



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()
UpperCAmelCase :				Optional[int]							=logging.get_logger(__name__)

UpperCAmelCase :				List[str]							="""Hello world! cécé herlolip"""



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase):
					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=5_14 ,						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__":
	UpperCAmelCase :				List[Any]							=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."""
	)
	UpperCAmelCase :				List[Any]							=parser.parse_args()
	convert_roberta_checkpoint_to_pytorch(
	    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)








"""simple docstring"""




UpperCamelCase											=     [
    [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
]






def 							lowerCAmelCase			(   UpperCamelCase_:				int  ,			UpperCamelCase_:				int  ,			UpperCamelCase_:				int  ,			UpperCamelCase_:				int       )							->				bool:







  '''simple docstring'''







  return 0 <= row < rows and 0 <= col < cols






def 							lowerCAmelCase			(   UpperCamelCase_:				int  ,			UpperCamelCase_:				int  ,			UpperCamelCase_:				set  ,			UpperCamelCase_:				list[list[int]]       )							->				int:







  '''simple docstring'''







  _a					      =						len(UpperCamelCase_       )
  _a					      =						len(mat[0]       )
  if is_safe(UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       ) and (row, col) not in seen and mat[row][col] == 1:
    seen.add((row, col)       )
    return (
        1
        + depth_first_search(row + 1  ,			UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       )
        + depth_first_search(row - 1  ,			UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       )
        + depth_first_search(UpperCamelCase_  ,			col + 1  ,			UpperCamelCase_  ,			UpperCamelCase_       )
        + depth_first_search(UpperCamelCase_  ,			col - 1  ,			UpperCamelCase_  ,			UpperCamelCase_       )
    )
  else:
    return 0






def 							lowerCAmelCase			(   UpperCamelCase_:				list[list[int]]       )							->				int:







  '''simple docstring'''







  _a					      =						set()

  _a					      =						0
  for row, line in enumerate(UpperCamelCase_       ):
    for col, item in enumerate(UpperCamelCase_       ):
      if item == 1 and (row, col) not in seen:
        # Maximizing the area
        _a					      =						max(UpperCamelCase_  ,			depth_first_search(UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       )       )
  return max_area


if __name__ == "__main__":
     import doctest

     print(find_max_area(matrix))  # Output -> 6
     doctest.testmod()


