




import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 						_A (  _lowerCamelCase      ,       unittest.TestCase				):
  _UpperCamelCase       :      int			=      CodeGenTokenizer
  _UpperCamelCase       :      Dict			=      CodeGenTokenizerFast
  _UpperCamelCase       :      Dict			=      True
  _UpperCamelCase       :      Union[str, Any]			=      {'''add_prefix_space''': True}
  _UpperCamelCase       :      List[str]			=      False






  def  __a      (  self		:     Dict				)		->   Dict:






       """simple docstring"""







       super().setUp()

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

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






  def  __a      (  self		:     Optional[Any]				,						**_A		:     int				)		->   int:






       """simple docstring"""







       kwargs.update(self.special_tokens_map				)
       return CodeGenTokenizer.from_pretrained(self.tmpdirname				,						**_A				)






  def  __a      (  self		:     Tuple				,						**_A		:     Dict				)		->   Optional[Any]:






       """simple docstring"""







       kwargs.update(self.special_tokens_map				)
       return CodeGenTokenizerFast.from_pretrained(self.tmpdirname				,						**_A				)






  def  __a      (  self		:     List[str]				,						_A		:     List[Any]				)		->   int:






       """simple docstring"""







       lowercase :       int       =			'''lower newer'''
       lowercase :       str       =			'''lower newer'''
       return input_text, output_text






  def  __a      (  self		:     int				)		->   List[str]:






       """simple docstring"""







       lowercase :       Tuple       =			CodeGenTokenizer(self.vocab_file				,						self.merges_file				,						**self.special_tokens_map				)
       lowercase :       Dict       =			'''lower newer'''
       lowercase :       Optional[int]       =			['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
       lowercase :       Optional[Any]       =			tokenizer.tokenize(_A				,						add_prefix_space=_A				)
       self.assertListEqual(_A				,						_A				)

       lowercase :       Union[str, Any]       =			tokens + [tokenizer.unk_token]
       lowercase :       Any       =			[14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(_A				)				,						_A				)






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






       """simple docstring"""







       if not self.test_rust_tokenizer:
            return

       lowercase :       List[str]       =			self.get_tokenizer()
       lowercase :       List[Any]       =			self.get_rust_tokenizer(add_prefix_space=_A				)

       lowercase :       Tuple       =			'''lower newer'''

       # Testing tokenization
       lowercase :       List[str]       =			tokenizer.tokenize(_A				,						add_prefix_space=_A				)
       lowercase :       Tuple       =			rust_tokenizer.tokenize(_A				)
       self.assertListEqual(_A				,						_A				)

       # Testing conversion to ids without special tokens
       lowercase :       Tuple       =			tokenizer.encode(_A				,						add_special_tokens=_A				,						add_prefix_space=_A				)
       lowercase :       Dict       =			rust_tokenizer.encode(_A				,						add_special_tokens=_A				)
       self.assertListEqual(_A				,						_A				)

       # Testing conversion to ids with special tokens
       lowercase :       Optional[int]       =			self.get_rust_tokenizer(add_prefix_space=_A				)
       lowercase :       List[str]       =			tokenizer.encode(_A				,						add_prefix_space=_A				)
       lowercase :       str       =			rust_tokenizer.encode(_A				)
       self.assertListEqual(_A				,						_A				)

       # Testing the unknown token
       lowercase :       List[str]       =			tokens + [rust_tokenizer.unk_token]
       lowercase :       int       =			[14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_A				)				,						_A				)






  def  __a      (  self		:     str				,						*_A		:     List[str]				,						**_A		:     Any				)		->   str:






       """simple docstring"""







       pass






  def  __a      (  self		:     Tuple				,						_A		:     Optional[int]=15				)		->   Any:






       """simple docstring"""







       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
                 lowercase :       Any       =			self.rust_tokenizer_class.from_pretrained(_A				,						**_A				)

                 # Simple input
                 lowercase :       str       =			'''This is a simple input'''
                 lowercase :       Dict       =			['''This is a simple input 1''', '''This is a simple input 2''']
                 lowercase :       int       =			('''This is a simple input''', '''This is a pair''')
                 lowercase :       str       =			[
                     ('''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(_A				,						tokenizer_r.encode				,						_A				,						max_length=_A				,						padding='''max_length'''				)

