

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()
UpperCamelCase__					: Tuple								=      logging.get_logger(__name__)

UpperCamelCase__					: Union[str, Any]								=      "Hello world! cécé herlolip"


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





    """simple docstring"""


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

    SCREAMING_SNAKE_CASE_													=  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_													=  roberta_sent_encoder.embed_tokens.weight
    SCREAMING_SNAKE_CASE_													=  roberta_sent_encoder.embed_positions.weight
    SCREAMING_SNAKE_CASE_													=  torch.zeros_like(
        model.roberta.embeddings.token_type_embeddings.weight    )  # just zero them out b/c RoBERTa doesn't use them.
    SCREAMING_SNAKE_CASE_													=  roberta_sent_encoder.emb_layer_norm.weight
    SCREAMING_SNAKE_CASE_													=  roberta_sent_encoder.emb_layer_norm.bias

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

        # self attention
        SCREAMING_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)    )
        )

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

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

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

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

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

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

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





import cva
import numpy as np


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						np.ndarray	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    try:
        return int(image[x_coordinate][y_coordinate] >= center    )
    except (IndexError, TypeError):
        return 0


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						np.ndarray	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  image[x_coordinate][y_coordinate]
    SCREAMING_SNAKE_CASE_													=  [1, 2, 4, 8, 16, 32, 64, 128]

    # skip get_neighbors_pixel if center is null
    if center is None:
        return 0

    # Starting from the top right, assigning value to pixels clockwise
    SCREAMING_SNAKE_CASE_													=  [
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate - 1	,				y_coordinate + 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				y_coordinate + 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate - 1	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate + 1	,				y_coordinate + 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate + 1	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate + 1	,				y_coordinate - 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				y_coordinate - 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate - 1	,				y_coordinate - 1	,				_SCREAMING_SNAKE_CASE    ),
    ]

    # Converting the binary value to decimal.
    return sum(
        binary_value * power for binary_value, power in zip(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )    )


if __name__ == "__main__":
       # Reading the image and converting it to grayscale.
       UpperCamelCase__					: Optional[int]								=      cva.imread(
           "digital_image_processing/image_data/lena.jpg", cva.IMREAD_GRAYSCALE
       )

       # Create a numpy array as the same height and width of read image
       UpperCamelCase__					: Dict								=      np.zeros((image.shape[0], image.shape[1]))

       # Iterating through the image and calculating the
       # local binary pattern value for each pixel.
       for i in range(0, image.shape[0]):
              for j in range(0, image.shape[1]):
                     UpperCamelCase__					: Any								=      local_binary_value(image, i, j)

       cva.imshow("local binary pattern", lbp_image)
       cva.waitKey(0)
       cva.destroyAllWindows()


