





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
UpperCAmelCase						:   int			  =     {
    '''/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  _SCREAMING_SNAKE_CASE				(   a  )      ->      Optional[Any]:
					# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
					# the original model
					__A	:						int       =				list(s_dict.keys()  )
					for key in keys:
										__A	:						Union[str, Any]       =				r'.*/layers_(\d+)'
										__A	:						Dict       =				key
										if re.match(a					,						a  ):
															__A	:						List[str]       =				re.sub(r'layers_(\d+)'					,						r'block/\1/layer'					,						a  )

										__A	:						List[Any]       =				r'(encoder|decoder)\/'

										if re.match(a					,						a  ):
															__A	:						Tuple       =				re.match(a					,						a  ).groups()
															if groups[0] == "encoder":
																				__A	:						Union[str, Any]       =				re.sub(r'/mlp/'					,						r'/1/mlp/'					,						a  )
																				__A	:						Any       =				re.sub(r'/pre_mlp_layer_norm/'					,						r'/1/layer_norm/'					,						a  )

															elif groups[0] == "decoder":
																				__A	:						Optional[int]       =				re.sub(r'/mlp/'					,						r'/2/mlp/'					,						a  )
																				__A	:						Optional[int]       =				re.sub(r'/pre_mlp_layer_norm/'					,						r'/2/layer_norm/'					,						a  )

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

										print(F"""{key} -> {new_key}"""  )
										__A	:						Dict       =				s_dict.pop(a  )

					if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
										__A	:						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:
										__A	:						List[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	:						Optional[int]       =				s_dict[key].shape[0]
															__A	:						Tuple       =				s_dict[key]
															for idx in range(a  ):
																				__A	:						str       =				expert_weihts[idx]
																				print(F"""{key} -> {key.replace("expert/"					,						"nested fstring"  )}"""  )

															s_dict.pop(a  )

					return s_dict


UpperCAmelCase						:   Tuple			  =     {
    '''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  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Optional[Any]:
					# Convert a google style config to the hugging face fromat
					import regex as re

					with open(a					,						'r'  ) as f:
										__A	:						List[str]       =				f.read()

					__A	:						int       =				re.findall(r'(.*) = ([0-9.]*)'					,						a  )
					__A	:						Optional[Any]       =				{}
					for param, value in regex_match:
										if param in GIN_TO_CONFIG_MAPPING and value != "":
															__A	:						str       =				float(a  ) if '.' in value else int(a  )

					__A	:						Any       =				re.findall(r'(.*activations) = \(\'(.*)\',\)'					,						a  )[0]
					__A	:						List[Any]       =				str(activation[1]  )

					__A	:						List[str]       =				num_experts
					__A	:						Union[str, Any]       =				SwitchTransformersConfig(**a  )
					return config







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a=None					,						a="./"					,						a=8  )      ->      List[Any]:
					# Initialise PyTorch model

					print(F"""Loading flax weights from : {flax_checkpoint_path}"""  )
					__A	:						List[Any]       =				checkpoints.load_tax_checkpoint(a  )

					if gin_file is not None:
										__A	:						str       =				convert_gin_to_config(a					,						a  )
					else:
										__A	:						int       =				SwitchTransformersConfig.from_pretrained(a  )

					__A	:						Union[str, Any]       =				SwitchTransformersForConditionalGeneration(a  )

					__A	:						Optional[Any]       =				flax_params['target']
					__A	:						Optional[int]       =				flatten_dict(a					,						sep='/'  )
					__A	:						Union[str, Any]       =				rename_keys(a  )
					__A	:						Any       =				unflatten_dict(a					,						sep='/'  )

