




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 lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE="attention" )			->							Tuple:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
			SCREAMING_SNAKE_CASE_								=						params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
			SCREAMING_SNAKE_CASE_								=						params[F'{prefix}/layers_{i}/{layer_name}/query/kernel']
			SCREAMING_SNAKE_CASE_								=						params[F'{prefix}/layers_{i}/{layer_name}/value/kernel']
			return k, o, q, v

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE=False )			->							Dict:


			'''simple docstring'''







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

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

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Optional[Any]:


			'''simple docstring'''







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

def lowercase    (  SCREAMING_SNAKE_CASE       ,    *, SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Any:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						traverse_util.flatten_dict(variables['target'] )
			SCREAMING_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
			SCREAMING_SNAKE_CASE_								=						'encoder/layers_0/mlp/wi_0/kernel' in old
			print('Split MLP:'       ,    SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE_								=						collections.OrderedDict()

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

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

						# Block i, layer 1 (MLP).
						SCREAMING_SNAKE_CASE_								=						tax_layer_norm_lookup(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    'encoder'       ,    'pre_mlp_layer_norm' )
						SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						tax_mlp_lookup(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    'encoder'       ,    SCREAMING_SNAKE_CASE )
						SCREAMING_SNAKE_CASE_								=						layer_norm
						if split_mlp_wi:
									SCREAMING_SNAKE_CASE_								=						wi[0].T
									SCREAMING_SNAKE_CASE_								=						wi[1].T
						else:
									SCREAMING_SNAKE_CASE_								=						wi.T
						SCREAMING_SNAKE_CASE_								=						wo.T

			SCREAMING_SNAKE_CASE_								=						old[
			    'encoder/relpos_bias/rel_embedding'
			].T
			SCREAMING_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).
									SCREAMING_SNAKE_CASE_								=						tax_layer_norm_lookup(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    'decoder'       ,    'pre_self_attention_layer_norm' )
									SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						tax_attention_lookup(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    'decoder'       ,    'self_attention' )
									SCREAMING_SNAKE_CASE_								=						layer_norm
									SCREAMING_SNAKE_CASE_								=						k.T
									SCREAMING_SNAKE_CASE_								=						o.T
									SCREAMING_SNAKE_CASE_								=						q.T
									SCREAMING_SNAKE_CASE_								=						v.T

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

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

						SCREAMING_SNAKE_CASE_								=						old['decoder/decoder_norm/scale']
						SCREAMING_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:
									SCREAMING_SNAKE_CASE_								=						old['decoder/logits_dense/kernel'].T

			return new

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Optional[Any]:


			'''simple docstring'''







			SCREAMING_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:
						SCREAMING_SNAKE_CASE_								=						state_dict['shared.weight']

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

			return state_dict

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Tuple:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						convert_tax_to_pytorch(SCREAMING_SNAKE_CASE       ,    num_layers=config.num_layers       ,    is_encoder_only=SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						make_state_dict(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
			model.load_state_dict(SCREAMING_SNAKE_CASE       ,    strict=SCREAMING_SNAKE_CASE )

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE = False )			->							Tuple:


			'''simple docstring'''







			SCREAMING_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:
						SCREAMING_SNAKE_CASE_								=						TaEncoderModel(SCREAMING_SNAKE_CASE )
			else:
						SCREAMING_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__     :     str		    =       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__     :     List[str]		    =       parser.parse_args()
				convert_tax_checkpoint_to_pytorch(
				    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
				)






def lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						len(SCREAMING_SNAKE_CASE )
			while cur > 1:
						# Find the maximum number in arr
						SCREAMING_SNAKE_CASE_								=						arr.index(max(arr[0:cur] ) )
						# Reverse from 0 to mi
						SCREAMING_SNAKE_CASE_								=						arr[mi::-1] + arr[mi + 1 : len(SCREAMING_SNAKE_CASE )]
						# Reverse whole list
						SCREAMING_SNAKE_CASE_								=						arr[cur - 1 :: -1] + arr[cur : len(SCREAMING_SNAKE_CASE )]
						cur -= 1
			return arr


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     List[str]		    =       input("Enter numbers separated by a comma:\n").strip()
				SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       [int(item) for item in user_input.split(",")]
				print(pancake_sort(unsorted))
