





'''simple docstring'''




import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


a__					:			Optional[int]          =     NewType('''DataClass''', Any)
a__					:			Any          =     NewType('''DataClassType''', Any)




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->int:
	if isinstance(UpperCAmelCase_					,				UpperCAmelCase_							):
		return v
	if v.lower() in ("yes", "true", "t", "y", "1"):
		return True
	elif v.lower() in ("no", "false", "f", "n", "0"):
		return False
	else:
		raise ArgumentTypeError(
		    f'''Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).'''							)




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->Callable[[str], Any]:
	snake_case__	       =   {str(UpperCAmelCase_							): choice for choice in choices}
	return lambda UpperCAmelCase_							: str_to_choice.get(UpperCAmelCase_					,				UpperCAmelCase_							)




def 						__lowerCamelCase			(	*,
    UpperCAmelCase_ = None					,				UpperCAmelCase_ = None					,				UpperCAmelCase_ = dataclasses.MISSING					,				UpperCAmelCase_ = dataclasses.MISSING					,				UpperCAmelCase_ = None					,				**UpperCAmelCase_					,				)   ->dataclasses.Field:
	if metadata is None:
		# Important, don't use as default param in function signature because dict is mutable and shared across function calls
		snake_case__	       =   {}
	if aliases is not None:
		snake_case__	       =   aliases
	if help is not None:
		snake_case__	       =   help

	return dataclasses.field(metadata=UpperCAmelCase_					,				default=UpperCAmelCase_					,				default_factory=UpperCAmelCase_					,				**UpperCAmelCase_							)







class 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  42





	def __init__(		self      ,       UpperCamelCase_      ,       **UpperCamelCase_		)      ->		int:
		# To make the default appear when using --help
		if "formatter_class" not in kwargs:
			snake_case__	       =   ArgumentDefaultsHelpFormatter
		super().__init__(**UpperCamelCase_		)
		if dataclasses.is_dataclass(UpperCamelCase_		):
			snake_case__	       =   [dataclass_types]
		snake_case__	       =   list(UpperCamelCase_		)
		for dtype in self.dataclass_types:
			self._add_dataclass_arguments(UpperCamelCase_		)





	@staticmethod
	def   _snake_case   (		UpperCamelCase_      ,       UpperCamelCase_		)      ->		Optional[int]:
		snake_case__	       =   F'''--{field.name}'''
		snake_case__	       =   field.metadata.copy()
		# field.metadata is not used at all by Data Classes,
		# it is provided as a third-party extension mechanism.
		if isinstance(field.type      ,       UpperCamelCase_		):
			raise RuntimeError(
			    'Unresolved type detected, which should have been done with the help of '
			    '`typing.get_type_hints` method by default'		)

		snake_case__	       =   kwargs.pop('aliases'      ,       []		)
		if isinstance(UpperCamelCase_      ,       UpperCamelCase_		):
			snake_case__	       =   [aliases]

		snake_case__	       =   getattr(field.type      ,       '__origin__'      ,       field.type		)
		if origin_type is Union or (hasattr(UpperCamelCase_      ,       'UnionType'		) and isinstance(UpperCamelCase_      ,       types.UnionType		)):
			if str not in field.type.__args__ and (
			    len(field.type.__args__		) != 2 or type(UpperCamelCase_		) not in field.type.__args__
			):
				raise ValueError(
				    'Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'
				    ' the argument parser only supports one type per argument.'
				    F''' Problem encountered in field \'{field.name}\'.'''		)
			if type(UpperCamelCase_		) not in field.type.__args__:
				# filter `str` in Union
				snake_case__	       =   field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
				snake_case__	       =   getattr(field.type      ,       '__origin__'      ,       field.type		)
			elif bool not in field.type.__args__:
				# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
				snake_case__	       =   (
				    field.type.__args__[0] if isinstance(UpperCamelCase_      ,       field.type.__args__[1]		) else field.type.__args__[1]
				)
				snake_case__	       =   getattr(field.type      ,       '__origin__'      ,       field.type		)

