

import os
import re
import shutil
import sys
import tempfile
import unittest

import black


A_       						=							os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
A_       						=							"    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"





class 							_snake_case (       unittest.TestCase						):
   def     __UpperCamelCase						( self			:      int							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					tempfile.mkdtemp()
     os.makedirs(os.path.join(self.transformer_dir     ,"models/bert/"							)							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					self.transformer_dir
     shutil.copy(
         os.path.join(SCREAMING_SNAKE_CASE__     ,"src/transformers/models/bert/modeling_bert.py"							)     ,os.path.join(self.transformer_dir     ,"models/bert/modeling_bert.py"							)     ,)
   def     __UpperCamelCase						( self			:      Any							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					"src/transformers"
     shutil.rmtree(self.transformer_dir							)
   def     __UpperCamelCase						( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      Dict=None							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
     if overwrite_result is not None:
       SCREAMING_SNAKE_CASE:Any						      =					comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
     SCREAMING_SNAKE_CASE:str						      =					black.Mode(target_versions={black.TargetVersion.PYaa}     ,line_length=119							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					black.format_str(SCREAMING_SNAKE_CASE__     ,mode=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Tuple						      =					os.path.join(self.transformer_dir     ,"new_code.py"							)
     with open(SCREAMING_SNAKE_CASE__     ,"w"     ,newline="\n"							) as f:
       f.write(SCREAMING_SNAKE_CASE__							)
     if overwrite_result is None:
       self.assertTrue(len(check_copies.is_copy_consistent(SCREAMING_SNAKE_CASE__							)							) == 0							)
     else:
       check_copies.is_copy_consistent(f.name     ,overwrite=SCREAMING_SNAKE_CASE__							)
       with open(SCREAMING_SNAKE_CASE__     ,"r"							) as f:
         self.assertTrue(f.read()     ,SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:List[str]						      =					check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"							)
     self.assertEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Dict							):
     # Base copy consistency
     self.check_copy_consistency(
         "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"     ,"BertLMPredictionHead"     ,REFERENCE_CODE + "\n"     ,)

     # With no empty line at the end
     self.check_copy_consistency(
         "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"     ,"BertLMPredictionHead"     ,SCREAMING_SNAKE_CASE__     ,)

     # Copy consistency with rename
     self.check_copy_consistency(
         "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"     ,"TestModelLMPredictionHead"     ,re.sub("Bert"     ,"TestModel"     ,SCREAMING_SNAKE_CASE__							)     ,)

     # Copy consistency with a really long name
     SCREAMING_SNAKE_CASE:List[Any]						      =					"TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
     self.check_copy_consistency(
         F'''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'''     ,F'''{long_class_name}LMPredictionHead'''     ,re.sub("Bert"     ,SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)     ,)

     # Copy consistency with overwrite
     self.check_copy_consistency(
         "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"     ,"TestModelLMPredictionHead"     ,SCREAMING_SNAKE_CASE__     ,overwrite_result=re.sub("Bert"     ,"TestModel"     ,SCREAMING_SNAKE_CASE__							)     ,)







   def     __UpperCamelCase						( self			:      str							):
     SCREAMING_SNAKE_CASE:List[str]						      =					check_copies.LOCALIZED_READMES["README_zh-hans.md"]

     SCREAMING_SNAKE_CASE:Dict						      =					(
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
         " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
         " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
         " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
         " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
         " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
         " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
         " method has been applied to compress GPT2 into"
         " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
         " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
         " Multilingual BERT into"
         " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
         " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
         " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
         " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
         " Luong, Quoc V. Le, Christopher D. Manning."
     )
     SCREAMING_SNAKE_CASE:Tuple						      =					(
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
         " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
         " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
         " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
     )
     SCREAMING_SNAKE_CASE:Any						      =					(
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
         " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
         " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
         " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
         " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
         " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
         " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
         " method has been applied to compress GPT2 into"
         " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
         " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
         " Multilingual BERT into"
         " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
         " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
         " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
         " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
         " Christopher D. Manning 发布。\n"
     )

