



"""simple docstring"""






import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				CONFIG_NAME



					@register_to_config
					def __init__(			self				:    Optional[Any],    lowerCamelCase				:    Optional[Any] = True,    lowerCamelCase				:    int = 8,    lowerCamelCase				:    List[str] = "lanczos",    lowerCamelCase				:    Any = True,    lowerCamelCase				:    List[str] = False,    )->   Tuple:
								super().__init__()



					@staticmethod
					def 						snake_case			(			lowerCamelCase				:    List[str]	)->   PIL.Image.Image:



								if images.ndim == 3:
											lowerCamelCase__						:						str						      =images[None, ...]
								lowerCamelCase__						:						Dict						      =(images * 255).round().astype('''uint8'''	)
								if images.shape[-1] == 1:
											# special case for grayscale (single channel) images
											lowerCamelCase__						:						Dict						      =[Image.fromarray(image.squeeze(),    mode='''L'''	) for image in images]
								else:
											lowerCamelCase__						:						Dict						      =[Image.fromarray(__snake_case	) for image in images]

								return pil_images



					@staticmethod
					def 						snake_case			(			lowerCamelCase				:    List[Any]	)->   np.ndarray:



								if not isinstance(__snake_case,    __snake_case	):
											lowerCamelCase__						:						Dict						      =[images]
								lowerCamelCase__						:						Optional[Any]						      =[np.array(__snake_case	).astype(np.floataa	) / 255.0 for image in images]
								lowerCamelCase__						:						str						      =np.stack(__snake_case,    axis=0	)

								return images



					@staticmethod
					def 						snake_case			(			lowerCamelCase				:    List[Any]	)->   torch.FloatTensor:



								if images.ndim == 3:
											lowerCamelCase__						:						Union[str, Any]						      =images[..., None]

								lowerCamelCase__						:						Optional[Any]						      =torch.from_numpy(images.transpose(0,    3,    1,    2	)	)
								return images



					@staticmethod
					def 						snake_case			(			lowerCamelCase				:    Union[str, Any]	)->   np.ndarray:
								lowerCamelCase__						:						Any						      =images.cpu().permute(0,    2,    3,    1	).float().numpy()
								return images



					@staticmethod
					def 						snake_case			(			lowerCamelCase				:    Optional[int]	)->   List[str]:
								return 2.0 * images - 1.0



					@staticmethod
					def 						snake_case			(			lowerCamelCase				:    Any	)->   List[str]:
								return (images / 2 + 0.5).clamp(0,    1	)



					@staticmethod
					def 						snake_case			(			lowerCamelCase				:    Tuple	)->   PIL.Image.Image:
								lowerCamelCase__						:						Dict						      =image.convert('''RGB'''	)
								return image



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    int,    lowerCamelCase				:    int = None,    lowerCamelCase				:    List[Any] = None,    )->   PIL.Image.Image:



								if height is None:
											lowerCamelCase__						:						List[str]						      =image.height
								if width is None:
											lowerCamelCase__						:						Optional[int]						      =image.width

								lowerCamelCase__						:						int						      =(
								    x - x % self.config.vae_scale_factor for x in (width, height)
								)  # resize to integer multiple of vae_scale_factor
								lowerCamelCase__						:						List[Any]						      =image.resize((width, height),    resample=PIL_INTERPOLATION[self.config.resample]	)
								return image



					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    Optional[int],    lowerCamelCase				:    Optional[int] = None,    lowerCamelCase				:    Optional[int] = None,    )->   torch.Tensor:
								lowerCamelCase__						:						Optional[int]						      =(PIL.Image.Image, np.ndarray, torch.Tensor)
								if isinstance(__snake_case,    __snake_case	):
											lowerCamelCase__						:						Tuple						      =[image]
								elif not (isinstance(__snake_case,    __snake_case	) and all(isinstance(__snake_case,    __snake_case	) for i in image	)):
											raise ValueError(
											    F'''Input is in incorrect format: {[type(__snake_case	) for i in image]}. Currently, we only support {", ".join(__snake_case	)}'''	)

								if isinstance(image[0],    PIL.Image.Image	):
											if self.config.do_convert_rgb:
														lowerCamelCase__						:						Any						      =[self.convert_to_rgb(__snake_case	) for i in image]
											if self.config.do_resize:
														lowerCamelCase__						:						Optional[Any]						      =[self.resize(__snake_case,    __snake_case,    __snake_case	) for i in image]
											lowerCamelCase__						:						List[Any]						      =self.pil_to_numpy(__snake_case	)  # to np
											lowerCamelCase__						:						List[str]						      =self.numpy_to_pt(__snake_case	)  # to pt

								elif isinstance(image[0],    np.ndarray	):
											lowerCamelCase__						:						List[str]						      =np.concatenate(__snake_case,    axis=0	) if image[0].ndim == 4 else np.stack(__snake_case,    axis=0	)
											lowerCamelCase__						:						int						      =self.numpy_to_pt(__snake_case	)
											lowerCamelCase__						:						Optional[int]						      =image.shape
											if self.config.do_resize and (
											    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
											):
														raise ValueError(
														    F'''Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'''
														    F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''	)

								elif isinstance(image[0],    torch.Tensor	):
											lowerCamelCase__						:						List[Any]						      =torch.cat(__snake_case,    axis=0	) if image[0].ndim == 4 else torch.stack(__snake_case,    axis=0	)
											lowerCamelCase__						:						Union[str, Any]						      =image.shape

											# don't need any preprocess if the image is latents
											if channel == 4:
														return image

											if self.config.do_resize and (
											    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
											):
														raise ValueError(
														    F'''Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'''
														    F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''	)

