






import copy
from typing import Dict, Optional

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


_a       =    logging.get_logger(__name__)

_a       =    {
    '''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					( snake_case__):





	"""simple docstring"""






	UpperCamelCase__								= "oneformer"
	UpperCamelCase__								= {"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 = 0.07			,       UpperCAmelCase = 1_2544			,       UpperCAmelCase = 3.0			,       UpperCAmelCase = 0.75			,       UpperCAmelCase = 0.02			,       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








import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
		raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
_a       =    logging.get_logger(__name__)

_a       =    '''Hello world! cécé herlolip'''


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    List[Any]:





						"""simple docstring"""



						_UpperCAmelCase							   =     FairseqRobertaModel.from_pretrained(__lowerCAmelCase      )
						roberta.eval()  # disable dropout
						_UpperCAmelCase							   =     roberta.model.encoder.sentence_encoder
						_UpperCAmelCase							   =     RobertaConfig(
						    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings					,						hidden_size=roberta.args.encoder_embed_dim					,						num_hidden_layers=roberta.args.encoder_layers					,						num_attention_heads=roberta.args.encoder_attention_heads					,						intermediate_size=roberta.args.encoder_ffn_embed_dim					,						max_position_embeddings=514					,						type_vocab_size=1					,						layer_norm_eps=1E-5					,						)
						if classification_head:
												_UpperCAmelCase							   =     roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
						print('Our BERT config:'					,						__lowerCAmelCase      )

						_UpperCAmelCase							   =     RobertaForSequenceClassification(__lowerCAmelCase      ) if classification_head else RobertaForMaskedLM(__lowerCAmelCase      )
						model.eval()

						# Now let's copy all the weights.
						# Embeddings
						_UpperCAmelCase							   =     roberta_sent_encoder.embed_tokens.weight
						_UpperCAmelCase							   =     roberta_sent_encoder.embed_positions.weight
						_UpperCAmelCase							   =     torch.zeros_like(
						    model.roberta.embeddings.token_type_embeddings.weight      )  # just zero them out b/c RoBERTa doesn't use them.
						_UpperCAmelCase							   =     roberta_sent_encoder.emb_layer_norm.weight
						_UpperCAmelCase							   =     roberta_sent_encoder.emb_layer_norm.bias

						for i in range(config.num_hidden_layers      ):
												# Encoder: start of layer
												_UpperCAmelCase							   =     model.roberta.encoder.layer[i]
												_UpperCAmelCase							   =     roberta_sent_encoder.layers[i]

												# self attention
												_UpperCAmelCase							   =     layer.attention.self
												assert (
												    roberta_layer.self_attn.k_proj.weight.data.shape
												    == roberta_layer.self_attn.q_proj.weight.data.shape
												    == roberta_layer.self_attn.v_proj.weight.data.shape
												    == torch.Size((config.hidden_size, config.hidden_size)      )
												)

												_UpperCAmelCase							   =     roberta_layer.self_attn.q_proj.weight
												_UpperCAmelCase							   =     roberta_layer.self_attn.q_proj.bias
												_UpperCAmelCase							   =     roberta_layer.self_attn.k_proj.weight
												_UpperCAmelCase							   =     roberta_layer.self_attn.k_proj.bias
												_UpperCAmelCase							   =     roberta_layer.self_attn.v_proj.weight
												_UpperCAmelCase							   =     roberta_layer.self_attn.v_proj.bias

												# self-attention output
												_UpperCAmelCase							   =     layer.attention.output
												assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
												_UpperCAmelCase							   =     roberta_layer.self_attn.out_proj.weight
												_UpperCAmelCase							   =     roberta_layer.self_attn.out_proj.bias
												_UpperCAmelCase							   =     roberta_layer.self_attn_layer_norm.weight
												_UpperCAmelCase							   =     roberta_layer.self_attn_layer_norm.bias

												# intermediate
												_UpperCAmelCase							   =     layer.intermediate
												assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
												_UpperCAmelCase							   =     roberta_layer.fca.weight
												_UpperCAmelCase							   =     roberta_layer.fca.bias

												# output
												_UpperCAmelCase							   =     layer.output
												assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
												_UpperCAmelCase							   =     roberta_layer.fca.weight
												_UpperCAmelCase							   =     roberta_layer.fca.bias
												_UpperCAmelCase							   =     roberta_layer.final_layer_norm.weight
												_UpperCAmelCase							   =     roberta_layer.final_layer_norm.bias
												# end of layer

						if classification_head:
												_UpperCAmelCase							   =     roberta.model.classification_heads['mnli'].dense.weight
												_UpperCAmelCase							   =     roberta.model.classification_heads['mnli'].dense.bias
												_UpperCAmelCase							   =     roberta.model.classification_heads['mnli'].out_proj.weight
												_UpperCAmelCase							   =     roberta.model.classification_heads['mnli'].out_proj.bias
						else:
												# LM Head
												_UpperCAmelCase							   =     roberta.model.encoder.lm_head.dense.weight
												_UpperCAmelCase							   =     roberta.model.encoder.lm_head.dense.bias
												_UpperCAmelCase							   =     roberta.model.encoder.lm_head.layer_norm.weight
												_UpperCAmelCase							   =     roberta.model.encoder.lm_head.layer_norm.bias
												_UpperCAmelCase							   =     roberta.model.encoder.lm_head.weight
												_UpperCAmelCase							   =     roberta.model.encoder.lm_head.bias

						# Let's check that we get the same results.
						_UpperCAmelCase							   =     roberta.encode(__lowerCAmelCase      ).unsqueeze(0      )  # batch of size 1

						_UpperCAmelCase							   =     model(__lowerCAmelCase      )[0]
						if classification_head:
												_UpperCAmelCase							   =     roberta.model.classification_heads['mnli'](roberta.extract_features(__lowerCAmelCase      )      )
						else:
												_UpperCAmelCase							   =     roberta.model(__lowerCAmelCase      )[0]
						print(our_output.shape					,						their_output.shape      )
						_UpperCAmelCase							   =     torch.max(torch.abs(our_output - their_output      )      ).item()
						print(F"""max_absolute_diff = {max_absolute_diff}"""      )  # ~ 1e-7
						_UpperCAmelCase							   =     torch.allclose(__lowerCAmelCase					,						__lowerCAmelCase					,						atol=1E-3      )
						print('Do both models output the same tensors?'					,						'🔥' if success else '💩'      )
						if not success:
												raise Exception('Something went wRoNg'      )

						pathlib.Path(__lowerCAmelCase      ).mkdir(parents=__lowerCAmelCase					,						exist_ok=__lowerCAmelCase      )
						print(F"""Saving model to {pytorch_dump_folder_path}"""      )
						model.save_pretrained(__lowerCAmelCase      )


if __name__ == "__main__":
		_a       =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--roberta_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		parser.add_argument(
		    '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
		)
		_a       =    parser.parse_args()
		convert_roberta_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)
