



'''simple docstring'''


import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[Any] ,  lowercase_    :      Union[str, Any] ,  lowercase_    :      int ,  lowercase_    :      int="attention"						):
						lowercase        =							params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
						lowercase        =							params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
						lowercase        =							params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
						lowercase        =							params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
						return k, o, q, v







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[str] ,  lowercase_    :      int ,  lowercase_    :      Any ,  lowercase_    :      Optional[Any]=False						):
						if split_mlp_wi:
												lowercase        =							params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
												lowercase        =							params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
												lowercase        =							(wi_a, wi_a)
						else:
												lowercase        =							params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

						lowercase        =							params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
						return wi, wo







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Any ,  lowercase_    :      Union[str, Any] ,  lowercase_    :      Dict ,  lowercase_    :      int						):
						return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      dict ,  *, lowercase_    :      int ,  lowercase_    :      bool						):
						lowercase        =							traverse_util.flatten_dict(variables["""target"""]						)
						lowercase        =							{"""/""".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        =							"""encoder/layers_0/mlp/wi_0/kernel""" in old
						print("""Split MLP:""" ,  lowercase_						)

						lowercase        =							collections.OrderedDict()

						# Shared embeddings.
						lowercase        =							old["""token_embedder/embedding"""]

						# Encoder.
						for i in range(lowercase_						):
												# Block i, layer 0 (Self Attention).
												lowercase        =							tax_layer_norm_lookup(lowercase_ ,  lowercase_ ,  """encoder""" ,  """pre_attention_layer_norm"""						)
												lowercase     ,   lowercase     ,   lowercase     ,   lowercase        =							tax_attention_lookup(lowercase_ ,  lowercase_ ,  """encoder""" ,  """attention"""						)
												lowercase        =							layer_norm
												lowercase        =							k.T
												lowercase        =							o.T
												lowercase        =							q.T
												lowercase        =							v.T

												# Block i, layer 1 (MLP).
												lowercase        =							tax_layer_norm_lookup(lowercase_ ,  lowercase_ ,  """encoder""" ,  """pre_mlp_layer_norm"""						)
												lowercase     ,   lowercase        =							tax_mlp_lookup(lowercase_ ,  lowercase_ ,  """encoder""" ,  lowercase_						)
												lowercase        =							layer_norm
												if split_mlp_wi:
																		lowercase        =							wi[0].T
																		lowercase        =							wi[1].T
												else:
																		lowercase        =							wi.T
												lowercase        =							wo.T

						lowercase        =							old[
						    """encoder/relpos_bias/rel_embedding"""
						].T
						lowercase        =							old["""encoder/encoder_norm/scale"""]

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

																		# Block i, layer 1 (Cross Attention).
																		lowercase        =							tax_layer_norm_lookup(lowercase_ ,  lowercase_ ,  """decoder""" ,  """pre_cross_attention_layer_norm"""						)
																		lowercase     ,   lowercase     ,   lowercase     ,   lowercase        =							tax_attention_lookup(lowercase_ ,  lowercase_ ,  """decoder""" ,  """encoder_decoder_attention"""						)
																		lowercase        =							layer_norm
																		lowercase        =							k.T
																		lowercase        =							o.T
																		lowercase        =							q.T
																		lowercase        =							v.T

																		# Block i, layer 2 (MLP).
																		lowercase        =							tax_layer_norm_lookup(lowercase_ ,  lowercase_ ,  """decoder""" ,  """pre_mlp_layer_norm"""						)
																		lowercase     ,   lowercase        =							tax_mlp_lookup(lowercase_ ,  lowercase_ ,  """decoder""" ,  lowercase_						)
																		lowercase        =							layer_norm
																		if split_mlp_wi:
																								lowercase        =							wi[0].T
																								lowercase        =							wi[1].T
																		else:
																								lowercase        =							wi.T
																		lowercase        =							wo.T

												lowercase        =							old["""decoder/decoder_norm/scale"""]
												lowercase        =							old[
												    """decoder/relpos_bias/rel_embedding"""
												].T

												# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
												if "decoder/logits_dense/kernel" in old:
																		lowercase        =							old["""decoder/logits_dense/kernel"""].T

						return new







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Dict ,  lowercase_    :      bool						):
						lowercase        =							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        =							state_dict["""shared.weight"""]

