
import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    Union[str, Any] 				=    OpenAIGPTTokenizer
   __UpperCamelCase		:    List[str] 				=    OpenAIGPTTokenizerFast
   __UpperCamelCase		:    int 				=    True
   __UpperCamelCase		:    Tuple 				=    False


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _A:  Tuple					=       [
          '''l''',
          '''o''',
          '''w''',
          '''e''',
          '''r''',
          '''s''',
          '''t''',
          '''i''',
          '''d''',
          '''n''',
          '''w</w>''',
          '''r</w>''',
          '''t</w>''',
          '''lo''',
          '''low''',
          '''er</w>''',
          '''low</w>''',
          '''lowest</w>''',
          '''newer</w>''',
          '''wider</w>''',
          '''<unk>''',
      ]
      _A:  int					=       dict(zip(lowerCAmelCase_			,			range(len(lowerCAmelCase_     )     )     )     )
      _A:  Tuple					=       ['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

      _A:  str					=       os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''vocab_file''']     )
      _A:  int					=       os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''merges_file''']     )
      with open(self.vocab_file			,			'''w'''     ) as fp:
         fp.write(json.dumps(lowerCAmelCase_     )     )
      with open(self.merges_file			,			'''w'''     ) as fp:
         fp.write('''\n'''.join(lowerCAmelCase_     )     )


   def  __magic_name__				(					self     :							List[Any]			,			lowerCAmelCase_     :							Dict     ):

      """simple docstring"""
      return "lower newer", "lower newer"


   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      _A:  Any					=       OpenAIGPTTokenizer(self.vocab_file			,			self.merges_file     )

      _A:  Union[str, Any]					=       '''lower'''
      _A:  List[str]					=       ['''low''', '''er</w>''']
      _A:  Optional[int]					=       tokenizer.tokenize(lowerCAmelCase_     )
      self.assertListEqual(lowerCAmelCase_			,			lowerCAmelCase_     )

      _A:  Union[str, Any]					=       tokens + ['''<unk>''']
      _A:  List[str]					=       [1_4, 1_5, 2_0]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_     )			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Tuple			,			lowerCAmelCase_     :							List[Any]=1_5     ):

      """simple docstring"""
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
            _A:  Union[str, Any]					=       self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_			,			**lowerCAmelCase_     )

            # Simple input
            _A:  Optional[Any]					=       '''This is a simple input'''
            _A:  List[str]					=       ['''This is a simple input 1''', '''This is a simple input 2''']
            _A:  Union[str, Any]					=       ('''This is a simple input''', '''This is a pair''')
            _A:  List[Any]					=       [
                ('''This is a simple input 1''', '''This is a simple input 2'''),
                ('''This is a simple pair 1''', '''This is a simple pair 2'''),
            ]

            # Simple input tests
            self.assertRaises(lowerCAmelCase_			,			tokenizer_r.encode			,			lowerCAmelCase_			,			max_length=lowerCAmelCase_			,			padding='''max_length'''     )

            # Simple input
            self.assertRaises(lowerCAmelCase_			,			tokenizer_r.encode_plus			,			lowerCAmelCase_			,			max_length=lowerCAmelCase_			,			padding='''max_length'''     )

            # Simple input
            self.assertRaises(
                lowerCAmelCase_			,			tokenizer_r.batch_encode_plus			,			lowerCAmelCase_			,			max_length=lowerCAmelCase_			,			padding='''max_length'''			,			)

            # Pair input
            self.assertRaises(lowerCAmelCase_			,			tokenizer_r.encode			,			lowerCAmelCase_			,			max_length=lowerCAmelCase_			,			padding='''max_length'''     )

            # Pair input
            self.assertRaises(lowerCAmelCase_			,			tokenizer_r.encode_plus			,			lowerCAmelCase_			,			max_length=lowerCAmelCase_			,			padding='''max_length'''     )

            # Pair input
            self.assertRaises(
                lowerCAmelCase_			,			tokenizer_r.batch_encode_plus			,			lowerCAmelCase_			,			max_length=lowerCAmelCase_			,			padding='''max_length'''			,			)


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      pass






@require_ftfy
@require_spacy
@require_tokenizers
class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   pass





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


UpperCAmelCase__		:     Any     			=      logging.get_logger(__name__)






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Dict 				=    '''maskformer-swin'''

   __UpperCamelCase		:    Any 				=    {
       '''num_attention_heads''': '''num_heads''',
       '''num_hidden_layers''': '''num_layers''',
   }


   def __init__(					self     :							Optional[Any]			,			lowerCAmelCase_     :							int=2_2_4			,			lowerCAmelCase_     :							Tuple=4			,			lowerCAmelCase_     :							Any=3			,			lowerCAmelCase_     :							Dict=9_6			,			lowerCAmelCase_     :							Union[str, Any]=[2, 2, 6, 2]			,			lowerCAmelCase_     :							Optional[Any]=[3, 6, 1_2, 2_4]			,			lowerCAmelCase_     :							Optional[Any]=7			,			lowerCAmelCase_     :							Optional[Any]=4.0			,			lowerCAmelCase_     :							int=True			,			lowerCAmelCase_     :							Optional[Any]=0.0			,			lowerCAmelCase_     :							Union[str, Any]=0.0			,			lowerCAmelCase_     :							Dict=0.1			,			lowerCAmelCase_     :							Optional[Any]="gelu"			,			lowerCAmelCase_     :							Optional[int]=False			,			lowerCAmelCase_     :							Dict=0.02			,			lowerCAmelCase_     :							str=1e-5			,			lowerCAmelCase_     :							Union[str, Any]=None			,			lowerCAmelCase_     :							Any=None			,			**lowerCAmelCase_     :							int			,			):

      """simple docstring"""
      super().__init__(**lowerCAmelCase_     )

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



