
"""simple docstring"""






import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()






def 		_snake_case			(      lowercase__						:    Dict ,	lowercase__						:    int ,	lowercase__						:    List[str] ,	lowercase__						:    int ,	lowercase__						:    List[str]						)    ->							List[str]:


			'''simple docstring'''





			lowerCAmelCase_       :List[Any]     	=    BertConfig.from_pretrained(
			    """bert-large-cased""" ,	vocab_size=lowercase__ ,	max_position_embeddings=5_1_2 ,	is_decoder=lowercase__ ,	add_cross_attention=lowercase__ ,	)
			lowerCAmelCase_       :Dict     	=    bert_config.to_dict()
			del bert_config_dict["type_vocab_size"]
			lowerCAmelCase_       :Optional[Any]     	=    BertGenerationConfig(**lowercase__						)
			if is_encoder:
						lowerCAmelCase_       :Union[str, Any]     	=    BertGenerationEncoder(lowercase__						)
			else:
						lowerCAmelCase_       :Tuple     	=    BertGenerationDecoder(lowercase__						)
			print(f"""Building PyTorch model from configuration: {config}"""						)

			# Load weights from tf checkpoint
			load_tf_weights_in_bert_generation(
			    lowercase__ ,	lowercase__ ,	model_class="""bert""" ,	is_encoder_named_decoder=lowercase__ ,	is_encoder=lowercase__ ,	)

			# Save pytorch-model
			print(f"""Save PyTorch model and config to {pytorch_dump_path}"""						)
			model.save_pretrained(lowercase__						)


if __name__ == "__main__":
				__UpperCAmelCase 					=    argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
				)
				parser.add_argument(
				    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				parser.add_argument(
				    '--is_encoder_named_decoder',
				    action='store_true',
				    help='If decoder has to be renamed to encoder in PyTorch model.',
				)
				parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
				parser.add_argument('--vocab_size', default=5_03_58, type=int, help='Vocab size of model')
				__UpperCAmelCase 					=    parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_hub_path,
				    args.pytorch_dump_path,
				    args.is_encoder_named_decoder,
				    args.vocab_size,
				    is_encoder=args.is_encoder,
				)








"""simple docstring"""

import copy
from typing import Dict, Optional

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


UpperCamelCase      :							List[str]          =					logging.get_logger(__name__)

UpperCamelCase      :							Dict          =					{
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}

class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		"oneformer"
     lowercase		  =		{"hidden_size": "hidden_dim"}





     def __init__(      self      ,	__UpperCAmelCase = None      ,	__UpperCAmelCase = 255      ,	__UpperCAmelCase = 150      ,	__UpperCAmelCase = 0.1      ,	__UpperCAmelCase = 2.0      ,	__UpperCAmelCase = 5.0      ,	__UpperCAmelCase = 5.0      ,	__UpperCAmelCase = 0.5      ,	__UpperCAmelCase = 0.0_7      ,	__UpperCAmelCase = 1_2544      ,	__UpperCAmelCase = 3.0      ,	__UpperCAmelCase = 0.7_5      ,	__UpperCAmelCase = 0.0_2      ,	__UpperCAmelCase = 1.0      ,	__UpperCAmelCase = 1E-05      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = True      ,	__UpperCAmelCase = True      ,	__UpperCAmelCase = [4, 8, 16, 32]      ,	__UpperCAmelCase = 77      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 77      ,	__UpperCAmelCase = 6      ,	__UpperCAmelCase = 4_9408      ,	__UpperCAmelCase = 2      ,	__UpperCAmelCase = 16      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 1024      ,	__UpperCAmelCase = "GN"      ,	__UpperCAmelCase = 6      ,	__UpperCAmelCase = 10      ,	__UpperCAmelCase = True      ,	__UpperCAmelCase = 8      ,	__UpperCAmelCase = 0.1      ,	__UpperCAmelCase = 2048      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = 2      ,	__UpperCAmelCase = 4      ,	**__UpperCAmelCase      ,	):

      '''simple docstring'''




      if backbone_config is None:
       logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.' )
       __UpperCamelCase							    =			CONFIG_MAPPING['swin'](
           image_size=224      ,	in_channels=3      ,	patch_size=4      ,	embed_dim=96      ,	depths=[2, 2, 6, 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']      ,	)
      elif isinstance(__UpperCAmelCase      ,	__UpperCAmelCase ):
       __UpperCamelCase							    =			backbone_config.get('model_type' )
       __UpperCamelCase							    =			CONFIG_MAPPING[backbone_model_type]
       __UpperCamelCase							    =			config_class.from_dict(__UpperCAmelCase )

      __UpperCamelCase							    =			backbone_config

      __UpperCamelCase							    =			ignore_value
      __UpperCamelCase							    =			num_queries
      __UpperCamelCase							    =			no_object_weight
      __UpperCamelCase							    =			class_weight
      __UpperCamelCase							    =			mask_weight
      __UpperCamelCase							    =			dice_weight
      __UpperCamelCase							    =			contrastive_weight
      __UpperCamelCase							    =			contrastive_temperature
      __UpperCamelCase							    =			train_num_points
      __UpperCamelCase							    =			oversample_ratio
      __UpperCamelCase							    =			importance_sample_ratio
      __UpperCamelCase							    =			init_std
      __UpperCamelCase							    =			init_xavier_std
      __UpperCamelCase							    =			layer_norm_eps
      __UpperCamelCase							    =			is_training
      __UpperCamelCase							    =			use_auxiliary_loss
      __UpperCamelCase							    =			output_auxiliary_logits
      __UpperCamelCase							    =			strides
      __UpperCamelCase							    =			task_seq_len
      __UpperCamelCase							    =			text_encoder_width
      __UpperCamelCase							    =			text_encoder_context_length
      __UpperCamelCase							    =			text_encoder_num_layers
      __UpperCamelCase							    =			text_encoder_vocab_size
      __UpperCamelCase							    =			text_encoder_proj_layers
      __UpperCamelCase							    =			text_encoder_n_ctx
      __UpperCamelCase							    =			conv_dim
      __UpperCamelCase							    =			mask_dim
      __UpperCamelCase							    =			hidden_dim
      __UpperCamelCase							    =			encoder_feedforward_dim
      __UpperCamelCase							    =			norm
      __UpperCamelCase							    =			encoder_layers
      __UpperCamelCase							    =			decoder_layers
      __UpperCamelCase							    =			use_task_norm
      __UpperCamelCase							    =			num_attention_heads
      __UpperCamelCase							    =			dropout
      __UpperCamelCase							    =			dim_feedforward
      __UpperCamelCase							    =			pre_norm
      __UpperCamelCase							    =			enforce_input_proj
      __UpperCamelCase							    =			query_dec_layers
      __UpperCamelCase							    =			common_stride
      __UpperCamelCase							    =			decoder_layers

      super().__init__(**__UpperCAmelCase )







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			copy.deepcopy(self.__dict__ )
      __UpperCamelCase							    =			self.backbone_config.to_dict()
      __UpperCamelCase							    =			self.__class__.model_type
      return output



