from typing import Any

class        UpperCamelCase						:





						def __init__(self			,     __UpperCamelCase						)				->				Dict:
												UpperCamelCase_		:							Any  =		data
												UpperCamelCase_		:							Optional[Any]  =		None





						def __repr__(self						)				->				Any:
												return f'''Node({self.data})'''


class        UpperCamelCase						:





						def __init__(self						)				->				Dict:
												UpperCamelCase_		:							Optional[Any]  =		None





						def __iter__(self						)				->				Optional[Any]:
												UpperCamelCase_		:							List[str]  =		self.head
												while node:
																		yield node.data
																		UpperCamelCase_		:							Tuple  =		node.next





						def __len__(self						)				->				Optional[int]:
												return sum(1 for _ in self						)





						def __repr__(self						)				->				int:
												return "->".join([str(__lowerCAmelCase						) for item in self]						)





						def __getitem__(self			,     __UpperCamelCase						)				->				Tuple:



												if not 0 <= index < len(self						):
																		raise ValueError("""list index out of range."""						)
												for i, node in enumerate(self						):
																		if i == index:
																								return node
												return None





						def __setitem__(self			,     __UpperCamelCase			,     __UpperCamelCase						)				->				int:



												if not 0 <= index < len(self						):
																		raise ValueError("""list index out of range."""						)
												UpperCamelCase_		:							int  =		self.head
												for _ in range(__lowerCAmelCase						):
																		UpperCamelCase_		:							Optional[Any]  =		current.next
												UpperCamelCase_		:							Optional[Any]  =		data





						def        A_							(self			,     __UpperCamelCase						)				->				Optional[Any]:
												self.insert_nth(len(self						)			,     __lowerCAmelCase						)





						def        A_							(self			,     __UpperCamelCase						)				->				Dict:
												self.insert_nth(0			,     __lowerCAmelCase						)





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase						)				->				Optional[Any]:



												if not 0 <= index <= len(self						):
																		raise IndexError("""list index out of range"""						)
												UpperCamelCase_		:							str  =		Node(__lowerCAmelCase						)
												if self.head is None:
																		UpperCamelCase_		:							Optional[Any]  =		new_node
												elif index == 0:
																		UpperCamelCase_		:							List[str]  =		self.head  # link new_node to head
																		UpperCamelCase_		:							Optional[Any]  =		new_node
												else:
																		UpperCamelCase_		:							List[Any]  =		self.head
																		for _ in range(index - 1						):
																								UpperCamelCase_		:							str  =		temp.next
																		UpperCamelCase_		:							str  =		temp.next
																		UpperCamelCase_		:							Union[str, Any]  =		new_node





						def        A_							(self						)				->				Dict:  # print every node data
												print(self						)





						def        A_							(self						)				->				Union[str, Any]:
												return self.delete_nth(0						)





						def        A_							(self						)				->				Dict:  # delete from tail
												return self.delete_nth(len(self						) - 1						)





						def        A_							(self			,     __UpperCamelCase = 0						)				->				Optional[int]:



												if not 0 <= index <= len(self						) - 1:  # test if index is valid
																		raise IndexError("""List index out of range."""						)
												UpperCamelCase_		:							Any  =		self.head  # default first node
												if index == 0:
																		UpperCamelCase_		:							Dict  =		self.head.next
												else:
																		UpperCamelCase_		:							Tuple  =		self.head
																		for _ in range(index - 1						):
																								UpperCamelCase_		:							Optional[int]  =		temp.next
																		UpperCamelCase_		:							List[Any]  =		temp.next
																		UpperCamelCase_		:							Optional[int]  =		temp.next.next
												return delete_node.data





						def        A_							(self						)				->				Optional[Any]:
												return self.head is None





						def        A_							(self						)				->				str:
												UpperCamelCase_		:							Union[str, Any]  =		None
												UpperCamelCase_		:							Union[str, Any]  =		self.head

												while current:
																		# Store the current node's next node.
																		UpperCamelCase_		:							Optional[Any]  =		current.next
																		# Make the current node's next point backwards
																		UpperCamelCase_		:							Tuple  =		prev
																		# Make the previous node be the current node
																		UpperCamelCase_		:							Union[str, Any]  =		current
																		# Make the current node the next node (to progress iteration)
																		UpperCamelCase_		:							str  =		next_node
												# Return prev in order to put the head at the end
												UpperCamelCase_		:							Optional[int]  =		prev






def     lowerCAmelCase_			(   ):
						UpperCamelCase_		:							Any  =		LinkedList()
						assert linked_list.is_empty() is True
						assert str(lowerCAmelCase__			) == ""

						try:
												linked_list.delete_head()
												raise AssertionError  # This should not happen.
						except IndexError:
												assert True  # This should happen.

