"""simple docstring"""







import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
SCREAMING_SNAKE_CASE									=						{
    '/attention/': '/0/SelfAttention/',
    '/self_attention/': '/0/SelfAttention/',
    '/encoder_decoder_attention/': '/1/EncDecAttention/',
    'value': 'v',
    'query': 'q',
    'key': 'k',
    'out': 'o',
    'pre_self_attention_layer_norm': '0/layer_norm',
    'pre_cross_attention_layer_norm': '1/layer_norm',
    'pre_attention_layer_norm': '0/layer_norm',  # previously 1, but seems wrong
    'token_embedder': 'shared',
    'encoder_norm': 'final_layer_norm',
    'decoder_norm': 'final_layer_norm',
    'relpos_bias/rel_embedding': 'block/0/layer/0/SelfAttention/relative_attention_bias/weight',
    'router/router_weights/w/': 'router/classifier/',
    'roer/roer_weights/w/': 'router/classifier/',
    'logits_dense': 'lm_head',
}


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Dict							=							list(s_dict.keys()			)
	for key in keys:
		_lowercase :    List[Any]							=							R'.*/layers_(\d+)'
		_lowercase :    Dict							=							key
		if re.match(__UpperCAmelCase     ,__UpperCAmelCase			):
			_lowercase :    Any							=							re.sub(R'layers_(\d+)'     ,R'block/\1/layer'     ,__UpperCAmelCase			)

		_lowercase :    List[Any]							=							R'(encoder|decoder)\/'

		if re.match(__UpperCAmelCase     ,__UpperCAmelCase			):
			_lowercase :    str							=							re.match(__UpperCAmelCase     ,__UpperCAmelCase			).groups()
			if groups[0] == "encoder":
				_lowercase :    List[str]							=							re.sub(R'/mlp/'     ,R'/1/mlp/'     ,__UpperCAmelCase			)
				_lowercase :    List[Any]							=							re.sub(R'/pre_mlp_layer_norm/'     ,R'/1/layer_norm/'     ,__UpperCAmelCase			)

			elif groups[0] == "decoder":
				_lowercase :    Optional[Any]							=							re.sub(R'/mlp/'     ,R'/2/mlp/'     ,__UpperCAmelCase			)
				_lowercase :    Any							=							re.sub(R'/pre_mlp_layer_norm/'     ,R'/2/layer_norm/'     ,__UpperCAmelCase			)

        # 2. Convert other classic mappings
		for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
			if old_key in new_key:
				_lowercase :    List[Any]							=							new_key.replace(__UpperCAmelCase     ,__UpperCAmelCase			)

		print(F'''{key} -> {new_key}'''			)
		_lowercase :    Optional[int]							=							s_dict.pop(__UpperCAmelCase			)

	if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
		_lowercase :    Any							=							s_dict[
		    'encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
		].T
	if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
		_lowercase :    Tuple							=							s_dict[
		    'decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
		].T

	# 3. Take extra care of the EXPERTS layer
	for key in list(s_dict.keys()			):
		if "expert" in key:
			_lowercase :    Union[str, Any]							=							s_dict[key].shape[0]
			_lowercase :    List[Any]							=							s_dict[key]
			for idx in range(__UpperCAmelCase			):
				_lowercase :    List[str]							=							expert_weihts[idx]
				print(F'''{key} -> {key.replace('expert/'     ,'nested fstring'			)}'''			)

			s_dict.pop(__UpperCAmelCase			)

	return s_dict


SCREAMING_SNAKE_CASE									=						{
    'NUM_ENCODER_LAYERS': 'num_layers',
    'NUM_DECODER_LAYERS': 'num_decoder_layers',
    'NUM_HEADS': 'num_heads',
    'HEAD_DIM': 'd_kv',
    'EMBED_DIM': 'd_model',
    'MLP_DIM': 'd_ff',
    'NUM_SELECTED_EXPERTS': 'num_selected_experts',
    'NUM_ENCODER_SPARSE_LAYERS': 'num_sparse_encoder_layers',
    'NUM_DECODER_SPARSE_LAYERS': 'num_sparse_decoder_layers',
    'dense.MlpBlock.activations': 'feed_forward_proj',
}


