




'''simple docstring'''





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		:  Optional[Any]   			=			logging.get_logger(__name__)

a		:  List[str]   			=			'''Hello world! cécé herlolip'''





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	):
				_a							:       List[Any]       					=			FairseqRobertaModel.from_pretrained(UpperCAmelCase_	)
				roberta.eval()  # disable dropout
				_a							:       str       					=			roberta.model.encoder.sentence_encoder
				_a							:       Optional[int]       					=			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:
								_a							:       int       					=			roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
				print("""Our BERT config:"""      ,  UpperCAmelCase_	)

				_a							:       Union[str, Any]       					=			RobertaForSequenceClassification(UpperCAmelCase_	) if classification_head else RobertaForMaskedLM(UpperCAmelCase_	)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				_a							:       Dict       					=			roberta_sent_encoder.embed_tokens.weight
				_a							:       str       					=			roberta_sent_encoder.embed_positions.weight
				_a							:       Dict       					=			torch.zeros_like(
				    model.roberta.embeddings.token_type_embeddings.weight	)  # just zero them out b/c RoBERTa doesn't use them.
				_a							:       Optional[int]       					=			roberta_sent_encoder.emb_layer_norm.weight
				_a							:       Union[str, Any]       					=			roberta_sent_encoder.emb_layer_norm.bias

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

								# self attention
								_a							:       BertSelfAttention       					=			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)	)
								)

								_a							:       Optional[Any]       					=			roberta_layer.self_attn.q_proj.weight
								_a							:       Optional[int]       					=			roberta_layer.self_attn.q_proj.bias
								_a							:       int       					=			roberta_layer.self_attn.k_proj.weight
								_a							:       Tuple       					=			roberta_layer.self_attn.k_proj.bias
								_a							:       List[str]       					=			roberta_layer.self_attn.v_proj.weight
								_a							:       Optional[int]       					=			roberta_layer.self_attn.v_proj.bias

								# self-attention output
								_a							:       BertSelfOutput       					=			layer.attention.output
								assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
								_a							:       Optional[Any]       					=			roberta_layer.self_attn.out_proj.weight
								_a							:       Optional[int]       					=			roberta_layer.self_attn.out_proj.bias
								_a							:       Union[str, Any]       					=			roberta_layer.self_attn_layer_norm.weight
								_a							:       Optional[Any]       					=			roberta_layer.self_attn_layer_norm.bias

								# intermediate
								_a							:       BertIntermediate       					=			layer.intermediate
								assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
								_a							:       Union[str, Any]       					=			roberta_layer.fca.weight
								_a							:       List[str]       					=			roberta_layer.fca.bias

								# output
								_a							:       BertOutput       					=			layer.output
								assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
								_a							:       List[Any]       					=			roberta_layer.fca.weight
								_a							:       Tuple       					=			roberta_layer.fca.bias
								_a							:       Any       					=			roberta_layer.final_layer_norm.weight
								_a							:       List[Any]       					=			roberta_layer.final_layer_norm.bias
								# end of layer

				if classification_head:
								_a							:       Any       					=			roberta.model.classification_heads["""mnli"""].dense.weight
								_a							:       str       					=			roberta.model.classification_heads["""mnli"""].dense.bias
								_a							:       Optional[int]       					=			roberta.model.classification_heads["""mnli"""].out_proj.weight
								_a							:       Optional[int]       					=			roberta.model.classification_heads["""mnli"""].out_proj.bias
				else:
								# LM Head
								_a							:       Tuple       					=			roberta.model.encoder.lm_head.dense.weight
								_a							:       Tuple       					=			roberta.model.encoder.lm_head.dense.bias
								_a							:       Union[str, Any]       					=			roberta.model.encoder.lm_head.layer_norm.weight
								_a							:       str       					=			roberta.model.encoder.lm_head.layer_norm.bias
								_a							:       List[Any]       					=			roberta.model.encoder.lm_head.weight
								_a							:       int       					=			roberta.model.encoder.lm_head.bias

				# Let's check that we get the same results.
				_a							:       torch.Tensor       					=			roberta.encode(UpperCAmelCase_	).unsqueeze(0	)  # batch of size 1

				_a							:       Dict       					=			model(UpperCAmelCase_	)[0]
				if classification_head:
								_a							:       List[Any]       					=			roberta.model.classification_heads["""mnli"""](roberta.extract_features(UpperCAmelCase_	)	)
				else:
								_a							:       List[str]       					=			roberta.model(UpperCAmelCase_	)[0]
				print(our_output.shape      ,  their_output.shape	)
				_a							:       Dict       					=			torch.max(torch.abs(our_output - their_output	)	).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''	)  # ~ 1e-7
				_a							:       List[Any]       					=			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		:  str   			=			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		:  Optional[int]   			=			parser.parse_args()
		convert_roberta_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)








'''simple docstring'''





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_=3	):
    _a							:       Optional[Any]       					=			int(mask / 2	)
    # copy image size
    _a							:       Optional[int]       					=			zeros_like(UpperCAmelCase_	)
    for i in range(UpperCAmelCase_      ,  gray_img.shape[0] - bd	):
        for j in range(UpperCAmelCase_      ,  gray_img.shape[1] - bd	):
            # get mask according with mask
            _a							:       List[str]       					=			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]	)
            # calculate mask median
            _a							:       Optional[Any]       					=			sort(UpperCAmelCase_	)[inta(divide((multiply(UpperCAmelCase_      ,  UpperCAmelCase_	))      ,  2	) + 1	)]
            _a							:       int       					=			median
    return median_img


if __name__ == "__main__":
  # read original image
  a		:  List[Any]   			=			imread('''../image_data/lena.jpg''')
  # turn image in gray scale value
  a		:  Optional[Any]   			=			cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  a		:  str   			=			median_filter(gray, 3)
  a		:  Optional[Any]   			=			median_filter(gray, 5)

  # show result images
  imshow('''median filter with 3x3 mask''', medianaxa)
  imshow('''median filter with 5x5 mask''', medianaxa)
  waitKey(0)