						try:
												linked_list.delete_tail()
												raise AssertionError  # This should not happen.
						except IndexError:
												assert True  # This should happen.

						for i in range(10			):
												assert len(lowerCAmelCase__			) == i
												linked_list.insert_nth(lowerCAmelCase__		, i + 1			)
						assert str(lowerCAmelCase__			) == "->".join(str(lowerCAmelCase__			) for i in range(1		, 11			)			)

						linked_list.insert_head(0			)
						linked_list.insert_tail(11			)
						assert str(lowerCAmelCase__			) == "->".join(str(lowerCAmelCase__			) for i in range(0		, 12			)			)

						assert linked_list.delete_head() == 0
						assert linked_list.delete_nth(9			) == 10
						assert linked_list.delete_tail() == 11
						assert len(lowerCAmelCase__			) == 9
						assert str(lowerCAmelCase__			) == "->".join(str(lowerCAmelCase__			) for i in range(1		, 10			)			)

						assert all(linked_list[i] == i + 1 for i in range(0		, 9			)			) is True

						for i in range(0		, 9			):
												UpperCamelCase_		:							Any  =		-i
						assert all(linked_list[i] == -i for i in range(0		, 9			)			) is True

						linked_list.reverse()
						assert str(lowerCAmelCase__			) == "->".join(str(lowerCAmelCase__			) for i in range(-8		, 1			)			)






def     lowerCAmelCase_			(   ):
						UpperCamelCase_		:							Tuple  =		[
						    -9,
						    100,
						    Node(7734_5112			),
						    """dlrow olleH""",
						    7,
						    5555,
						    0,
						    -192.55_555,
						    """Hello, world!""",
						    77.9,
						    Node(10			),
						    None,
						    None,
						    12.20,
						]
						UpperCamelCase_		:							List[str]  =		LinkedList()

						for i in test_input:
												linked_list.insert_tail(lowerCAmelCase__			)

						# Check if it's empty or not
						assert linked_list.is_empty() is False
						assert (
						    str(lowerCAmelCase__			) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
						    "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
						)

						# Delete the head
						UpperCamelCase_		:							Tuple  =		linked_list.delete_head()
						assert result == -9
						assert (
						    str(lowerCAmelCase__			) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
						    "Hello, world!->77.9->Node(10)->None->None->12.2"
						)

						# Delete the tail
						UpperCamelCase_		:							Union[str, Any]  =		linked_list.delete_tail()
						assert result == 12.2
						assert (
						    str(lowerCAmelCase__			) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
						    "Hello, world!->77.9->Node(10)->None->None"
						)

						# Delete a node in specific location in linked list
						UpperCamelCase_		:							List[Any]  =		linked_list.delete_nth(10			)
						assert result is None
						assert (
						    str(lowerCAmelCase__			) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
						    "Hello, world!->77.9->Node(10)->None"
						)

						# Add a Node instance to its head
						linked_list.insert_head(Node("""Hello again, world!"""			)			)
						assert (
						    str(lowerCAmelCase__			)
						    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
						    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
						)

						# Add None to its tail
						linked_list.insert_tail(lowerCAmelCase__			)
						assert (
						    str(lowerCAmelCase__			)
						    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
						    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
						)

						# Reverse the linked list
						linked_list.reverse()
						assert (
						    str(lowerCAmelCase__			)
						    == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
						    "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
						)






def     lowerCAmelCase_			(   ):
						from doctest import testmod

						testmod()

						UpperCamelCase_		:							Optional[int]  =		LinkedList()
						linked_list.insert_head(input("""Inserting 1st at head """			).strip()			)
						linked_list.insert_head(input("""Inserting 2nd at head """			).strip()			)
						print("""\nPrint list:"""			)
						linked_list.print_list()
						linked_list.insert_tail(input("""\nInserting 1st at tail """			).strip()			)
						linked_list.insert_tail(input("""Inserting 2nd at tail """			).strip()			)
						print("""\nPrint list:"""			)
						linked_list.print_list()
						print("""\nDelete head"""			)
						linked_list.delete_head()
						print("""Delete tail"""			)
						linked_list.delete_tail()
						print("""\nPrint list:"""			)
						linked_list.print_list()
						print("""\nReverse linked list"""			)
						linked_list.reverse()
						print("""\nPrint list:"""			)
						linked_list.print_list()
						print("""\nString representation of linked list:"""			)
						print(lowerCAmelCase__			)
						print("""\nReading/changing Node data using indexing:"""			)
						print(f'''Element at Position 1: {linked_list[1]}'''			)
						UpperCamelCase_		:							Union[str, Any]  =		input("""Enter New Value: """			).strip()
						print("""New list:"""			)
						print(lowerCAmelCase__			)
						print(f'''length of linked_list is : {len(lowerCAmelCase__			)}'''			)


if __name__ == "__main__":
	main()