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	import regex as re

	with open(__UpperCAmelCase     ,'r'			) as f:
		_lowercase :    Any							=							f.read()

	_lowercase :    Union[str, Any]							=							re.findall(R'(.*) = ([0-9.]*)'     ,__UpperCAmelCase			)
	_lowercase :    List[Any]							=							{}
	for param, value in regex_match:
		if param in GIN_TO_CONFIG_MAPPING and value != "":
			_lowercase :    Union[str, Any]							=							float(__UpperCAmelCase			) if '.' in value else int(__UpperCAmelCase			)

	_lowercase :    List[Any]							=							re.findall(R'(.*activations) = \(\'(.*)\',\)'     ,__UpperCAmelCase			)[0]
	_lowercase :    List[Any]							=							str(activation[1]			)

	_lowercase :    List[Any]							=							num_experts
	_lowercase :    List[str]							=							SwitchTransformersConfig(**__UpperCAmelCase			)
	return config


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase=None     ,__UpperCAmelCase="./"     ,__UpperCAmelCase=8			):






	"""simple docstring"""





	print(F'''Loading flax weights from : {flax_checkpoint_path}'''			)
	_lowercase :    str							=							checkpoints.load_tax_checkpoint(__UpperCAmelCase			)

	if gin_file is not None:
		_lowercase :    Tuple							=							convert_gin_to_config(__UpperCAmelCase     ,__UpperCAmelCase			)
	else:
		_lowercase :    Union[str, Any]							=							SwitchTransformersConfig.from_pretrained(__UpperCAmelCase			)

	_lowercase :    Any							=							SwitchTransformersForConditionalGeneration(__UpperCAmelCase			)

	_lowercase :    List[str]							=							flax_params['target']
	_lowercase :    Optional[Any]							=							flatten_dict(__UpperCAmelCase     ,sep='/'			)
	_lowercase :    Optional[int]							=							rename_keys(__UpperCAmelCase			)
	_lowercase :    List[Any]							=							unflatten_dict(__UpperCAmelCase     ,sep='/'			)

	# Load the flax params in the PT model
	load_flax_weights_in_pytorch_model(__UpperCAmelCase     ,__UpperCAmelCase			)

	print(F'''Save PyTorch model to {pytorch_dump_path}'''			)
	pt_model.save_pretrained(__UpperCAmelCase			)


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--switch_t5x_checkpoint_path',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        'The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'
						        ' model architecture. If not provided, a `gin_file` has to be provided.'
						    ),
						)
						parser.add_argument(
						    '--gin_file',
						    default=None,
						    type=str,
						    required=False,
						    help='Path to the gin config file. If not provided, a `config_file` has to be passed   ',
						)
						parser.add_argument(
						    '--config_name', default=None, type=str, required=False, help='Config name of SwitchTransformers model.'
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output pytorch model.'
						)
						parser.add_argument('--num_experts', default=8, type=int, required=False, help='Number of experts')
						SCREAMING_SNAKE_CASE									=						parser.parse_args()
						convert_flax_checkpoint_to_pytorch(
						    args.switch_tax_checkpoint_path,
						    args.config_name,
						    args.gin_file,
						    args.pytorch_dump_folder_path,
						    args.num_experts,
						)




"""simple docstring"""







import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  ""
				_snake_case     =  (
				    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
				)
				_snake_case     =  None  # compression type in fsspec. ex: "gzip"
				_snake_case     =  None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz







				def __init__( self    :       str			,					lowerCamelCase_    :       str = ""			,					lowerCamelCase_    :       Optional[str] = None			,					lowerCamelCase_    :       Optional[dict] = None			,					**lowerCamelCase_    :       List[str]						):

