

'''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 	lowerCAmelCase  (   A ,      unittest.TestCase ):
       lowerCAmelCase_												=			DebertaTokenizer
       lowerCAmelCase_												=			True
       lowerCAmelCase_												=			DebertaTokenizerFast







       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            __lowercase      		=[
                'l',
                'o',
                'w',
                'e',
                'r',
                's',
                't',
                'i',
                'd',
                'n',
                '\u0120',
                '\u0120l',
                '\u0120n',
                '\u0120lo',
                '\u0120low',
                'er',
                '\u0120lowest',
                '\u0120newer',
                '\u0120wider',
                '[UNK]',
            ]
            __lowercase      		=dict(zip(__lowercase    ,   range(len(__lowercase   )   )   )   )
            __lowercase      		=['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
            __lowercase      		={'unk_token': '[UNK]'}

            __lowercase      		=os.path.join(self.tmpdirname    ,   VOCAB_FILES_NAMES['vocab_file']   )
            __lowercase      		=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(__lowercase   ) + '\n'   )
            with open(self.merges_file    ,   'w'    ,   encoding='utf-8'   ) as fp:
                 fp.write('\n'.join(__lowercase   )   )







       def       snake_case (				self      :			Dict    ,   **__lowercase      :			List[str]   ):







            """simple docstring"""







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







       def       snake_case (				self      :			List[Any]    ,   __lowercase      :			Tuple   ):







            """simple docstring"""







            __lowercase      		='lower newer'
            __lowercase      		='lower newer'
            return input_text, output_text







       def       snake_case (				self      :			str   ):







            """simple docstring"""







            __lowercase      		=self.get_tokenizer()
            __lowercase      		='lower newer'
            __lowercase      		=['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
            __lowercase      		=tokenizer.tokenize(__lowercase   )
            self.assertListEqual(__lowercase    ,   __lowercase   )

            __lowercase      		=tokens + [tokenizer.unk_token]
            __lowercase      		=[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase   )    ,   __lowercase   )







       def       snake_case (				self      :			str   ):







            """simple docstring"""







            __lowercase      		=self.get_tokenizer()
            __lowercase      		=tokenizer('Hello'    ,   'World'   )
            __lowercase      		=[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
            self.assertListEqual(tokd['token_type_ids']    ,   __lowercase   )







       @slow
       def       snake_case (				self      :			Dict   ):







            """simple docstring"""







            __lowercase      		=self.tokenizer_class.from_pretrained('microsoft/deberta-base'   )

            __lowercase      		=tokenizer.encode('sequence builders'    ,   add_special_tokens=__lowercase   )
            __lowercase      		=tokenizer.encode('multi-sequence build'    ,   add_special_tokens=__lowercase   )

            __lowercase      		=tokenizer.encode(
                'sequence builders'    ,   add_special_tokens=__lowercase    ,   add_prefix_space=__lowercase   )
            __lowercase      		=tokenizer.encode(
                'sequence builders'    ,   'multi-sequence build'    ,   add_special_tokens=__lowercase    ,   add_prefix_space=__lowercase   )

            __lowercase      		=tokenizer.build_inputs_with_special_tokens(__lowercase   )
            __lowercase      		=tokenizer.build_inputs_with_special_tokens(__lowercase    ,   __lowercase   )

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode







       @slow
       def       snake_case (				self      :			Any   ):







            """simple docstring"""







            __lowercase      		=[self.tokenizer_class]
            if self.test_rust_tokenizer:
                 tokenizer_classes.append(self.rust_tokenizer_class   )

            for tokenizer_class in tokenizer_classes:
                 __lowercase      		=tokenizer_class.from_pretrained('microsoft/deberta-base'   )

                 __lowercase      		=[
                     '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.',
                 ]

                 __lowercase      		=tokenizer(__lowercase    ,   padding=__lowercase   )
                 __lowercase      		=[tokenizer.decode(__lowercase    ,   skip_special_tokens=__lowercase   ) for seq in encoding['input_ids']]

                 # fmt: off
                 __lowercase      		={
                     'input_ids': [
                         [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 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, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 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, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 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

                 __lowercase      		=[
                     '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    ,   __lowercase   )

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





'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	{
    '''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}


class 	lowerCAmelCase  (   A ,      A ):
       lowerCAmelCase_												=			"dinat"

       lowerCAmelCase_												=			{
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }







       def __init__(				self      :			int    ,   __lowercase      :			str=4    ,   __lowercase      :			str=3    ,   __lowercase      :			int=64    ,   __lowercase      :			int=[3, 4, 6, 5]    ,   __lowercase      :			Union[str, Any]=[2, 4, 8, 16]    ,   __lowercase      :			Optional[int]=7    ,   __lowercase      :			str=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]    ,   __lowercase      :			Any=3.0    ,   __lowercase      :			Optional[Any]=True    ,   __lowercase      :			List[Any]=0.0    ,   __lowercase      :			Optional[Any]=0.0    ,   __lowercase      :			int=0.1    ,   __lowercase      :			Union[str, Any]="gelu"    ,   __lowercase      :			Any=0.0_2    ,   __lowercase      :			int=1E-5    ,   __lowercase      :			Optional[int]=0.0    ,   __lowercase      :			Optional[int]=None    ,   __lowercase      :			str=None    ,   **__lowercase      :			Any    ,   ):







            """simple docstring"""







            super().__init__(**__lowercase   )

            __lowercase      		=patch_size
            __lowercase      		=num_channels
            __lowercase      		=embed_dim
            __lowercase      		=depths
            __lowercase      		=len(__lowercase   )
            __lowercase      		=num_heads
            __lowercase      		=kernel_size
            __lowercase      		=dilations
            __lowercase      		=mlp_ratio
            __lowercase      		=qkv_bias
            __lowercase      		=hidden_dropout_prob
            __lowercase      		=attention_probs_dropout_prob
            __lowercase      		=drop_path_rate
            __lowercase      		=hidden_act
            __lowercase      		=layer_norm_eps
            __lowercase      		=initializer_range
            # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
            # this indicates the channel dimension after the last stage of the model
            __lowercase      		=int(embed_dim * 2 ** (len(__lowercase   ) - 1)   )
            __lowercase      		=layer_scale_init_value
            __lowercase      		=['stem'] + [f'''stage{idx}''' for idx in range(1    ,   len(__lowercase   ) + 1   )]
            __lowercase		,  __lowercase      		=get_aligned_output_features_output_indices(
                out_features=__lowercase    ,   out_indices=__lowercase    ,   stage_names=self.stage_names   )