        # expected range [0,1], normalize to [-1,1]
								lowerCamelCase__						:						int						      =self.config.do_normalize
								if image.min() < 0:
											warnings.warn(
											    '''Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '''
											    F'''when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]''',    __snake_case,    )
											lowerCamelCase__						:						List[Any]						      =False

								if do_normalize:
											lowerCamelCase__						:						int						      =self.normalize(__snake_case	)

								return image




					def 						snake_case			(			self				:    List[Any],    lowerCamelCase				:    int,    lowerCamelCase				:    Optional[int] = "pil",    lowerCamelCase				:    List[str] = None,    )->   Any:



								if not isinstance(__snake_case,    torch.Tensor	):
											raise ValueError(
											    F'''Input for postprocessing is in incorrect format: {type(__snake_case	)}. We only support pytorch tensor'''	)
								if output_type not in ["latent", "pt", "np", "pil"]:
											lowerCamelCase__						:						Optional[Any]						      =(
											    F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
											    "`pil`, `np`, `pt`, `latent`"
											)
											deprecate('''Unsupported output_type''',    '''1.0.0''',    __snake_case,    standard_warn=__snake_case	)
											lowerCamelCase__						:						Optional[Any]						      ="np"

								if output_type == "latent":
											return image

								if do_denormalize is None:
											lowerCamelCase__						:						Union[str, Any]						      =[self.config.do_normalize] * image.shape[0]

								lowerCamelCase__						:						List[Any]						      =torch.stack(
								    [self.denormalize(image[i]	) if do_denormalize[i] else image[i] for i in range(image.shape[0]	)]	)

								if output_type == "pt":
											return image

								lowerCamelCase__						:						str						      =self.pt_to_numpy(__snake_case	)

								if output_type == "np":
											return image

								if output_type == "pil":
											return self.numpy_to_pil(__snake_case	)





class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				CONFIG_NAME



					@register_to_config
					def __init__(			self				:    Optional[int],    lowerCamelCase				:    str = True,    lowerCamelCase				:    Any = 8,    lowerCamelCase				:    int = "lanczos",    lowerCamelCase				:    Tuple = True,    )->   List[Any]:
								super().__init__()



					@staticmethod
					def 						snake_case			(			lowerCamelCase				:    int	)->   str:



								if images.ndim == 3:
											lowerCamelCase__						:						int						      =images[None, ...]
								lowerCamelCase__						:						Union[str, Any]						      =(images * 255).round().astype('''uint8'''	)
								if images.shape[-1] == 1:
											# special case for grayscale (single channel) images
											lowerCamelCase__						:						Tuple						      =[Image.fromarray(image.squeeze(),    mode='''L'''	) for image in images]
								else:
											lowerCamelCase__						:						int						      =[Image.fromarray(image[:, :, :3]	) for image in images]

								return pil_images



					@staticmethod
					def 						snake_case			(			lowerCamelCase				:    List[Any]	)->   str:
								return image[:, :, 1] * 2**8 + image[:, :, 2]



					def 						snake_case			(			self				:    Dict,    lowerCamelCase				:    List[str]	)->   int:



								if images.ndim == 3:
											lowerCamelCase__						:						int						      =images[None, ...]
								lowerCamelCase__						:						Tuple						      =images[:, :, :, 3:]
								if images.shape[-1] == 6:
											lowerCamelCase__						:						List[Any]						      =(images_depth * 255).round().astype('''uint8'''	)
											lowerCamelCase__						:						List[Any]						      =[
											    Image.fromarray(self.rgblike_to_depthmap(__snake_case	),    mode='''I;16'''	) for image_depth in images_depth
											]
								elif images.shape[-1] == 4:
											lowerCamelCase__						:						Optional[int]						      =(images_depth * 6_5535.0).astype(np.uintaa	)
											lowerCamelCase__						:						Union[str, Any]						      =[Image.fromarray(__snake_case,    mode='''I;16'''	) for image_depth in images_depth]
								else:
											raise Exception('''Not supported'''	)

								return pil_images




					def 						snake_case			(			self				:    int,    lowerCamelCase				:    Tuple,    lowerCamelCase				:    Optional[Any] = "pil",    lowerCamelCase				:    List[Any] = None,    )->   Tuple:



								if not isinstance(__snake_case,    torch.Tensor	):
											raise ValueError(
											    F'''Input for postprocessing is in incorrect format: {type(__snake_case	)}. We only support pytorch tensor'''	)
								if output_type not in ["latent", "pt", "np", "pil"]:
											lowerCamelCase__						:						str						      =(
											    F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
											    "`pil`, `np`, `pt`, `latent`"
											)
											deprecate('''Unsupported output_type''',    '''1.0.0''',    __snake_case,    standard_warn=__snake_case	)
											lowerCamelCase__						:						str						      ="np"

								if do_denormalize is None:
											lowerCamelCase__						:						Tuple						      =[self.config.do_normalize] * image.shape[0]

								lowerCamelCase__						:						Union[str, Any]						      =torch.stack(
								    [self.denormalize(image[i]	) if do_denormalize[i] else image[i] for i in range(image.shape[0]	)]	)

								lowerCamelCase__						:						Optional[Any]						      =self.pt_to_numpy(__snake_case	)

								if output_type == "np":
											if image.shape[-1] == 6:
														lowerCamelCase__						:						Optional[Any]						      =np.stack([self.rgblike_to_depthmap(im[:, :, 3:]	) for im in image],    axis=0	)
											else:
														lowerCamelCase__						:						Optional[int]						      =image[:, :, :, 3:]
											return image[:, :, :, :3], image_depth

								if output_type == "pil":
											return self.numpy_to_pil(__snake_case	), self.numpy_to_depth(__snake_case	)
								else:
											raise Exception(F'''This type {output_type} is not supported'''	)






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)