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

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

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

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

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






  def  __a      (  self		:     Any				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       str       =			CodeGenTokenizer.from_pretrained(self.tmpdirname				,						pad_token='''<pad>'''				)

       # Simple input
       lowercase :       List[str]       =			'''This is a simple input'''
       lowercase :       List[str]       =			['''This is a simple input looooooooong''', '''This is a simple input''']
       lowercase :       str       =			('''This is a simple input''', '''This is a pair''')
       lowercase :       Tuple       =			[
           ('''This is a simple input loooooong''', '''This is a simple input'''),
           ('''This is a simple pair loooooong''', '''This is a simple pair'''),
       ]

       lowercase :       Dict       =			tokenizer.pad_token_id

       lowercase :       int       =			tokenizer(_A				,						padding='''max_length'''				,						max_length=30				,						return_tensors='''np'''				)
       lowercase :       Optional[int]       =			tokenizer(_A				,						padding=_A				,						truncate=_A				,						return_tensors='''np'''				)
       lowercase :       int       =			tokenizer(*_A				,						padding='''max_length'''				,						max_length=60				,						return_tensors='''np'''				)
       lowercase :       Optional[Any]       =			tokenizer(_A				,						padding=_A				,						truncate=_A				,						return_tensors='''np'''				)

       # s
       # test single string max_length padding
       self.assertEqual(out_s['''input_ids'''].shape[-1]				,						30				)
       self.assertTrue(pad_token_id in out_s['''input_ids''']				)
       self.assertTrue(0 in out_s['''attention_mask''']				)

       # s2
       # test automatic padding
       self.assertEqual(out_sa['''input_ids'''].shape[-1]				,						33				)
       # long slice doesn't have padding
       self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]				)
       self.assertFalse(0 in out_sa['''attention_mask'''][0]				)
       # short slice does have padding
       self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]				)
       self.assertTrue(0 in out_sa['''attention_mask'''][1]				)

       # p
       # test single pair max_length padding
       self.assertEqual(out_p['''input_ids'''].shape[-1]				,						60				)
       self.assertTrue(pad_token_id in out_p['''input_ids''']				)
       self.assertTrue(0 in out_p['''attention_mask''']				)

       # p2
       # test automatic padding pair
       self.assertEqual(out_pa['''input_ids'''].shape[-1]				,						52				)
       # long slice pair doesn't have padding
       self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]				)
       self.assertFalse(0 in out_pa['''attention_mask'''][0]				)
       # short slice pair does have padding
       self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]				)
       self.assertTrue(0 in out_pa['''attention_mask'''][1]				)






  def  __a      (  self		:     Optional[int]				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       Dict       =			'''$$$'''
       lowercase :       Optional[int]       =			CodeGenTokenizer.from_pretrained(self.tmpdirname				,						bos_token=_A				,						add_bos_token=_A				)

       lowercase :       Any       =			'''This is a simple input'''
       lowercase :       str       =			['''This is a simple input 1''', '''This is a simple input 2''']

       lowercase :       Tuple       =			tokenizer.bos_token_id

       lowercase :       str       =			tokenizer(_A				)
       lowercase :       str       =			tokenizer(_A				)

       self.assertEqual(out_s.input_ids[0]				,						_A				)
       self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids				)				)

       lowercase :       Optional[Any]       =			tokenizer.decode(out_s.input_ids				)
       lowercase :       List[Any]       =			tokenizer.batch_decode(out_sa.input_ids				)

       self.assertEqual(decode_s.split()[0]				,						_A				)
       self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa				)				)






  @slow
  def  __a      (  self		:     Union[str, Any]				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       Optional[Any]       =			CodeGenTokenizer.from_pretrained('''Salesforce/codegen-350M-mono'''				)

       lowercase :       Optional[int]       =			'''\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'''
       lowercase :       Optional[int]       =			'''\nif len_a > len_b:      result = a\nelse:      result = b'''

       lowercase :       Dict       =			tokenizer.encode(_A				)
       lowercase :       int       =			['''^#''', re.escape('''<|endoftext|>'''				), '''^\'\'\'''', '''^"""''', '''\n\n\n''']
       lowercase :       List[str]       =			tokenizer.decode(_A				,						truncate_before_pattern=_A				)
       self.assertEqual(_A				,						_A				)






  def  __a      (  self		:     Any				)		->   Tuple:






