case__						)
         lowerCAmelCase     :     List[Any]								=							tokenizer.encode(
             "sequence builders"		,     "multi-sequence build"		,     add_special_tokens=snake_case__		,     add_prefix_space=snake_case__						)

         lowerCAmelCase     :     Optional[int]								=							tokenizer.build_inputs_with_special_tokens(snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							tokenizer.build_inputs_with_special_tokens(snake_case__		,     snake_case__						)

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode







       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							[self.tokenizer_class]
         if self.test_rust_tokenizer:
           tokenizer_classes.append(self.rust_tokenizer_class						)

         for tokenizer_class in tokenizer_classes:
           lowerCAmelCase     :     Tuple								=							tokenizer_class.from_pretrained("microsoft/deberta-base"						)

           lowerCAmelCase     :     Tuple								=							[
               "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
               "ALBERT incorporates two parameter reduction techniques",
               "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
               " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
               " vocabulary embedding.",
           ]

           lowerCAmelCase     :     List[Any]								=							tokenizer(snake_case__		,     padding=snake_case__						)
           lowerCAmelCase     :     List[Any]								=							[tokenizer.decode(snake_case__		,     skip_special_tokens=snake_case__						) for seq in encoding["input_ids"]]

           # fmt: off
           lowerCAmelCase     :     List[str]								=							{
               "input_ids": [
                   [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
               ],
               "token_type_ids": [
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
               ],
               "attention_mask": [
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
               ]
           }
           # fmt: on

           lowerCAmelCase     :     int								=							[
               "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
               "ALBERT incorporates two parameter reduction techniques",
               "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
               " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
               " vocabulary embedding.",
           ]

           self.assertDictEqual(encoding.data		,     snake_case__						)

           for expected, decoded in zip(snake_case__		,     snake_case__						):
             self.assertEqual(snake_case__		,     snake_case__						)









"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {
    '''facebook/data2vec-vision-base-ft''': (
        '''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
    ),
}


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				List[Any]   ="data2vec-vision"

       def __init__(						self		,     snake_case__=768		,     snake_case__=12		,     snake_case__=12		,     snake_case__=3_072		,     snake_case__="gelu"		,     snake_case__=0.0		,     snake_case__=0.0		,     snake_case__=0.02		,     snake_case__=1e-12		,     snake_case__=224		,     snake_case__=16		,     snake_case__=3		,     snake_case__=False		,     snake_case__=False		,     snake_case__=False		,     snake_case__=False		,     snake_case__=0.1		,     snake_case__=0.1		,     snake_case__=True		,     snake_case__=[3, 5, 7, 11]		,     snake_case__=[1, 2, 3, 6]		,     snake_case__=True		,     snake_case__=0.4		,     snake_case__=256		,     snake_case__=1		,     snake_case__=False		,     snake_case__=255		,     **snake_case__		,     ):






         """simple docstring"""





         super().__init__(**snake_case__						)

         lowerCAmelCase     :     Tuple								=							hidden_size
         lowerCAmelCase     :     List[Any]								=							num_hidden_layers
         lowerCAmelCase     :     Tuple								=							num_attention_heads
         lowerCAmelCase     :     Optional[int]								=							intermediate_size
         lowerCAmelCase     :     Optional[int]								=							hidden_act
         lowerCAmelCase     :     Dict								=							hidden_dropout_prob
         lowerCAmelCase     :     Optional[Any]								=							attention_probs_dropout_prob
         lowerCAmelCase     :     int								=							initializer_range
         lowerCAmelCase     :     Dict								=							layer_norm_eps

         lowerCAmelCase     :     Optional[int]								=							image_size
         lowerCAmelCase     :     Optional[Any]								=							patch_size
         lowerCAmelCase     :     Optional[Any]								=							num_channels
         lowerCAmelCase     :     Union[str, Any]								=							use_mask_token
         lowerCAmelCase     :     str								=							use_absolute_position_embeddings
         lowerCAmelCase     :     Any								=							use_relative_position_bias
         lowerCAmelCase     :     List[str]								=							use_shared_relative_position_bias
         lowerCAmelCase     :     str								=							layer_scale_init_value
         lowerCAmelCase     :     Union[str, Any]								=							drop_path_rate
         lowerCAmelCase     :     Any								=							use_mean_pooling
         # decode head attributes (semantic segmentation)
         lowerCAmelCase     :     Optional[int]								=							out_indices
         lowerCAmelCase     :     Union[str, Any]								=							pool_scales
         # auxiliary head attributes (semantic segmentation)
         lowerCAmelCase     :     str								=							use_auxiliary_head
         lowerCAmelCase     :     int								=							auxiliary_loss_weight
         lowerCAmelCase     :     Tuple								=							auxiliary_channels
         lowerCAmelCase     :     List[str]								=							auxiliary_num_convs
         lowerCAmelCase     :     Tuple								=							auxiliary_concat_input
         lowerCAmelCase     :     List[str]								=							semantic_loss_ignore_index


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Union[str, Any]   =version.parse("1.11"				)

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]						)







       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return 1e-4