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

						return state_dict







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[Any] ,  lowercase_    :      Tuple ,  lowercase_    :      int ,  lowercase_    :      Any						):
						lowercase        =							checkpoints.load_tax_checkpoint(lowercase_						)
						lowercase        =							convert_tax_to_pytorch(lowercase_ ,  num_layers=config.num_layers ,  is_encoder_only=lowercase_						)
						lowercase        =							make_state_dict(lowercase_ ,  lowercase_						)
						model.load_state_dict(lowercase_ ,  strict=lowercase_						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[Any] ,  lowercase_    :      str ,  lowercase_    :      List[Any] ,  lowercase_    :      bool = False						):
						lowercase        =							TaConfig.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        =							TaEncoderModel(lowercase_						)
						else:
												lowercase        =							TaForConditionalGeneration(lowercase_						)

						# Load weights from tf checkpoint
						load_tax_weights_in_ta(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__":
						lowercase_      :				Optional[int]   					=    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
						)
						lowercase_      :				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
						)







'''simple docstring'''


import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
lowercase_      :				Tuple   					=    logging.getLogger(__name__)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
						lowercase        =							git.Repo(search_parent_directories=lowercase_						)
						lowercase        =							{
						    """repo_id""": str(lowercase_						),
						    """repo_sha""": str(repo.head.object.hexsha						),
						    """repo_branch""": str(repo.active_branch						),
						}

						with open(os.path.join(lowercase_ ,  """git_log.json"""						) ,  """w"""						) as f:
												json.dump(lowercase_ ,  lowercase_ ,  indent=4						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
						if params.n_gpu <= 0:
												lowercase        =							0
												lowercase        =							-1
												lowercase        =							True
												lowercase        =							False
												return

						assert torch.cuda.is_available()

						logger.info("""Initializing GPUs"""						)
						if params.n_gpu > 1:
												assert params.local_rank != -1

												lowercase        =							int(os.environ["""WORLD_SIZE"""]						)
												lowercase        =							int(os.environ["""N_GPU_NODE"""]						)
												lowercase        =							int(os.environ["""RANK"""]						)

												# number of nodes / node ID
												lowercase        =							params.world_size // params.n_gpu_per_node
												lowercase        =							params.global_rank // params.n_gpu_per_node
												lowercase        =							True

												assert params.n_nodes == int(os.environ["""N_NODES"""]						)
												assert params.node_id == int(os.environ["""NODE_RANK"""]						)

						# local job (single GPU)
						else:
												assert params.local_rank == -1

												lowercase        =							1
												lowercase        =							0
												lowercase        =							0
												lowercase        =							0
												lowercase        =							1
												lowercase        =							1
												lowercase        =							False

						# sanity checks
						assert params.n_nodes >= 1
						assert 0 <= params.node_id < params.n_nodes
						assert 0 <= params.local_rank <= params.global_rank < params.world_size
						assert params.world_size == params.n_nodes * params.n_gpu_per_node

						# define whether this is the master process / if we are in multi-node distributed mode
						lowercase        =							params.node_id == 0 and params.local_rank == 0
						lowercase        =							params.n_nodes > 1

						# summary
						lowercase        =							F"""--- Global rank: {params.global_rank} - """
						logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes						)
						logger.info(PREFIX + """Node ID        : %i""" % params.node_id						)
						logger.info(PREFIX + """Local rank     : %i""" % params.local_rank						)
						logger.info(PREFIX + """World size     : %i""" % params.world_size						)
						logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node						)
						logger.info(PREFIX + """Master         : %s""" % str(params.is_master						)						)
						logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node						)						)
						logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu						)						)
						logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname()						)

						# set GPU device
						torch.cuda.set_device(params.local_rank						)

						# initialize multi-GPU
						if params.multi_gpu:
												logger.info("""Initializing PyTorch distributed"""						)
												torch.distributed.init_process_group(
												    init_method="""env://""" ,  backend="""nccl""" ,  )







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[Any]						):
						np.random.seed(args.seed						)
						torch.manual_seed(args.seed						)
						if args.n_gpu > 0:
												torch.cuda.manual_seed_all(args.seed						)


