





import copy
from typing import Dict, Optional

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


lowerCAmelCase_        =			logging.get_logger(__name__)

lowerCAmelCase_        =			{
    """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			(	UpperCAmelCase_							):


  '''simple docstring'''
  a_							:				List[Any]					    ="""oneformer"""
  a_							:				List[Any]					    ={"""hidden_size""": """hidden_dim"""}







  def __init__(					self					:    List[str]					, UpperCamelCase					:    int = None					, UpperCamelCase					:    List[Any] = 2_55					, UpperCamelCase					:    Any = 1_50					, UpperCamelCase					:    Tuple = 0.1					, UpperCamelCase					:    List[str] = 2.0					, UpperCamelCase					:    Dict = 5.0					, UpperCamelCase					:    Any = 5.0					, UpperCamelCase					:    Optional[int] = 0.5					, UpperCamelCase					:    Optional[int] = 0.07					, UpperCamelCase					:    Optional[int] = 1_25_44					, UpperCamelCase					:    str = 3.0					, UpperCamelCase					:    str = 0.75					, UpperCamelCase					:    Tuple = 0.02					, UpperCamelCase					:    Union[str, Any] = 1.0					, UpperCamelCase					:    Optional[int] = 1e-0_5					, UpperCamelCase					:    Any = False					, UpperCamelCase					:    int = True					, UpperCamelCase					:    Tuple = True					, UpperCamelCase					:    Any = [4, 8, 16, 32]					, UpperCamelCase					:    List[str] = 77					, UpperCamelCase					:    List[str] = 2_56					, UpperCamelCase					:    Union[str, Any] = 77					, UpperCamelCase					:    str = 6					, UpperCamelCase					:    List[str] = 4_94_08					, UpperCamelCase					:    List[str] = 2					, UpperCamelCase					:    List[str] = 16					, UpperCamelCase					:    Dict = 2_56					, UpperCamelCase					:    Tuple = 2_56					, UpperCamelCase					:    Any = 2_56					, UpperCamelCase					:    Optional[int] = 10_24					, UpperCamelCase					:    Union[str, Any] = "GN"					, UpperCamelCase					:    Optional[Any] = 6					, UpperCamelCase					:    Any = 10					, UpperCamelCase					:    str = True					, UpperCamelCase					:    Union[str, Any] = 8					, UpperCamelCase					:    Any = 0.1					, UpperCamelCase					:    Union[str, Any] = 20_48					, UpperCamelCase					:    List[Any] = False					, UpperCamelCase					:    Any = False					, UpperCamelCase					:    Tuple = 2					, UpperCamelCase					:    Union[str, Any] = 4					, **UpperCamelCase					:    List[Any]					, ):


    '''simple docstring'''





    if backbone_config is None:
      logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'							)
      _snake_case      : Dict														=		CONFIG_MAPPING['swin'](
          image_size=2_24					, 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							):
      _snake_case      : Any														=		backbone_config.get('model_type'							)
      _snake_case      : Any														=		CONFIG_MAPPING[backbone_model_type]
      _snake_case      : Optional[int]														=		config_class.from_dict(__UpperCAmelCase							)

