import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def    A			(					_lowercase	,						_lowercase	,						_lowercase		):

						'''simple docstring'''





						return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase="attention"		):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE							:					List[str]           =							np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]		)
						SCREAMING_SNAKE_CASE							:					int           =							k_tmp.reshape(k_tmp.shape[0]	,						k_tmp.shape[1] * k_tmp.shape[2]		)
						SCREAMING_SNAKE_CASE							:					Dict           =							np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]		)
						SCREAMING_SNAKE_CASE							:					Tuple           =							o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]	,						o_tmp.shape[2]		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]		)
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							q_tmp.reshape(q_tmp.shape[0]	,						q_tmp.shape[1] * q_tmp.shape[2]		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]		)
						SCREAMING_SNAKE_CASE							:					str           =							v_tmp.reshape(v_tmp.shape[0]	,						v_tmp.shape[1] * v_tmp.shape[2]		)
						return k, o, q, v
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase=False		):

						'''simple docstring'''





						if split_mlp_wi:
												SCREAMING_SNAKE_CASE							:					str           =							params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
												SCREAMING_SNAKE_CASE							:					str           =							params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
												SCREAMING_SNAKE_CASE							:					int           =							(wi_a, wi_a)
						else:
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

						SCREAMING_SNAKE_CASE							:					Optional[int]           =							params[f"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
						return wi, wo
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase		):

						'''simple docstring'''





						return params[f"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]
def    A			(					_lowercase	,						*, _lowercase	,						_lowercase	,						_lowercase = False		):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE							:					Tuple           =							traverse_util.flatten_dict(variables['''target''']		)
						SCREAMING_SNAKE_CASE							:					Dict           =							{"/".join(__snake_case		): v for k, v in old.items()}

						# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							"encoder/encoder/mlp/wi_0/kernel" in old
						print('''Split MLP:'''	,						__snake_case		)

						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							collections.OrderedDict()

						# Shared embeddings.
						SCREAMING_SNAKE_CASE							:					List[str]           =							old["token_embedder/embedding"]

						# Encoder.
						for i in range(__snake_case		):
												# Block i, layer 0 (Self Attention).
												SCREAMING_SNAKE_CASE							:					Tuple           =							tax_layer_norm_lookup(__snake_case	,						__snake_case	,						'''encoder'''	,						'''pre_attention_layer_norm'''		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							tax_attention_lookup(__snake_case	,						__snake_case	,						'''encoder'''	,						'''attention'''		)
												SCREAMING_SNAKE_CASE							:					str           =							layer_norm
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							k.T
												SCREAMING_SNAKE_CASE							:					Tuple           =							o.T
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							q.T
												SCREAMING_SNAKE_CASE							:					List[str]           =							v.T

												# Block i, layer 1 (MLP).
												SCREAMING_SNAKE_CASE							:					Any           =							tax_layer_norm_lookup(__snake_case	,						__snake_case	,						'''encoder'''	,						'''pre_mlp_layer_norm'''		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tax_mlp_lookup(__snake_case	,						__snake_case	,						'''encoder'''	,						__snake_case		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							layer_norm
												if split_mlp_wi:
																		SCREAMING_SNAKE_CASE							:					int           =							wi[0].T
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							wi[1].T
												else:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							wi.T
												SCREAMING_SNAKE_CASE							:					Dict           =							wo.T
												if scalable_attention:
																		# convert the rel_embedding of each layer
																		SCREAMING_SNAKE_CASE							:					str           =							tax_relpos_bias_lookup(
																		    __snake_case	,						__snake_case	,						'''encoder'''		).T

