
from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


snake_case_      :					Optional[Any]				      =     logging.get_logger(__name__)





class 		__lowerCamelCase     (   lowercase		):
							lowerCamelCase__:				Union[str, Any]      =							['''input_values''', '''attention_mask''']



							def __init__(     self	,				__snake_case=1	,				__snake_case=1_6_0_0_0	,				__snake_case=0.0	,				__snake_case=False	,				__snake_case=True	,				**__snake_case	,				)						->     List[Any]:






								"""simple docstring"""



								super().__init__(feature_size=__snake_case	,				sampling_rate=__snake_case	,				padding_value=__snake_case	,				**__snake_case							)
								UpperCAmelCase:					Tuple 						=     return_attention_mask
								UpperCAmelCase:					str 						=     do_normalize



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case = 0.0							)						->     List[np.ndarray]:






								"""simple docstring"""



								if attention_mask is not None:
									UpperCAmelCase:					Optional[int] 						=     np.array(__snake_case	,				np.intaa							)
									UpperCAmelCase:					Dict 						=     []

									for vector, length in zip(__snake_case	,				attention_mask.sum(-1							)							):
										UpperCAmelCase:					Any 						=     (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7							)
										if length < normed_slice.shape[0]:
											UpperCAmelCase:					int 						=     padding_value

										normed_input_values.append(__snake_case							)
								else:
									UpperCAmelCase:					Tuple 						=     [(x - x.mean()) / np.sqrt(x.var() + 1e-7							) for x in input_values]

								return normed_input_values




							def __call__(     self	,				__snake_case	,				__snake_case = False	,				__snake_case = None	,				__snake_case = False	,				__snake_case = None	,				__snake_case = None	,				__snake_case = None	,				__snake_case = None	,				**__snake_case	,				)						->     BatchFeature:






								"""simple docstring"""



								if sampling_rate is not None:
									if sampling_rate != self.sampling_rate:
										raise ValueError(
										    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
										    F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
										    F' {self.sampling_rate} and not {sampling_rate}.'							)
								else:
									logger.warning(
									    "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
									    "Failing to do so can result in silent errors that might be hard to debug."							)

								UpperCAmelCase:					Optional[Any] 						=     isinstance(__snake_case	,				np.ndarray							) and len(raw_speech.shape							) > 1
								if is_batched_numpy and len(raw_speech.shape							) > 2:
									raise ValueError(F'Only mono-channel audio is supported for input to {self}'							)
								UpperCAmelCase:					Any 						=     is_batched_numpy or (
								    isinstance(__snake_case	,				(list, tuple)							) and (isinstance(raw_speech[0]	,				(np.ndarray, tuple, list)							))
								)

								# always return batch
								if not is_batched:
									UpperCAmelCase:					Dict 						=     [raw_speech]

								# convert into correct format for padding
								UpperCAmelCase:					List[str] 						=     BatchFeature({"input_values": raw_speech}							)

								UpperCAmelCase:					Optional[int] 						=     self.pad(
								    __snake_case	,				padding=__snake_case	,				max_length=__snake_case	,				truncation=__snake_case	,				pad_to_multiple_of=__snake_case	,				return_attention_mask=__snake_case	,				)

								# convert input values to correct format
								UpperCAmelCase:					Optional[Any] 						=     padded_inputs["input_values"]
								if not isinstance(input_values[0]	,				np.ndarray							):
									UpperCAmelCase:					Optional[int] 						=     [np.asarray(__snake_case	,				dtype=np.floataa							) for array in input_values]
								elif (
								    not isinstance(__snake_case	,				np.ndarray							)
								    and isinstance(input_values[0]	,				np.ndarray							)
								    and input_values[0].dtype is np.dtype(np.floataa							)
								):
									UpperCAmelCase:					List[Any] 						=     [array.astype(np.floataa							) for array in input_values]
								elif isinstance(__snake_case	,				np.ndarray							) and input_values.dtype is np.dtype(np.floataa							):
									UpperCAmelCase:					List[Any] 						=     input_values.astype(np.floataa							)

								# convert attention_mask to correct format
								UpperCAmelCase:					List[Any] 						=     padded_inputs.get("attention_mask"							)
								if attention_mask is not None:
									UpperCAmelCase:					Tuple 						=     [np.asarray(__snake_case	,				dtype=np.intaa							) for array in attention_mask]