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

					print(F"""Save PyTorch model to {pytorch_dump_path}"""  )
					pt_model.save_pretrained(a  )


if __name__ == "__main__":
				UpperCAmelCase						:   Tuple			  =     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''')
				UpperCAmelCase						:   Optional[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,
				)











from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A = None     ):
									if components is None:
														__A	:						int       =				[]
									__A	:						Tuple       =				list(_A     )



				def __len__(			self     ):
									return len(self.__components     )



				def __str__(			self     ):
									return "(" + ",".join(map(_A     ,					self.__components     )     ) + ")"



				def __add__(			self     ,					_A     ):
									__A	:						Optional[int]       =				len(self     )
									if size == len(_A     ):
														__A	:						Any       =				[self.__components[i] + other.component(_A     ) for i in range(_A     )]
														return Vector(_A     )
									else:
														raise Exception('must have the same size'     )



				def __sub__(			self     ,					_A     ):
									__A	:						Tuple       =				len(self     )
									if size == len(_A     ):
														__A	:						Union[str, Any]       =				[self.__components[i] - other.component(_A     ) for i in range(_A     )]
														return Vector(_A     )
									else:  # error case
														raise Exception('must have the same size'     )



				@overload
				def __mul__(			self     ,					_A     ):
									...



				@overload
				def __mul__(			self     ,					_A     ):
									...



				def __mul__(			self     ,					_A     ):
									if isinstance(_A     ,					(float, int)     ):
														__A	:						str       =				[c * other for c in self.__components]
														return Vector(_A     )
									elif isinstance(_A     ,					_A     ) and len(self     ) == len(_A     ):
														__A	:						Union[str, Any]       =				len(self     )
														__A	:						Dict       =				[self.__components[i] * other.component(_A     ) for i in range(_A     )]
														return sum(_A     )
									else:  # error case
														raise Exception('invalid operand!'     )



				def 					UpperCAmelCase_		(			self     ):
									return Vector(self.__components     )



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									if isinstance(_A     ,					_A     ) and -len(self.__components     ) <= i < len(self.__components     ):
														return self.__components[i]
									else:
														raise Exception('index out of range'     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									assert -len(self.__components     ) <= pos < len(self.__components     )
									__A	:						Optional[int]       =				value



				def 					UpperCAmelCase_		(			self     ):
									if len(self.__components     ) == 0:
														raise Exception('Vector is empty'     )
									__A	:						Optional[Any]       =				[c**2 for c in self.__components]
									return math.sqrt(sum(_A     )     )





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = False     ):
									__A	:						Optional[Any]       =				self * other
									__A	:						Optional[Any]       =				self.euclidean_length() * other.euclidean_length()
									if deg:
														return math.degrees(math.acos(num / den     )     )
									else:
														return math.acos(num / den     )







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Vector:
					assert isinstance(a					,						a  )
					return Vector([0] * dimension  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Vector:
					assert isinstance(a					,						a  ) and (isinstance(a					,						a  ))
					__A	:						Optional[Any]       =				[0] * dimension
					__A	:						Tuple       =				1
					return Vector(a  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      Vector:
					assert (
					    isinstance(a					,						a  )
					    and isinstance(a					,						a  )
					    and (isinstance(a					,						(int, float)  ))
					)
					return x * scalar + y







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      Vector:
					random.seed(a  )
					__A	:						str       =				[random.randint(a					,						a  ) for _ in range(a  )]
					return Vector(a  )





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A     ,					_A     ):
									__A	:						Optional[Any]       =				matrix
									__A	:						Dict       =				w
									__A	:						Optional[int]       =				h



				def __str__(			self     ):
									__A	:						Tuple       =				''
									for i in range(self.__height     ):
														ans += "|"
														for j in range(self.__width     ):
																			if j < self.__width - 1:
																								ans += str(self.__matrix[i][j]     ) + ","
																			else:
																								ans += str(self.__matrix[i][j]     ) + "|\n"
									return ans



				def __add__(			self     ,					_A     ):
									if self.__width == other.width() and self.__height == other.height():
														__A	:						Optional[Any]       =				[]
														for i in range(self.__height     ):
																			__A	:						Optional[Any]       =				[
																			    self.__matrix[i][j] + other.component(_A     ,					_A     )
																			    for j in range(self.__width     )
																			]
																			matrix.append(_A     )
														return Matrix(_A     ,					self.__width     ,					self.__height     )
									else:
														raise Exception('matrix must have the same dimension!'     )



