






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

							if split_mlp_wi:
														__lowercase      :							Dict				=     params[F"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
														__lowercase      :							Optional[Any]				=     params[F"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
														__lowercase      :							Union[str, Any]				=     (wi_a, wi_a)
							else:
														__lowercase      :							Optional[Any]				=     params[F"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]

							__lowercase      :							int				=     params[F"{prefix}/{prefix}/mlp/wo/kernel"][:, i, :]
							return wi, wo
def       snake_case_						(     lowerCAmelCase_       :						Dict       ,       lowerCAmelCase_       :						Tuple       ,       lowerCAmelCase_       :						List[Any]       ,       lowerCAmelCase_       :						Any      ):
							return params[F"{prefix}/{prefix}/{layer_name}/scale"][:, i]
def       snake_case_						(     lowerCAmelCase_       :						List[str]       ,       *, lowerCAmelCase_       :						List[Any]       ,       lowerCAmelCase_       :						Optional[int]       ,       lowerCAmelCase_       :						Optional[int] = False      ):
							__lowercase      :							int				=     traverse_util.flatten_dict(variables["""target"""]      )
							__lowercase      :							str				=     {"/".join(lowercase_      ): v for k, v in old.items()}

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

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

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

							# Encoder.
							for i in range(lowercase_      ):
														# Block i, layer 0 (Self Attention).
														__lowercase      :							int				=     tax_layer_norm_lookup(lowercase_       ,       lowercase_       ,       """encoder"""       ,       """pre_attention_layer_norm"""      )
														__lowercase      :							str				=     tax_attention_lookup(lowercase_       ,       lowercase_       ,       """encoder"""       ,       """attention"""      )
														__lowercase      :							List[Any]				=     layer_norm
														__lowercase      :							str				=     k.T
														__lowercase      :							Optional[Any]				=     o.T
														__lowercase      :							List[str]				=     q.T
														__lowercase      :							int				=     v.T

														# Block i, layer 1 (MLP).
														__lowercase      :							Dict				=     tax_layer_norm_lookup(lowercase_       ,       lowercase_       ,       """encoder"""       ,       """pre_mlp_layer_norm"""      )
														__lowercase      :							int				=     tax_mlp_lookup(lowercase_       ,       lowercase_       ,       """encoder"""       ,       lowercase_      )
														__lowercase      :							int				=     layer_norm
														if split_mlp_wi:
																					__lowercase      :							Optional[int]				=     wi[0].T
																					__lowercase      :							Optional[Any]				=     wi[1].T
														else:
																					__lowercase      :							Any				=     wi.T
														__lowercase      :							Optional[Any]				=     wo.T
														if scalable_attention:
																					# convert the rel_embedding of each layer
																					__lowercase      :							Optional[int]				=     tax_relpos_bias_lookup(
																					    lowercase_       ,       lowercase_       ,       """encoder"""      ).T

							__lowercase      :							str				=     old["encoder/encoder_norm/scale"]

							if not scalable_attention:
														__lowercase      :							Any				=     tax_relpos_bias_lookup(
														    lowercase_       ,       0       ,       """encoder"""      ).T
														__lowercase      :							str				=     tax_relpos_bias_lookup(
														    lowercase_       ,       0       ,       """decoder"""      ).T

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

																					# Block i, layer 1 (Cross Attention).
																					__lowercase      :							Dict				=     tax_layer_norm_lookup(lowercase_       ,       lowercase_       ,       """decoder"""       ,       """pre_cross_attention_layer_norm"""      )
																					__lowercase      :							int				=     tax_attention_lookup(lowercase_       ,       lowercase_       ,       """decoder"""       ,       """encoder_decoder_attention"""      )
																					__lowercase      :							List[str]				=     layer_norm
																					__lowercase      :							str				=     k.T
																					__lowercase      :							List[Any]				=     o.T
																					__lowercase      :							int				=     q.T
																					__lowercase      :							List[Any]				=     v.T

																					# Block i, layer 2 (MLP).
																					__lowercase      :							Tuple				=     tax_layer_norm_lookup(lowercase_       ,       lowercase_       ,       """decoder"""       ,       """pre_mlp_layer_norm"""      )
																					__lowercase      :							Tuple				=     tax_mlp_lookup(lowercase_       ,       lowercase_       ,       """decoder"""       ,       lowercase_      )
																					__lowercase      :							Optional[int]				=     layer_norm
																					if split_mlp_wi:
																												__lowercase      :							List[Any]				=     wi[0].T
																												__lowercase      :							Optional[int]				=     wi[1].T
																					else:
																												__lowercase      :							str				=     wi.T
																					__lowercase      :							Union[str, Any]				=     wo.T

																					if scalable_attention:
																												# convert the rel_embedding of each layer
																												__lowercase      :							Optional[int]				=     tax_relpos_bias_lookup(lowercase_       ,       lowercase_       ,       """decoder"""      ).T

														__lowercase      :							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:
																					__lowercase      :							Any				=     old["decoder/logits_dense/kernel"].T

							return new
def       snake_case_						(     lowerCAmelCase_       :						List[Any]       ,       lowerCAmelCase_       :						Optional[int]      ):
							__lowercase      :							Dict				=     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:
														__lowercase      :							str				=     state_dict["shared.weight"]

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					__lowercase      :							Optional[int]				=     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."""      )
																					__lowercase      :							int				=     state_dict["shared.weight"]

							return state_dict
def       snake_case_						(     lowerCAmelCase_       :						Optional[Any]       ,       lowerCAmelCase_       :						List[Any]       ,       lowerCAmelCase_       :						Dict       ,       lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						Dict      ):
							__lowercase      :							int				=     checkpoints.load_tax_checkpoint(lowercase_      )
							__lowercase      :							List[str]				=     convert_tax_to_pytorch(
							    lowercase_       ,       num_layers=config.num_layers       ,       is_encoder_only=lowercase_       ,       scalable_attention=lowercase_      )
							__lowercase      :							Union[str, Any]				=     make_state_dict(lowercase_       ,       lowercase_      )
							model.load_state_dict(lowercase_       ,       strict=lowercase_      )
def       snake_case_						(     lowerCAmelCase_       :						Any       ,       lowerCAmelCase_       :						Optional[int]       ,       lowerCAmelCase_       :						Tuple       ,       lowerCAmelCase_       :						str = False       ,       lowerCAmelCase_       :						Any = False       ,       ):
							__lowercase      :							int				=     MTaConfig.from_json_file(lowercase_      )
							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:
														__lowercase      :							Optional[int]				=     UMTaEncoderModel(lowercase_      )
							else:
														__lowercase      :							Optional[int]				=     UMTaForConditionalGeneration(lowercase_      )

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

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

							# Verify that we can load the checkpoint.
							model.from_pretrained(lowercase_      )
							print("""Done"""      )


if __name__ == "__main__":
	lowerCamelCase : 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,
	)
	lowerCamelCase : Dict						   =       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,
	)







def       snake_case_		(     lowerCAmelCase_       :						int       ,       lowerCAmelCase_       :						int      ):
							return int((input_a, input_a).count(0      ) == 0      )
def       snake_case_		(     ):
							assert and_gate(0       ,       0      ) == 0
							assert and_gate(0       ,       1      ) == 0
							assert and_gate(1       ,       0      ) == 0
							assert and_gate(1       ,       1      ) == 1


if __name__ == "__main__":
	test_and_gate()
	print(and_gate(1, 0))
	print(and_gate(0, 0))
	print(and_gate(0, 1))
	print(and_gate(1, 1))