"""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__			(							lowerCAmelCase__	:Optional[int]   ,						lowerCAmelCase__	:Tuple   ,						lowerCAmelCase__	:Optional[int]   ,						lowerCAmelCase__	:Optional[int]="attention"						)					->       str:
      '''simple docstring'''


      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      UpperCAmelCase__			(							lowerCAmelCase__	:Union[str, Any]   ,						lowerCAmelCase__	:Optional[Any]   ,						lowerCAmelCase__	:Optional[Any]   ,						lowerCAmelCase__	:Union[str, Any]=False						)					->       str:
      '''simple docstring'''


      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      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:Tuple   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:int						)					->       Any:
      '''simple docstring'''


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


def      UpperCAmelCase__			(							lowerCAmelCase__	:dict   ,						*, lowerCAmelCase__	:int   ,						lowerCAmelCase__	:bool						)					->       List[str]:
      '''simple docstring'''


      lowercase								=      traverse_util.flatten_dict(variables["""target"""]						)
      lowercase								=      {"""/""".join(lowerCAmelCase__						): 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:"""   ,						lowerCAmelCase__						)

      lowercase								=      collections.OrderedDict()

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

      # Encoder.
      for i in range(lowerCAmelCase__						):
            # Block i, layer 0 (Self Attention).
            lowercase								=      tax_layer_norm_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""encoder"""   ,						"""pre_attention_layer_norm"""						)
            lowercase				,				lowercase				,				lowercase				,				lowercase								=      tax_attention_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""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(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""encoder"""   ,						"""pre_mlp_layer_norm"""						)
            lowercase				,				lowercase								=      tax_mlp_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""encoder"""   ,						lowerCAmelCase__						)
            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(lowerCAmelCase__						):
                  # Block i, layer 0 (Self Attention).
                  lowercase								=      tax_layer_norm_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						"""pre_self_attention_layer_norm"""						)
                  lowercase				,				lowercase				,				lowercase				,				lowercase								=      tax_attention_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""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(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						"""pre_cross_attention_layer_norm"""						)
                  lowercase				,				lowercase				,				lowercase				,				lowercase								=      tax_attention_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""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(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						"""pre_mlp_layer_norm"""						)
                  lowercase				,				lowercase								=      tax_mlp_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						lowerCAmelCase__						)
                  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      UpperCAmelCase__			(							lowerCAmelCase__	:List[Any]   ,						lowerCAmelCase__	:bool						)					->       int:
      '''simple docstring'''


      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      UpperCAmelCase__			(							lowerCAmelCase__	:Optional[Any]   ,						lowerCAmelCase__	:Optional[int]   ,						lowerCAmelCase__	:Optional[Any]   ,						lowerCAmelCase__	:List[Any]						)					->       Union[str, Any]:
      '''simple docstring'''


      lowercase								=      checkpoints.load_tax_checkpoint(lowerCAmelCase__						)
      lowercase								=      convert_tax_to_pytorch(lowerCAmelCase__   ,						num_layers=config.num_layers   ,						is_encoder_only=lowerCAmelCase__						)
      lowercase								=      make_state_dict(lowerCAmelCase__   ,						lowerCAmelCase__						)
      model.load_state_dict(lowerCAmelCase__   ,						strict=lowerCAmelCase__						)


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:Union[str, Any]   ,						lowerCAmelCase__	:bool = False						)					->       Tuple:
      '''simple docstring'''


      lowercase								=      TaConfig.from_json_file(lowerCAmelCase__						)
      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(lowerCAmelCase__						)
      else:
            lowercase								=      TaForConditionalGeneration(lowerCAmelCase__						)

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

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

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


if __name__ == "__main__":
       __lowerCAmelCase       :			List[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
       )
       __lowerCAmelCase       :			List[Any]   	=parser.parse_args()
       convert_tax_checkpoint_to_pytorch(
           args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
       )