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
		snake_case__	       =   {}
		if origin_type is Literal or (isinstance(field.type      ,       UpperCamelCase_		) and issubclass(field.type      ,       UpperCamelCase_		)):
			if origin_type is Literal:
				snake_case__	       =   field.type.__args__
			else:
				snake_case__	       =   [x.value for x in field.type]

			snake_case__	       =   make_choice_type_function(kwargs['choices']		)

			if field.default is not dataclasses.MISSING:
				snake_case__	       =   field.default
			else:
				snake_case__	       =   True
		elif field.type is bool or field.type == Optional[bool]:
			# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
			# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
			snake_case__	       =   copy(UpperCamelCase_		)

			# Hack because type=bool in argparse does not behave as we want.
			snake_case__	       =   string_to_bool
			if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
				# Default value is False if we have no default when of type bool.
				snake_case__	       =   False if field.default is dataclasses.MISSING else field.default
				# This is the value that will get picked if we don't include --field_name in any way
				snake_case__	       =   default
				# This tells argparse we accept 0 or 1 value after --field_name
				snake_case__	       =   '?'
				# This is the value that will get picked if we do --field_name (without value)
				snake_case__	       =   True
		elif isclass(UpperCamelCase_		) and issubclass(UpperCamelCase_      ,       UpperCamelCase_		):
			snake_case__	       =   field.type.__args__[0]
			snake_case__	       =   '+'
			if field.default_factory is not dataclasses.MISSING:
				snake_case__	       =   field.default_factory()
			elif field.default is dataclasses.MISSING:
				snake_case__	       =   True
		else:
			snake_case__	       =   field.type
			if field.default is not dataclasses.MISSING:
				snake_case__	       =   field.default
			elif field.default_factory is not dataclasses.MISSING:
				snake_case__	       =   field.default_factory()
			else:
				snake_case__	       =   True
		parser.add_argument(UpperCamelCase_      ,       *UpperCamelCase_      ,       **UpperCamelCase_		)

		# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
		# Order is important for arguments with the same destination!
		# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
		# here and we do not need those changes/additional keys.
		if field.default is True and (field.type is bool or field.type == Optional[bool]):
			snake_case__	       =   False
			parser.add_argument(F'''--no_{field.name}'''      ,       action='store_false'      ,       dest=field.name      ,       **UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Optional[int]:
		if hasattr(UpperCamelCase_      ,       '_argument_group_name'		):
			snake_case__	       =   self.add_argument_group(dtype._argument_group_name		)
		else:
			snake_case__	       =   self

		try:
			snake_case__	       =   get_type_hints(UpperCamelCase_		)
		except NameError:
			raise RuntimeError(
			    F'''Type resolution failed for {dtype}. Try declaring the class in global scope or '''
			    'removing line of `from __future__ import annotations` which opts in Postponed '
			    'Evaluation of Annotations (PEP 563)'		)
		except TypeError as ex:
			# Remove this block when we drop Python 3.9 support
			if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(UpperCamelCase_		):
				snake_case__	       =   '.'.join(map(UpperCamelCase_      ,       sys.version_info[:3]		)		)
				raise RuntimeError(
				    F'''Type resolution failed for {dtype} on Python {python_version}. Try removing '''
				    'line of `from __future__ import annotations` which opts in union types as '
				    '`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '
				    'support Python versions that lower than 3.10, you need to use '
				    '`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '
				    '`X | None`.'		) from ex
			raise

		for field in dataclasses.fields(UpperCamelCase_		):
			if not field.init:
				continue
			snake_case__	       =   type_hints[field.name]
			self._parse_dataclass_field(UpperCamelCase_      ,       UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_=None      ,       UpperCamelCase_=False      ,       UpperCamelCase_=True      ,       UpperCamelCase_=None      ,       UpperCamelCase_=None      ,       )      ->		Tuple[DataClass, ...]:

		if args_file_flag or args_filename or (look_for_args_file and len(sys.argv		)):
			snake_case__	       =   []

			if args_filename:
				args_files.append(Path(UpperCamelCase_		)		)
			elif look_for_args_file and len(sys.argv		):
				args_files.append(Path(sys.argv[0]		).with_suffix('.args'		)		)

			# args files specified via command line flag should overwrite default args files so we add them last
			if args_file_flag:
				# Create special parser just to extract the args_file_flag values
				snake_case__	       =   ArgumentParser()
				args_file_parser.add_argument(UpperCamelCase_      ,       type=UpperCamelCase_      ,       action='append'		)

