


'''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    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Optional[Any]:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=    mega_args
									_UpperCAmelCase					=    nn.Embedding(__SCREAMING_SNAKE_CASE    ,					self.mega_args.encoder_embed_dim		)
									_UpperCAmelCase					=    nn.ModuleList([MegaEncoderLayer(self.mega_args		) for _ in range(__SCREAMING_SNAKE_CASE		)]		)
									_UpperCAmelCase					=    depth






		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE=0		)				-> Optional[int]:




									"""simple docstring"""





									if batch_first:
																_UpperCAmelCase					=    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					=    1 - attention_mask

									# get token embeddings from IDs
									_UpperCAmelCase					=    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					=    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    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Union[str, Any]:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=    MegaLM(__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    nn.Linear(mega_args.encoder_embed_dim    ,					__SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    nn.Dropout(p=0.1		)






		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE=0		)				-> List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=    self.mega(__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE		)
									return self.mlm_head(self.dropout(__SCREAMING_SNAKE_CASE		)		)



def  lowerCAmelCase__				(    a__:	Optional[int]    ,						a__:	List[str]    ,						a__:	str )						->	Optional[Any]:


							'''simple docstring'''





							with open(os.path.join(snake_case__    ,						'model_args.pkl' )    ,						'rb' ) as f:
														_UpperCAmelCase					=    pkl.load(snake_case__ )

							# load the original encoder
							_UpperCAmelCase					=    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					=    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					=    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					=    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					=    original_mlm.mega.encoders.state_dict()
							_UpperCAmelCase					=    {}
							for module_name in original_state_dict.keys():
														_UpperCAmelCase					=    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					=    module_name.replace('move.beta'    ,						'ema_gate.ema_expansion_matrix' )
																					elif "mega_layer.beta" in module_name:
																												_UpperCAmelCase					=    module_name.replace('beta'    ,						'qk_bias' )
																					else:
																												_UpperCAmelCase					=    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					=    module_name.replace('move.gamma'    ,						'ema_gate.kernel_projection_matrix' )
																					elif "mega_layer.gamma" in module_name:
																												_UpperCAmelCase					=    module_name.replace('gamma'    ,						'qk_weight' )
																					else:
																												_UpperCAmelCase					=    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					=    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					=    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					=    module_name.replace('move.omega'    ,						'ema_gate.residual_weight' )

														if new_module_name:
																					_UpperCAmelCase					=    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					=    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					=    torch.randint(0    ,						hf_config.vocab_size    ,						size=(4, 2_5_6) )
							_UpperCAmelCase					=    torch.ones_like(snake_case__ )
							# mask a few tokens to make sure masking is applied appropriately :)
							_UpperCAmelCase					=    0

							# run forward passes
							_UpperCAmelCase					=    original_mlm(snake_case__    ,						snake_case__    ,						batch_first=snake_case__    ,						ignore_mask_value=0 )
							_UpperCAmelCase					=    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					=    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					=    AutoTokenizer.from_pretrained(snake_case__ )
														tokenizer.save_pretrained(snake_case__ )


if __name__ == "__main__":
		lowerCAmelCase__						: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''',
		)

		lowerCAmelCase__						:Union[str, Any]         =							parser.parse_args()

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




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__						:int         =							logging.get_logger(__name__)

lowerCAmelCase__						:Optional[int]         =							{
    '''facebook/sam-vit-huge''': '''https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json''',
    '''facebook/sam-vit-large''': '''https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json''',
    '''facebook/sam-vit-base''': '''https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json''',
}

class 	__a   (       UpperCAmelCase    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=256    ,					_SCREAMING_SNAKE_CASE=1024    ,					_SCREAMING_SNAKE_CASE=16    ,					_SCREAMING_SNAKE_CASE=16    ,					_SCREAMING_SNAKE_CASE=4    ,					_SCREAMING_SNAKE_CASE="gelu"    ,					_SCREAMING_SNAKE_CASE=1e-6    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> Tuple:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    hidden_size
									_UpperCAmelCase					=    image_size
									_UpperCAmelCase					=    patch_size
									_UpperCAmelCase					=    image_size // patch_size
									_UpperCAmelCase					=    mask_input_channels
									_UpperCAmelCase					=    num_point_embeddings
									_UpperCAmelCase					=    hidden_act
									_UpperCAmelCase					=    layer_norm_eps



