



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             =logging.get_logger(__name__)

__A             ="Hello world! cécé herlolip"

def 	a						(				_UpperCAmelCase							: str			,       _UpperCAmelCase							: str			,       _UpperCAmelCase							: bool						):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     FairseqRobertaModel.from_pretrained(_UpperCAmelCase						)
		roberta.eval()  # disable dropout
		__UpperCAmelCase   :					List[str]													=     roberta.model.encoder.sentence_encoder
		__UpperCAmelCase   :					Any													=     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   :					Union[str, Any]													=     roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
		print('''Our BERT config:'''			,       _UpperCAmelCase						)

		__UpperCAmelCase   :					Optional[Any]													=     RobertaForSequenceClassification(_UpperCAmelCase						) if classification_head else RobertaForMaskedLM(_UpperCAmelCase						)
		model.eval()

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

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

				# self attention
				__UpperCAmelCase   :					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)						)
				)

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

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

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

				# output
				__UpperCAmelCase   :					BertOutput													=     layer.output
				assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
				__UpperCAmelCase   :					Any													=     roberta_layer.fca.weight
				__UpperCAmelCase   :					Tuple													=     roberta_layer.fca.bias
				__UpperCAmelCase   :					int													=     roberta_layer.final_layer_norm.weight
				__UpperCAmelCase   :					int													=     roberta_layer.final_layer_norm.bias
				# end of layer

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

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

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










def 	a						(				_UpperCAmelCase							: Optional[int]			,       _UpperCAmelCase							: List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     len(_UpperCAmelCase						)
		# Create a stack and push index of fist element to it
		__UpperCAmelCase   :					List[Any]													=     []
		st.append(0						)

		# Span value of first element is always 1
		__UpperCAmelCase   :					Any													=     1

		# Calculate span values for rest of the elements
		for i in range(1			,       _UpperCAmelCase						):
				# Pop elements from stack while stack is not
				# empty and top of stack is smaller than price[i]
				while len(_UpperCAmelCase						) > 0 and price[st[0]] <= price[i]:
						st.pop()

				# If stack becomes empty, then price[i] is greater
				# than all elements on left of it, i.e. price[0],
				# price[1], ..price[i-1]. Else the price[i]  is
				# greater than elements after top of stack
				__UpperCAmelCase   :					int													=     i + 1 if len(_UpperCAmelCase						) <= 0 else (i - st[0])

				# Push this element to stack
				st.append(_UpperCAmelCase						)

def 	a						(				_UpperCAmelCase							: Dict			,       _UpperCAmelCase							: List[str]						):







		'''simple docstring'''





		for i in range(0			,       _UpperCAmelCase						):
				print(arr[i]			,       end=''' '''						)


# Driver program to test above function
__A             =[1_0, 4, 5, 9_0, 1_2_0, 8_0]
__A             =[0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)
print_array(S, len(price))





