


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()
SCREAMING_SNAKE_CASE_      :			Dict		  =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      :			List[Any]		  =      '''Hello world! cécé herlolip'''



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case       )		->						List[str]:
		__lowercase					=    FairseqRobertaModel.from_pretrained(snake_case       )
		roberta.eval()  # disable dropout
		__lowercase					=    roberta.model.encoder.sentence_encoder
		__lowercase					=    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:
				__lowercase					=    roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
		print('Our BERT config:'	,      snake_case       )

		__lowercase					=    RobertaForSequenceClassification(snake_case       ) if classification_head else RobertaForMaskedLM(snake_case       )
		model.eval()

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

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

				# self attention
				__lowercase					=    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)       )
				)

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

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

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

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

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

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

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

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


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_      :			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.'''
		)
		SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      parser.parse_args()
		convert_roberta_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)











from __future__ import annotations



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case	,      snake_case       )		->						None:
		__lowercase	,			__lowercase					=    m - 1, 0
		while i >= 0 and j < n:
				if key == mat[i][j]:
						print(F"Key {key} found at row- {i + 1} column- {j + 1}"       )
						return
				if key < mat[i][j]:
						i -= 1
				else:
						j += 1
		print(F"Key {key} not found"       )



def    SCREAMING_SNAKE_CASE							(			)		->						None:
		__lowercase					=    [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
		__lowercase					=    int(input('Enter the element to be searched:'       )       )
		print(snake_case       )
		search_in_a_sorted_matrix(snake_case	,      len(snake_case       )	,      len(mat[0]       )	,      snake_case       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()




