


import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
														# expert layer
														__snake_case		:      Union[str, Any]										=       flax_key_tuple[:-1] + ("weight",)
														__snake_case		:      str										=       torch.permute(__lowerCamelCase							,     (0, 2, 1)     )
							elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCamelCase     ):
														# linear layer
														__snake_case		:      int										=       flax_key_tuple[:-1] + ("weight",)
														__snake_case		:      str										=       flax_tensor.T
							elif flax_key_tuple[-1] in ["scale", "embedding"]:
														__snake_case		:      Optional[int]										=       flax_key_tuple[:-1] + ("weight",)

							return flax_key_tuple, flax_tensor







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							if "metadata" in layer:
														__snake_case		:      Union[str, Any]										=       layer.split("metadata"     )
														__snake_case		:      str										=       "".join(split_layer[0]     )[:-1]
														__snake_case		:      Optional[int]										=       [tuple(("metadata" + split_layer[1]).split("/"     )     )]
							elif "kvstore" in layer:
														__snake_case		:      List[Any]										=       layer.split("kvstore"     )
														__snake_case		:      List[Any]										=       "".join(split_layer[0]     )[:-1]
														__snake_case		:      Union[str, Any]										=       [tuple(("kvstore" + split_layer[1]).split("/"     )     )]

							else:
														__snake_case		:      int										=       layer.split("/"     )
														__snake_case		:      List[Any]										=       "/".join(split_layer[:-1]     )
														__snake_case		:      Tuple										=       (split_layer[-1],)

							if "kvstore/path" in layer:
														__snake_case		:      str										=       F'{switch_checkpoint_path}/{checkpoint_info[layer]}'
							elif "kvstore/driver" in layer:
														__snake_case		:      Optional[Any]										=       "file"
							else:
														__snake_case		:      str										=       checkpoint_info[layer]

							return curr_real_layer_name, split_layer, content







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      List[str]										=       rename_keys(__lowerCamelCase     )
							__snake_case		:      List[str]										=       {}
							for k, v in current_block.items():
														__snake_case		:      Union[str, Any]										=       v
							__snake_case		:      Any										=       new_current_block
							torch.save(__lowerCamelCase							,     __lowerCamelCase     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase = WEIGHTS_NAME     ):
							__snake_case		:      str										=       convert_file_size_to_int(__lowerCamelCase     )
							__snake_case		:      Union[str, Any]										=       []
							__snake_case		:      List[str]										=       {}
							__snake_case		:      Optional[int]										=       0
							__snake_case		:      int										=       0

							os.makedirs(__lowerCamelCase							,     exist_ok=__lowerCamelCase     )
							with gfile.GFile(switch_checkpoint_path + "/checkpoint"							,     "rb"     ) as fp:
														__snake_case		:      List[str]										=       serialization.msgpack_restore(fp.read()     )["optimizer"]["target"]
														__snake_case		:      str										=       flatten_dict(__lowerCamelCase							,     sep="/"     )

							__snake_case		:      int										=       {}
							for layer in checkpoint_info.keys():
														__snake_case			,      __snake_case			,      __snake_case		:      int										=       get_key_and_tensorstore_dict(
														    __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     )
														if curr_real_layer_name in all_layers:
																					__snake_case		:      Optional[Any]										=       content
														else:
																					__snake_case		:      List[Any]										=       {split_layer[-1]: content}

							for key in all_layers.keys():
														# open tensorstore file
														__snake_case		:      Any										=       ts.open(unflatten_dict(all_layers[key]     )     ).result().read().result()
														__snake_case		:      Dict										=       torch.tensor(__lowerCamelCase     )
														__snake_case		:      Any										=       raw_weights.numel() * dtype_byte_size(raw_weights.dtype     )

														# use the renaming pattern from the small conversion scripts
														__snake_case			,      __snake_case		:      List[str]										=       rename_base_flax_keys(tuple(key.split("/"     )     )							,     __lowerCamelCase     )
														__snake_case		:      Union[str, Any]										=       "/".join(__lowerCamelCase     )

														# If this weight is going to tip up over the maximal size, we split.
														if current_block_size + weight_size > max_shard_size:
																					__snake_case		:      Dict										=       os.path.join(
																					    __lowerCamelCase							,     weights_name.replace(".bin"							,     F'-{len(__lowerCamelCase     )+1:05d}-of-???.bin'     )     )
																					rename_and_save_block(__lowerCamelCase							,     __lowerCamelCase     )
																					sharded_state_dicts.append(current_block.keys()     )
																					del current_block
																					__snake_case		:      str										=       {}
																					__snake_case		:      int										=       0

														__snake_case		:      Dict										=       raw_weights.to(getattr(__lowerCamelCase							,     __lowerCamelCase     )     )
														current_block_size += weight_size
														total_size += weight_size

							# Add the last block
							__snake_case		:      Dict										=       os.path.join(__lowerCamelCase							,     weights_name.replace(".bin"							,     F'-{len(__lowerCamelCase     )+1:05d}-of-???.bin'     )     )
							rename_and_save_block(__lowerCamelCase							,     __lowerCamelCase     )
							sharded_state_dicts.append(current_block.keys()     )

