


import math
import sys

import cva
import numpy as np


def    __lowerCamelCase   (      A__						: Optional[int]						,						A__						: Union[str, Any]					)     ->    str:
   lowerCamelCase_						:   Any		= math.sqrt(a__					)
   lowerCamelCase_						:   Dict		= 1 / (sigma * math.sqrt(2 * math.pi					))
   return cons * np.exp(-((img / sigma) ** 2) * 0.5					)


def    __lowerCamelCase   (      A__						: str						,						A__						: str						,						A__						: str						,						A__						: Any					)     ->    List[str]:
   lowerCamelCase_						:   Optional[Any]		= kernel_size // 2
   return img[x - half : x + half + 1, y - half : y + half + 1]


def    __lowerCamelCase   (      A__						: Any						,						A__						: List[str]					)     ->    List[str]:
   lowerCamelCase_						:   Tuple		= np.zeros((kernel_size, kernel_size)					)
   for i in range(0						,						a__					):
      for j in range(0						,						a__					):
         lowerCamelCase_						:   Tuple		= math.sqrt(
             abs(i - kernel_size // 2					) ** 2 + abs(j - kernel_size // 2					) ** 2					)
   return vec_gaussian(a__						,						a__					)


def    __lowerCamelCase   (      A__						: Tuple						,						A__						: Optional[int]						,						A__						: Any						,						A__						: Tuple						,						)     ->    Union[str, Any]:
   lowerCamelCase_						:   Optional[Any]		= np.zeros(img.shape					)
   lowerCamelCase_						:   Dict		= get_gauss_kernel(a__						,						a__					)
   lowerCamelCase_,	lowerCamelCase_						:   str		= img.shape
   for i in range(kernel_size // 2						,						size_x - kernel_size // 2					):
      for j in range(kernel_size // 2						,						size_y - kernel_size // 2					):
         lowerCamelCase_						:   List[Any]		= get_slice(a__						,						a__						,						a__						,						a__					)
         lowerCamelCase_						:   List[str]		= img_s - img_s[kernel_size // 2, kernel_size // 2]
         lowerCamelCase_						:   Optional[int]		= vec_gaussian(a__						,						a__					)
         lowerCamelCase_						:   List[Any]		= np.multiply(a__						,						a__					)
         lowerCamelCase_						:   List[Any]		= np.multiply(a__						,						a__					)
         lowerCamelCase_						:   Optional[Any]		= np.sum(a__					) / np.sum(a__					)
         lowerCamelCase_						:   Union[str, Any]		= val
   return imga


def    __lowerCamelCase   (      A__						: Optional[Any]					)     ->    Optional[int]:
   lowerCamelCase_						:   str		= args[1] if args[1:] else """../image_data/lena.jpg"""
   lowerCamelCase_						:   List[Any]		= float(args[2]					) if args[2:] else 1.0
   lowerCamelCase_						:   Optional[int]		= float(args[3]					) if args[3:] else 1.0
   if args[4:]:
      lowerCamelCase_						:   Dict		= int(args[4]					)
      lowerCamelCase_						:   List[str]		= kernel_size + abs(kernel_size % 2 - 1					)
   else:
      lowerCamelCase_						:   List[str]		= 5
   return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
      snake_case__				:	Any					       =					parse_args(sys.argv)
      snake_case__				:	Union[str, Any]					       =					cva.imread(filename, 0)
      cva.imshow('input image', img)

      snake_case__				:	Optional[int]					       =					img / 255
      snake_case__				:	Optional[Any]					       =					out.astype('float32')
      snake_case__				:	Tuple					       =					bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
      snake_case__				:	List[Any]					       =					out * 255
      snake_case__				:	Any					       =					np.uinta(out)
      cva.imshow('output image', out)
      cva.waitKey(0)
      cva.destroyAllWindows()








'''simple docstring'''



import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
  raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
  raise Exception('requires fairseq < v2')

logging.set_verbosity_info()
UpperCAmelCase					:			List[str]       =	logging.get_logger(__name__)

UpperCAmelCase					:			Tuple       =	'Hello, World!'
UpperCAmelCase					:			Any       =	'en_XX'







def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = Path("""data_bin"""		)
     __SCREAMING_SNAKE_CASE      = FairseqXmodModel.from_pretrained(
         model_name_or_path=str(Path(a__		).parent		) ,       checkpoint_file=Path(a__		).name ,       _name="""xmod_base""" ,       arch="""xmod_base""" ,       task="""multilingual_masked_lm""" ,       data_name_or_path=str(a__		) ,       bpe="""sentencepiece""" ,       sentencepiece_model=str(Path(a__		).parent / """sentencepiece.bpe.model"""		) ,       src_dict=str(data_dir / """dict.txt"""		) ,       )
     xmod.eval()  # disable dropout
     print(a__		)

