
"""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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE="attention" )  ->     Optional[Any]:







							"""simple docstring"""


							__snake_case     			=       params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
							__snake_case     			=       params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
							__snake_case     			=       params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
							__snake_case     			=       params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
							return k, o, q, v
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=False )  ->     Union[str, Any]:







							"""simple docstring"""


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

							__snake_case     			=       params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
							return wi, wo
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    *, SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Tuple:







							"""simple docstring"""


							__snake_case     			=       traverse_util.flatten_dict(variables["target"] )
							__snake_case     			=       {"/".join(SCREAMING_SNAKE_CASE ): v for k, v in old.items()}

							# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
							__snake_case     			=       "encoder/layers_0/mlp/wi_0/kernel" in old
							print("Split MLP:"							,    SCREAMING_SNAKE_CASE )

							__snake_case     			=       collections.OrderedDict()

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

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

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

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

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

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

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

														__snake_case     			=       old["decoder/decoder_norm/scale"]
														__snake_case     			=       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:
																					__snake_case     			=       old["decoder/logits_dense/kernel"].T

							return new
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Union[str, Any]:







							"""simple docstring"""


							__snake_case     			=       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:
														__snake_case     			=       state_dict["shared.weight"]

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

							return state_dict
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Any:







							"""simple docstring"""


							__snake_case     			=       checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE )
							__snake_case     			=       convert_tax_to_pytorch(SCREAMING_SNAKE_CASE							,    num_layers=config.num_layers							,    is_encoder_only=SCREAMING_SNAKE_CASE )
							__snake_case     			=       make_state_dict(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							model.load_state_dict(SCREAMING_SNAKE_CASE							,    strict=SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = False )  ->     List[str]:







							"""simple docstring"""


							__snake_case     			=       TaConfig.from_json_file(SCREAMING_SNAKE_CASE )
							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:
														__snake_case     			=       TaEncoderModel(SCREAMING_SNAKE_CASE )
							else:
														__snake_case     			=       TaForConditionalGeneration(SCREAMING_SNAKE_CASE )

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

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

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


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      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
		)
		_SCREAMING_SNAKE_CASE	       =      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"""







from __future__ import annotations

from typing import Any
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							if not postfix_notation:
														return 0

							__snake_case     			=       {"+", "-", "*", "/"}
							__snake_case     			=       []

							for token in postfix_notation:
														if token in operations:
																					__snake_case			,   __snake_case     			=       stack.pop(), stack.pop()
																					if token == "+":
																												stack.append(a + b )
																					elif token == "-":
																												stack.append(a - b )
																					elif token == "*":
																												stack.append(a * b )
																					else:
																												if a * b < 0 and a % b != 0:
																																			stack.append(a // b + 1 )
																												else:
																																			stack.append(a // b )
														else:
																					stack.append(int(SCREAMING_SNAKE_CASE ) )

							return stack.pop()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
