
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 			UpperCamelCase__		(     nn.Module ):







	'''simple docstring'''





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





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__=True					,							UpperCamelCase__=0		):

					# Mega expects embeddings to be (time, batch, embedding size), but
					# Hugging Face returns tokens as (batch, time)
					if batch_first:
									A__	:						List[str]								=				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:
									A__	:						Union[str, Any]								=				1 - attention_mask

					# get token embeddings from IDs
					A__	:						Dict								=				self.embedding_layer(UpperCamelCase__		)

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

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







class 			UpperCamelCase__		(     nn.Module ):







	'''simple docstring'''





	def __init__(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__		):
					super().__init__()
					A__	:						Dict								=				MegaLM(UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__		)
					A__	:						int								=				nn.Linear(mega_args.encoder_embed_dim					,							UpperCamelCase__		)
					A__	:						Optional[int]								=				nn.Dropout(p=0.1		)





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__=True					,							UpperCamelCase__=0		):
					A__	:						str								=				self.mega(UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__		)
					return self.mlm_head(self.dropout(UpperCamelCase__		)		)




def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Optional[Any]				,    __UpperCamelCase			:    int				,    __UpperCamelCase			:    Tuple							)			->							str:

				"""simple docstring"""







				with open(os.path.join(__UpperCamelCase				,    '''model_args.pkl'''							)				,    '''rb'''							) as f:
								A__	:						Dict								=				pkl.load(__UpperCamelCase							)

				# load the original encoder
				A__	:						List[str]								=				OriginalMegaForMaskedLM(**__UpperCamelCase							).eval()

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

				# create a new config from the old one
				A__	:						Optional[Any]								=				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=__UpperCamelCase				,    norm_affine=__UpperCamelCase				,    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=__UpperCamelCase				,    add_lm_hidden_dense_layer=__UpperCamelCase				,    )

				A__	:						List[str]								=				MegaForMaskedLM(__UpperCamelCase							).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
				A__	:						Any								=				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
				A__	:						Union[str, Any]								=				original_mlm.mega.encoders.state_dict()
				A__	:						Optional[int]								=				{}
				for module_name in original_state_dict.keys():
								A__	:						List[str]								=				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:
																A__	:						int								=				module_name.replace('''move.beta'''				,    '''ema_gate.ema_expansion_matrix'''							)
												elif "mega_layer.beta" in module_name:
																A__	:						Union[str, Any]								=				module_name.replace('''beta'''				,    '''qk_bias'''							)
												else:
																A__	:						List[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:
																A__	:						List[Any]								=				module_name.replace('''move.gamma'''				,    '''ema_gate.kernel_projection_matrix'''							)
												elif "mega_layer.gamma" in module_name:
																A__	:						Optional[Any]								=				module_name.replace('''gamma'''				,    '''qk_weight'''							)
												else:
																A__	:						Optional[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:
												A__	:						List[str]								=				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:
												A__	:						List[str]								=				module_name.replace('''move.delta'''				,    '''ema_gate.damping_factor'''							)
								# omega is only used in EMA; renaming to improve readability
								elif "omega" in module_name:
												A__	:						List[Any]								=				module_name.replace('''move.omega'''				,    '''ema_gate.residual_weight'''							)

								if new_module_name:
												A__	:						List[Any]								=				new_module_name

				if len(__UpperCamelCase							) != 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():
								A__	:						Optional[int]								=				original_state_dict.pop(__UpperCamelCase							)

				# 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(__UpperCamelCase							)							)

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

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

				# run forward passes
				A__	:						Dict								=				original_mlm(__UpperCamelCase				,    __UpperCamelCase				,    batch_first=__UpperCamelCase				,    ignore_mask_value=0							)
				A__	:						List[str]								=				hf_mlm(__UpperCamelCase				,    __UpperCamelCase							)[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
				A__	:						str								=				torch.allclose(__UpperCamelCase				,    __UpperCamelCase				,    atol=1e-3							)

				if success:
								print('''Yay!'''							)
								hf_mlm.save_pretrained(__UpperCamelCase							)
				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'''							)
								A__	:						Optional[Any]								=				AutoTokenizer.from_pretrained(__UpperCamelCase							)
								tokenizer.save_pretrained(__UpperCamelCase							)


if __name__ == "__main__":
					_SCREAMING_SNAKE_CASE     :   Optional[Any]							 =      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',
					)