     SCREAMING_SNAKE_CASE:int						      =					check_copies.convert_to_localized_md(
         SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,localized_readme["format_model_list"]							)

     self.assertFalse(SCREAMING_SNAKE_CASE__							)
     self.assertEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Any						      =					check_copies.convert_to_localized_md(
         SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,localized_readme["format_model_list"]							)

     # Check whether the number of models is equal to README.md after conversion.
     self.assertTrue(SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Dict						      =					(
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
         " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
         " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
         " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
     )
     SCREAMING_SNAKE_CASE:int						      =					(
         "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
         " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
         " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
         " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
     )
     SCREAMING_SNAKE_CASE:int						      =					(
         "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
         " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
         " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
         " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
     )

     SCREAMING_SNAKE_CASE:Tuple						      =					check_copies.convert_to_localized_md(
         SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,localized_readme["format_model_list"]							)

     # Check if the model link is synchronized.
     self.assertEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)









'''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_       						=							{
    "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_       						=							logging.get_logger(__name__)





class 							_snake_case (       _a						):
   _A       :     List[Any]       =					'''mask2former'''
   _A       :     str       =					['''swin''']
   _A       :     Tuple       =					{'''hidden_size''': '''hidden_dim'''}
   def __init__( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Optional[Dict] = None     ,SCREAMING_SNAKE_CASE__			:      int = 256     ,SCREAMING_SNAKE_CASE__			:      int = 256     ,SCREAMING_SNAKE_CASE__			:      int = 256     ,SCREAMING_SNAKE_CASE__			:      int = 1_024     ,SCREAMING_SNAKE_CASE__			:      str = "relu"     ,SCREAMING_SNAKE_CASE__			:      int = 6     ,SCREAMING_SNAKE_CASE__			:      int = 10     ,SCREAMING_SNAKE_CASE__			:      int = 8     ,SCREAMING_SNAKE_CASE__			:      float = 0.0     ,SCREAMING_SNAKE_CASE__			:      int = 2_048     ,SCREAMING_SNAKE_CASE__			:      bool = False     ,SCREAMING_SNAKE_CASE__			:      bool = False     ,SCREAMING_SNAKE_CASE__			:      int = 4     ,SCREAMING_SNAKE_CASE__			:      int = 255     ,SCREAMING_SNAKE_CASE__			:      int = 100     ,SCREAMING_SNAKE_CASE__			:      float = 0.1     ,SCREAMING_SNAKE_CASE__			:      float = 2.0     ,SCREAMING_SNAKE_CASE__			:      float = 5.0     ,SCREAMING_SNAKE_CASE__			:      float = 5.0     ,SCREAMING_SNAKE_CASE__			:      int = 12_544     ,SCREAMING_SNAKE_CASE__			:      float = 3.0     ,SCREAMING_SNAKE_CASE__			:      float = 0.75     ,SCREAMING_SNAKE_CASE__			:      float = 0.02     ,SCREAMING_SNAKE_CASE__			:      float = 1.0     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      List[int] = [4, 8, 16, 32]     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,**SCREAMING_SNAKE_CASE__			:      Dict     ,):
     if backbone_config is None:
       logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."							)
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					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=SCREAMING_SNAKE_CASE__     ,out_features=["stage1", "stage2", "stage3", "stage4"]     ,)

     if isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:Any						      =					backbone_config.pop("model_type"							)
       SCREAMING_SNAKE_CASE:List[str]						      =					CONFIG_MAPPING[backbone_model_type]
       SCREAMING_SNAKE_CASE:List[str]						      =					config_class.from_dict(SCREAMING_SNAKE_CASE__							)

     # 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							)}'''							)

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

     super().__init__(**SCREAMING_SNAKE_CASE__							)
   @classmethod
   def     __UpperCamelCase						( cls			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      PretrainedConfig     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]							):
     return cls(
         backbone_config=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__     ,)







   def     __UpperCamelCase						( self			:      List[Any]							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					copy.deepcopy(self.__dict__							)
     SCREAMING_SNAKE_CASE:str						      =					self.backbone_config.to_dict()
     SCREAMING_SNAKE_CASE:List[str]						      =					self.__class__.model_type
     return output