								# zero-mean and unit-variance normalization
								if self.do_normalize:
									UpperCAmelCase:					Union[str, Any] 						=     (
									    attention_mask
									    if self._get_padding_strategies(__snake_case	,				max_length=__snake_case							) is not PaddingStrategy.DO_NOT_PAD
									    else None
									)
									UpperCAmelCase:					str 						=     self.zero_mean_unit_var_norm(
									    padded_inputs["input_values"]	,				attention_mask=__snake_case	,				padding_value=self.padding_value							)

								if return_tensors is not None:
									UpperCAmelCase:					int 						=     padded_inputs.convert_to_tensors(__snake_case							)

								return padded_inputs


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 		__lowerCamelCase     (   nn.Module		):



							def __init__(     self	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Dict:






								"""simple docstring"""



								super().__init__()
								UpperCAmelCase:					int 						=     mega_args
								UpperCAmelCase:					Union[str, Any] 						=     nn.Embedding(__snake_case	,				self.mega_args.encoder_embed_dim							)
								UpperCAmelCase:					List[str] 						=     nn.ModuleList([MegaEncoderLayer(self.mega_args							) for _ in range(__snake_case							)]							)
								UpperCAmelCase:					List[str] 						=     depth




							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case=True	,				__snake_case=0							)						->     List[str]:






								"""simple docstring"""



								if batch_first:
									UpperCAmelCase:					int 						=     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:					Union[str, Any] 						=     1 - attention_mask

								# get token embeddings from IDs
								UpperCAmelCase:					Dict 						=     self.embedding_layer(__snake_case							)

								# pass through the Mega layers
								# input is (time, batch, encoder dim) and output is the same
								for encoder in self.encoders:
									UpperCAmelCase:					Any 						=     encoder(__snake_case	,				__snake_case							)

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





class 		__lowerCamelCase     (   nn.Module		):



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






								"""simple docstring"""



								super().__init__()
								UpperCAmelCase:					int 						=     MegaLM(__snake_case	,				__snake_case	,				__snake_case							)
								UpperCAmelCase:					Tuple 						=     nn.Linear(mega_args.encoder_embed_dim	,				__snake_case							)
								UpperCAmelCase:					List[str] 						=     nn.Dropout(p=0.1							)




							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case=True	,				__snake_case=0							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     self.mega(__snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)
								return self.mlm_head(self.dropout(__snake_case							)							)



def      __UpperCAmelCase    (  snake_case_							:		Optional[Any]				,					snake_case_							:		Dict				,					snake_case_							:		Tuple				):



	'''simple docstring'''



	with open(os.path.join(snake_case_				,					"model_args.pkl"				)				,					"rb"				) as f:
		UpperCAmelCase:					Union[str, Any] 						=     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:					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:					Union[str, 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:					str 						=     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:					List[str] 						=     original_mlm.mega.encoders.state_dict()
	UpperCAmelCase:					Optional[Any] 						=     {}
	for module_name in original_state_dict.keys():
		UpperCAmelCase:					Optional[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:					Any 						=     module_name.replace("move.beta"				,					"ema_gate.ema_expansion_matrix"				)
			elif "mega_layer.beta" in module_name:
				UpperCAmelCase:					Optional[int] 						=     module_name.replace("beta"				,					"qk_bias"				)
			else:
				UpperCAmelCase:					List[str] 						=     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:					Union[str, Any] 						=     module_name.replace("move.gamma"				,					"ema_gate.kernel_projection_matrix"				)
			elif "mega_layer.gamma" in module_name:
				UpperCAmelCase:					Any 						=     module_name.replace("gamma"				,					"qk_weight"				)
			else:
				UpperCAmelCase:					List[Any] 						=     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:					Tuple 						=     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:					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:					Optional[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:					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:					Optional[int] 						=     torch.randint(0				,					hf_config.vocab_size				,					size=(4, 2_5_6)				)
	UpperCAmelCase:					int 						=     torch.ones_like(snake_case_				)
	# mask a few tokens to make sure masking is applied appropriately :)
	UpperCAmelCase:					int 						=     0

	# run forward passes
	UpperCAmelCase:					Optional[int] 						=     original_mlm(snake_case_				,					snake_case_				,					batch_first=snake_case_				,					ignore_mask_value=0				)
	UpperCAmelCase:					Optional[int] 						=     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:					Tuple 						=     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:					List[str] 						=     AutoTokenizer.from_pretrained(snake_case_				)
		tokenizer.save_pretrained(snake_case_				)


if __name__ == "__main__":
	snake_case_      :					str				      =     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',
	)

	snake_case_      :					str				      =     parser.parse_args()

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