'''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__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__="attention"      ):


						'''simple docstring'''






						_lowerCAmelCase										=_lowerCAmelCase										=np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]      )
						_lowerCAmelCase										=k_tmp.reshape(k_tmp.shape[0]			,      k_tmp.shape[1] * k_tmp.shape[2]      )
						_lowerCAmelCase										=np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]      )
						_lowerCAmelCase										=o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]			,      o_tmp.shape[2]      )
						_lowerCAmelCase										=np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]      )
						_lowerCAmelCase										=q_tmp.reshape(q_tmp.shape[0]			,      q_tmp.shape[1] * q_tmp.shape[2]      )
						_lowerCAmelCase										=np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]      )
						_lowerCAmelCase										=v_tmp.reshape(v_tmp.shape[0]			,      v_tmp.shape[1] * v_tmp.shape[2]      )
						return k, o, q, v



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__=False      ):


						'''simple docstring'''






						if split_mlp_wi:
												_lowerCAmelCase										=params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
												_lowerCAmelCase										=params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
												_lowerCAmelCase										=(wi_a, wi_a)
						else:
												_lowerCAmelCase										=params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

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



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__      ):


						'''simple docstring'''






						return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]



def 							UpperCamelCase__			(			a__			,      *, a__			,      a__			,      a__ = False      ):


						'''simple docstring'''






						_lowerCAmelCase										=traverse_util.flatten_dict(variables['target']      )
						_lowerCAmelCase										={'/'.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
						_lowerCAmelCase										='encoder/encoder/mlp/wi_0/kernel' in old
						print('Split MLP:'			,      a__      )

						_lowerCAmelCase										=collections.OrderedDict()

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

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

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

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

						if not scalable_attention:
												_lowerCAmelCase										=tax_relpos_bias_lookup(
												    a__			,      0			,      'encoder'      ).T
												_lowerCAmelCase										=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).
																		_lowerCAmelCase										=tax_layer_norm_lookup(a__			,      a__			,      'decoder'			,      'pre_self_attention_layer_norm'      )
																		_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=tax_attention_lookup(a__			,      a__			,      'decoder'			,      'self_attention'      )
																		_lowerCAmelCase										=layer_norm
																		_lowerCAmelCase										=k.T
																		_lowerCAmelCase										=o.T
																		_lowerCAmelCase										=q.T
																		_lowerCAmelCase										=v.T

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

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

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

												_lowerCAmelCase										=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										=old['decoder/logits_dense/kernel'].T

						return new



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=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										=state_dict['shared.weight']

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

						return state_dict



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=checkpoints.load_tax_checkpoint(a__      )
						_lowerCAmelCase										=convert_tax_to_pytorch(
						    a__			,      num_layers=config.num_layers			,      is_encoder_only=a__			,      scalable_attention=a__      )
						_lowerCAmelCase										=make_state_dict(a__			,      a__      )
						model.load_state_dict(a__			,      strict=a__      )



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__ = False			,      a__ = False			,      ):


						'''simple docstring'''






						_lowerCAmelCase										=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:
												_lowerCAmelCase										=UMTaEncoderModel(a__      )
						else:
												_lowerCAmelCase										=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__":
							lowercase_     		= 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_     		= 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'''

def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=set()
						# To detect a back edge, keep track of vertices currently in the recursion stack
						_lowerCAmelCase										=set()
						return any(
						    node not in visited and depth_first_search(a__			,      a__			,      a__			,      a__      )
						    for node in graph      )



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__      ):


						'''simple docstring'''






						visited.add(a__      )
						rec_stk.add(a__      )

						for node in graph[vertex]:
												if node not in visited:
																		if depth_first_search(a__			,      a__			,      a__			,      a__      ):
																								return True
												elif node in rec_stk:
																		return True

    # The node needs to be removed from recursion stack before function ends
						rec_stk.remove(a__      )
						return False


if __name__ == "__main__":
							from doctest import testmod

							testmod()