					_SCREAMING_SNAKE_CASE     :   List[str]							 =      parser.parse_args()

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

import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
					import librosa  # noqa: E402

					_SCREAMING_SNAKE_CASE     :   Tuple							 =      True
					_SCREAMING_SNAKE_CASE     :   List[Any]							 =      ''
except Exception as e:
					_SCREAMING_SNAKE_CASE     :   Optional[Any]							 =      False
					_SCREAMING_SNAKE_CASE     :   Any							 =      (
					    f"""Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."""
					)


from PIL import Image  # noqa: E402







class 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''
	_lowerCAmelCase											=					"mel_config.json"





	@register_to_config
	def __init__(      self					,							UpperCamelCase__ = 256					,							UpperCamelCase__ = 256					,							UpperCamelCase__ = 2_2050					,							UpperCamelCase__ = 2048					,							UpperCamelCase__ = 512					,							UpperCamelCase__ = 80					,							UpperCamelCase__ = 32					,							):
					A__	:						Any								=				hop_length
					A__	:						List[Any]								=				sample_rate
					A__	:						Union[str, Any]								=				n_fft
					A__	:						Optional[int]								=				top_db
					A__	:						Optional[Any]								=				n_iter
					self.set_resolution(UpperCamelCase__					,							UpperCamelCase__		)
					A__	:						str								=				None

					if not _librosa_can_be_imported:
									raise ValueError(_import_error		)





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__		):
					A__	:						int								=				x_res
					A__	:						List[Any]								=				y_res
					A__	:						List[str]								=				self.y_res
					A__	:						int								=				self.x_res * self.hop_length - 1





	def 	__snake_case					(      self					,							UpperCamelCase__ = None					,							UpperCamelCase__ = None		):
					if audio_file is not None:
									A__							,				A__	:						Union[str, Any]								=				librosa.load(UpperCamelCase__					,							mono=UpperCamelCase__					,							sr=self.sr		)
					else:
									A__	:						Tuple								=				raw_audio

					# Pad with silence if necessary.
					if len(self.audio		) < self.x_res * self.hop_length:
									A__	:						List[Any]								=				np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio		),)		)]		)





	def 	__snake_case					(      self		):
					return len(self.audio		) // self.slice_size





	def 	__snake_case					(      self					,							UpperCamelCase__ = 0		):
					return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]





	def 	__snake_case					(      self		):
					return self.sr





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					A__	:						Optional[Any]								=				librosa.feature.melspectrogram(
					    y=self.get_audio_slice(UpperCamelCase__		)					,							sr=self.sr					,							n_fft=self.n_fft					,							hop_length=self.hop_length					,							n_mels=self.n_mels		)
					A__	:						str								=				librosa.power_to_db(UpperCamelCase__					,							ref=np.max					,							top_db=self.top_db		)
					A__	:						str								=				(((log_S + self.top_db) * 255 / self.top_db).clip(0					,							255		) + 0.5).astype(np.uinta		)
					A__	:						Dict								=				Image.fromarray(UpperCamelCase__		)
					return image





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					A__	:						List[Any]								=				np.frombuffer(image.tobytes()					,							dtype='''uint8'''		).reshape((image.height, image.width)		)
					A__	:						Dict								=				bytedata.astype('''float'''		) * self.top_db / 255 - self.top_db
					A__	:						Union[str, Any]								=				librosa.db_to_power(UpperCamelCase__		)
					A__	:						Dict								=				librosa.feature.inverse.mel_to_audio(
					    UpperCamelCase__					,							sr=self.sr					,							n_fft=self.n_fft					,							hop_length=self.hop_length					,							n_iter=self.n_iter		)
					return audio