							# If we only have one shard, we return it
							if len(__lowerCamelCase     ) == 1:
														return {weights_name: sharded_state_dicts[0]}, None

							# Otherwise, let's build the index
							__snake_case		:      Dict										=       {}
							__snake_case		:      Any										=       {}
							for idx, shard in enumerate(__lowerCamelCase     ):
														__snake_case		:      List[Any]										=       weights_name.replace(
														    ".bin"							,     F'-{idx+1:05d}-of-{len(__lowerCamelCase     ):05d}.bin'     )  # len(sharded_state_dicts):05d}
														__snake_case		:      List[Any]										=       os.path.join(__lowerCamelCase							,     weights_name.replace(".bin"							,     F'-{idx+1:05d}-of-???.bin'     )     )
														os.rename(__lowerCamelCase							,     os.path.join(__lowerCamelCase							,     __lowerCamelCase     )     )
														__snake_case		:      Optional[Any]										=       shard
														for key in shard:
																					__snake_case		:      List[str]										=       shard_file

    # Add the metadata
							__snake_case		:      Optional[Any]										=       {"total_size": total_size}
							__snake_case		:      int										=       {"metadata": metadata, "weight_map": weight_map}

							with open(os.path.join(__lowerCamelCase							,     __lowerCamelCase     )							,     "w"							,     encoding="utf-8"     ) as f:
														__snake_case		:      Dict										=       json.dumps(__lowerCamelCase							,     indent=2							,     sort_keys=__lowerCamelCase     ) + "\n"
														f.write(__lowerCamelCase     )

							return metadata, index


if __name__ == "__main__":
			_snake_case					:		Tuple      =    argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--switch_t5x_checkpoint_path",
			    default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600",
			    type=str,
			    required=False,
			    help="Path to a directory containing a folder per layer. Follows the original Google format.",
			)
			parser.add_argument("--max_shard_size", default="10GB", required=False, help="Max shard size")
			parser.add_argument("--dtype", default="bfloat16", type=str, required=False, help="dtype of the saved model")
			parser.add_argument(
			    "--pytorch_dump_folder_path",
			    default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted",
			    type=str,
			    required=False,
			    help="Path to the output pytorch model.",
			)
			_snake_case					:		Any      =    parser.parse_args()
			shard_on_the_fly(
			    args.switch_tax_checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.max_shard_size,
			    args.dtype,
			)







def     lowerCAmelCase_					( ):
							from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

							__snake_case		:      Any										=       SwitchTransformersConfig.from_pretrained("google/switch-base-8"     )
							config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted"     )
							__snake_case		:      Tuple										=       SwitchTransformersForConditionalGeneration.from_pretrained(
							    "/home/arthur_huggingface_co/transformers/switch_converted"							,     device_map="auto"     )

							__snake_case		:      str										=       TaTokenizer.from_pretrained("t5-small"     )
							__snake_case		:      Dict										=       "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

							__snake_case		:      List[str]										=       tokenizer(__lowerCamelCase							,     return_tensors="pt"     ).input_ids
							__snake_case		:      int										=       model.generate(__lowerCamelCase							,     decoder_start_token_id=0     )
							print(tokenizer.decode(out[0]     )     )





import numpy

class     a		:




							"""simple docstring"""


							def __init__(							self	:  str   ,     lowerCamelCase	:  numpy.ndarray   ,     lowerCamelCase	:  numpy.ndarray    )     ->   None:
														__snake_case		:      Any										=       input_array

														# Random initial weights are assigned where first argument is the
														# number of nodes in previous layer and second argument is the
														# number of nodes in the next layer.

														# Random initial weights are assigned.
														# self.input_array.shape[1] is used to represent number of nodes in input layer.
														# First hidden layer consists of 4 nodes.
														__snake_case		:      int										=       numpy.random.rand(
														    self.input_array.shape[1]   ,     4    )

														# Random initial values for the first hidden layer.
														# First hidden layer has 4 nodes.
														# Second hidden layer has 3 nodes.
														__snake_case		:      Optional[int]										=       numpy.random.rand(
														    4   ,     3    )

														# Random initial values for the second hidden layer.
														# Second hidden layer has 3 nodes.
														# Output layer has 1 node.
														__snake_case		:      int										=       numpy.random.rand(3   ,     1    )

														# Real output values provided.
														__snake_case		:      Optional[Any]										=       output_array

														# Predicted output values by the neural network.
														# Predicted_output array initially consists of zeroes.
														__snake_case		:      Optional[int]										=       numpy.zeros(output_array.shape    )

							def    __snake_case (							self	:  List[Any]    )     ->   numpy.ndarray:
														__snake_case		:      List[str]										=       sigmoid(
														    numpy.dot(self.input_array   ,     self.input_layer_and_first_hidden_layer_weights    )    )

														# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
														# connecting the first hidden set of nodes with the second hidden set of nodes.
														__snake_case		:      str										=       sigmoid(
														    numpy.dot(
														        self.layer_between_input_and_first_hidden_layer   ,     self.first_hidden_layer_and_second_hidden_layer_weights   ,     )    )

														# layer_between_second_hidden_layer_and_output is the layer connecting
														# second hidden layer with the output node.
														__snake_case		:      str										=       sigmoid(
														    numpy.dot(
														        self.layer_between_first_hidden_layer_and_second_hidden_layer   ,     self.second_hidden_layer_and_output_layer_weights   ,     )    )

														return self.layer_between_second_hidden_layer_and_output

							def    __snake_case (							self	:  Union[str, Any]    )     ->   None:
														__snake_case		:      Optional[int]										=       numpy.dot(
														    self.layer_between_first_hidden_layer_and_second_hidden_layer.T   ,     2
														    * (self.output_array - self.predicted_output)
														    * sigmoid_derivative(self.predicted_output    )   ,     )
														__snake_case		:      Dict										=       numpy.dot(
														    self.layer_between_input_and_first_hidden_layer.T   ,     numpy.dot(
														        2
														        * (self.output_array - self.predicted_output)
														        * sigmoid_derivative(self.predicted_output    )   ,     self.second_hidden_layer_and_output_layer_weights.T   ,     )
														    * sigmoid_derivative(
														        self.layer_between_first_hidden_layer_and_second_hidden_layer    )   ,     )
														__snake_case		:      Optional[Any]										=       numpy.dot(
														    self.input_array.T   ,     numpy.dot(
														        numpy.dot(
														            2
														            * (self.output_array - self.predicted_output)
														            * sigmoid_derivative(self.predicted_output    )   ,     self.second_hidden_layer_and_output_layer_weights.T   ,     )
														        * sigmoid_derivative(
														            self.layer_between_first_hidden_layer_and_second_hidden_layer    )   ,     self.first_hidden_layer_and_second_hidden_layer_weights.T   ,     )
														    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer    )   ,     )

														self.input_layer_and_first_hidden_layer_weights += (
														    updated_input_layer_and_first_hidden_layer_weights
														)
														self.first_hidden_layer_and_second_hidden_layer_weights += (
														    updated_first_hidden_layer_and_second_hidden_layer_weights
														)
														self.second_hidden_layer_and_output_layer_weights += (
														    updated_second_hidden_layer_and_output_layer_weights
														)

							def    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  numpy.ndarray   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  bool    )     ->   None:
														for iteration in range(1   ,     iterations + 1    ):
																					__snake_case		:      Any										=       self.feedforward()
																					self.back_propagation()
																					if give_loss:
																												__snake_case		:      str										=       numpy.mean(numpy.square(output - self.feedforward()    )    )
																												print(F'Iteration {iteration} Loss: {loss}'    )






							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  numpy.ndarray    )     ->   int:
														__snake_case		:      Any										=       input_arr

														__snake_case		:      List[str]										=       sigmoid(
														    numpy.dot(self.array   ,     self.input_layer_and_first_hidden_layer_weights    )    )

														__snake_case		:      List[Any]										=       sigmoid(
														    numpy.dot(
														        self.layer_between_input_and_first_hidden_layer   ,     self.first_hidden_layer_and_second_hidden_layer_weights   ,     )    )

														__snake_case		:      Any										=       sigmoid(
														    numpy.dot(
														        self.layer_between_first_hidden_layer_and_second_hidden_layer   ,     self.second_hidden_layer_and_output_layer_weights   ,     )    )

														return int(self.layer_between_second_hidden_layer_and_output > 0.6    )







def     lowerCAmelCase_					( __lowerCamelCase     ):
							return 1 / (1 + numpy.exp(-value     ))







def     lowerCAmelCase_					( __lowerCamelCase     ):
							return (value) * (1 - (value))







def     lowerCAmelCase_					( ):
							__snake_case		:      Dict										=       numpy.array(
							    (
							        [0, 0, 0],
							        [0, 0, 1],
							        [0, 1, 0],
							        [0, 1, 1],
							        [1, 0, 0],
							        [1, 0, 1],
							        [1, 1, 0],
							        [1, 1, 1],
							    )							,     dtype=numpy.floataa							,     )

							# True output values for the given input values.
							__snake_case		:      Union[str, Any]										=       numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])							,     dtype=numpy.floataa     )

							# Calling neural network class.
							__snake_case		:      int										=       TwoHiddenLayerNeuralNetwork(
							    input_array=__lowerCamelCase							,     output_array=__lowerCamelCase     )

							# Calling training function.
							# Set give_loss to True if you want to see loss in every iteration.
							neural_network.train(output=__lowerCamelCase							,     iterations=1_0							,     give_loss=__lowerCamelCase     )

							return neural_network.predict(numpy.array(([1, 1, 1])							,     dtype=numpy.floataa     )     )


if __name__ == "__main__":
			example()

