



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






def 							__lowercase     (				__lowerCAmelCase							:       Tuple      ,					__lowerCAmelCase							:       Optional[Any]      ,					__lowerCAmelCase							:       Optional[int]      ,					__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							:       Any      ,					__lowerCAmelCase							:       List[Any]      ,					__lowerCAmelCase							:       List[Any]      ,					__lowerCAmelCase							:       Tuple=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							:       Dict      ,					__lowerCAmelCase							:       Dict      ,					__lowerCAmelCase							:       Tuple      ,					__lowerCAmelCase							:       Optional[Any]     ):
							return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]






def 							__lowercase     (				__lowerCAmelCase							:       List[str]      ,					*, __lowerCAmelCase							:       Dict      ,					__lowerCAmelCase							:       Optional[int]      ,					__lowerCAmelCase							:       Optional[Any] = False     ):
							a__    =  traverse_util.flatten_dict(variables['target']     )
							a__    =  {'/'.join(a__     ): 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:'      ,					a__     )

							a__    =  collections.OrderedDict()

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

							# Encoder.
							for i in range(a__     ):
														# Block i, layer 0 (Self Attention).
														a__    =  tax_layer_norm_lookup(a__      ,					a__      ,					'encoder'      ,					'pre_attention_layer_norm'     )
														a__   ,      a__   ,      a__   ,      a__    =  tax_attention_lookup(a__      ,					a__      ,					'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(a__      ,					a__      ,					'encoder'      ,					'pre_mlp_layer_norm'     )
														a__   ,      a__    =  tax_mlp_lookup(a__      ,					a__      ,					'encoder'      ,					a__     )
														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(
																					    a__      ,					a__      ,					'encoder'     ).T

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

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

							if not is_encoder_only:
														# Decoder.
														for i in range(a__     ):
																					# Block i, layer 0 (Self Attention).
																					a__    =  tax_layer_norm_lookup(a__      ,					a__      ,					'decoder'      ,					'pre_self_attention_layer_norm'     )
																					a__   ,      a__   ,      a__   ,      a__    =  tax_attention_lookup(a__      ,					a__      ,					'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(a__      ,					a__      ,					'decoder'      ,					'pre_cross_attention_layer_norm'     )
																					a__   ,      a__   ,      a__   ,      a__    =  tax_attention_lookup(a__      ,					a__      ,					'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(a__      ,					a__      ,					'decoder'      ,					'pre_mlp_layer_norm'     )
																					a__   ,      a__    =  tax_mlp_lookup(a__      ,					a__      ,					'decoder'      ,					a__     )
																					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(a__      ,					a__      ,					'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							:       List[Any]      ,					__lowerCAmelCase							:       List[Any]     ):
							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							:       Union[str, Any]      ,					__lowerCAmelCase							:       Tuple      ,					__lowerCAmelCase							:       List[str]      ,					__lowerCAmelCase							:       Optional[Any]     ):
							a__    =  checkpoints.load_tax_checkpoint(a__     )
							a__    =  convert_tax_to_pytorch(
							    a__      ,					num_layers=config.num_layers      ,					is_encoder_only=a__      ,					scalable_attention=a__     )
							a__    =  make_state_dict(a__      ,					a__     )
							model.load_state_dict(a__      ,					strict=a__     )






def 							__lowercase     (				__lowerCAmelCase							:       Optional[int]      ,					__lowerCAmelCase							:       Union[str, Any]      ,					__lowerCAmelCase							:       Optional[Any]      ,					__lowerCAmelCase							:       Any = False      ,					__lowerCAmelCase							:       Tuple = False      ,					):
							a__    =  MTaConfig.from_json_file(a__     )
							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(a__     )
							else:
														a__    =  UMTaForConditionalGeneration(a__     )

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

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

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


if __name__ == "__main__":
						snake_case							: 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,
						)
						snake_case							: Union[str, 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,
						)








from math import pi






def 							__lowercase     (				__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       int     ):
							return 2 * pi * radius * (angle / 3_6_0)


if __name__ == "__main__":
						print(arc_length(90, 10))




