





from __future__ import annotations

from typing import Any


def        A__						(							*SCREAMING_SNAKE_CASE_		) ->  list[list[int]]:
		if all(_check_not_integer(SCREAMING_SNAKE_CASE_		) for m in matrix_s		):
				for i in matrix_s[1:]:
						_verify_matrix_sizes(matrix_s[0]				,							SCREAMING_SNAKE_CASE_		)
				return [[sum(SCREAMING_SNAKE_CASE_		) for t in zip(*SCREAMING_SNAKE_CASE_		)] for m in zip(*SCREAMING_SNAKE_CASE_		)]
		raise TypeError('''Expected a matrix, got int/list instead'''		)


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  list[list[int]]:
		if (
		    _check_not_integer(SCREAMING_SNAKE_CASE_		)
		    and _check_not_integer(SCREAMING_SNAKE_CASE_		)
		    and _verify_matrix_sizes(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)
		):
				return [[i - j for i, j in zip(*SCREAMING_SNAKE_CASE_		)] for m in zip(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)]
		raise TypeError('''Expected a matrix, got int/list instead'''		)


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  list[list[float]]:
		return [[x * n for x in row] for row in matrix]


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  list[list[int]]:
		if _check_not_integer(SCREAMING_SNAKE_CASE_		) and _check_not_integer(SCREAMING_SNAKE_CASE_		):
				lowerCamelCase		, lowerCamelCase :			Optional[int]    		=_verify_matrix_sizes(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)

		if cols[0] != rows[1]:
				lowerCamelCase :			Optional[int]    		=(
				    '''Cannot multiply matrix of dimensions '''
				    F"({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})"
				)
				raise ValueError(SCREAMING_SNAKE_CASE_		)
		return [
		    [sum(m * n for m, n in zip(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)		) for j in zip(*SCREAMING_SNAKE_CASE_		)] for i in matrix_a
		]


def        A__						(							SCREAMING_SNAKE_CASE_		) ->  list[list[int]]:
		lowerCamelCase :			int    		=int(SCREAMING_SNAKE_CASE_		)
		return [[int(row == column		) for column in range(SCREAMING_SNAKE_CASE_		)] for row in range(SCREAMING_SNAKE_CASE_		)]


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ = True		) ->  list[list[int]] | map[list[int]]:
		if _check_not_integer(SCREAMING_SNAKE_CASE_		):
				if return_map:
						return map(SCREAMING_SNAKE_CASE_				,							zip(*SCREAMING_SNAKE_CASE_		)		)
				else:
						return list(map(SCREAMING_SNAKE_CASE_				,							zip(*SCREAMING_SNAKE_CASE_		)		)		)
		raise TypeError('''Expected a matrix, got int/list instead'''		)


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  list[list[int]]:
		lowerCamelCase :			Any    		=matrix[:row] + matrix[row + 1 :]
		return [row[:column] + row[column + 1 :] for row in minor]


def        A__						(							SCREAMING_SNAKE_CASE_		) ->  Any:
		if len(SCREAMING_SNAKE_CASE_		) == 1:
				return matrix[0][0]

		return sum(
		    x * determinant(minor(SCREAMING_SNAKE_CASE_				,							0				,							SCREAMING_SNAKE_CASE_		)		) * (-1) ** i
		    for i, x in enumerate(matrix[0]		)		)


def        A__						(							SCREAMING_SNAKE_CASE_		) ->  list[list[float]] | None:
		lowerCamelCase :			Optional[int]    		=determinant(SCREAMING_SNAKE_CASE_		)
		if det == 0:
				return None

		lowerCamelCase :			Any    		=[
		    [determinant(minor(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)		) for j in range(len(SCREAMING_SNAKE_CASE_		)		)]
		    for i in range(len(SCREAMING_SNAKE_CASE_		)		)
		]

		lowerCamelCase :			int    		=[
		    [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]		)]
		    for row in range(len(SCREAMING_SNAKE_CASE_		)		)
		]
		lowerCamelCase :			Any    		=list(transpose(SCREAMING_SNAKE_CASE_		)		)
		return scalar_multiply(SCREAMING_SNAKE_CASE_				,							1 / det		)


def        A__						(							SCREAMING_SNAKE_CASE_		) ->  bool:
		return not isinstance(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) and not isinstance(matrix[0]				,							SCREAMING_SNAKE_CASE_		)


def        A__						(							SCREAMING_SNAKE_CASE_		) ->  tuple[int, int]:
		return len(SCREAMING_SNAKE_CASE_		), len(matrix[0]		)


