"""simple docstring"""




import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_a   :	Any    					=     {
    'facebook/mask2former-swin-small-coco-instance': (
        'https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

_a   :	Optional[int]    					=     logging.get_logger(__name__)
class 		__A							(				__lowerCamelCase	):
     _UpperCamelCase							:    Optional[int]	      =	"mask2former"
     _UpperCamelCase							:    Optional[Any]	      =	["swin"]
     _UpperCamelCase							:    Any	      =	{"hidden_size": "hidden_dim"}






     def __init__(	self       ,  a__ = None       ,  a__ = 256       ,  a__ = 256       ,  a__ = 256       ,  a__ = 1024       ,  a__ = "relu"       ,  a__ = 6       ,  a__ = 10       ,  a__ = 8       ,  a__ = 0.0       ,  a__ = 2048       ,  a__ = False       ,  a__ = False       ,  a__ = 4       ,  a__ = 255       ,  a__ = 100       ,  a__ = 0.1       ,  a__ = 2.0       ,  a__ = 5.0       ,  a__ = 5.0       ,  a__ = 12544       ,  a__ = 3.0       ,  a__ = 0.7_5       ,  a__ = 0.0_2       ,  a__ = 1.0       ,  a__ = True       ,  a__ = [4, 8, 16, 32]       ,  a__ = None       ,  **a__       ,  ):
       if backbone_config is None:
         logger.info("""`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."""						)
         _lowerCAmelCase		: Tuple						    =  CONFIG_MAPPING['swin'](
             image_size=224       ,  in_channels=3       ,  patch_size=4       ,  embed_dim=96       ,  depths=[2, 2, 18, 2]       ,  num_heads=[3, 6, 12, 24]       ,  window_size=7       ,  drop_path_rate=0.3       ,  use_absolute_embeddings=UpperCAmelCase_       ,  out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]       ,  )

       if isinstance(UpperCAmelCase_       ,  UpperCAmelCase_						):
         _lowerCAmelCase		: Optional[Any]						    =  backbone_config.pop("""model_type"""						)
         _lowerCAmelCase		: Union[str, Any]						    =  CONFIG_MAPPING[backbone_model_type]
         _lowerCAmelCase		: Dict						    =  config_class.from_dict(UpperCAmelCase_						)

       # 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 Mask2Former. "
             F"Supported model types: {','.join(self.backbones_supported						)}"						)

       _lowerCAmelCase		: Optional[Any]						    =  backbone_config
       _lowerCAmelCase		: List[Any]						    =  feature_size
       _lowerCAmelCase		: Dict						    =  mask_feature_size
       _lowerCAmelCase		: Optional[Any]						    =  hidden_dim
       _lowerCAmelCase		: Any						    =  encoder_feedforward_dim
       _lowerCAmelCase		: Optional[Any]						    =  activation_function
       _lowerCAmelCase		: Tuple						    =  encoder_layers
       _lowerCAmelCase		: Optional[int]						    =  decoder_layers
       _lowerCAmelCase		: int						    =  num_attention_heads
       _lowerCAmelCase		: Optional[int]						    =  dropout
       _lowerCAmelCase		: Dict						    =  dim_feedforward
       _lowerCAmelCase		: List[str]						    =  pre_norm
       _lowerCAmelCase		: Dict						    =  enforce_input_projection
       _lowerCAmelCase		: Union[str, Any]						    =  common_stride
       _lowerCAmelCase		: Dict						    =  ignore_value
       _lowerCAmelCase		: List[Any]						    =  num_queries
       _lowerCAmelCase		: List[Any]						    =  no_object_weight
       _lowerCAmelCase		: Optional[int]						    =  class_weight
       _lowerCAmelCase		: Dict						    =  mask_weight
       _lowerCAmelCase		: Tuple						    =  dice_weight
       _lowerCAmelCase		: Any						    =  train_num_points
       _lowerCAmelCase		: str						    =  oversample_ratio
       _lowerCAmelCase		: Any						    =  importance_sample_ratio
       _lowerCAmelCase		: Union[str, Any]						    =  init_std
       _lowerCAmelCase		: str						    =  init_xavier_std
       _lowerCAmelCase		: List[Any]						    =  use_auxiliary_loss
       _lowerCAmelCase		: List[str]						    =  feature_strides
       _lowerCAmelCase		: str						    =  output_auxiliary_logits
       _lowerCAmelCase		: List[Any]						    =  decoder_layers

       super().__init__(**UpperCAmelCase_						)






