


"""simple docstring"""






import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
					from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
					raise ImportError("""You need to install the version of fairseq from the Mega repo!""")






class     A(     nn.Module		):

		"""simple docstring"""





		def __init__(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )			->  Optional[int]:

					"""simple docstring"""



					super().__init__()
					_UpperCamelCase				:List[str]				    =  mega_args
					_UpperCamelCase				:Dict				    =  nn.Embedding(SCREAMING_SNAKE_CASE__				,							self.mega_args.encoder_embed_dim      )
					_UpperCamelCase				:Optional[Any]				    =  nn.ModuleList([MegaEncoderLayer(self.mega_args      ) for _ in range(SCREAMING_SNAKE_CASE__      )]      )
					_UpperCamelCase				:Tuple				    =  depth





		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__=True				,							SCREAMING_SNAKE_CASE__=0      )			->  Dict:

					"""simple docstring"""



					if batch_first:
								_UpperCamelCase				:Tuple				    =  input_ids.T

					# to make things more confusing, Mega expects the attention mask to
					# be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
					# which is the opposite of what HF returns
					if ignore_mask_value == 0:
								_UpperCamelCase				:str				    =  1 - attention_mask

					# get token embeddings from IDs
					_UpperCamelCase				:Union[str, Any]				    =  self.embedding_layer(SCREAMING_SNAKE_CASE__      )

					# pass through the Mega layers
					# input is (time, batch, encoder dim) and output is the same
					for encoder in self.encoders:
								_UpperCamelCase				:Union[str, Any]				    =  encoder(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

					# return according to the shape specified
					if batch_first:
								# (T, B, H) --> (B, T, H)
								return torch.transpose(SCREAMING_SNAKE_CASE__				,							0				,							1      )
					else:
								return embeds






class     A(     nn.Module		):

		"""simple docstring"""





		def __init__(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )			->  Any:

					"""simple docstring"""



					super().__init__()
					_UpperCamelCase				:Dict				    =  MegaLM(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:str				    =  nn.Linear(mega_args.encoder_embed_dim				,							SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:int				    =  nn.Dropout(p=0.1      )





		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__=True				,							SCREAMING_SNAKE_CASE__=0      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  self.mega(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
					return self.mlm_head(self.dropout(SCREAMING_SNAKE_CASE__      )      )





def 							A_					(   snake_case__					,				snake_case__					,				snake_case__		)				->     Union[str, Any]:
			with open(os.path.join(snake_case__					,				'''model_args.pkl'''		)					,				'''rb'''		) as f:
						_UpperCamelCase				:List[str]				    =  pkl.load(snake_case__		)

			# load the original encoder
			_UpperCamelCase				:Tuple				    =  OriginalMegaForMaskedLM(**snake_case__		).eval()

			# load its weights
			print(
			    '''Original Mega encoder:'''					,				original_mlm.mega.load_state_dict(
			        torch.load(os.path.join(snake_case__					,				'''encoder_weights.pt'''		)					,				map_location='''cpu'''		)		)					,				)
			print(
			    '''Original Mega MLM layer:'''					,				original_mlm.mlm_head.load_state_dict(
			        torch.load(os.path.join(snake_case__					,				'''mlm_head_weights.pt'''		)					,				map_location='''cpu'''		)		)					,				)

