




"""simple docstring"""






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 							UpperCAmelCase		(		snake_case			:					Dict      ,       snake_case			:					Union[str, Any]      ,       snake_case			:					List[str]    ):
					return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]





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





def 							UpperCAmelCase		(		snake_case			:					Dict      ,       snake_case			:					int      ,       snake_case			:					Union[str, Any]      ,       snake_case			:					Optional[int]=False    ):
					if split_mlp_wi:
										_lowerCAmelCase:Optional[int]									=  params[F'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
										_lowerCAmelCase:Optional[Any]									=  params[F'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
										_lowerCAmelCase:List[Any]									=  (wi_a, wi_a)
					else:
										_lowerCAmelCase:Optional[int]									=  params[F'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

					_lowerCAmelCase:Dict									=  params[F'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
					return wi, wo





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					Optional[int]      ,       snake_case			:					Union[str, Any]      ,       snake_case			:					Optional[Any]    ):
					return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]





def 							UpperCAmelCase		(		snake_case			:					dict      ,       *, snake_case			:					int      ,       snake_case			:					bool      ,       snake_case			:					bool = False    ):
					_lowerCAmelCase:Optional[int]									=  traverse_util.flatten_dict(variables['''target''']    )
					_lowerCAmelCase:int									=  {'''/'''.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
					_lowerCAmelCase:int									=  '''encoder/encoder/mlp/wi_0/kernel''' in old
					print('''Split MLP:'''      ,       snake_case    )

					_lowerCAmelCase:List[Any]									=  collections.OrderedDict()

					# Shared embeddings.
					_lowerCAmelCase:Any									=  old['''token_embedder/embedding''']

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

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

					_lowerCAmelCase:Union[str, Any]									=  old['''encoder/encoder_norm/scale''']

					if not scalable_attention:
										_lowerCAmelCase:str									=  tax_relpos_bias_lookup(
										    snake_case      ,       0      ,       '''encoder'''    ).T
										_lowerCAmelCase:Optional[int]									=  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).
															_lowerCAmelCase:List[str]									=  tax_layer_norm_lookup(snake_case      ,       snake_case      ,       '''decoder'''      ,       '''pre_self_attention_layer_norm'''    )
															_lowerCAmelCase:str									=  tax_attention_lookup(snake_case      ,       snake_case      ,       '''decoder'''      ,       '''self_attention'''    )
															_lowerCAmelCase:Any									=  layer_norm
															_lowerCAmelCase:int									=  k.T
															_lowerCAmelCase:Union[str, Any]									=  o.T
															_lowerCAmelCase:Tuple									=  q.T
															_lowerCAmelCase:Dict									=  v.T

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

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

															if scalable_attention:
																				# convert the rel_embedding of each layer
																				_lowerCAmelCase:int									=  tax_relpos_bias_lookup(snake_case      ,       snake_case      ,       '''decoder'''    ).T

										_lowerCAmelCase:str									=  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:
															_lowerCAmelCase:Optional[int]									=  old['''decoder/logits_dense/kernel'''].T

					return new





def 							UpperCAmelCase		(		snake_case			:					Optional[int]      ,       snake_case			:					bool    ):
					_lowerCAmelCase: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:
										_lowerCAmelCase:Union[str, Any]									=  state_dict['''shared.weight''']

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															_lowerCAmelCase:Dict									=  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.'''    )
															_lowerCAmelCase:Optional[Any]									=  state_dict['''shared.weight''']

					return state_dict





def 							UpperCAmelCase		(		snake_case			:					Any      ,       snake_case			:					Any      ,       snake_case			:					Optional[int]      ,       snake_case			:					Any      ,       snake_case			:					str    ):
					_lowerCAmelCase:str									=  checkpoints.load_tax_checkpoint(snake_case    )
					_lowerCAmelCase:Any									=  convert_tax_to_pytorch(
					    snake_case      ,       num_layers=config.num_layers      ,       is_encoder_only=snake_case      ,       scalable_attention=snake_case    )
					_lowerCAmelCase:Tuple									=  make_state_dict(snake_case      ,       snake_case    )
					model.load_state_dict(snake_case      ,       strict=snake_case    )





def 							UpperCAmelCase		(		snake_case			:					Optional[int]      ,       snake_case			:					Tuple      ,       snake_case			:					List[str]      ,       snake_case			:					bool = False      ,       snake_case			:					bool = False      ,       ):
					_lowerCAmelCase:str									=  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:
										_lowerCAmelCase:int									=  UMTaEncoderModel(snake_case    )
					else:
										_lowerCAmelCase:Optional[int]									=  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__						=			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__						=			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,
				)












"""simple docstring"""






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


UpperCamelCase__						=			logging.get_logger(__name__)

UpperCamelCase__						=			{
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}




class   a__			(       UpperCamelCase_  ):
						snake_case__             = '''roc_bert'''





						def __init__(							self      :	Union[str, Any]     ,a__      :	Union[str, Any]=3_0522     ,a__      :	List[Any]=768     ,a__      :	Tuple=12     ,a__      :	Optional[int]=12     ,a__      :	Any=3072     ,a__      :	Optional[int]="gelu"     ,a__      :	Union[str, Any]=0.1     ,a__      :	List[str]=0.1     ,a__      :	Dict=512     ,a__      :	int=2     ,a__      :	Dict=0.02     ,a__      :	Dict=1E-12     ,a__      :	int=True     ,a__      :	Optional[int]=0     ,a__      :	Union[str, Any]="absolute"     ,a__      :	List[Any]=None     ,a__      :	str=True     ,a__      :	str=True     ,a__      :	List[str]=768     ,a__      :	Optional[int]=910     ,a__      :	Any=512     ,a__      :	str=2_4858     ,a__      :	List[str]=True     ,**a__      :	str     ,)						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:Tuple									=  vocab_size
											_lowerCAmelCase:Any									=  max_position_embeddings
											_lowerCAmelCase:Union[str, Any]									=  hidden_size
											_lowerCAmelCase:Optional[Any]									=  num_hidden_layers
											_lowerCAmelCase:int									=  num_attention_heads
											_lowerCAmelCase:int									=  intermediate_size
											_lowerCAmelCase:Union[str, Any]									=  hidden_act
											_lowerCAmelCase:Any									=  hidden_dropout_prob
											_lowerCAmelCase:List[Any]									=  attention_probs_dropout_prob
											_lowerCAmelCase:List[Any]									=  initializer_range
											_lowerCAmelCase:Dict									=  type_vocab_size
											_lowerCAmelCase:Dict									=  layer_norm_eps
											_lowerCAmelCase:str									=  use_cache
											_lowerCAmelCase:Any									=  enable_pronunciation
											_lowerCAmelCase:List[str]									=  enable_shape
											_lowerCAmelCase:Optional[int]									=  pronunciation_embed_dim
											_lowerCAmelCase:Union[str, Any]									=  pronunciation_vocab_size
											_lowerCAmelCase:str									=  shape_embed_dim
											_lowerCAmelCase:List[Any]									=  shape_vocab_size
											_lowerCAmelCase:str									=  concat_input
											_lowerCAmelCase:Optional[int]									=  position_embedding_type
											_lowerCAmelCase:Any									=  classifier_dropout
											super().__init__(pad_token_id=a__     ,**a__)