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  tuple[tuple[int, int], tuple[int, int]]:
		lowerCamelCase :			str    		=_shape(SCREAMING_SNAKE_CASE_		) + _shape(SCREAMING_SNAKE_CASE_		)
		if shape[0] != shape[3] or shape[1] != shape[2]:
				lowerCamelCase :			Optional[Any]    		=(
				    '''operands could not be broadcast together with shape '''
				    F"({shape[0], shape[1]}), ({shape[2], shape[3]})"
				)
				raise ValueError(SCREAMING_SNAKE_CASE_		)
		return (shape[0], shape[2]), (shape[1], shape[3])


def        A__						(							) ->  None:
		lowerCamelCase :			Tuple    		=[[1_2, 1_0], [3, 9]]
		lowerCamelCase :			Dict    		=[[3, 4], [7, 4]]
		lowerCamelCase :			Tuple    		=[[1_1, 1_2, 1_3, 1_4], [2_1, 2_2, 2_3, 2_4], [3_1, 3_2, 3_3, 3_4], [4_1, 4_2, 4_3, 4_4]]
		lowerCamelCase :			List[Any]    		=[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
		print(F"Add Operation, {add(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) = } \n"		)
		print(
		    F"Multiply Operation, {multiply(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) = } \n"				,							)
		print(F"Identity: {identity(5		)}\n"		)
		print(F"Minor of {matrix_c} = {minor(SCREAMING_SNAKE_CASE_				,							1				,							2		)} \n"		)
		print(F"Determinant of {matrix_b} = {determinant(SCREAMING_SNAKE_CASE_		)} \n"		)
		print(F"Inverse of {matrix_d} = {inverse(SCREAMING_SNAKE_CASE_		)}\n"		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()








import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  List[Any]:
		# Initialise PyTorch model
		lowerCamelCase :			Dict    		=FNetConfig.from_json_file(SCREAMING_SNAKE_CASE_		)
		print(F"Building PyTorch model from configuration: {config}"		)
		lowerCamelCase :			Tuple    		=FNetForPreTraining(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			List[Any]    		=restore_checkpoint(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			List[str]    		=checkpoint_dict['''target''']

		# Embeddings
		# Position IDs
		lowerCamelCase :			Optional[Any]    		=fnet_pretraining_model.state_dict()

		lowerCamelCase :			Tuple    		=state_dict['''fnet.embeddings.position_ids''']
		lowerCamelCase :			str    		={'''fnet.embeddings.position_ids''': position_ids}
		# Embedding Layers
		lowerCamelCase :			int    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']		)
		lowerCamelCase :			int    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''position''']['''embedding'''][0]		)
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''type''']['''embedding''']		)
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''kernel''']		).T
		lowerCamelCase :			List[Any]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''bias''']		)
		lowerCamelCase :			List[str]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''scale''']		)
		lowerCamelCase :			Tuple    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''bias''']		)

		# Encoder Layers
		for layer in range(config.num_hidden_layers		):
				lowerCamelCase :			int    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''mixing_layer_norm''']['''scale''']		)
				lowerCamelCase :			int    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''mixing_layer_norm''']['''bias''']		)

				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''intermediate''']['''kernel''']		).T
				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''intermediate''']['''bias''']		)

				lowerCamelCase :			str    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''output''']['''kernel''']		).T
				lowerCamelCase :			Tuple    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''output''']['''bias''']		)

				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''output_layer_norm''']['''scale''']		)
				lowerCamelCase :			Optional[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''output_layer_norm''']['''bias''']		)

		# Pooler Layers
		lowerCamelCase :			Any    		=torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''kernel''']		).T
		lowerCamelCase :			str    		=torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''bias''']		)

		# Masked LM Layers
		lowerCamelCase :			Optional[int]    		=torch.tensor(
		    pretrained_model_params['''predictions_dense''']['''kernel''']		).T
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''predictions_dense''']['''bias''']		)
		lowerCamelCase :			Optional[Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_layer_norm''']['''scale''']		)
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_layer_norm''']['''bias''']		)
		lowerCamelCase :			Optional[int]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']		)
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_output''']['''output_bias''']		)
		lowerCamelCase :			Tuple    		=torch.tensor(pretrained_model_params['''predictions_output''']['''output_bias''']		)

		# Seq Relationship Layers
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''classification''']['''output_kernel''']		)
		lowerCamelCase :			Any    		=torch.tensor(
		    pretrained_model_params['''classification''']['''output_bias''']		)

		# Load State Dict
		fnet_pretraining_model.load_state_dict(SCREAMING_SNAKE_CASE_		)

		# Save PreTrained
		print(F"Saving pretrained model to {save_path}"		)
		fnet_pretraining_model.save_pretrained(SCREAMING_SNAKE_CASE_		)


if __name__ == "__main__":
				snake_case_				=      argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
				)
				parser.add_argument(
				    '''--fnet_config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        '''The config json file corresponding to the pre-trained FNet model. \n'''
				        '''This specifies the model architecture.'''
				    ),
				)
				parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
				snake_case_				=      parser.parse_args()
				convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)