			# create a new config from the old one
			_UpperCamelCase				:Optional[int]				    =  MegaConfig(
			    num_hidden_layers=mega_original_args['''depth''']					,				vocab_size=mega_original_args['''vocab_size''']					,				hidden_size=mega_original_args['''mega_args'''].encoder_embed_dim					,				shared_representation_size=mega_original_args['''mega_args'''].encoder_z_dim					,				intermediate_size=mega_original_args['''mega_args'''].encoder_hidden_dim					,				ema_projection_size=mega_original_args['''mega_args'''].encoder_n_dim					,				dropout_prob=mega_original_args['''mega_args'''].dropout					,				attention_probs_dropout_prob=mega_original_args['''mega_args'''].attention_dropout					,				hidden_dropout_prob=mega_original_args['''mega_args'''].hidden_dropout					,				activation=mega_original_args['''mega_args'''].activation_fn					,				attention_activation=mega_original_args['''mega_args'''].attention_activation_fn					,				bidirectional=mega_original_args['''mega_args'''].bidirectional					,				use_chunking=mega_original_args['''mega_args'''].encoder_chunk_size > 0					,				chunk_size=mega_original_args['''mega_args'''].encoder_chunk_size					,				truncation=mega_original_args['''mega_args'''].truncation_length					,				normalization_type=mega_original_args['''mega_args'''].normalization_type					,				normalize_before_mega=snake_case__					,				norm_affine=snake_case__					,				use_feature_dropout=mega_original_args['''mega_args'''].feature_dropout					,				relative_positional_bias=mega_original_args['''mega_args'''].rel_pos_bias					,				max_positions=mega_original_args['''mega_args'''].max_source_positions					,				nffn_hidden_size=mega_original_args['''mega_args'''].encoder_ffn_embed_dim					,				normalize_before_ffn=mega_original_args['''mega_args'''].normalize_before					,				nffn_activation_dropout_prob=0.0					,				add_token_type_embeddings=snake_case__					,				add_lm_hidden_dense_layer=snake_case__					,				)

			_UpperCamelCase				:Optional[Any]				    =  MegaForMaskedLM(snake_case__		).eval()

			# the originl checkpoint just uses nn.Embedding for the word embeddings
			# we use a wrapper module for embeddings to add support for positional embeddings
			_UpperCamelCase				:Dict				    =  original_mlm.mega.embedding_layer.weight

			# modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
			# ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
			# also renaming previously confusing parameter names
			_UpperCamelCase				:Union[str, Any]				    =  original_mlm.mega.encoders.state_dict()
			_UpperCamelCase				:List[str]				    =  {}
			for module_name in original_state_dict.keys():
						_UpperCamelCase				:Any				    =  None
						# have to handle gamma, beta, and alpha differently due to their use
						# in multiple modules within the original repository;
						# beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
						# the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
						if "beta" in module_name:
									# EMA sub-layers were always called "move" in the original repo
									if "move.beta" in module_name:
												_UpperCamelCase				:List[Any]				    =  module_name.replace('''move.beta'''					,				'''ema_gate.ema_expansion_matrix'''		)
									elif "mega_layer.beta" in module_name:
												_UpperCamelCase				:List[str]				    =  module_name.replace('''beta'''					,				'''qk_bias'''		)
									else:
												_UpperCamelCase				:Union[str, Any]				    =  module_name.replace('''beta'''					,				'''b_param'''		)
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
						elif "gamma" in module_name:
									if "move.gamma" in module_name:
												_UpperCamelCase				:List[str]				    =  module_name.replace('''move.gamma'''					,				'''ema_gate.kernel_projection_matrix'''		)
									elif "mega_layer.gamma" in module_name:
												_UpperCamelCase				:str				    =  module_name.replace('''gamma'''					,				'''qk_weight'''		)
									else:
												_UpperCamelCase				:List[str]				    =  module_name.replace('''gamma'''					,				'''g_param'''		)
        # alpha is used in EMA and positional bias; renaming to improve readability
						elif "move.alpha" in module_name:
									_UpperCamelCase				:Optional[Any]				    =  module_name.replace('''move.alpha'''					,				'''ema_gate.decay_factor'''		)
						# delta is only used in EMA; renaming to improve readability
						elif "move.delta" in module_name:
									_UpperCamelCase				:Union[str, Any]				    =  module_name.replace('''move.delta'''					,				'''ema_gate.damping_factor'''		)
						# omega is only used in EMA; renaming to improve readability
						elif "omega" in module_name:
									_UpperCamelCase				:int				    =  module_name.replace('''move.omega'''					,				'''ema_gate.residual_weight'''		)

						if new_module_name:
									_UpperCamelCase				:List[Any]				    =  new_module_name

			if len(snake_case__		) != 0:
						print(f"Renaming these keys: {updated_keys.keys()}"		)
			else:
						print('''No need to rename state dict entries'''		)
			for old, new in updated_keys.items():
						_UpperCamelCase				:List[str]				    =  original_state_dict.pop(snake_case__		)

