

'''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 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()
snake_case_					  =	logging.get_logger(__name__)

snake_case_					  =	'Hello world! cécé herlolip'


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     str  ,  SCREAMING_SNAKE_CASE_					:     str  ,  SCREAMING_SNAKE_CASE_					:     bool   )							->       Tuple:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[str]					=	FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE_   )
      roberta.eval()  # disable dropout
      SCREAMING_SNAKE_CASE_				:      Dict					=	roberta.model.encoder.sentence_encoder
      SCREAMING_SNAKE_CASE_				:      Optional[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_1_4  ,  type_vocab_size=1  ,  layer_norm_eps=1E-5  ,  )
      if classification_head:
            SCREAMING_SNAKE_CASE_				:      Optional[int]					=	roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
      print("Our BERT config:"  ,  SCREAMING_SNAKE_CASE_   )

      SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	RobertaForSequenceClassification(SCREAMING_SNAKE_CASE_   ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE_   )
      model.eval()

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

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

            # self attention
            SCREAMING_SNAKE_CASE_				:      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)   )
            )

            SCREAMING_SNAKE_CASE_				:      str					=	roberta_layer.self_attn.q_proj.weight
            SCREAMING_SNAKE_CASE_				:      str					=	roberta_layer.self_attn.q_proj.bias
            SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	roberta_layer.self_attn.k_proj.weight
            SCREAMING_SNAKE_CASE_				:      Any					=	roberta_layer.self_attn.k_proj.bias
            SCREAMING_SNAKE_CASE_				:      str					=	roberta_layer.self_attn.v_proj.weight
            SCREAMING_SNAKE_CASE_				:      List[Any]					=	roberta_layer.self_attn.v_proj.bias

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

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

            # output
            SCREAMING_SNAKE_CASE_				:      BertOutput					=	layer.output
            assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
            SCREAMING_SNAKE_CASE_				:      List[Any]					=	roberta_layer.fca.weight
            SCREAMING_SNAKE_CASE_				:      Optional[int]					=	roberta_layer.fca.bias
            SCREAMING_SNAKE_CASE_				:      Dict					=	roberta_layer.final_layer_norm.weight
            SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	roberta_layer.final_layer_norm.bias
            # end of layer

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

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

      SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	model(SCREAMING_SNAKE_CASE_   )[0]
      if classification_head:
            SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	roberta.model.classification_heads["mnli"](roberta.extract_features(SCREAMING_SNAKE_CASE_   )   )
      else:
            SCREAMING_SNAKE_CASE_				:      Tuple					=	roberta.model(SCREAMING_SNAKE_CASE_   )[0]
      print(our_output.shape  ,  their_output.shape   )
      SCREAMING_SNAKE_CASE_				:      Dict					=	torch.max(torch.abs(our_output - their_output   )   ).item()
      print(F"max_absolute_diff = {max_absolute_diff}"   )  # ~ 1e-7
      SCREAMING_SNAKE_CASE_				:      Dict					=	torch.allclose(SCREAMING_SNAKE_CASE_  ,  SCREAMING_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(SCREAMING_SNAKE_CASE_   ).mkdir(parents=SCREAMING_SNAKE_CASE_  ,  exist_ok=SCREAMING_SNAKE_CASE_   )
      print(F"Saving model to {pytorch_dump_folder_path}"   )
      model.save_pretrained(SCREAMING_SNAKE_CASE_   )


if __name__ == "__main__":
      snake_case_					  =	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.'
      )
      snake_case_					  =	parser.parse_args()
      convert_roberta_checkpoint_to_pytorch(
          args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )




'''simple docstring'''






from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
snake_case_					  =	[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     int   )							->       list[int]:
      """simple docstring"""

      return [int(SCREAMING_SNAKE_CASE_   ) for c in F"{ruleset:08}"[:8]]


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     list[list[int]]  ,  SCREAMING_SNAKE_CASE_					:     list[int]  ,  SCREAMING_SNAKE_CASE_					:     int   )							->       list[int]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	len(cells[0]   )  # 31
      SCREAMING_SNAKE_CASE_				:      Dict					=	[]
      for i in range(SCREAMING_SNAKE_CASE_   ):
            # Get the neighbors of each cell
            # Handle neighbours outside bounds by using 0 as their value
            SCREAMING_SNAKE_CASE_				:      Any					=	0 if i == 0 else cells[time][i - 1]
            SCREAMING_SNAKE_CASE_				:      Dict					=	0 if i == population - 1 else cells[time][i + 1]
            # Define a new cell and add it to the new generation
            SCREAMING_SNAKE_CASE_				:      List[Any]					=	7 - int(F"{left_neighbor}{cells[time][i]}{right_neighbor}"  ,  2   )
            next_generation.append(rule[situation]   )
      return next_generation


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     list[list[int]]   )							->       Image.Image:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Tuple					=	Image.new("RGB"  ,  (len(cells[0]   ), len(SCREAMING_SNAKE_CASE_   ))   )
      SCREAMING_SNAKE_CASE_				:      Optional[int]					=	img.load()
      # Generates image
      for w in range(img.width   ):
            for h in range(img.height   ):
                  SCREAMING_SNAKE_CASE_				:      List[Any]					=	2_5_5 - int(2_5_5 * cells[h][w]   )
                  SCREAMING_SNAKE_CASE_				:      Optional[int]					=	(color, color, color)
      return img


if __name__ == "__main__":
      snake_case_					  =	bin(int(input('Rule:\n').strip()))[2:]
      snake_case_					  =	format_ruleset(int(rule_num))
      for time in range(1_6):
            CELLS.append(new_generation(CELLS, rule, time))
      snake_case_					  =	generate_image(CELLS)
      # Uncomment to save the image
      # img.save(f"rule_{rule_num}.png")
      img.show()