				# Use only remaining args for further parsing (remove the args_file_flag)
				snake_case__       ,  snake_case__	       =   args_file_parser.parse_known_args(args=UpperCamelCase_		)
				snake_case__	       =   vars(UpperCamelCase_		).get(args_file_flag.lstrip('-'		)      ,       UpperCamelCase_		)

				if cmd_args_file_paths:
					args_files.extend([Path(UpperCamelCase_		) for p in cmd_args_file_paths]		)

			snake_case__	       =   []
			for args_file in args_files:
				if args_file.exists():
					file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
			snake_case__	       =   file_args + args if args is not None else file_args + sys.argv[1:]
		snake_case__       ,  snake_case__	       =   self.parse_known_args(args=UpperCamelCase_		)
		snake_case__	       =   []
		for dtype in self.dataclass_types:
			snake_case__	       =   {f.name for f in dataclasses.fields(UpperCamelCase_		) if f.init}
			snake_case__	       =   {k: v for k, v in vars(UpperCamelCase_		).items() if k in keys}
			for k in keys:
				delattr(UpperCamelCase_      ,       UpperCamelCase_		)
			snake_case__	       =   dtype(**UpperCamelCase_		)
			outputs.append(UpperCamelCase_		)
		if len(namespace.__dict__		) > 0:
			# additional namespace.
			outputs.append(UpperCamelCase_		)
		if return_remaining_strings:
			return (*outputs, remaining_args)
		else:
			if remaining_args:
				raise ValueError(F'''Some specified arguments are not used by the HfArgumentParser: {remaining_args}'''		)

			return (*outputs,)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_ = False		)      ->		Tuple[DataClass, ...]:
		snake_case__	       =   set(args.keys()		)
		snake_case__	       =   []
		for dtype in self.dataclass_types:
			snake_case__	       =   {f.name for f in dataclasses.fields(UpperCamelCase_		) if f.init}
			snake_case__	       =   {k: v for k, v in args.items() if k in keys}
			unused_keys.difference_update(inputs.keys()		)
			snake_case__	       =   dtype(**UpperCamelCase_		)
			outputs.append(UpperCamelCase_		)
		if not allow_extra_keys and unused_keys:
			raise ValueError(F'''Some keys are not used by the HfArgumentParser: {sorted(UpperCamelCase_		)}'''		)
		return tuple(UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_ = False		)      ->		Tuple[DataClass, ...]:
		with open(Path(UpperCamelCase_		)      ,       encoding='utf-8'		) as open_json_file:
			snake_case__	       =   json.loads(open_json_file.read()		)
		snake_case__	       =   self.parse_dict(UpperCamelCase_      ,       allow_extra_keys=UpperCamelCase_		)
		return tuple(UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_ = False		)      ->		Tuple[DataClass, ...]:
		snake_case__	       =   self.parse_dict(yaml.safe_load(Path(UpperCamelCase_		).read_text()		)      ,       allow_extra_keys=UpperCamelCase_		)
		return tuple(UpperCamelCase_		)










'''simple docstring'''




import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=None							)   ->List[str]:
	# set parameter of one layer
	assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
	snake_case__	       =   nn.Parameter(UpperCAmelCase_							)
	if bias is not None:
		assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
		snake_case__	       =   nn.Parameter(UpperCAmelCase_							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# set torch weights for 1-to-1 comparison
	snake_case__	       =   np.asarray(weights[0]							)
	snake_case__	       =   np.asarray(weights[1]							)
	snake_case__	       =   np.asarray(weights[2]							)

	set_param(
	    torch_layer.self_attention.query_key					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.self_attention.value					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.output.dense					,				torch.tensor(UpperCAmelCase_							).view(-1					,				UpperCAmelCase_							).contiguous().transpose(0					,				1							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# set torch weights for 1-to-1 comparison
	snake_case__	       =   np.asarray(weights[0]							)
	snake_case__	       =   np.asarray(weights[1]							)
	snake_case__	       =   np.asarray(weights[2]							)
	snake_case__	       =   np.asarray(weights[3]							)

