
"""simple docstring"""




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 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''






					if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
										# expert layer
										lowercase						     =						flax_key_tuple[:-1] + ('''weight''',)
										lowercase						     =						torch.permute(lowerCAmelCase__			,      (0, 2, 1)		)
					elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCAmelCase__		):
										# linear layer
										lowercase						     =						flax_key_tuple[:-1] + ('''weight''',)
										lowercase						     =						flax_tensor.T
					elif flax_key_tuple[-1] in ["scale", "embedding"]:
										lowercase						     =						flax_key_tuple[:-1] + ('''weight''',)

					return flax_key_tuple, flax_tensor



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''






					if "metadata" in layer:
										lowercase						     =						layer.split('''metadata'''		)
										lowercase						     =						''''''.join(split_layer[0]		)[:-1]
										lowercase						     =						[tuple(('''metadata''' + split_layer[1]).split('''/'''		)		)]
					elif "kvstore" in layer:
										lowercase						     =						layer.split('''kvstore'''		)
										lowercase						     =						''''''.join(split_layer[0]		)[:-1]
										lowercase						     =						[tuple(('''kvstore''' + split_layer[1]).split('''/'''		)		)]

					else:
										lowercase						     =						layer.split('''/'''		)
										lowercase						     =						'''/'''.join(split_layer[:-1]		)
										lowercase						     =						(split_layer[-1],)

					if "kvstore/path" in layer:
										lowercase						     =						f'{switch_checkpoint_path}/{checkpoint_info[layer]}'
					elif "kvstore/driver" in layer:
										lowercase						     =						'''file'''
					else:
										lowercase						     =						checkpoint_info[layer]

					return curr_real_layer_name, split_layer, content



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						rename_keys(lowerCAmelCase__		)
					lowercase						     =						{}
					for k, v in current_block.items():
										lowercase						     =						v
					lowercase						     =						new_current_block
					torch.save(lowerCAmelCase__			,      lowerCAmelCase__		)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__ = WEIGHTS_NAME		):
					'''simple docstring'''
					lowercase						     =						convert_file_size_to_int(lowerCAmelCase__		)
					lowercase						     =						[]
					lowercase						     =						{}
					lowercase						     =						0
					lowercase						     =						0

					os.makedirs(lowerCAmelCase__			,      exist_ok=lowerCAmelCase__		)
					with gfile.GFile(switch_checkpoint_path + '''/checkpoint'''			,      '''rb'''		) as fp:
										lowercase						     =						serialization.msgpack_restore(fp.read()		)['''optimizer''']['''target''']
										lowercase						     =						flatten_dict(lowerCAmelCase__			,      sep='''/'''		)

					lowercase						     =						{}
					for layer in checkpoint_info.keys():
										lowercase     ,						lowercase     ,						lowercase						     =						get_key_and_tensorstore_dict(
										    lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		)
										if curr_real_layer_name in all_layers:
															lowercase						     =						content
										else:
															lowercase						     =						{split_layer[-1]: content}

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

										# use the renaming pattern from the small conversion scripts
										lowercase     ,						lowercase						     =						rename_base_flax_keys(tuple(key.split('''/'''		)		)			,      lowerCAmelCase__		)
										lowercase						     =						'''/'''.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:
															lowercase						     =						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
															lowercase						     =						{}
															lowercase						     =						0

										lowercase						     =						raw_weights.to(getattr(lowerCAmelCase__			,      lowerCAmelCase__		)		)
										current_block_size += weight_size
										total_size += weight_size

					# Add the last block
					lowercase						     =						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
					lowercase						     =						{}
					lowercase						     =						{}
					for idx, shard in enumerate(lowerCAmelCase__		):
										lowercase						     =						weights_name.replace(
										    '''.bin'''			,      f'-{idx+1:05d}-of-{len(lowerCAmelCase__		):05d}.bin'		)  # len(sharded_state_dicts):05d}
										lowercase						     =						os.path.join(lowerCAmelCase__			,      weights_name.replace('''.bin'''			,      f'-{idx+1:05d}-of-???.bin'		)		)
										os.rename(lowerCAmelCase__			,      os.path.join(lowerCAmelCase__			,      lowerCAmelCase__		)		)
										lowercase						     =						shard
										for key in shard:
															lowercase						     =						shard_file