			# now attempt to load the state dictionary with updated names
			# note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
			print('''HF Mega encoder:'''					,				hf_mlm.mega.layers.load_state_dict(snake_case__		)		)

			# load the MLM head weights directly
			print(
			    '''HF Mega MLM layer:'''					,				hf_mlm.mlm_head.load_state_dict(
			        torch.load(os.path.join(snake_case__					,				'''mlm_head_weights.pt'''		)					,				map_location='''cpu'''		)		)					,				)

			# test on a randomly generated input sequence
			_UpperCamelCase				:Dict				    =  torch.randint(0					,				hf_config.vocab_size					,				size=(4, 2_56)		)
			_UpperCamelCase				:Tuple				    =  torch.ones_like(snake_case__		)
			# mask a few tokens to make sure masking is applied appropriately :)
			_UpperCamelCase				:str				    =  0

			# run forward passes
			_UpperCamelCase				:str				    =  original_mlm(snake_case__					,				snake_case__					,				batch_first=snake_case__					,				ignore_mask_value=0		)
			_UpperCamelCase				:str				    =  hf_mlm(snake_case__					,				snake_case__		)[0]

			# print shapes and diff
			print(f"original output {original_output.shape}"		)
			print(f"hf output {hf_output.shape}"		)
			print(f"max diff: {(original_output - hf_output).max()}"		)  # 0.0
			_UpperCamelCase				:str				    =  torch.allclose(snake_case__					,				snake_case__					,				atol=1E-3		)

			if success:
						print('''Yay!'''		)
						hf_mlm.save_pretrained(snake_case__		)
			else:
						raise RuntimeError(f"Something's broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}"		)

			if includes_tokenizer:
						print('''Transferring tokenizer'''		)
						_UpperCamelCase				:Optional[Any]				    =  AutoTokenizer.from_pretrained(snake_case__		)
						tokenizer.save_pretrained(snake_case__		)


if __name__ == "__main__":
					UpperCamelCase__							:Optional[int]       =  argparse.ArgumentParser()

					parser.add_argument(
					    """--pretrained_checkpoint_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Point to the directory containing your model weights using the official Mega repo""",
					)

					parser.add_argument(
					    """--output_path""", default=None, type=str, required=True, help="""Location to save the Hugging Face version"""
					)

					parser.add_argument(
					    """--includes_tokenizer""",
					    action="""store_true""",
					    help="""Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo""",
					)

					UpperCamelCase__							:Dict       =  parser.parse_args()

					convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)





"""simple docstring"""






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve





def 							A_					(   snake_case__		)				->     str:
			_UpperCamelCase				:List[Any]				    =  np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]		)
			_UpperCamelCase				:Dict				    =  np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]		)

			_UpperCamelCase				:List[str]				    =  np.abs(img_convolve(snake_case__					,				snake_case__		)		)
			_UpperCamelCase				:Dict				    =  np.abs(img_convolve(snake_case__					,				snake_case__		)		)
			# modify the pix within [0, 255]
			_UpperCamelCase				:List[Any]				    =  dst_x * 2_55 / np.max(snake_case__		)
			_UpperCamelCase				:Tuple				    =  dst_y * 2_55 / np.max(snake_case__		)

			_UpperCamelCase				:Tuple				    =  np.sqrt((np.square(snake_case__		)) + (np.square(snake_case__		))		)
			_UpperCamelCase				:Any				    =  dst_xy * 2_55 / np.max(snake_case__		)
			_UpperCamelCase				:Optional[int]				    =  dst_xy.astype(np.uinta		)

			_UpperCamelCase				:str				    =  np.arctana(snake_case__					,				snake_case__		)
			return dst, theta


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

					UpperCamelCase__      , UpperCamelCase__							:Optional[int]       =  sobel_filter(gray)

					# show result images
					imshow("""sobel filter""", sobel_grad)
					imshow("""sobel theta""", sobel_theta)
					waitKey(0)