				def __sub__(			self     ,					_A     ):
									if self.__width == other.width() and self.__height == other.height():
														__A	:						Tuple       =				[]
														for i in range(self.__height     ):
																			__A	:						str       =				[
																			    self.__matrix[i][j] - other.component(_A     ,					_A     )
																			    for j in range(self.__width     )
																			]
																			matrix.append(_A     )
														return Matrix(_A     ,					self.__width     ,					self.__height     )
									else:
														raise Exception('matrices must have the same dimension!'     )



				@overload
				def __mul__(			self     ,					_A     ):
									...



				@overload
				def __mul__(			self     ,					_A     ):
									...



				def __mul__(			self     ,					_A     ):
									if isinstance(_A     ,					_A     ):  # matrix-vector
														if len(_A     ) == self.__width:
																			__A	:						List[Any]       =				zero_vector(self.__height     )
																			for i in range(self.__height     ):
																								__A	:						List[str]       =				[
																								    self.__matrix[i][j] * other.component(_A     )
																								    for j in range(self.__width     )
																								]
																								ans.change_component(_A     ,					sum(_A     )     )
																			return ans
														else:
																			raise Exception(
																			    'vector must have the same size as the '
																			    'number of columns of the matrix!'     )
									elif isinstance(_A     ,					(int, float)     ):  # matrix-scalar
														__A	:						List[str]       =				[
														    [self.__matrix[i][j] * other for j in range(self.__width     )]
														    for i in range(self.__height     )
														]
														return Matrix(_A     ,					self.__width     ,					self.__height     )
									return None



				def 					UpperCAmelCase_		(			self     ):
									return self.__height



				def 					UpperCAmelCase_		(			self     ):
									return self.__width



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									if 0 <= x < self.__height and 0 <= y < self.__width:
														return self.__matrix[x][y]
									else:
														raise Exception('change_component: indices out of bounds'     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									if 0 <= x < self.__height and 0 <= y < self.__width:
														__A	:						int       =				value
									else:
														raise Exception('change_component: indices out of bounds'     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									if self.__height != self.__width:
														raise Exception('Matrix is not square'     )
									__A	:						List[str]       =				self.__matrix[:x] + self.__matrix[x + 1 :]
									for i in range(len(_A     )     ):
														__A	:						Optional[int]       =				minor[i][:y] + minor[i][y + 1 :]
									return Matrix(_A     ,					self.__width - 1     ,					self.__height - 1     ).determinant()



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									if self.__height != self.__width:
														raise Exception('Matrix is not square'     )
									if 0 <= x < self.__height and 0 <= y < self.__width:
														return (-1) ** (x + y) * self.minor(_A     ,					_A     )
									else:
														raise Exception('Indices out of bounds'     )





				def 					UpperCAmelCase_		(			self     ):
									if self.__height != self.__width:
														raise Exception('Matrix is not square'     )
									if self.__height < 1:
														raise Exception('Matrix has no element'     )
									elif self.__height == 1:
														return self.__matrix[0][0]
									elif self.__height == 2:
														return (
														    self.__matrix[0][0] * self.__matrix[1][1]
														    - self.__matrix[0][1] * self.__matrix[1][0]
														)
									else:
														__A	:						List[str]       =				[
														    self.__matrix[0][y] * self.cofactor(0     ,					_A     ) for y in range(self.__width     )
														]
														return sum(_A     )







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Matrix:
					__A	:						list[list[float]]       =				[[0] * n for _ in range(a  )]
					return Matrix(a					,						a					,						a  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						a  )      ->      Matrix:
					random.seed(a  )
					__A	:						list[list[float]]       =				[
					    [random.randint(a					,						a  ) for _ in range(a  )] for _ in range(a  )
					]
					return Matrix(a					,						a					,						a  )




