"""simple docstring"""
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE = False  )      ->Any:
  if device_specific:
    seed += AcceleratorState().process_index
  random.seed(_SCREAMING_SNAKE_CASE  )
  np.random.seed(_SCREAMING_SNAKE_CASE  )
  torch.manual_seed(_SCREAMING_SNAKE_CASE  )
  if is_xpu_available():
    torch.xpu.manual_seed_all(_SCREAMING_SNAKE_CASE  )
  elif is_npu_available():
    torch.npu.manual_seed_all(_SCREAMING_SNAKE_CASE  )
  else:
    torch.cuda.manual_seed_all(_SCREAMING_SNAKE_CASE  )
  # ^^ safe to call this function even if cuda is not available
  if is_tpu_available():
    xm.set_rng_state(_SCREAMING_SNAKE_CASE  )







def 		__a  (      _SCREAMING_SNAKE_CASE = None						,	_SCREAMING_SNAKE_CASE = None  )      ->Tuple:
  # Get the proper rng state
  if rng_type == RNGType.TORCH:
    a__:	int     				=      torch.get_rng_state()
  elif rng_type == RNGType.CUDA:
    a__:	int     				=      torch.cuda.get_rng_state()
  elif rng_type == RNGType.XLA:
    assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
    a__:	Optional[Any]     				=      torch.tensor(xm.get_rng_state()  )
  elif rng_type == RNGType.NPU:
    assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
    a__:	int     				=      torch.npu.get_rng_state()
  elif rng_type == RNGType.XPU:
    assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
    a__:	List[Any]     				=      torch.xpu.get_rng_state()
  elif rng_type == RNGType.GENERATOR:
    assert generator is not None, "Need a generator to synchronize its seed."
    a__:	Tuple     				=      generator.get_state()

  # Broadcast the rng state from device 0 to other devices
  a__:	int     				=      AcceleratorState()
  if state.distributed_type == DistributedType.TPU:
    a__:	List[Any]     				=      rng_state.to(xm.xla_device()  )
    xm.collective_broadcast([rng_state]  )
    xm.mark_step()
    a__:	Any     				=      rng_state.cpu()
  elif (
      state.distributed_type in CUDA_DISTRIBUTED_TYPES
      or state.distributed_type == DistributedType.MULTI_NPU
      or state.distributed_type == DistributedType.MULTI_XPU
  ):
    a__:	Optional[Any]     				=      rng_state.to(state.device  )
    torch.distributed.broadcast(_SCREAMING_SNAKE_CASE						,	0  )
    a__:	List[str]     				=      rng_state.cpu()
  elif state.distributed_type == DistributedType.MULTI_CPU:
    torch.distributed.broadcast(_SCREAMING_SNAKE_CASE						,	0  )

  # Set the broadcast rng state
  if rng_type == RNGType.TORCH:
    torch.set_rng_state(_SCREAMING_SNAKE_CASE  )
  elif rng_type == RNGType.CUDA:
    torch.cuda.set_rng_state(_SCREAMING_SNAKE_CASE  )
  elif rng_type == RNGType.NPU:
    torch.npu.set_rng_state(_SCREAMING_SNAKE_CASE  )
  elif rng_type == RNGType.XPU:
    torch.xpu.set_rng_state(_SCREAMING_SNAKE_CASE  )
  elif rng_type == RNGType.XLA:
    xm.set_rng_state(rng_state.item()  )
  elif rng_type == RNGType.GENERATOR:
    generator.set_state(_SCREAMING_SNAKE_CASE  )







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE = None  )      ->Optional[Any]:
  for rng_type in rng_types:
    synchronize_rng_state(RNGType(_SCREAMING_SNAKE_CASE  )						,	generator=_SCREAMING_SNAKE_CASE  )










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       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    List[Any]:





						"""simple docstring"""



						_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=514					,						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__":
		_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
		)
