




"""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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
		raise Exception('requires fairseq >= 1.0.0a')

logging.set_verbosity_info()
_lowerCamelCase  :			Dict        =       logging.get_logger(__name__)

_lowerCamelCase  :			Optional[int]        =       'Hello world! cécé herlolip'

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				str	   =		FairseqRobertaModel.from_pretrained(lowerCAmelCase__	)
	roberta.eval()  # disable dropout
	A_  :				List[Any]	   =		roberta.model.encoder.sentence_encoder
	A_  :				Any	   =		XLMRobertaConfig(
	    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings  ,      hidden_size=roberta.cfg.model.encoder_embed_dim  ,      num_hidden_layers=roberta.cfg.model.encoder_layers  ,      num_attention_heads=roberta.cfg.model.encoder_attention_heads  ,      intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim  ,      max_position_embeddings=514  ,      type_vocab_size=1  ,      layer_norm_eps=1E-5  ,      )
	if classification_head:
		A_  :				Any	   =		roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

	print('''Our RoBERTa config:'''  ,      lowerCAmelCase__	)

	A_  :				List[Any]	   =		XLMRobertaXLForSequenceClassification(lowerCAmelCase__	) if classification_head else XLMRobertaXLForMaskedLM(lowerCAmelCase__	)
	model.eval()

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

	A_  :				Union[str, Any]	   =		roberta_sent_encoder.layer_norm.weight
	A_  :				List[Any]	   =		roberta_sent_encoder.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]

		A_  :				RobertaAttention	   =		layer.attention
		A_  :				List[Any]	   =		roberta_layer.self_attn_layer_norm.weight
		A_  :				List[Any]	   =		roberta_layer.self_attn_layer_norm.bias

		# 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_  :				List[str]	   =		roberta_layer.self_attn.q_proj.weight
		A_  :				Optional[int]	   =		roberta_layer.self_attn.q_proj.bias
		A_  :				Any	   =		roberta_layer.self_attn.k_proj.weight
		A_  :				Optional[Any]	   =		roberta_layer.self_attn.k_proj.bias
		A_  :				Any	   =		roberta_layer.self_attn.v_proj.weight
		A_  :				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_  :				Union[str, Any]	   =		roberta_layer.self_attn.out_proj.bias

		# this one is final layer norm
		A_  :				str	   =		roberta_layer.final_layer_norm.weight
		A_  :				List[Any]	   =		roberta_layer.final_layer_norm.bias

		# intermediate
		A_  :				BertIntermediate	   =		layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		A_  :				Tuple	   =		roberta_layer.fca.weight
		A_  :				List[Any]	   =		roberta_layer.fca.bias

		# output
		A_  :				BertOutput	   =		layer.output
		assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
		A_  :				Dict	   =		roberta_layer.fca.weight
		A_  :				Optional[Any]	   =		roberta_layer.fca.bias
		# end of layer

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

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

	A_  :				Any	   =		model(lowerCAmelCase__	)[0]
	if classification_head:
		A_  :				Optional[int]	   =		roberta.model.classification_heads['mnli'](roberta.extract_features(lowerCAmelCase__	)	)
	else:
		A_  :				Optional[Any]	   =		roberta.model(lowerCAmelCase__	)[0]
	print(our_output.shape  ,      their_output.shape	)
	A_  :				Any	   =		torch.max(torch.abs(our_output - their_output	)	).item()
	print(f"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
	A_  :				List[Any]	   =		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__":
		_lowerCamelCase  :			int        =       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.'
		)
		_lowerCamelCase  :			Any        =       parser.parse_args()
		convert_xlm_roberta_xl_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)











"""simple docstring"""

# Function to print upper half of diamond (pyramid)

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	for i in range(0  ,      _UpperCAmelCase	):
		for _ in range(0  ,      n - i - 1	):  # printing spaces
			print(''' '''  ,      end=''''''	)
		for _ in range(0  ,      i + 1	):  # printing stars
			print('''* '''  ,      end=''''''	)
		print()

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	for i in range(_UpperCAmelCase  ,      0  ,      -1	):
		for _ in range(_UpperCAmelCase  ,      0  ,      -1	):  # printing stars
			print('''* '''  ,      end=''''''	)
		print()
		for _ in range(n - i + 1  ,      0  ,      -1	):  # printing spaces
			print(''' '''  ,      end=''''''	)

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	if n <= 0:
		print('''       ...       ....        nothing printing :('''	)
		return
	floyd(_UpperCAmelCase	)  # upper half
	reverse_floyd(_UpperCAmelCase	)  # lower half


if __name__ == "__main__":
		print(r'| /\ | |- |  |-  |--| |\  /| |-')
		print(r'|/  \| |- |_ |_  |__| | \/ | |_')
		_lowerCamelCase  :			Tuple        =       1
		while K:
				_lowerCamelCase  :			Optional[int]        =       int(input('enter the number and , and see the magic : '))
				print()
				pretty_print(user_number)
				_lowerCamelCase  :			Optional[Any]        =       int(input('press 0 to exit... and 1 to continue...'))

		print('Good Bye...')





