
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
lowercase__	: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					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		list(s_dict.keys()	)
					for key in keys:
										lowercase					=		R'''.*/layers_(\d+)'''
										lowercase					=		key
										if re.match(lowerCAmelCase__				,    lowerCAmelCase__	):
															lowercase					=		re.sub(R'''layers_(\d+)'''				,    R'''block/\1/layer'''				,    lowerCAmelCase__	)

										lowercase					=		R'''(encoder|decoder)\/'''

										if re.match(lowerCAmelCase__				,    lowerCAmelCase__	):
															lowercase					=		re.match(lowerCAmelCase__				,    lowerCAmelCase__	).groups()
															if groups[0] == "encoder":
																				lowercase					=		re.sub(R'''/mlp/'''				,    R'''/1/mlp/'''				,    lowerCAmelCase__	)
																				lowercase					=		re.sub(R'''/pre_mlp_layer_norm/'''				,    R'''/1/layer_norm/'''				,    lowerCAmelCase__	)

															elif groups[0] == "decoder":
																				lowercase					=		re.sub(R'''/mlp/'''				,    R'''/2/mlp/'''				,    lowerCAmelCase__	)
																				lowercase					=		re.sub(R'''/pre_mlp_layer_norm/'''				,    R'''/2/layer_norm/'''				,    lowerCAmelCase__	)

        # 2. Convert other classic mappings
										for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
															if old_key in new_key:
																				lowercase					=		new_key.replace(lowerCAmelCase__				,    lowerCAmelCase__	)

										print(f'{key} -> {new_key}'	)
										lowercase					=		s_dict.pop(lowerCAmelCase__	)

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

															s_dict.pop(lowerCAmelCase__	)

					return s_dict


lowercase__	:Dict										=					{
    "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					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					import regex as re

					with open(lowerCAmelCase__				,    '''r'''	) as f:
										lowercase					=		f.read()

					lowercase					=		re.findall(R'''(.*) = ([0-9.]*)'''				,    lowerCAmelCase__	)
					lowercase					=		{}
					for param, value in regex_match:
										if param in GIN_TO_CONFIG_MAPPING and value != "":
															lowercase					=		float(lowerCAmelCase__	) if '''.''' in value else int(lowerCAmelCase__	)

					lowercase					=		re.findall(R'''(.*activations) = \(\'(.*)\',\)'''				,    lowerCAmelCase__	)[0]
					lowercase					=		str(activation[1]	)

					lowercase					=		num_experts
					lowercase					=		SwitchTransformersConfig(**lowerCAmelCase__	)
					return config



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__=None				,    lowerCAmelCase__="./"				,    lowerCAmelCase__=8	):
					'''simple docstring'''
					print(f'Loading flax weights from : {flax_checkpoint_path}'	)
					lowercase					=		checkpoints.load_tax_checkpoint(lowerCAmelCase__	)

					if gin_file is not None:
										lowercase					=		convert_gin_to_config(lowerCAmelCase__				,    lowerCAmelCase__	)
					else:
										lowercase					=		SwitchTransformersConfig.from_pretrained(lowerCAmelCase__	)

					lowercase					=		SwitchTransformersForConditionalGeneration(lowerCAmelCase__	)

					lowercase					=		flax_params['''target''']
					lowercase					=		flatten_dict(lowerCAmelCase__				,    sep='''/'''	)
					lowercase					=		rename_keys(lowerCAmelCase__	)
					lowercase					=		unflatten_dict(lowerCAmelCase__				,    sep='''/'''	)

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

					print(f'Save PyTorch model to {pytorch_dump_path}'	)
					pt_model.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
							lowercase__	:str										=					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")
							lowercase__	:Dict										=					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,
							)



from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__ = 10**-10	):
					'''simple docstring'''
					lowercase					=		a
					while True:
										lowercase					=		Decimal(lowerCAmelCase__	) - (
										    Decimal(eval(lowerCAmelCase__	)	) / Decimal(eval(str(diff(lowerCAmelCase__	)	)	)	)  # noqa: S307
										)
										# This number dictates the accuracy of the answer
										if abs(eval(lowerCAmelCase__	)	) < precision:  # noqa: S307
															return float(lowerCAmelCase__	)


# Let's Execute
if __name__ == "__main__":
							# Find root of trigonometric function
							# Find value of pi
							print(F'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
							# Find root of polynomial
							print(F'The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}')
							# Find Square Root of 5
							print(F'The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}')
							# Exponential Roots
							print(F'The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}')