     @classmethod
     def 					__A						(	cls       ,  a__       ,  **a__						):
       return cls(
           backbone_config=UpperCAmelCase_       ,  **UpperCAmelCase_       ,  )






     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  copy.deepcopy(self.__dict__						)
       _lowerCAmelCase		: Union[str, Any]						    =  self.backbone_config.to_dict()
       _lowerCAmelCase		: Any						    =  self.__class__.model_type
       return output





"""simple docstring"""




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							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Dict	      =	CodeGenTokenizer
     _UpperCamelCase							:    Dict	      =	CodeGenTokenizerFast
     _UpperCamelCase							:    Tuple	      =	True
     _UpperCamelCase							:    List[Any]	      =	{"add_prefix_space": True}
     _UpperCamelCase							:    str	      =	False






     def 					__A						(	self						):
       super().setUp()

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

       _lowerCAmelCase		: List[str]						    =  os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["""vocab_file"""]						)
       _lowerCAmelCase		: List[Any]						    =  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       ,  **a__						):
       kwargs.update(self.special_tokens_map						)
       return CodeGenTokenizer.from_pretrained(self.tmpdirname       ,  **a__						)






     def 					__A						(	self       ,  **a__						):
       kwargs.update(self.special_tokens_map						)
       return CodeGenTokenizerFast.from_pretrained(self.tmpdirname       ,  **a__						)






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: str						    =  """lower newer"""
       _lowerCAmelCase		: Tuple						    =  """lower newer"""
       return input_text, output_text






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  CodeGenTokenizer(self.vocab_file       ,  self.merges_file       ,  **self.special_tokens_map						)
       _lowerCAmelCase		: int						    =  """lower newer"""
       _lowerCAmelCase		: List[str]						    =  ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
       _lowerCAmelCase		: Optional[int]						    =  tokenizer.tokenize(a__       ,  add_prefix_space=a__						)
       self.assertListEqual(a__       ,  a__						)

       _lowerCAmelCase		: Tuple						    =  tokens + [tokenizer.unk_token]
       _lowerCAmelCase		: List[str]						    =  [14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(a__						)       ,  a__						)






     def 					__A						(	self						):
       if not self.test_rust_tokenizer:
         return

       _lowerCAmelCase		: Optional[int]						    =  self.get_tokenizer()
       _lowerCAmelCase		: Optional[int]						    =  self.get_rust_tokenizer(add_prefix_space=a__						)

       _lowerCAmelCase		: Any						    =  """lower newer"""

       # Testing tokenization
       _lowerCAmelCase		: Any						    =  tokenizer.tokenize(a__       ,  add_prefix_space=a__						)
       _lowerCAmelCase		: int						    =  rust_tokenizer.tokenize(a__						)
       self.assertListEqual(a__       ,  a__						)

       # Testing conversion to ids without special tokens
       _lowerCAmelCase		: Union[str, Any]						    =  tokenizer.encode(a__       ,  add_special_tokens=a__       ,  add_prefix_space=a__						)
       _lowerCAmelCase		: Dict						    =  rust_tokenizer.encode(a__       ,  add_special_tokens=a__						)
       self.assertListEqual(a__       ,  a__						)

       # Testing conversion to ids with special tokens
       _lowerCAmelCase		: int						    =  self.get_rust_tokenizer(add_prefix_space=a__						)
       _lowerCAmelCase		: Optional[int]						    =  tokenizer.encode(a__       ,  add_prefix_space=a__						)
       _lowerCAmelCase		: Any						    =  rust_tokenizer.encode(a__						)
       self.assertListEqual(a__       ,  a__						)

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






     def 					__A						(	self       ,  *a__       ,  **a__						):
       # It's very difficult to mix/test pretokenization with byte-level
       # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
       pass






     def 					__A						(	self       ,  a__=15						):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"						):
           _lowerCAmelCase		: List[Any]						    =  self.rust_tokenizer_class.from_pretrained(a__       ,  **a__						)

           # Simple input
           _lowerCAmelCase		: Dict						    =  """This is a simple input"""
           _lowerCAmelCase		: Optional[int]						    =  ["""This is a simple input 1""", """This is a simple input 2"""]
           _lowerCAmelCase		: Optional[int]						    =  ("""This is a simple input""", """This is a pair""")
           _lowerCAmelCase		: 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						):
       _lowerCAmelCase		: Any						    =  CodeGenTokenizer.from_pretrained(self.tmpdirname       ,  pad_token="""<pad>"""						)

       # Simple input
       _lowerCAmelCase		: Union[str, Any]						    =  """This is a simple input"""
       _lowerCAmelCase		: Dict						    =  ["""This is a simple input looooooooong""", """This is a simple input"""]
       _lowerCAmelCase		: Any						    =  ("""This is a simple input""", """This is a pair""")
       _lowerCAmelCase		: Optional[int]						    =  [
           ("""This is a simple input loooooong""", """This is a simple input"""),
           ("""This is a simple pair loooooong""", """This is a simple pair"""),
       ]

       _lowerCAmelCase		: Optional[int]						    =  tokenizer.pad_token_id

       _lowerCAmelCase		: Any						    =  tokenizer(a__       ,  padding="""max_length"""       ,  max_length=30       ,  return_tensors="""np"""						)
       _lowerCAmelCase		: str						    =  tokenizer(a__       ,  padding=a__       ,  truncate=a__       ,  return_tensors="""np"""						)
       _lowerCAmelCase		: int						    =  tokenizer(*a__       ,  padding="""max_length"""       ,  max_length=60       ,  return_tensors="""np"""						)
       _lowerCAmelCase		: int						    =  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						):
       _lowerCAmelCase		: List[str]						    =  """$$$"""
       _lowerCAmelCase		: str						    =  CodeGenTokenizer.from_pretrained(self.tmpdirname       ,  bos_token=a__       ,  add_bos_token=a__						)

       _lowerCAmelCase		: Tuple						    =  """This is a simple input"""
       _lowerCAmelCase		: Union[str, Any]						    =  ["""This is a simple input 1""", """This is a simple input 2"""]

       _lowerCAmelCase		: List[str]						    =  tokenizer.bos_token_id

       _lowerCAmelCase		: str						    =  tokenizer(a__						)
       _lowerCAmelCase		: Union[str, Any]						    =  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						)						)

       _lowerCAmelCase		: Optional[Any]						    =  tokenizer.decode(out_s.input_ids						)
       _lowerCAmelCase		: Optional[int]						    =  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						):
       _lowerCAmelCase		: int						    =  CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono"""						)

       _lowerCAmelCase		: Optional[int]						    =  """\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"""
       _lowerCAmelCase		: List[Any]						    =  """\nif len_a > len_b:      result = a\nelse:      result = b"""

       _lowerCAmelCase		: Tuple						    =  tokenizer.encode(a__						)
       _lowerCAmelCase		: Optional[Any]						    =  ["""^#""", re.escape("""<|endoftext|>"""						), """^'''""", """^\"\"\"""", """\n\n\n"""]
       _lowerCAmelCase		: int						    =  tokenizer.decode(a__       ,  truncate_before_pattern=a__						)
       self.assertEqual(a__       ,  a__						)






     def 					__A						(	self						):
       pass