    _snake_case      : Dict														=		backbone_config

    _snake_case      : Any														=		ignore_value
    _snake_case      : Union[str, Any]														=		num_queries
    _snake_case      : Optional[int]														=		no_object_weight
    _snake_case      : Optional[Any]														=		class_weight
    _snake_case      : List[Any]														=		mask_weight
    _snake_case      : Optional[int]														=		dice_weight
    _snake_case      : Optional[int]														=		contrastive_weight
    _snake_case      : Any														=		contrastive_temperature
    _snake_case      : List[str]														=		train_num_points
    _snake_case      : str														=		oversample_ratio
    _snake_case      : List[Any]														=		importance_sample_ratio
    _snake_case      : List[Any]														=		init_std
    _snake_case      : int														=		init_xavier_std
    _snake_case      : List[str]														=		layer_norm_eps
    _snake_case      : Optional[int]														=		is_training
    _snake_case      : str														=		use_auxiliary_loss
    _snake_case      : List[str]														=		output_auxiliary_logits
    _snake_case      : Optional[Any]														=		strides
    _snake_case      : List[Any]														=		task_seq_len
    _snake_case      : List[Any]														=		text_encoder_width
    _snake_case      : str														=		text_encoder_context_length
    _snake_case      : Union[str, Any]														=		text_encoder_num_layers
    _snake_case      : int														=		text_encoder_vocab_size
    _snake_case      : Any														=		text_encoder_proj_layers
    _snake_case      : Optional[int]														=		text_encoder_n_ctx
    _snake_case      : List[str]														=		conv_dim
    _snake_case      : Optional[int]														=		mask_dim
    _snake_case      : str														=		hidden_dim
    _snake_case      : int														=		encoder_feedforward_dim
    _snake_case      : Optional[Any]														=		norm
    _snake_case      : Union[str, Any]														=		encoder_layers
    _snake_case      : Optional[Any]														=		decoder_layers
    _snake_case      : Union[str, Any]														=		use_task_norm
    _snake_case      : Optional[Any]														=		num_attention_heads
    _snake_case      : List[Any]														=		dropout
    _snake_case      : List[Any]														=		dim_feedforward
    _snake_case      : Dict														=		pre_norm
    _snake_case      : Optional[int]														=		enforce_input_proj
    _snake_case      : Optional[Any]														=		query_dec_layers
    _snake_case      : Union[str, Any]														=		common_stride
    _snake_case      : List[Any]														=		decoder_layers

    super().__init__(**__UpperCAmelCase							)







  def UpperCamelCase_       (					self					:    Optional[Any]							):


    '''simple docstring'''





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






"""simple docstring"""


import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
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     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> Any:
	_lowerCAmelCase						,							_lowerCAmelCase								=os.path.split(__UpperCamelCase       )
	_lowerCAmelCase								=DataaVecTextModel.from_pretrained(
	    __UpperCamelCase    , checkpoint_file=__UpperCamelCase       )
	dataavec.eval()  # disable dropout
	_lowerCAmelCase								=dataavec.models[0]
	_lowerCAmelCase								=dataavec_model.encoder.sentence_encoder
	_lowerCAmelCase								=DataaVecTextConfig(
	    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings    , hidden_size=dataavec_model.args.encoder_embed_dim    , num_hidden_layers=dataavec_model.args.encoder_layers    , num_attention_heads=dataavec_model.args.encoder_attention_heads    , intermediate_size=dataavec_model.args.encoder_ffn_embed_dim    , max_position_embeddings=514    , type_vocab_size=1    , layer_norm_eps=1E-5    , )
	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
	print("""Our BERT config:"""    , __UpperCamelCase       )

	_lowerCAmelCase								=DataaVecTextForSequenceClassification(__UpperCamelCase       ) if classification_head else DataaVecTextForMaskedLM(__UpperCamelCase       )
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	_lowerCAmelCase								=dataavec_sent_encoder.embed_tokens.weight
	_lowerCAmelCase								=dataavec_sent_encoder.embed_positions.weight
	_lowerCAmelCase								=torch.zeros_like(
	    model.dataavec_text.embeddings.token_type_embeddings.weight       )  # just zero them out b/c data2vec doesn't use them.
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.weight
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.bias

	for i in range(config.num_hidden_layers       ):
		# Encoder: start of layer
		_lowerCAmelCase								=model.dataavec_text.encoder.layer[i]
		_lowerCAmelCase								=dataavec_sent_encoder.layers[i]

		# self attention
		_lowerCAmelCase								=layer.attention.self
		assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)       ), (
		    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
		)
		assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)       ), (
		    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
		)
		assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)       ), (
		    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
		)

		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.bias

		# self-attention output
		_lowerCAmelCase								=layer.attention.output
		assert (
		    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
		), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.bias

		# intermediate
		_lowerCAmelCase								=layer.intermediate
		assert (
		    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
		), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias

		# output
		_lowerCAmelCase								=layer.output
		assert (
		    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
		), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.bias
		# end of layer

	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.bias
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.bias
	else:
		# LM Head
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.bias

	# Let's check that we get the same results.
	_lowerCAmelCase								=dataavec.encode(__UpperCamelCase       ).unsqueeze(0       )  # batch of size 1

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

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


if __name__ == "__main__":
			__A       						=       argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--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_dataavec_checkpoint_to_pytorch(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)