class 	__a   (       UpperCAmelCase    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=256    ,					_SCREAMING_SNAKE_CASE="relu"    ,					_SCREAMING_SNAKE_CASE=2048    ,					_SCREAMING_SNAKE_CASE=2    ,					_SCREAMING_SNAKE_CASE=8    ,					_SCREAMING_SNAKE_CASE=2    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=256    ,					_SCREAMING_SNAKE_CASE=1e-6    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> List[Any]:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    hidden_size
									_UpperCAmelCase					=    hidden_act
									_UpperCAmelCase					=    mlp_dim
									_UpperCAmelCase					=    num_hidden_layers
									_UpperCAmelCase					=    num_attention_heads
									_UpperCAmelCase					=    attention_downsample_rate
									_UpperCAmelCase					=    num_multimask_outputs
									_UpperCAmelCase					=    iou_head_depth
									_UpperCAmelCase					=    iou_head_hidden_dim
									_UpperCAmelCase					=    layer_norm_eps



class 	__a   (       UpperCAmelCase    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=768    ,					_SCREAMING_SNAKE_CASE=256    ,					_SCREAMING_SNAKE_CASE=12    ,					_SCREAMING_SNAKE_CASE=12    ,					_SCREAMING_SNAKE_CASE=3    ,					_SCREAMING_SNAKE_CASE=1024    ,					_SCREAMING_SNAKE_CASE=16    ,					_SCREAMING_SNAKE_CASE="gelu"    ,					_SCREAMING_SNAKE_CASE=1e-0_6    ,					_SCREAMING_SNAKE_CASE=0.0    ,					_SCREAMING_SNAKE_CASE=1e-1_0    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE=4.0    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE=14    ,					_SCREAMING_SNAKE_CASE=[2, 5, 8, 11]    ,					_SCREAMING_SNAKE_CASE=128    ,					_SCREAMING_SNAKE_CASE=None    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> List[str]:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    hidden_size
									_UpperCAmelCase					=    output_channels
									_UpperCAmelCase					=    num_hidden_layers
									_UpperCAmelCase					=    num_attention_heads
									_UpperCAmelCase					=    num_channels
									_UpperCAmelCase					=    image_size
									_UpperCAmelCase					=    patch_size
									_UpperCAmelCase					=    hidden_act
									_UpperCAmelCase					=    layer_norm_eps
									_UpperCAmelCase					=    attention_dropout
									_UpperCAmelCase					=    initializer_range
									_UpperCAmelCase					=    qkv_bias
									_UpperCAmelCase					=    mlp_ratio
									_UpperCAmelCase					=    use_abs_pos
									_UpperCAmelCase					=    use_rel_pos
									_UpperCAmelCase					=    window_size
									_UpperCAmelCase					=    global_attn_indexes
									_UpperCAmelCase					=    num_pos_feats
									_UpperCAmelCase					=    int(hidden_size * mlp_ratio		) if mlp_dim is None else mlp_dim



class 	__a   (       UpperCAmelCase    ):
		_a			:						Any    			=			'sam'
		_a			:						Any    			=			True
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=0.02    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> Tuple:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    vision_config if vision_config is not None else {}
									_UpperCAmelCase					=    prompt_encoder_config if prompt_encoder_config is not None else {}
									_UpperCAmelCase					=    mask_decoder_config if mask_decoder_config is not None else {}

									if isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    vision_config.to_dict()
									if isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    prompt_encoder_config.to_dict()
									if isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    mask_decoder_config.to_dict()

									_UpperCAmelCase					=    SamVisionConfig(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    SamPromptEncoderConfig(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    SamMaskDecoderConfig(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    initializer_range






		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									_UpperCAmelCase					=    copy.deepcopy(self.__dict__		)
									_UpperCAmelCase					=    self.vision_config.to_dict()
									_UpperCAmelCase					=    self.prompt_encoder_config.to_dict()
									_UpperCAmelCase					=    self.mask_decoder_config.to_dict()
									_UpperCAmelCase					=    self.__class__.model_type
									return output