	set_param(
	    torch_layer.self_attention.query					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.self_attention.key					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.self_attention.value					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.output.dense					,				torch.tensor(UpperCAmelCase_							).view(-1					,				UpperCAmelCase_							).contiguous().transpose(0					,				1							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Dict:
	# layernorm 1
	snake_case__	       =   weights[0][0][0]
	snake_case__	       =   np.asarray(layer_norm_a[0]							)
	snake_case__	       =   np.asarray(layer_norm_a[1]							)
	set_param(
	    torch_block.attention.layer_norm					,				torch.tensor(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# lsh weights + output
	snake_case__	       =   weights[0][1]
	if len(UpperCAmelCase_							) < 4:
		set_layer_weights_in_torch_lsh(UpperCAmelCase_					,				torch_block.attention					,				UpperCAmelCase_							)
	else:
		set_layer_weights_in_torch_local(UpperCAmelCase_					,				torch_block.attention					,				UpperCAmelCase_							)

	# intermediate weighs
	snake_case__	       =   weights[2][0][1][2]

	# Chunked Feed Forward
	if len(UpperCAmelCase_							) == 4:
		snake_case__	       =   intermediate_weights[2]

	# layernorm 2
	snake_case__	       =   np.asarray(intermediate_weights[0][0]							)
	snake_case__	       =   np.asarray(intermediate_weights[0][1]							)
	set_param(
	    torch_block.feed_forward.layer_norm					,				torch.tensor(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# intermediate dense
	snake_case__	       =   np.asarray(intermediate_weights[1][0]							)
	snake_case__	       =   np.asarray(intermediate_weights[1][1]							)
	set_param(
	    torch_block.feed_forward.dense.dense					,				torch.tensor(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)

	# intermediate out
	snake_case__	       =   np.asarray(intermediate_weights[4][0]							)
	snake_case__	       =   np.asarray(intermediate_weights[4][1]							)
	set_param(
	    torch_block.feed_forward.output.dense					,				torch.tensor(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Union[str, Any]:
	# reformer model
	snake_case__	       =   torch_model.reformer

	# word embeds
	snake_case__	       =   np.asarray(weights[1]							)
	set_param(
	    torch_model_reformer.embeddings.word_embeddings					,				torch.tensor(UpperCAmelCase_							)					,				)

	if isinstance(weights[3]					,				UpperCAmelCase_							):
		snake_case__	       =   torch_model_reformer.embeddings.position_embeddings
		for emb_idx in range(len(position_embeddings.weights							)							):
			snake_case__	       =   np.asarray(weights[3][emb_idx][0]							)
			assert (
			    position_embeddings.weights[emb_idx].shape == emb_weights.shape
			), f'''{position_embeddings[emb_idx]} emb does not match'''
			snake_case__	       =   nn.Parameter(torch.tensor(UpperCAmelCase_							)							)

	snake_case__	       =   weights[5]
	assert len(torch_model_reformer.encoder.layers							) * 4 == len(
	    UpperCAmelCase_							), "HF and trax model do not have the same number of layers"
	for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers							):
		snake_case__	       =   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
		set_block_weights_in_torch(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)

	# output layer norm
	snake_case__	       =   np.asarray(weights[7][0]							)
	snake_case__	       =   np.asarray(weights[7][1]							)
	set_param(
	    torch_model_reformer.encoder.layer_norm					,				torch.tensor(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# output embeddings
	snake_case__	       =   np.asarray(weights[9][0]							)
	snake_case__	       =   np.asarray(weights[9][1]							)
	set_param(
	    torch_model.lm_head.decoder					,				torch.tensor(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# Initialise PyTorch model
	snake_case__	       =   ReformerConfig.from_json_file(UpperCAmelCase_							)
	print(f'''Building PyTorch model from configuration: {config}'''							)
	snake_case__	       =   ReformerModelWithLMHead(UpperCAmelCase_							)

	with open(UpperCAmelCase_					,				'rb'							) as f:
		snake_case__	       =   pickle.load(UpperCAmelCase_							)['weights']

	set_model_weights_in_torch(UpperCAmelCase_					,				UpperCAmelCase_					,				config.hidden_size							)

	# Save pytorch-model
	print(f'''Save PyTorch model to {pytorch_dump_path}'''							)
	torch.save(model.state_dict()					,				UpperCAmelCase_							)


if __name__ == "__main__":
			a__					:			Optional[int]          =     argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained Reformer model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			a__					:			Dict          =     parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