					"""simple docstring"""






					super().__init__(self			,					**lowerCamelCase_						)
					# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
					_lowercase :    Union[str, Any]							=							fsspec.open(
					    lowerCamelCase_			,					mode='rb'			,					protocol=lowerCamelCase_			,					compression=self.compression			,					client_kwargs={
					        'requote_redirect_url': False,  # see https://github.com/huggingface/datasets/pull/5459
					        'trust_env': True,  # Enable reading proxy env variables.
					        **(target_options or {}).pop('client_kwargs'			,					{}						),  # To avoid issues if it was already passed.
					    }			,					**(target_options or {})			,					)
					_lowercase :    str							=							os.path.basename(self.file.path.split('::'						)[0]						)
					_lowercase :    Optional[int]							=							(
					    self.compressed_name[: self.compressed_name.rindex('.'						)]
					    if '.' in self.compressed_name
					    else self.compressed_name
					)
					_lowercase :    str							=							None







				@classmethod
				def 							__UpperCAmelCase					( cls    :       int			,					lowerCamelCase_    :       List[str]						):

					"""simple docstring"""






					return super()._strip_protocol(lowerCamelCase_						).lstrip('/'						)







				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






					if self.dir_cache is None:
						_lowercase :    Tuple							=							{**self.file.fs.info(self.file.path						), 'name': self.uncompressed_name}
						_lowercase :    int							=							{f['name']: f}







				def 							__UpperCAmelCase					( self    :       List[str]			,					lowerCamelCase_    :       str						):

					"""simple docstring"""






					return self.file.open().read()







				def 							__UpperCAmelCase					( self    :       Dict			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       str = "rb"			,					lowerCamelCase_    :       str=None			,					lowerCamelCase_    :       str=True			,					lowerCamelCase_    :       Union[str, Any]=None			,					**lowerCamelCase_    :       Optional[int]			,					):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							self._strip_protocol(lowerCamelCase_						)
					if mode != "rb":
						raise ValueError(F'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\''''						)
					return self.file.open()



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  "bz2"
				_snake_case     =  "bz2"
				_snake_case     =  ".bz2"



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  "gzip"
				_snake_case     =  "gzip"
				_snake_case     =  ".gz"



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  "lz4"
				_snake_case     =  "lz4"
				_snake_case     =  ".lz4"



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  "xz"
				_snake_case     =  "xz"
				_snake_case     =  ".xz"



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  "zstd"
				_snake_case     =  "zstd"
				_snake_case     =  ".zst"







				def __init__( self    :       Union[str, Any]			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       str = "rb"			,					lowerCamelCase_    :       Optional[str] = None			,					lowerCamelCase_    :       Optional[dict] = None			,					lowerCamelCase_    :       int = DEFAULT_BLOCK_SIZE			,					**lowerCamelCase_    :       int			,					):

					"""simple docstring"""






					super().__init__(
					    fo=lowerCamelCase_			,					mode=lowerCamelCase_			,					target_protocol=lowerCamelCase_			,					target_options=lowerCamelCase_			,					block_size=lowerCamelCase_			,					**lowerCamelCase_			,					)
					# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
					#
					# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
					#     out.close = close
					# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
					#
					# see https://github.com/intake/filesystem_spec/issues/725
					_lowercase :    Any							=							self.file.__enter__



					class  _lowerCamelCase			:







									def __init__( self    :       Any			,					lowerCamelCase_    :       List[Any]						):

										"""simple docstring"""






										_lowercase :    Tuple							=							file_







									def __enter__( self    :       str						):

										"""simple docstring"""






										self._file.__enter__()
										return self







									def __exit__( self    :       Union[str, Any]			,					*lowerCamelCase_    :       Optional[Any]			,					**lowerCamelCase_    :       Union[str, Any]						):

										"""simple docstring"""






										self._file.__exit__(*lowerCamelCase_			,					**lowerCamelCase_						)







									def __iter__( self    :       Optional[int]						):

										"""simple docstring"""






										return iter(self._file						)







									def 							__UpperCAmelCase					( self    :       int						):

										"""simple docstring"""






										return next(self._file						)







									def __getattr__( self    :       List[str]			,					lowerCamelCase_    :       str						):

										"""simple docstring"""






										return getattr(self._file			,					lowerCamelCase_						)

					def fixed_enter(*lowerCamelCase_    :       List[Any]			,					**lowerCamelCase_    :       int						):
						return WrappedFile(_enter(*lowerCamelCase_			,					**lowerCamelCase_						)						)

					_lowercase :    Optional[int]							=							fixed_enter



