

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
A_	:			Optional[Any]  	=      {
    "/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 						UpperCamelCase (lowercase_:			List[str] )					->				Dict:
	# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
	# the original model
	A__							:   List[str]					       =		list(s_dict.keys() )
	for key in keys:
		A__							:   int					       =		R".*/layers_(\d+)"
		A__							:   List[str]					       =		key
		if re.match(_UpperCAmelCase   ,				_UpperCAmelCase ):
			A__							:   Optional[int]					       =		re.sub(r"""layers_(\d+)"""   ,				r"""block/\1/layer"""   ,				_UpperCAmelCase )

		A__							:   Any					       =		R"(encoder|decoder)\/"

		if re.match(_UpperCAmelCase   ,				_UpperCAmelCase ):
			A__							:   List[Any]					       =		re.match(_UpperCAmelCase   ,				_UpperCAmelCase ).groups()
			if groups[0] == "encoder":
				A__							:   Optional[Any]					       =		re.sub(r"""/mlp/"""   ,				r"""/1/mlp/"""   ,				_UpperCAmelCase )
				A__							:   List[Any]					       =		re.sub(r"""/pre_mlp_layer_norm/"""   ,				r"""/1/layer_norm/"""   ,				_UpperCAmelCase )

			elif groups[0] == "decoder":
				A__							:   List[Any]					       =		re.sub(r"""/mlp/"""   ,				r"""/2/mlp/"""   ,				_UpperCAmelCase )
				A__							:   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:
				A__							:   List[str]					       =		new_key.replace(_UpperCAmelCase   ,				_UpperCAmelCase )

		print(f"""{key} -> {new_key}""" )
		A__							:   Any					       =		s_dict.pop(_UpperCAmelCase )

	if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
		A__							:   Tuple					       =		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:
		A__							:   Optional[Any]					       =		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:
			A__							:   Any					       =		s_dict[key].shape[0]
			A__							:   str					       =		s_dict[key]
			for idx in range(_UpperCAmelCase ):
				A__							:   Optional[Any]					       =		expert_weihts[idx]
				print(f"""{key} -> {key.replace('expert/'   ,				'nested fstring' )}""" )

			s_dict.pop(_UpperCAmelCase )

	return s_dict


A_	:			Optional[Any]  	=      {
    "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 						UpperCamelCase (lowercase_:			int   ,				lowercase_:			int )					->				Dict:
	# Convert a google style config to the hugging face fromat
	import regex as re

	with open(_UpperCAmelCase   ,				"""r""" ) as f:
		A__							:   List[Any]					       =		f.read()

	A__							:   Union[str, Any]					       =		re.findall(r"""(.*) = ([0-9.]*)"""   ,				_UpperCAmelCase )
	A__							:   Dict					       =		{}
	for param, value in regex_match:
		if param in GIN_TO_CONFIG_MAPPING and value != "":
			A__							:   Tuple					       =		float(_UpperCAmelCase ) if "." in value else int(_UpperCAmelCase )

	A__							:   str					       =		re.findall(r"""(.*activations) = \(\'(.*)\',\)"""   ,				_UpperCAmelCase )[0]
	A__							:   int					       =		str(activation[1] )

	A__							:   int					       =		num_experts
	A__							:   List[str]					       =		SwitchTransformersConfig(**_UpperCAmelCase )
	return config

def 						UpperCamelCase (lowercase_:			Union[str, Any]   ,				lowercase_:			Optional[int]   ,				lowercase_:			Optional[int]=None   ,				lowercase_:			Union[str, Any]="./"   ,				lowercase_:			Dict=8 )					->				List[Any]:
	# Initialise PyTorch model

	print(f"""Loading flax weights from : {flax_checkpoint_path}""" )
	A__							:   Dict					       =		checkpoints.load_tax_checkpoint(_UpperCAmelCase )

	if gin_file is not None:
		A__							:   int					       =		convert_gin_to_config(_UpperCAmelCase   ,				_UpperCAmelCase )
	else:
		A__							:   int					       =		SwitchTransformersConfig.from_pretrained(_UpperCAmelCase )

	A__							:   Any					       =		SwitchTransformersForConditionalGeneration(_UpperCAmelCase )

	A__							:   str					       =		flax_params["target"]
	A__							:   Any					       =		flatten_dict(_UpperCAmelCase   ,				sep="""/""" )
	A__							:   Optional[Any]					       =		rename_keys(_UpperCAmelCase )
	A__							:   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__":
				A_	:			int  	=      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')
				A_	:			int  	=      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,
				)






import argparse
from collections import defaultdict

def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			Optional[int]   ,				lowercase_:			Optional[Any]   ,				lowercase_:			Union[str, Any]   ,				lowercase_:			Any )					->				int:
	A__							:   Optional[Any]					       =		f"""{file}_{class_name}_{test_name}"""
	done_test[_id] += 1

	with open(lowercase_   ,				"""r""" ) as f:
		A__							:   Union[str, Any]					       =		f.readlines()

	A__							:   str					       =		f"""class {class_name}("""
	A__							:   Optional[Any]					       =		f"""{4 * ' '}def {test_name}("""
	A__							:   Union[str, Any]					       =		f"""{8 * ' '}{correct_line.split()[0]}"""
	A__							:   Optional[int]					       =		f"""{16 * ' '}{correct_line.split()[0]}"""
	A__							:   int					       =		False
	A__							:   str					       =		False
	A__							:   Tuple					       =		False
	A__							:   Optional[int]					       =		False
	A__							:   Optional[Any]					       =		0
	A__							:   Dict					       =		0

	A__							:   List[str]					       =		[]
	for line in lines:
		if line.startswith(lowercase_ ):
			A__							:   Dict					       =		True
		elif in_class and line.startswith(lowercase_ ):
			A__							:   Optional[Any]					       =		True
		elif in_class and in_func and (line.startswith(lowercase_ ) or line.startswith(lowercase_ )):
			A__							:   Tuple					       =		len(line.split(correct_line.split()[0] )[0] )
			count += 1

			if count == done_test[_id]:
				A__							:   Any					       =		True

		if in_class and in_func and in_line:
			if ")" not in line:
				continue
			else:
				A__							:   Dict					       =		True

		if in_class and in_func and in_line and insert_line:
			new_lines.append(f"""{spaces * ' '}{correct_line}""" )
			A__							:   List[str]					       =		False
		else:
			new_lines.append(lowercase_ )

	with open(lowercase_   ,				"""w""" ) as f:
		for line in new_lines:
			f.write(lowercase_ )

def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			Optional[Any]=None )					->				Any:
	if fail is not None:
		with open(lowercase_   ,				"""r""" ) as f:
			A__							:   Dict					       =		{l.strip() for l in f.readlines()}
	else:
		A__							:   List[str]					       =		None

	with open(lowercase_   ,				"""r""" ) as f:
		A__							:   int					       =		f.readlines()

	A__							:   Union[str, Any]					       =		defaultdict(lowercase_ )
	for line in correct_lines:
		A__       ,       A__       ,       A__       ,       A__							:   Optional[int]					       =		line.split(""";""" )
		if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
			overwrite_file(lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_ )


if __name__ == "__main__":
	A_	:			Optional[Any]  	=      argparse.ArgumentParser()
	parser.add_argument('--correct_filename', help='filename of tests with expected result')
	parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
	A_	:			Optional[Any]  	=      parser.parse_args()

	main(args.correct_filename, args.fail_filename)



