


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 : Any 			=				logging.get_logger(__name__)

__UpperCAmelCase : List[str] 			=				"Hello, World!"
__UpperCAmelCase : int 			=				"en_XX"


def      a	(							SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		bool       ):






							"""simple docstring"""





							UpperCamelCase    :	int   	=		Path('''data_bin'''       )
							UpperCamelCase    :	Union[str, Any]   	=		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_       )

							UpperCamelCase    :	Any   	=		xmod.model.encoder.sentence_encoder
							UpperCamelCase    :	List[str]   	=		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_1_4							,			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:
														UpperCamelCase    :	Dict   	=		xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

							print('''Our X-MOD config:'''							,			a_       )

							UpperCamelCase    :	List[Any]   	=		XmodForSequenceClassification(a_       ) if classification_head else XmodForMaskedLM(a_       )
							model.eval()

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

							UpperCamelCase    :	List[Any]   	=		xmod_sent_encoder.layernorm_embedding.weight
							UpperCamelCase    :	Optional[int]   	=		xmod_sent_encoder.layernorm_embedding.bias

							for i in range(config.num_hidden_layers       ):
														# Encoder: start of layer
														UpperCamelCase    :	Union[str, Any]   	=		model.roberta.encoder.layer[i]
														UpperCamelCase    :	List[str]   	=		xmod_sent_encoder.layers[i]

														# self attention
														UpperCamelCase    :	Optional[int]   	=		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.'''       )

														UpperCamelCase    :	Optional[int]   	=		xmod_layer.self_attn.q_proj.weight
														UpperCamelCase    :	List[str]   	=		xmod_layer.self_attn.q_proj.bias
														UpperCamelCase    :	str   	=		xmod_layer.self_attn.k_proj.weight
														UpperCamelCase    :	Optional[Any]   	=		xmod_layer.self_attn.k_proj.bias
														UpperCamelCase    :	Dict   	=		xmod_layer.self_attn.v_proj.weight
														UpperCamelCase    :	Optional[int]   	=		xmod_layer.self_attn.v_proj.bias

														# self-attention output
														UpperCamelCase    :	Optional[int]   	=		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.'''       )
														UpperCamelCase    :	List[Any]   	=		xmod_layer.self_attn.out_proj.weight
														UpperCamelCase    :	Union[str, Any]   	=		xmod_layer.self_attn.out_proj.bias
														UpperCamelCase    :	str   	=		xmod_layer.self_attn_layer_norm.weight
														UpperCamelCase    :	List[Any]   	=		xmod_layer.self_attn_layer_norm.bias

														# intermediate
														UpperCamelCase    :	Optional[int]   	=		layer.intermediate
														if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError('''Dimensions of intermediate weights do not match.'''       )
														UpperCamelCase    :	int   	=		xmod_layer.fca.weight
														UpperCamelCase    :	Union[str, Any]   	=		xmod_layer.fca.bias

														# output
														UpperCamelCase    :	List[str]   	=		layer.output
														if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError('''Dimensions of feed-forward weights do not match.'''       )
														UpperCamelCase    :	str   	=		xmod_layer.fca.weight
														UpperCamelCase    :	int   	=		xmod_layer.fca.bias
														UpperCamelCase    :	List[Any]   	=		xmod_layer.final_layer_norm.weight
														UpperCamelCase    :	List[str]   	=		xmod_layer.final_layer_norm.bias
														if bert_output.adapter_layer_norm is not None:
																					UpperCamelCase    :	List[str]   	=		xmod_layer.adapter_layer_norm.weight
																					UpperCamelCase    :	int   	=		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():
																					UpperCamelCase    :	Optional[int]   	=		bert_output.adapter_modules[lang_code]
																					UpperCamelCase    :	Dict   	=		xmod_layer.adapter_modules[lang_code]
																					UpperCamelCase    :	List[Any]   	=		from_adapter.fca.weight
																					UpperCamelCase    :	List[Any]   	=		from_adapter.fca.bias
																					UpperCamelCase    :	Dict   	=		from_adapter.fca.weight
																					UpperCamelCase    :	Optional[Any]   	=		from_adapter.fca.bias

														# end of layer

							if xmod_sent_encoder.layer_norm is not None:
														UpperCamelCase    :	Dict   	=		xmod_sent_encoder.layer_norm.weight
														UpperCamelCase    :	List[Any]   	=		xmod_sent_encoder.layer_norm.bias

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

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

							UpperCamelCase    :	Any   	=		model(a_       )[0]
							if classification_head:
														UpperCamelCase    :	Dict   	=		xmod.model.classification_heads['''mnli'''](xmod.extract_features(a_       )       )
							else:
														UpperCamelCase    :	int   	=		xmod.model(a_							,			lang_id=[SAMPLE_LANGUAGE]       )[0]
							print(our_output.shape							,			their_output.shape       )
							UpperCamelCase    :	List[str]   	=		torch.max(torch.abs(our_output - their_output       )       ).item()
							print(F"""max_absolute_diff = {max_absolute_diff}"""       )  # ~ 1e-7
							UpperCamelCase    :	str   	=		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 : Dict 			=				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 : int 			=				parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)





from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros


def      a	(							SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		List[str]       ):






       """simple docstring"""





       UpperCamelCase    :	List[str]   	=		k_size // 2
       UpperCamelCase	, UpperCamelCase    :	Optional[int]   	=		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       UpperCamelCase    :	Dict   	=		1 / (2 * pi * sigma) * exp(-(square(SCREAMING_SNAKE_CASE_       ) + square(SCREAMING_SNAKE_CASE_       )) / (2 * square(SCREAMING_SNAKE_CASE_       ))       )
       return g


def      a	(							SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase	, UpperCamelCase    :	Tuple   	=		image.shape[0], image.shape[1]
       # dst image height and width
       UpperCamelCase    :	str   	=		height - k_size + 1
       UpperCamelCase    :	Optional[int]   	=		width - k_size + 1

       # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
       UpperCamelCase    :	List[Any]   	=		zeros((dst_height * dst_width, k_size * k_size)       )
       UpperCamelCase    :	Tuple   	=		0
       for i, j in product(range(SCREAMING_SNAKE_CASE_       )							,			range(SCREAMING_SNAKE_CASE_       )       ):
              UpperCamelCase    :	Dict   	=		ravel(image[i : i + k_size, j : j + k_size]       )
              UpperCamelCase    :	Dict   	=		window
              row += 1

       #  turn the kernel into shape(k*k, 1)
       UpperCamelCase    :	Optional[int]   	=		gen_gaussian_kernel(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	List[Any]   	=		ravel(SCREAMING_SNAKE_CASE_       )

       # reshape and get the dst image
       UpperCamelCase    :	Optional[int]   	=		dot(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       ).reshape(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       ).astype(SCREAMING_SNAKE_CASE_       )

       return dst


if __name__ == "__main__":
  # read original image
  __UpperCAmelCase : Union[str, Any] 			=				imread(r"../image_data/lena.jpg")
  # turn image in gray scale value
  __UpperCAmelCase : Optional[Any] 			=				cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  __UpperCAmelCase : Optional[int] 			=				gaussian_filter(gray, 3, sigma=1)
  __UpperCAmelCase : List[Any] 			=				gaussian_filter(gray, 5, sigma=0.8)

  # show result images
  imshow("gaussian filter with 3x3 mask", gaussianaxa)
  imshow("gaussian filter with 5x5 mask", gaussianaxa)
  waitKey()