       """simple docstring"""







       pass





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCAmelCase_						=			{
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCAmelCase_						=			logging.get_logger(__name__)
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      int			=      '''maskformer'''
  _UpperCamelCase       :      Union[str, Any]			=      {'''hidden_size''': '''mask_feature_size'''}
  _UpperCamelCase       :      Dict			=      ['''resnet''', '''swin''']
  _UpperCamelCase       :      Optional[int]			=      ['''detr''']






  def __init__(  self		:     Any				,						_A		:     int = 256				,						_A		:     int = 256				,						_A		:     float = 0.1				,						_A		:     bool = False				,						_A		:     Optional[Dict] = None				,						_A		:     Optional[Dict] = None				,						_A		:     float = 0.02				,						_A		:     float = 1.0				,						_A		:     float = 1.0				,						_A		:     float = 1.0				,						_A		:     float = 20.0				,						_A		:     Optional[bool] = None				,						**_A		:     str				,						)		->   str:






       """simple docstring"""







       if backbone_config is None:
            # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
            lowercase :       List[str]       =			SwinConfig(
                image_size=384				,						in_channels=3				,						patch_size=4				,						embed_dim=128				,						depths=[2, 2, 18, 2]				,						num_heads=[4, 8, 16, 32]				,						window_size=12				,						drop_path_rate=0.3				,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				,						)

       if isinstance(_A				,						_A				):
            lowercase :       Optional[int]       =			backbone_config.pop('''model_type'''				)
            lowercase :       List[str]       =			CONFIG_MAPPING[backbone_model_type]
            lowercase :       Union[str, Any]       =			config_class.from_dict(_A				)

       # verify that the backbone is supported
       if backbone_config.model_type not in self.backbones_supported:
            logger.warning_once(
                f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
                f"""Supported model types: {','.join(self.backbones_supported				)}"""				)

       if decoder_config is None:
            # fall back to https://huggingface.co/facebook/detr-resnet-50
            lowercase :       Any       =			DetrConfig()
       else:
            # verify that the decoder is supported
            lowercase :       Union[str, Any]       =			(
                decoder_config.pop('''model_type'''				) if isinstance(_A				,						_A				) else decoder_config.model_type
            )
            if decoder_type not in self.decoders_supported:
                 raise ValueError(
                     f"""Transformer Decoder {decoder_type} not supported, please use one of"""
                     f""" {','.join(self.decoders_supported				)}"""				)
            if isinstance(_A				,						_A				):
                 lowercase :       str       =			CONFIG_MAPPING[decoder_type]
                 lowercase :       Dict       =			config_class.from_dict(_A				)

       lowercase :       Tuple       =			backbone_config
       lowercase :       List[Any]       =			decoder_config
       # main feature dimension for the model
       lowercase :       Optional[int]       =			fpn_feature_size
       lowercase :       List[Any]       =			mask_feature_size
       # initializer
       lowercase :       Union[str, Any]       =			init_std
       lowercase :       Tuple       =			init_xavier_std
       # Hungarian matcher && loss
       lowercase :       List[str]       =			cross_entropy_weight
       lowercase :       int       =			dice_weight
       lowercase :       List[Any]       =			mask_weight
       lowercase :       Tuple       =			use_auxiliary_loss
       lowercase :       Tuple       =			no_object_weight
       lowercase :       int       =			output_auxiliary_logits

       lowercase :       List[Any]       =			self.decoder_config.encoder_attention_heads
       lowercase :       List[Any]       =			self.decoder_config.num_hidden_layers
       super().__init__(**_A				)






  @classmethod
  def  __a      (  cls		:     Optional[int]				,						_A		:     PretrainedConfig				,						_A		:     PretrainedConfig				,						**_A		:     str				)		->   Any:






       """simple docstring"""







       return cls(
           backbone_config=_A				,						decoder_config=_A				,						**_A				,						)






  def  __a      (  self		:     Optional[int]				)		->   Dict[str, any]:






       """simple docstring"""







       lowercase :       str       =			copy.deepcopy(self.__dict__				)
       lowercase :       Optional[Any]       =			self.backbone_config.to_dict()
       lowercase :       List[Any]       =			self.decoder_config.to_dict()
       lowercase :       Optional[int]       =			self.__class__.model_type
       return output