						SCREAMING_SNAKE_CASE							:					int           =							old["encoder/encoder_norm/scale"]

						if not scalable_attention:
												SCREAMING_SNAKE_CASE							:					List[Any]           =							tax_relpos_bias_lookup(
												    __snake_case	,						0	,						'''encoder'''		).T
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tax_relpos_bias_lookup(
												    __snake_case	,						0	,						'''decoder'''		).T

						if not is_encoder_only:
												# Decoder.
												for i in range(__snake_case		):
																		# Block i, layer 0 (Self Attention).
																		SCREAMING_SNAKE_CASE							:					Dict           =							tax_layer_norm_lookup(__snake_case	,						__snake_case	,						'''decoder'''	,						'''pre_self_attention_layer_norm'''		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							tax_attention_lookup(__snake_case	,						__snake_case	,						'''decoder'''	,						'''self_attention'''		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							layer_norm
																		SCREAMING_SNAKE_CASE							:					Tuple           =							k.T
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							o.T
																		SCREAMING_SNAKE_CASE							:					Tuple           =							q.T
																		SCREAMING_SNAKE_CASE							:					List[str]           =							v.T

																		# Block i, layer 1 (Cross Attention).
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							tax_layer_norm_lookup(__snake_case	,						__snake_case	,						'''decoder'''	,						'''pre_cross_attention_layer_norm'''		)
																		SCREAMING_SNAKE_CASE							:					int           =							tax_attention_lookup(__snake_case	,						__snake_case	,						'''decoder'''	,						'''encoder_decoder_attention'''		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							layer_norm
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							k.T
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							o.T
																		SCREAMING_SNAKE_CASE							:					str           =							q.T
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							v.T

																		# Block i, layer 2 (MLP).
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							tax_layer_norm_lookup(__snake_case	,						__snake_case	,						'''decoder'''	,						'''pre_mlp_layer_norm'''		)
																		SCREAMING_SNAKE_CASE							:					Dict           =							tax_mlp_lookup(__snake_case	,						__snake_case	,						'''decoder'''	,						__snake_case		)
																		SCREAMING_SNAKE_CASE							:					int           =							layer_norm
																		if split_mlp_wi:
																								SCREAMING_SNAKE_CASE							:					Optional[Any]           =							wi[0].T
																								SCREAMING_SNAKE_CASE							:					List[str]           =							wi[1].T
																		else:
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							wi.T
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							wo.T

																		if scalable_attention:
																								# convert the rel_embedding of each layer
																								SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tax_relpos_bias_lookup(__snake_case	,						__snake_case	,						'''decoder'''		).T

												SCREAMING_SNAKE_CASE							:					Dict           =							old["decoder/decoder_norm/scale"]

												# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
												if "decoder/logits_dense/kernel" in old:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							old["decoder/logits_dense/kernel"].T

						return new
def    A			(					_lowercase	,						_lowercase		):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							collections.OrderedDict([(k, torch.from_numpy(v.copy()		)) for (k, v) in converted_params.items()]		)

						# Add what is missing.
						if "encoder.embed_tokens.weight" not in state_dict:
												SCREAMING_SNAKE_CASE							:					Tuple           =							state_dict["shared.weight"]

						if not is_encoder_only:
												if "decoder.embed_tokens.weight" not in state_dict:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							state_dict["shared.weight"]

												if "lm_head.weight" not in state_dict:  # For old 1.0 models.
																		print('''Using shared word embeddings as lm_head.'''		)
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							state_dict["shared.weight"]

						return state_dict
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase		):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE							:					List[str]           =							checkpoints.load_tax_checkpoint(__snake_case		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							convert_tax_to_pytorch(
						    __snake_case	,						num_layers=config.num_layers	,						is_encoder_only=__snake_case	,						scalable_attention=__snake_case		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							make_state_dict(__snake_case	,						__snake_case		)
						model.load_state_dict(__snake_case	,						strict=__snake_case		)
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase = False	,						_lowercase = False	,						):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE							:					Dict           =							MTaConfig.from_json_file(__snake_case		)
						print(f"""Building PyTorch model from configuration: {config}"""		)
						# Non-v1.1 checkpoints could also use T5Model, but this works for all.
						# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
						if is_encoder_only:
												SCREAMING_SNAKE_CASE							:					Any           =							UMTaEncoderModel(__snake_case		)
						else:
												SCREAMING_SNAKE_CASE							:					List[Any]           =							UMTaForConditionalGeneration(__snake_case		)

						# Load weights from tf checkpoint
						load_tax_weights_in_ta(__snake_case	,						__snake_case	,						__snake_case	,						__snake_case	,						__snake_case		)

