



'''simple docstring'''





from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

_UpperCamelCase											=		TypeVar('T')
_UpperCamelCase											=		TypeVar('U')






class    lowerCamelCase_       (							Generic[T, U]	):
  """simple docstring"""




  def __init__(					self :      int     ,      _a :      Union[str, Any]     ,      _a :      str  )   ->   List[Any]:
    __lowerCamelCase			:			Tuple       			=					key
    __lowerCamelCase			:			int       			=					val
    __lowerCamelCase			:			Tuple       			=					None
    __lowerCamelCase			:			Dict       			=					None




  def __repr__(					self :      Union[str, Any]  )   ->   str:
    return (
        f'Node: key: {self.key}, val: {self.val}, '
        f'has next: {bool(self.next  )}, has prev: {bool(self.prev  )}'
    )






class    lowerCamelCase_       (							Generic[T, U]	):
  """simple docstring"""




  def __init__(					self :      str  )   ->   None:
    __lowerCamelCase			:			Union[str, Any]       			=					DoubleLinkedListNode(_a     ,      _a  )
    __lowerCamelCase			:			int       			=					DoubleLinkedListNode(_a     ,      _a  )
    __lowerCamelCase			,__lowerCamelCase			:			Optional[int]       			=					self.rear, self.head




  def __repr__(					self :      Union[str, Any]  )   ->   str:
    __lowerCamelCase			:			List[Any]       			=					['DoubleLinkedList']
    __lowerCamelCase			:			Optional[int]       			=					self.head
    while node.next is not None:
      rep.append(str(_a  )  )
      __lowerCamelCase			:			int       			=					node.next
    rep.append(str(self.rear  )  )
    return ",\n    ".join(_a  )




  def 		_lowercase	(					self :      str     ,      _a :      int  )   ->   None:
    __lowerCamelCase			:			Optional[int]       			=					self.rear.prev

    # All nodes other than self.head are guaranteed to have non-None previous
    assert previous is not None

    __lowerCamelCase			:			Any       			=					node
    __lowerCamelCase			:			Any       			=					previous
    __lowerCamelCase			:			List[Any]       			=					node
    __lowerCamelCase			:			Tuple       			=					self.rear




  def 		_lowercase	(					self :      Any     ,      _a :      Union[str, Any]  )   ->   DoubleLinkedListNode[T, U] | None:

    if node.prev is None or node.next is None:
      return None

    __lowerCamelCase			:			Optional[Any]       			=					node.next
    __lowerCamelCase			:			List[str]       			=					node.prev
    __lowerCamelCase			:			Union[str, Any]       			=					None
    __lowerCamelCase			:			str       			=					None
    return node






class    lowerCamelCase_       (							Generic[T, U]	):
  """simple docstring"""




  a_				   ={}




  def __init__(					self :      Optional[int]     ,      _a :      Tuple  )   ->   str:
    __lowerCamelCase			:			List[str]       			=					DoubleLinkedList()
    __lowerCamelCase			:			Tuple       			=					capacity
    __lowerCamelCase			:			List[str]       			=					0
    __lowerCamelCase			:			str       			=					0
    __lowerCamelCase			:			int       			=					0
    __lowerCamelCase			:			Optional[int]       			=					{}




  def __repr__(					self :      Optional[int]  )   ->   str:
    return (
        f'CacheInfo(hits={self.hits}, misses={self.miss}, '
        f'capacity={self.capacity}, current size={self.num_keys})'
    )




  def __contains__(					self :      Optional[Any]     ,      _a :      Any  )   ->   bool:
    return key in self.cache




  def 		_lowercase	(					self :      Dict     ,      _a :      int  )   ->   U | None:
    # Note: pythonic interface would throw KeyError rather than return None

    if key in self.cache:
      self.hits += 1
      __lowerCamelCase			:			Union[str, Any]       			=					self.cache[key]
      __lowerCamelCase			:			Any       			=					self.list.remove(self.cache[key]  )
      assert node == value_node

      # node is guaranteed not None because it is in self.cache
      assert node is not None
      self.list.add(_a  )
      return node.val
    self.miss += 1
    return None




  def 		_lowercase	(					self :      Any     ,      _a :      List[str]     ,      _a :      Union[str, Any]  )   ->   None:

    if key not in self.cache:
      if self.num_keys >= self.capacity:
        # delete first node (oldest) when over capacity
        __lowerCamelCase			:			str       			=					self.list.head.next

