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

import black


_UpperCAmelCase            =		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.
_UpperCAmelCase            =		"""    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""





class __a						( unittest.TestCase  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[str]         =				tempfile.mkdtemp()
						os.makedirs(os.path.join(self.transformer_dir		,     'models/bert/' ) )
						A_		:    Tuple         =				self.transformer_dir
						shutil.copy(
						    os.path.join(lowercase		,     'src/transformers/models/bert/modeling_bert.py' )		,     os.path.join(self.transformer_dir		,     'models/bert/modeling_bert.py' )		,     )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				'src/transformers'
						shutil.rmtree(self.transformer_dir )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase=None ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
						if overwrite_result is not None:
								A_		:    Dict         =				comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
						A_		:    Optional[int]         =				black.Mode(target_versions={black.TargetVersion.PYaa}		,     line_length=1_1_9 )
						A_		:    int         =				black.format_str(lowercase		,     mode=lowercase )
						A_		:    List[Any]         =				os.path.join(self.transformer_dir		,     'new_code.py' )
						with open(lowercase		,     'w'		,     newline='\n' ) as f:
								f.write(lowercase )
						if overwrite_result is None:
								self.assertTrue(len(check_copies.is_copy_consistent(lowercase ) ) == 0 )
						else:
								check_copies.is_copy_consistent(f.name		,     overwrite=lowercase )
								with open(lowercase		,     'r' ) as f:
										self.assertTrue(f.read()		,     lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Dict         =				check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead' )
						self.assertEqual(lowercase		,     lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						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'		,     lowercase		,     )

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

						# Copy consistency with a really long name
						A_		:    Optional[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'		,     lowercase		,     lowercase )		,     )

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







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Tuple         =				check_copies.LOCALIZED_READMES['README_zh-hans.md']

						A_		:    str         =				(
						    '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.'
						)
						A_		:    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。\n'
						)
						A_		:    List[str]         =				(
						    '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'
						)

						A_		:    str         =				check_copies.convert_to_localized_md(
						    lowercase		,     lowercase		,     localized_readme['format_model_list'] )

						self.assertFalse(lowercase )
						self.assertEqual(lowercase		,     lowercase )

						A_		:    Tuple         =				check_copies.convert_to_localized_md(
						    lowercase		,     lowercase		,     localized_readme['format_model_list'] )

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

						A_		:    Optional[int]         =				(
						    '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.'
						)
						A_		:    Tuple         =				(
						    '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'
						)
						A_		:    Dict         =				(
						    '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'
						)

						A_		:    int         =				check_copies.convert_to_localized_md(
						    lowercase		,     lowercase		,     localized_readme['format_model_list'] )

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







import copy
from typing import Dict, List, Optional

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


_UpperCAmelCase            =		{
    """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
}

_UpperCAmelCase            =		logging.get_logger(__name__)





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = '''mask2former'''
				lowerCamelCase_    = ['''swin''']
				lowerCamelCase_    = {'''hidden_size''': '''hidden_dim'''}
				def __init__(						self		,     lowercase = None		,     lowercase = 2_5_6		,     lowercase = 2_5_6		,     lowercase = 2_5_6		,     lowercase = 1_0_2_4		,     lowercase = "relu"		,     lowercase = 6		,     lowercase = 1_0		,     lowercase = 8		,     lowercase = 0.0		,     lowercase = 2_0_4_8		,     lowercase = False		,     lowercase = False		,     lowercase = 4		,     lowercase = 2_5_5		,     lowercase = 1_0_0		,     lowercase = 0.1		,     lowercase = 2.0		,     lowercase = 5.0		,     lowercase = 5.0		,     lowercase = 1_2_5_4_4		,     lowercase = 3.0		,     lowercase = 0.75		,     lowercase = 0.02		,     lowercase = 1.0		,     lowercase = True		,     lowercase = [4, 8, 1_6, 3_2]		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						if backbone_config is None:
								logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.' )
								A_		:    List[str]         =				CONFIG_MAPPING['swin'](
								    image_size=2_2_4		,     in_channels=3		,     patch_size=4		,     embed_dim=9_6		,     depths=[2, 2, 1_8, 2]		,     num_heads=[3, 6, 1_2, 2_4]		,     window_size=7		,     drop_path_rate=0.3		,     use_absolute_embeddings=lowercase		,     out_features=['stage1', 'stage2', 'stage3', 'stage4']		,     )

						if isinstance(lowercase		,     lowercase ):
								A_		:    str         =				backbone_config.pop('model_type' )
								A_		:    List[str]         =				CONFIG_MAPPING[backbone_model_type]
								A_		:    Tuple         =				config_class.from_dict(lowercase )

						# 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 )}''' )

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

						super().__init__(**lowercase )
				@classmethod
				def   lowerCAmelCase_       (						cls		,     lowercase		,     **lowercase ):



						"""simple docstring"""





						return cls(
						    backbone_config=lowercase		,     **lowercase		,     )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





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