						# Save pytorch-model
						print(f"""Save PyTorch model to {pytorch_dump_path}"""		)
						model.save_pretrained(__snake_case		)

						# Verify that we can load the checkpoint.
						model.from_pretrained(__snake_case		)
						print('''Done'''		)


if __name__ == "__main__":
					__UpperCamelCase	: Dict    		=  argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
					# Required parameters
					parser.add_argument(
					    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					parser.add_argument(
					    '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
					)
					parser.add_argument(
					    '--scalable_attention',
					    action='store_true',
					    help='Whether the model uses scaled attention (umt5 model)',
					    default=False,
					)
					__UpperCamelCase	: str    		=  parser.parse_args()
					convert_tax_checkpoint_to_pytorch(
					    args.tax_checkpoint_path,
					    args.config_file,
					    args.pytorch_dump_path,
					    args.is_encoder_only,
					    args.scalable_attention,
					)






from dataclasses import asdict, dataclass
from typing import Optional

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


__UpperCamelCase	: Tuple    		=  logging.get_logger(__name__)

# TODO Update this
__UpperCamelCase	: List[str]    		=  {
    'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.json',
    # See all ESM models at https://huggingface.co/models?filter=esm
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""esm"""


						def __init__( self			:       Tuple					,	UpperCamelCase__			:       Optional[Any]=None					,	UpperCamelCase__			:       int=None					,	UpperCamelCase__			:       List[str]=None					,	UpperCamelCase__			:       Any=768					,	UpperCamelCase__			:       Optional[Any]=12					,	UpperCamelCase__			:       str=12					,	UpperCamelCase__			:       Optional[int]=3072					,	UpperCamelCase__			:       Optional[Any]=0.1					,	UpperCamelCase__			:       Any=0.1					,	UpperCamelCase__			:       Union[str, Any]=1026					,	UpperCamelCase__			:       Dict=0.02					,	UpperCamelCase__			:       Any=1E-12					,	UpperCamelCase__			:       Dict="absolute"					,	UpperCamelCase__			:       Optional[Any]=True					,	UpperCamelCase__			:       int=None					,	UpperCamelCase__			:       Optional[int]=False					,	UpperCamelCase__			:       Tuple=False					,	UpperCamelCase__			:       List[str]=None					,	UpperCamelCase__			:       Optional[Any]=None					,	**UpperCamelCase__			:       Any					,	):





												'''simple docstring'''





												super().__init__(pad_token_id=UpperCamelCase__					,	mask_token_id=UpperCamelCase__					,	**UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							vocab_size
												SCREAMING_SNAKE_CASE							:					Any           =							hidden_size
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							num_hidden_layers
												SCREAMING_SNAKE_CASE							:					Dict           =							num_attention_heads
												SCREAMING_SNAKE_CASE							:					Any           =							intermediate_size
												SCREAMING_SNAKE_CASE							:					str           =							hidden_dropout_prob
												SCREAMING_SNAKE_CASE							:					List[Any]           =							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:					str           =							max_position_embeddings
												SCREAMING_SNAKE_CASE							:					Tuple           =							initializer_range
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							layer_norm_eps
												SCREAMING_SNAKE_CASE							:					Dict           =							position_embedding_type
												SCREAMING_SNAKE_CASE							:					Any           =							use_cache
												SCREAMING_SNAKE_CASE							:					Dict           =							emb_layer_norm_before
												SCREAMING_SNAKE_CASE							:					List[str]           =							token_dropout
												SCREAMING_SNAKE_CASE							:					List[Any]           =							is_folding_model
												if is_folding_model:
																		if esmfold_config is None:
																								logger.info('''No esmfold_config supplied for folding model, using default values.'''		)
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							EsmFoldConfig()
																		elif isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							EsmFoldConfig(**UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							esmfold_config
																		if vocab_list is None:
																								logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'''		)
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							get_default_vocab_list()
																		else:
																								SCREAMING_SNAKE_CASE							:					Optional[Any]           =							vocab_list
												else:
																		SCREAMING_SNAKE_CASE							:					str           =							None
																		SCREAMING_SNAKE_CASE							:					int           =							None
												if self.esmfold_config is not None and getattr(self.esmfold_config					,	'''use_esm_attn_map'''					,	UpperCamelCase__		):
																		raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'''		)



						def  __A ( self			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							super().to_dict()
												if isinstance(self.esmfold_config					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.esmfold_config.to_dict()
												return output