     __SCREAMING_SNAKE_CASE      = xmod.model.encoder.sentence_encoder
     __SCREAMING_SNAKE_CASE      = XmodConfig(
         vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings ,       hidden_size=xmod.cfg.model.encoder_embed_dim ,       num_hidden_layers=xmod.cfg.model.encoder_layers ,       num_attention_heads=xmod.cfg.model.encoder_attention_heads ,       intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim ,       max_position_embeddings=5_14 ,       type_vocab_size=1 ,       layer_norm_eps=1E-5 ,       pre_norm=xmod.cfg.model.encoder_normalize_before ,       adapter_reduction_factor=getattr(xmod.cfg.model ,       """bottleneck""" ,       2		) ,       adapter_layer_norm=xmod.cfg.model.adapter_layer_norm ,       adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm ,       ln_before_adapter=xmod.cfg.model.ln_before_adapter ,       languages=xmod.cfg.model.languages ,       )
     if classification_head:
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

     print("""Our X-MOD config:""" ,       a__		)

     __SCREAMING_SNAKE_CASE      = XmodForSequenceClassification(a__		) if classification_head else XmodForMaskedLM(a__		)
     model.eval()

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

     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layernorm_embedding.weight
     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layernorm_embedding.bias

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

          # self attention
          __SCREAMING_SNAKE_CASE      = layer.attention.self
          if not (
              xmod_layer.self_attn.k_proj.weight.data.shape
              == xmod_layer.self_attn.q_proj.weight.data.shape
              == xmod_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)		)
          ):
               raise AssertionError("""Dimensions of self-attention weights do not match."""		)

          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.q_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.q_proj.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.k_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.k_proj.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.v_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.v_proj.bias

          # self-attention output
          __SCREAMING_SNAKE_CASE      = layer.attention.output
          if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
               raise AssertionError("""Dimensions of self-attention output weights do not match."""		)
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.out_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.out_proj.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn_layer_norm.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn_layer_norm.bias

          # intermediate
          __SCREAMING_SNAKE_CASE      = layer.intermediate
          if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError("""Dimensions of intermediate weights do not match."""		)
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.bias

          # output
          __SCREAMING_SNAKE_CASE      = layer.output
          if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError("""Dimensions of feed-forward weights do not match."""		)
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.final_layer_norm.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.final_layer_norm.bias
          if bert_output.adapter_layer_norm is not None:
               __SCREAMING_SNAKE_CASE      = xmod_layer.adapter_layer_norm.weight
               __SCREAMING_SNAKE_CASE      = xmod_layer.adapter_layer_norm.bias

          if sorted(bert_output.adapter_modules.keys()		) != sorted(xmod_layer.adapter_modules.keys()		):
               raise AssertionError("""Lists of language adapters do not match."""		)
          for lang_code, adapter in xmod_layer.adapter_modules.items():
               __SCREAMING_SNAKE_CASE      = bert_output.adapter_modules[lang_code]
               __SCREAMING_SNAKE_CASE      = xmod_layer.adapter_modules[lang_code]
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.weight
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.bias
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.weight
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.bias

          # end of layer

     if xmod_sent_encoder.layer_norm is not None:
          __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layer_norm.weight
          __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layer_norm.bias

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

     # Let's check that we get the same results.
     __SCREAMING_SNAKE_CASE      = xmod.encode(a__		).unsqueeze(0		)  # batch of size 1
     model.roberta.set_default_language(a__		)

     __SCREAMING_SNAKE_CASE      = model(a__		)[0]
     if classification_head:
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""](xmod.extract_features(a__		)		)
     else:
          __SCREAMING_SNAKE_CASE      = xmod.model(a__ ,       lang_id=[SAMPLE_LANGUAGE]		)[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(a__ ,       a__ ,       atol=1E-3		)
     print("""Do both models output the same tensors?""" ,       """🔥""" if success else """💩"""		)
     if not success:
          raise Exception("""Something went wRoNg"""		)

     Path(a__		).mkdir(parents=a__ ,       exist_ok=a__		)
     print(F'Saving model to {pytorch_dump_folder_path}'		)
     model.save_pretrained(a__		)


if __name__ == "__main__":
  UpperCAmelCase					:			Tuple       =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--xmod_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					:			Optional[int]       =	parser.parse_args()
  convert_xmod_checkpoint_to_pytorch(
      args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )






