


'''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   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       Union[str, Any]						,				_SCREAMING_SNAKE_CASE				:       Union[str, Any]       ):
					return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]






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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       Union[str, Any]						,				_SCREAMING_SNAKE_CASE				:       Optional[Any]						,				_SCREAMING_SNAKE_CASE				:       int=False       ):
					if split_mlp_wi:
										__a      :	List[Any]    =  params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
										__a      :	List[Any]    =  params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
										__a      :	Optional[Any]    =  (wi_a, wi_a)
					else:
										__a      :	List[str]    =  params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Tuple						,				_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       str						,				_SCREAMING_SNAKE_CASE				:       int       ):
					return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       dict						,				*, _SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       bool						,				_SCREAMING_SNAKE_CASE				:       bool = False       ):
					__a      :	Optional[Any]    =  traverse_util.flatten_dict(variables['target']       )
					__a      :	Optional[Any]    =  {'/'.join(_SCREAMING_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
					__a      :	Tuple    =  'encoder/encoder/mlp/wi_0/kernel' in old
					print('Split MLP:'						,				_SCREAMING_SNAKE_CASE       )

					__a      :	int    =  collections.OrderedDict()

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

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

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

					__a      :	List[Any]    =  old['encoder/encoder_norm/scale']

					if not scalable_attention:
										__a      :	str    =  tax_relpos_bias_lookup(
										    _SCREAMING_SNAKE_CASE						,				0						,				'encoder'       ).T
										__a      :	Any    =  tax_relpos_bias_lookup(
										    _SCREAMING_SNAKE_CASE						,				0						,				'decoder'       ).T

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

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

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

															if scalable_attention:
																				# convert the rel_embedding of each layer
																				__a      :	Optional[int]    =  tax_relpos_bias_lookup(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				'decoder'       ).T

										__a      :	Tuple    =  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:
															__a      :	List[Any]    =  old['decoder/logits_dense/kernel'].T

					return new






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Dict						,				_SCREAMING_SNAKE_CASE				:       bool       ):
					__a      :	List[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:
										__a      :	Optional[Any]    =  state_dict['shared.weight']

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															__a      :	str    =  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.'       )
															__a      :	Tuple    =  state_dict['shared.weight']

					return state_dict






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str						,				_SCREAMING_SNAKE_CASE				:       Optional[Any]						,				_SCREAMING_SNAKE_CASE				:       List[str]						,				_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       List[str]       ):
					__a      :	str    =  checkpoints.load_tax_checkpoint(_SCREAMING_SNAKE_CASE       )
					__a      :	str    =  convert_tax_to_pytorch(
					    _SCREAMING_SNAKE_CASE						,				num_layers=config.num_layers						,				is_encoder_only=_SCREAMING_SNAKE_CASE						,				scalable_attention=_SCREAMING_SNAKE_CASE       )
					__a      :	List[Any]    =  make_state_dict(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
					model.load_state_dict(_SCREAMING_SNAKE_CASE						,				strict=_SCREAMING_SNAKE_CASE       )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       Dict						,				_SCREAMING_SNAKE_CASE				:       List[str]						,				_SCREAMING_SNAKE_CASE				:       bool = False						,				_SCREAMING_SNAKE_CASE				:       bool = False						,				):
					__a      :	Tuple    =  MTaConfig.from_json_file(_SCREAMING_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:
										__a      :	Union[str, Any]    =  UMTaEncoderModel(_SCREAMING_SNAKE_CASE       )
					else:
										__a      :	Any    =  UMTaForConditionalGeneration(_SCREAMING_SNAKE_CASE       )

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

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

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


if __name__ == "__main__":
	__lowercase     :						Any	=     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,
	)
	__lowercase     :						List[Any]	=     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 __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


__lowercase     :						List[Any]	=     {
    'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
    'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   "ernie_m"
	A_           =   {"dropout": "classifier_dropout", "num_classes": "num_labels"}




	def __init__(       self						,    __a = 25_0002						,    __a = 768						,    __a = 12						,    __a = 12						,    __a = 3072						,    __a = "gelu"						,    __a = 0.1						,    __a = 0.1						,    __a = 514						,    __a = 0.02						,    __a = 1						,    __a = 1E-0_5						,    __a=None						,    __a=False						,    __a=0.0						,    **__a						,    ):

						'''simple docstring'''





						super().__init__(pad_token_id=__a						,    **__a							)
						__a      :	int    =  vocab_size
						__a      :	Dict    =  hidden_size
						__a      :	str    =  num_hidden_layers
						__a      :	Dict    =  num_attention_heads
						__a      :	List[str]    =  intermediate_size
						__a      :	Union[str, Any]    =  hidden_act
						__a      :	List[Any]    =  hidden_dropout_prob
						__a      :	str    =  attention_probs_dropout_prob
						__a      :	Any    =  max_position_embeddings
						__a      :	int    =  initializer_range
						__a      :	Dict    =  layer_norm_eps
						__a      :	int    =  classifier_dropout
						__a      :	Dict    =  is_decoder
						__a      :	int    =  act_dropout



