




lowercase__    :					str					=						[0.33, 0.44, 0.55, 0.44, 0.33]
lowercase__    :					Optional[Any]					=						3
lowercase__    :					Optional[int]					=						5

# Buffer Space (with Parameters Space)
lowercase__    :					list[float]					=						[]
lowercase__    :					list[float]					=						[]

# Machine Time
lowercase__    :					str					=						0


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	global buffer_space, params_space, machine_time, K, m, t

	# Choosing Dynamical Systems (All)
	for key, value in enumerate(_A							):
		# Evolution Parameter
		snake_case_		   =					float(seed / value							)

		# Control Theory: Orbit Change
		snake_case_		   =					(buffer_space[(key + 1) % m] + e) % 1

		# Control Theory: Trajectory Change
		snake_case_		   =					(params_space[key] + e) % 1 + 3

		# Modification (Transition Function) - Jumps
		snake_case_		   =					round(float(r * value * (1 - value)							)					,     10							)
		snake_case_		   =					r  # Saving to Parameters Space

	# Logistic Map
	assert max(_A							) < 1
	assert max(_A							) < 4

	# Machine Time
	machine_time += 1


def 						lowerCamelCase__   (		):







	'''simple docstring'''





	global buffer_space, params_space, machine_time, K, m, t

	# PRNG (Xorshift by George Marsaglia)
	def xorshift(_A					,     _A							):
		x ^= y >> 13
		y ^= x << 17
		x ^= y >> 5
		return x

	# Choosing Dynamical Systems (Increment)
	snake_case_		   =					machine_time % m

	# Evolution (Time Length)
	for _ in range(0					,     _A							):
		# Variables (Position + Parameters)
		snake_case_		   =					params_space[key]
		snake_case_		   =					buffer_space[key]

		# Modification (Transition Function) - Flow
		snake_case_		   =					round(float(r * value * (1 - value)							)					,     10							)
		snake_case_		   =					(machine_time * 0.01 + r * 1.01) % 1 + 3

	# Choosing Chaotic Data
	snake_case_		   =					int(buffer_space[(key + 2) % m] * (10**10)							)
	snake_case_		   =					int(buffer_space[(key - 2) % m] * (10**10)							)

	# Machine Time
	machine_time += 1

	return xorshift(_A					,     _A							) % 0xFFFFFFFF


def 						lowerCamelCase__   (		):







	'''simple docstring'''





	global buffer_space, params_space, machine_time, K, m, t

	snake_case_		   =					K
	snake_case_		   =					[0] * m
	snake_case_		   =					0


if __name__ == "__main__":
		# Initialization
		reset()

		# Pushing Data (Input)
		import random

		lowercase__    :					int					=						random.sample(range(0Xf_f_f_f_f_f_f_f), 100)
		for chunk in message:
				push(chunk)

		# for controlling
		lowercase__    :					Union[str, Any]					=						""

		# Pulling Data (Output)
		while inp in ("e", "E"):
				print(f'''{format(pull(), '#04x')}''')
				print(buffer_space)
				print(params_space)
				lowercase__    :					Dict					=						input("(e)exit? ").strip()






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

lowercase__    :					Any					=						"Hello world! cécé herlolip"


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					FairseqRobertaModel.from_pretrained(_A							)
	roberta.eval()  # disable dropout
	snake_case_		   =					roberta.model.encoder.sentence_encoder
	snake_case_		   =					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:
		snake_case_		   =					roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
	print("Our BERT config:"					,     _A							)

	snake_case_		   =					RobertaForSequenceClassification(_A							) if classification_head else RobertaForMaskedLM(_A							)
	model.eval()

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

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

		# self attention
		snake_case_		   =					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)							)
		)

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

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

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

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

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

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

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

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


if __name__ == "__main__":
		lowercase__    :					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."
		)
		lowercase__    :					Optional[Any]					=						parser.parse_args()
		convert_roberta_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)
