


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 							__lowercase   ( __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				Optional[Any]					,    __lowerCAmelCase							:				str   ):
							return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]






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






def 							__lowercase   ( __lowerCAmelCase							:				Tuple					,    __lowerCAmelCase							:				Optional[Any]					,    __lowerCAmelCase							:				Optional[Any]					,    __lowerCAmelCase							:				Optional[int]=False   ):
							if split_mlp_wi:
														a__             =     params[F'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
														a__             =     params[F'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
														a__             =     (wi_a, wi_a)
							else:
														a__             =     params[F'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

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






def 							__lowercase   ( __lowerCAmelCase							:				List[Any]					,    __lowerCAmelCase							:				Any					,    __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				int   ):
							return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]






def 							__lowercase   ( __lowerCAmelCase							:				dict					,    *, __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				bool					,    __lowerCAmelCase							:				bool = False   ):
							a__             =     traverse_util.flatten_dict(variables['target']   )
							a__             =     {'/'.join(__lowerCAmelCase   ): v for k, v in old.items()}

							# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
							a__             =     'encoder/encoder/mlp/wi_0/kernel' in old
							print('Split MLP:'					,    __lowerCAmelCase   )

							a__             =     collections.OrderedDict()

							# Shared embeddings.
							a__             =     old['token_embedder/embedding']

							# Encoder.
							for i in range(__lowerCAmelCase   ):
														# Block i, layer 0 (Self Attention).
														a__             =     tax_layer_norm_lookup(__lowerCAmelCase					,    __lowerCAmelCase					,    'encoder'					,    'pre_attention_layer_norm'   )
														a__      ,					a__      ,					a__      ,					a__             =     tax_attention_lookup(__lowerCAmelCase					,    __lowerCAmelCase					,    'encoder'					,    'attention'   )
														a__             =     layer_norm
														a__             =     k.T
														a__             =     o.T
														a__             =     q.T
														a__             =     v.T

														# Block i, layer 1 (MLP).
														a__             =     tax_layer_norm_lookup(__lowerCAmelCase					,    __lowerCAmelCase					,    'encoder'					,    'pre_mlp_layer_norm'   )
														a__      ,					a__             =     tax_mlp_lookup(__lowerCAmelCase					,    __lowerCAmelCase					,    'encoder'					,    __lowerCAmelCase   )
														a__             =     layer_norm
														if split_mlp_wi:
																					a__             =     wi[0].T
																					a__             =     wi[1].T
														else:
																					a__             =     wi.T
														a__             =     wo.T
														if scalable_attention:
																					# convert the rel_embedding of each layer
																					a__             =     tax_relpos_bias_lookup(
																					    __lowerCAmelCase					,    __lowerCAmelCase					,    'encoder'   ).T

							a__             =     old['encoder/encoder_norm/scale']

							if not scalable_attention:
														a__             =     tax_relpos_bias_lookup(
														    __lowerCAmelCase					,    0					,    'encoder'   ).T
														a__             =     tax_relpos_bias_lookup(
														    __lowerCAmelCase					,    0					,    'decoder'   ).T

							if not is_encoder_only:
														# Decoder.
														for i in range(__lowerCAmelCase   ):
																					# Block i, layer 0 (Self Attention).
																					a__             =     tax_layer_norm_lookup(__lowerCAmelCase					,    __lowerCAmelCase					,    'decoder'					,    'pre_self_attention_layer_norm'   )
																					a__      ,					a__      ,					a__      ,					a__             =     tax_attention_lookup(__lowerCAmelCase					,    __lowerCAmelCase					,    'decoder'					,    'self_attention'   )
																					a__             =     layer_norm
																					a__             =     k.T
																					a__             =     o.T
																					a__             =     q.T
																					a__             =     v.T

																					# Block i, layer 1 (Cross Attention).
																					a__             =     tax_layer_norm_lookup(__lowerCAmelCase					,    __lowerCAmelCase					,    'decoder'					,    'pre_cross_attention_layer_norm'   )
																					a__      ,					a__      ,					a__      ,					a__             =     tax_attention_lookup(__lowerCAmelCase					,    __lowerCAmelCase					,    'decoder'					,    'encoder_decoder_attention'   )
																					a__             =     layer_norm
																					a__             =     k.T
																					a__             =     o.T
																					a__             =     q.T
																					a__             =     v.T

																					# Block i, layer 2 (MLP).
																					a__             =     tax_layer_norm_lookup(__lowerCAmelCase					,    __lowerCAmelCase					,    'decoder'					,    'pre_mlp_layer_norm'   )
																					a__      ,					a__             =     tax_mlp_lookup(__lowerCAmelCase					,    __lowerCAmelCase					,    'decoder'					,    __lowerCAmelCase   )
																					a__             =     layer_norm
																					if split_mlp_wi:
																												a__             =     wi[0].T
																												a__             =     wi[1].T
																					else:
																												a__             =     wi.T
																					a__             =     wo.T

																					if scalable_attention:
																												# convert the rel_embedding of each layer
																												a__             =     tax_relpos_bias_lookup(__lowerCAmelCase					,    __lowerCAmelCase					,    'decoder'   ).T

														a__             =     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__             =     old['decoder/logits_dense/kernel'].T

							return new






def 							__lowercase   ( __lowerCAmelCase							:				Optional[int]					,    __lowerCAmelCase							:				bool   ):
							a__             =     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__             =     state_dict['shared.weight']

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					a__             =     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__             =     state_dict['shared.weight']

							return state_dict






def 							__lowercase   ( __lowerCAmelCase							:				Tuple					,    __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				Dict					,    __lowerCAmelCase							:				Union[str, Any]					,    __lowerCAmelCase							:				Union[str, Any]   ):
							a__             =     checkpoints.load_tax_checkpoint(__lowerCAmelCase   )
							a__             =     convert_tax_to_pytorch(
							    __lowerCAmelCase					,    num_layers=config.num_layers					,    is_encoder_only=__lowerCAmelCase					,    scalable_attention=__lowerCAmelCase   )
							a__             =     make_state_dict(__lowerCAmelCase					,    __lowerCAmelCase   )
							model.load_state_dict(__lowerCAmelCase					,    strict=__lowerCAmelCase   )






def 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				Optional[int]					,    __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				bool = False					,    __lowerCAmelCase							:				bool = False					,    ):
							a__             =     MTaConfig.from_json_file(__lowerCAmelCase   )
							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__             =     UMTaEncoderModel(__lowerCAmelCase   )
							else:
														a__             =     UMTaForConditionalGeneration(__lowerCAmelCase   )

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

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

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


if __name__ == "__main__":
						snake_case							:       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,
						)
						snake_case							:       Tuple     			=      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 							__lowercase   ( __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				float					,    __lowerCAmelCase							:				float   ):
							return round(float(moles / volume   ) * nfactor   )






def 							__lowercase   ( __lowerCAmelCase							:				float					,    __lowerCAmelCase							:				float					,    __lowerCAmelCase							:				float   ):
							return round(float((moles * 0.0_821 * temperature) / (volume)   )   )






def 							__lowercase   ( __lowerCAmelCase							:				float					,    __lowerCAmelCase							:				float					,    __lowerCAmelCase							:				float   ):
							return round(float((moles * 0.0_821 * temperature) / (pressure)   )   )






def 							__lowercase   ( __lowerCAmelCase							:				float					,    __lowerCAmelCase							:				float					,    __lowerCAmelCase							:				float   ):
							return round(float((pressure * volume) / (0.0_821 * moles)   )   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




