




import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin


class    lowerCamelCase    (		_lowerCamelCase				,unittest.TestCase						):






   '''simple docstring'''





   UpperCamelCase__								=BlenderbotSmallTokenizer
   UpperCamelCase__								=False

   def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   str:
        super().setUp()

        __magic_name__							:	Union[str, Any]    							=							['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
        __magic_name__							:	Tuple    							=							dict(zip(lowerCamelCase_       ,						range(len(lowerCamelCase_  )  )  )  )

        __magic_name__							:	Tuple    							=							['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
        __magic_name__							:	List[Any]    							=							{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

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

   def 							UpperCAmelCase__       ( self			:   List[str]       ,						**lowerCamelCase_			:   Optional[Any]  )						->   List[Any]:
        kwargs.update(self.special_tokens_map  )
        return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname       ,						**lowerCamelCase_  )

   def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Optional[int]:
        __magic_name__							:	Union[str, Any]    							=							'''adapt act apte'''
        __magic_name__							:	Dict    							=							'''adapt act apte'''
        return input_text, output_text

   def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Any:
        __magic_name__							:	int    							=							BlenderbotSmallTokenizer(self.vocab_file       ,						self.merges_file       ,						**self.special_tokens_map  )
        __magic_name__							:	str    							=							'''adapt act apte'''
        __magic_name__							:	Any    							=							['''adapt''', '''act''', '''ap@@''', '''te''']
        __magic_name__							:	List[str]    							=							tokenizer.tokenize(lowerCamelCase_  )
        self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )

        __magic_name__							:	Union[str, Any]    							=							[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

        __magic_name__							:	List[Any]    							=							[0, 1, 2, 3, 4, 5]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_  )       ,						lowerCamelCase_  )

   def 							UpperCAmelCase__       ( self			:   int  )						->   int:
        __magic_name__							:	Any    							=							BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
        assert tok('''sam'''  ).input_ids == [1384]
        __magic_name__							:	Dict    							=							'''I am a small frog.'''
        __magic_name__							:	Tuple    							=							tok([src_text]       ,						padding=lowerCamelCase_       ,						truncation=lowerCamelCase_  )['''input_ids''']
        __magic_name__							:	Tuple    							=							tok.batch_decode(lowerCamelCase_       ,						skip_special_tokens=lowerCamelCase_       ,						clean_up_tokenization_spaces=lowerCamelCase_  )[0]
        assert src_text != decoded  # I wish it did!
        assert decoded == "i am a small frog ."


   def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Dict:
        __magic_name__							:	Tuple    							=							BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
        __magic_name__							:	Any    							=							'''I am a small frog .'''
        __magic_name__							:	List[str]    							=							'''.'''
        __magic_name__							:	Tuple    							=							tok(lowerCamelCase_  )['''input_ids''']
        __magic_name__							:	Optional[Any]    							=							tok(lowerCamelCase_  )['''input_ids''']

        assert encoded[-1] == encoded_dot[0]









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


__lowerCamelCase   :						Optional[int]				=			logging.get_logger(__name__)

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


class    lowerCamelCase    (		_lowerCamelCase				,_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								='''nat'''

			UpperCamelCase__								={
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}

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

								__magic_name__							:	List[Any]    							=							patch_size
								__magic_name__							:	str    							=							num_channels
								__magic_name__							:	Union[str, Any]    							=							embed_dim
								__magic_name__							:	Dict    							=							depths
								__magic_name__							:	str    							=							len(lowerCamelCase_  )
								__magic_name__							:	List[str]    							=							num_heads
								__magic_name__							:	Optional[int]    							=							kernel_size
								__magic_name__							:	Any    							=							mlp_ratio
								__magic_name__							:	Any    							=							qkv_bias
								__magic_name__							:	Union[str, Any]    							=							hidden_dropout_prob
								__magic_name__							:	Any    							=							attention_probs_dropout_prob
								__magic_name__							:	List[str]    							=							drop_path_rate
								__magic_name__							:	str    							=							hidden_act
								__magic_name__							:	Tuple    							=							layer_norm_eps
								__magic_name__							:	Any    							=							initializer_range
								# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								__magic_name__							:	str    							=							int(embed_dim * 2 ** (len(lowerCamelCase_  ) - 1)  )
								__magic_name__							:	str    							=							layer_scale_init_value
								__magic_name__							:	Dict    							=							['''stem'''] + [F'''stage{idx}''' for idx in range(1       ,						len(lowerCamelCase_  ) + 1  )]
								__magic_name__							,    __magic_name__							:	int    							=							get_aligned_output_features_output_indices(
								    out_features=lowerCamelCase_       ,						out_indices=lowerCamelCase_       ,						stage_names=self.stage_names  )