@dataclass
class       lowercase__		:
						UpperCamelCase_  =	None
						UpperCamelCase_  =	True
						UpperCamelCase_  =	False
						UpperCamelCase_  =	False
						UpperCamelCase_  =	False
						UpperCamelCase_  =	0

						UpperCamelCase_  =	True
						UpperCamelCase_  =	False

						UpperCamelCase_  =	128
						UpperCamelCase_  =	None


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												if self.trunk is None:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							TrunkConfig()
												elif isinstance(self.trunk					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Tuple           =							TrunkConfig(**self.trunk		)



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							asdict(self		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.trunk.to_dict()
												return output






@dataclass
class       lowercase__		:
						UpperCamelCase_  =	48
						UpperCamelCase_  =	1_024
						UpperCamelCase_  =	128
						UpperCamelCase_  =	32
						UpperCamelCase_  =	32
						UpperCamelCase_  =	32
						UpperCamelCase_  =	0
						UpperCamelCase_  =	0
						UpperCamelCase_  =	False
						UpperCamelCase_  =	4
						UpperCamelCase_  =	128
						UpperCamelCase_  =	None


						def  __A ( self			:       Any		):





												'''simple docstring'''





												if self.structure_module is None:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							StructureModuleConfig()
												elif isinstance(self.structure_module					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							StructureModuleConfig(**self.structure_module		)

												if self.max_recycles <= 0:
																		raise ValueError(f"""`max_recycles` should be positive, got {self.max_recycles}."""		)
												if self.sequence_state_dim % self.sequence_state_dim != 0:
																		raise ValueError(
																		    '''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
																		    f""" {self.sequence_state_dim} and {self.sequence_state_dim}."""		)
												if self.pairwise_state_dim % self.pairwise_state_dim != 0:
																		raise ValueError(
																		    '''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
																		    f""" {self.pairwise_state_dim} and {self.pairwise_state_dim}."""		)

												SCREAMING_SNAKE_CASE							:					Dict           =							self.sequence_state_dim // self.sequence_head_width
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.pairwise_state_dim // self.pairwise_head_width

												if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
																		raise ValueError(
																		    '''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
																		    f""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."""		)
												if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
																		raise ValueError(
																		    '''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
																		    f""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."""		)
												if self.pairwise_state_dim % 2 != 0:
																		raise ValueError(f"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."""		)

												if self.dropout >= 0.4:
																		raise ValueError(f"""`dropout` should not be greater than 0.4, got {self.dropout}."""		)



						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							asdict(self		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.structure_module.to_dict()
												return output






@dataclass
class       lowercase__		:
						UpperCamelCase_  =	384
						UpperCamelCase_  =	128
						UpperCamelCase_  =	16
						UpperCamelCase_  =	128
						UpperCamelCase_  =	12
						UpperCamelCase_  =	4
						UpperCamelCase_  =	8
						UpperCamelCase_  =	0.1
						UpperCamelCase_  =	8
						UpperCamelCase_  =	1
						UpperCamelCase_  =	2
						UpperCamelCase_  =	7
						UpperCamelCase_  =	10
						UpperCamelCase_  =	1E-8
						UpperCamelCase_  =	1E5


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												return asdict(self		)






def    A			(					):
						return (
						    "<cls>",
						    "<pad>",
						    "<eos>",
						    "<unk>",
						    "L",
						    "A",
						    "G",
						    "V",
						    "S",
						    "E",
						    "R",
						    "T",
						    "I",
						    "D",
						    "P",
						    "K",
						    "Q",
						    "N",
						    "F",
						    "Y",
						    "M",
						    "H",
						    "W",
						    "C",
						    "X",
						    "B",
						    "U",
						    "Z",
						    "O",
						    ".",
						    "-",
						    "<null_1>",
						    "<mask>",
						)