        # guaranteed to have a non-None first node when num_keys > 0
        # explain to type checker via assertions
        assert first_node is not None
        assert first_node.key is not None
        assert (
            self.list.remove(_a  ) is not None
        )  # node guaranteed to be in list assert node.key is not None

        del self.cache[first_node.key]
        self.num_keys -= 1
      __lowerCamelCase			:			Optional[Any]       			=					DoubleLinkedListNode(_a     ,      _a  )
      self.list.add(self.cache[key]  )
      self.num_keys += 1

    else:
      # bump node to the end of the list, update value
      __lowerCamelCase			:			List[str]       			=					self.list.remove(self.cache[key]  )
      assert node is not None  # node guaranteed to be in list
      __lowerCamelCase			:			List[str]       			=					value
      self.list.add(_a  )




  @classmethod
  def 		_lowercase	(					cls :      int     ,      _a :      Dict = 128  )   ->   Callable[[Callable[[T], U]], Callable[..., U]]:

    def cache_decorator_inner(_a :      List[Any]  ) -> Callable[..., U]:
      def cache_decorator_wrapper(*_a :      Optional[Any]  ) -> U:
        if func not in cls.decorator_function_to_instance_map:
          __lowerCamelCase			:			List[Any]       			=					LRUCache(_a  )

        __lowerCamelCase			:			Dict       			=					cls.decorator_function_to_instance_map[func].get(args[0]  )
        if result is None:
          __lowerCamelCase			:			str       			=					func(*_a  )
          cls.decorator_function_to_instance_map[func].put(args[0]     ,      _a  )
        return result

      def cache_info() -> LRUCache[T, U]:
        return cls.decorator_function_to_instance_map[func]

      setattr(_a     ,      'cache_info'     ,      _a  )  # noqa: B010

      return cache_decorator_wrapper

    return cache_decorator_inner


if __name__ == "__main__":
   import doctest

   doctest.testmod()














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     (	a_				,	a_				,	a_				,	a_="attention"):
							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     (	a_				,	a_				,	a_				,	a_=False):
							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     (	a_				,	a_				,	a_				,	a_):
							return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']





def 		__UpperCAmelCase     (	a_				,	*, a_				,	a_):
							snake_case_           =				traverse_util.flatten_dict(variables['target'])
							snake_case_           =				{'/'.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
							snake_case_           =				'encoder/layers_0/mlp/wi_0/kernel' in old
							print('Split MLP:'				,	a_)

							snake_case_           =				collections.OrderedDict()

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

							# Encoder.
							for i in range(a_):
														# Block i, layer 0 (Self Attention).
														snake_case_           =				tax_layer_norm_lookup(a_				,	a_				,	'encoder'				,	'pre_attention_layer_norm')
														snake_case_     ,						snake_case_     ,						snake_case_     ,						snake_case_           =				tax_attention_lookup(a_				,	a_				,	'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(a_				,	a_				,	'encoder'				,	'pre_mlp_layer_norm')
														snake_case_     ,						snake_case_           =				tax_mlp_lookup(a_				,	a_				,	'encoder'				,	a_)
														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(a_):
																					# Block i, layer 0 (Self Attention).
																					snake_case_           =				tax_layer_norm_lookup(a_				,	a_				,	'decoder'				,	'pre_self_attention_layer_norm')
																					snake_case_     ,						snake_case_     ,						snake_case_     ,						snake_case_           =				tax_attention_lookup(a_				,	a_				,	'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(a_				,	a_				,	'decoder'				,	'pre_cross_attention_layer_norm')
																					snake_case_     ,						snake_case_     ,						snake_case_     ,						snake_case_           =				tax_attention_lookup(a_				,	a_				,	'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(a_				,	a_				,	'decoder'				,	'pre_mlp_layer_norm')
																					snake_case_     ,						snake_case_           =				tax_mlp_lookup(a_				,	a_				,	'decoder'				,	a_)
																					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     (	a_				,	a_):
							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     (	a_				,	a_				,	a_				,	a_):
							snake_case_           =				checkpoints.load_tax_checkpoint(a_)
							snake_case_           =				convert_tax_to_pytorch(a_				,	num_layers=config.num_layers				,	is_encoder_only=a_)
							snake_case_           =				make_state_dict(a_				,	a_)
							model.load_state_dict(a_				,	strict=a_)





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_ = False):
							snake_case_           =				TaConfig.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:
														snake_case_           =				TaEncoderModel(a_)
							else:
														snake_case_           =				TaForConditionalGeneration(a_)

							# Load weights from tf checkpoint
							load_tax_weights_in_ta(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
	)
	lowercase    				=   parser.parse_args()
	convert_tax_checkpoint_to_pytorch(
	    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
	)





