


"""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()
snake_case__		:     List[str]    				=				logging.get_logger(__name__)

snake_case__		:     Tuple    				=				'''Hello world! cécé herlolip'''


def      _snake_case						(						_snake_case				:			str ,   _snake_case				:			str ,   _snake_case				:			bool			):
      lowerCAmelCase					:	Any       =   FairseqRobertaModel.from_pretrained(_snake_case			)
      roberta.eval()  # disable dropout
      lowerCAmelCase					:	Optional[int]       =   roberta.model.encoder.sentence_encoder
      lowerCAmelCase					:	Union[str, 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:
            lowerCAmelCase					:	Any       =   roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

      lowerCAmelCase					:	List[str]       =   XLMRobertaXLForSequenceClassification(_snake_case			) if classification_head else XLMRobertaXLForMaskedLM(_snake_case			)
      model.eval()

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

      lowerCAmelCase					:	Any       =   roberta_sent_encoder.layer_norm.weight
      lowerCAmelCase					:	Any       =   roberta_sent_encoder.layer_norm.bias

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

            lowerCAmelCase					:	RobertaAttention       =   layer.attention
            lowerCAmelCase					:	Optional[Any]       =   roberta_layer.self_attn_layer_norm.weight
            lowerCAmelCase					:	str       =   roberta_layer.self_attn_layer_norm.bias

            # self attention
            lowerCAmelCase					:	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)			)
            )

            lowerCAmelCase					:	Dict       =   roberta_layer.self_attn.q_proj.weight
            lowerCAmelCase					:	Tuple       =   roberta_layer.self_attn.q_proj.bias
            lowerCAmelCase					:	Dict       =   roberta_layer.self_attn.k_proj.weight
            lowerCAmelCase					:	Union[str, Any]       =   roberta_layer.self_attn.k_proj.bias
            lowerCAmelCase					:	Tuple       =   roberta_layer.self_attn.v_proj.weight
            lowerCAmelCase					:	str       =   roberta_layer.self_attn.v_proj.bias

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

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

            # intermediate
            lowerCAmelCase					:	BertIntermediate       =   layer.intermediate
            assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
            lowerCAmelCase					:	Any       =   roberta_layer.fca.weight
            lowerCAmelCase					:	Any       =   roberta_layer.fca.bias

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

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

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

      lowerCAmelCase					:	Tuple       =   model(_snake_case			)[0]
      if classification_head:
            lowerCAmelCase					:	int       =   roberta.model.classification_heads['''mnli'''](roberta.extract_features(_snake_case			)			)
      else:
            lowerCAmelCase					:	Any       =   roberta.model(_snake_case			)[0]
      print(our_output.shape ,   their_output.shape			)
      lowerCAmelCase					:	int       =   torch.max(torch.abs(our_output - their_output			)			).item()
      print(f'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
      lowerCAmelCase					:	Dict       =   torch.allclose(_snake_case ,   _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(_snake_case			).mkdir(parents=_snake_case ,   exist_ok=_snake_case			)
      print(f'''Saving model to {pytorch_dump_folder_path}'''			)
      model.save_pretrained(_snake_case			)


if __name__ == "__main__":
 snake_case__		:     Optional[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.'''
 )
 snake_case__		:     int    				=				parser.parse_args()
 convert_xlm_roberta_xl_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 glider example
snake_case__		:     int    				=				[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
snake_case__		:     Any    				=				[[0, 1, 0], [0, 1, 0], [0, 1, 0]]


def      _snake_case						(						_snake_case				:			list[list[int]]			):
      lowerCAmelCase					:	Union[str, Any]       =   []
      for i in range(len(_snake_case			)			):
            lowerCAmelCase					:	Any       =   []
            for j in range(len(cells[i]			)			):
                  # Get the number of live neighbours
                  lowerCAmelCase					:	Optional[int]       =   0
                  if i > 0 and j > 0:
                        neighbour_count += cells[i - 1][j - 1]
                  if i > 0:
                        neighbour_count += cells[i - 1][j]
                  if i > 0 and j < len(cells[i]			) - 1:
                        neighbour_count += cells[i - 1][j + 1]
                  if j > 0:
                        neighbour_count += cells[i][j - 1]
                  if j < len(cells[i]			) - 1:
                        neighbour_count += cells[i][j + 1]
                  if i < len(_snake_case			) - 1 and j > 0:
                        neighbour_count += cells[i + 1][j - 1]
                  if i < len(_snake_case			) - 1:
                        neighbour_count += cells[i + 1][j]
                  if i < len(_snake_case			) - 1 and j < len(cells[i]			) - 1:
                        neighbour_count += cells[i + 1][j + 1]

                  # Rules of the game of life (excerpt from Wikipedia):
                  # 1. Any live cell with two or three live neighbours survives.
                  # 2. Any dead cell with three live neighbours becomes a live cell.
                  # 3. All other live cells die in the next generation.
                  #    Similarly, all other dead cells stay dead.
                  lowerCAmelCase					:	str       =   cells[i][j] == 1
                  if (
                      (alive and 2 <= neighbour_count <= 3)
                      or not alive
                      and neighbour_count == 3
                  ):
                        next_generation_row.append(1			)
                  else:
                        next_generation_row.append(0			)

            next_generation.append(_snake_case			)
      return next_generation


def      _snake_case						(						_snake_case				:			list[list[int]] ,   _snake_case				:			int			):
      lowerCAmelCase					:	int       =   []
      for _ in range(_snake_case			):
            # Create output image
            lowerCAmelCase					:	Union[str, Any]       =   Image.new('''RGB''' ,   (len(cells[0]			), len(_snake_case			))			)
            lowerCAmelCase					:	Union[str, Any]       =   img.load()

            # Save cells to image
            for x in range(len(_snake_case			)			):
                  for y in range(len(cells[0]			)			):
                        lowerCAmelCase					:	Optional[int]       =   255 - cells[y][x] * 255
                        lowerCAmelCase					:	List[Any]       =   (colour, colour, colour)

        # Save image
            images.append(_snake_case			)
            lowerCAmelCase					:	Union[str, Any]       =   new_generation(_snake_case			)
      return images


if __name__ == "__main__":
 snake_case__		:     Union[str, Any]    				=				generate_images(GLIDER, 16)
 images[0].save('''out.gif''', save_all=True, append_images=images[1:])