    # Add the metadata
					lowercase						     =						{'''total_size''': total_size}
					lowercase						     =						{'''metadata''': metadata, '''weight_map''': weight_map}

					with open(os.path.join(lowerCAmelCase__			,      lowerCAmelCase__		)			,      '''w'''			,      encoding='''utf-8'''		) as f:
										lowercase						     =						json.dumps(lowerCAmelCase__			,      indent=2			,      sort_keys=lowerCAmelCase__		) + '''\n'''
										f.write(lowerCAmelCase__		)

					return metadata, index


if __name__ == "__main__":
							lowercase__		:Union[str, Any]			  =   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.",
							)
							lowercase__		:List[str]			  =   parser.parse_args()
							shard_on_the_fly(
							    args.switch_tax_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.max_shard_size,
							    args.dtype,
							)



def 	UpperCamelCase   (							):
					'''simple docstring'''
					from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

					lowercase						     =						SwitchTransformersConfig.from_pretrained('''google/switch-base-8'''		)
					config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted'''		)
					lowercase						     =						SwitchTransformersForConditionalGeneration.from_pretrained(
					    '''/home/arthur_huggingface_co/transformers/switch_converted'''			,      device_map='''auto'''		)

					lowercase						     =						TaTokenizer.from_pretrained('''t5-small'''		)
					lowercase						     =						'''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

					lowercase						     =						tokenizer(lowerCAmelCase__			,      return_tensors='''pt'''		).input_ids
					lowercase						     =						model.generate(lowerCAmelCase__			,      decoder_start_token_id=0		)
					print(tokenizer.decode(out[0]		)		)






import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						np.inf

					def set_batch_size(lowerCAmelCase__		) -> None:
										nonlocal batch_size
										if isinstance(lowerCAmelCase__			,      lowerCAmelCase__		):
															lowercase						     =						min(lowerCAmelCase__			,      config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS		)
										elif isinstance(lowerCAmelCase__			,      lowerCAmelCase__		):
															lowercase						     =						min(lowerCAmelCase__			,      config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS		)
										elif isinstance(lowerCAmelCase__			,      lowerCAmelCase__		) and feature.dtype == "binary":
															lowercase						     =						min(lowerCAmelCase__			,      config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS		)

					_visit(lowerCAmelCase__			,      lowerCAmelCase__		)

					return None if batch_size is np.inf else batch_size







class     lowercase		( SCREAMING_SNAKE_CASE__		):




							def __init__(    self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = False       ,A__ = False       ,A__ = None       ,**A__       ,):
												super().__init__(
												    A__       ,split=A__       ,features=A__       ,cache_dir=A__       ,keep_in_memory=A__       ,streaming=A__       ,num_proc=A__       ,**A__       ,)
												lowercase						     =						path_or_paths if isinstance(A__       ,A__) else {self.split: path_or_paths}
												lowercase						     =						_PACKAGED_DATASETS_MODULES['''parquet'''][1]
												lowercase						     =						Parquet(
												    cache_dir=A__       ,data_files=A__       ,features=A__       ,hash=A__       ,**A__       ,)





							def 			A__				(    self):
												# Build iterable dataset
												if self.streaming:
																	lowercase						     =						self.builder.as_streaming_dataset(split=self.split)
												# Build regular (map-style) dataset
												else:
																	lowercase						     =						None
																	lowercase						     =						None
																	lowercase						     =						None
																	lowercase						     =						None

																	self.builder.download_and_prepare(
																	    download_config=A__       ,download_mode=A__       ,verification_mode=A__       ,base_path=A__       ,num_proc=self.num_proc       ,)
																	lowercase						     =						self.builder.as_dataset(
																	    split=self.split       ,verification_mode=A__       ,in_memory=self.keep_in_memory)
												return dataset







class     lowercase		:




							def __init__(    self       ,A__       ,A__       ,A__ = None       ,**A__       ,):
												lowercase						     =						dataset
												lowercase						     =						path_or_buf
												lowercase						     =						batch_size or get_writer_batch_size(dataset.features)
												lowercase						     =						parquet_writer_kwargs




							def 			A__				(    self):
												lowercase						     =						self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

												if isinstance(self.path_or_buf       ,(str, bytes, os.PathLike)):
																	with open(self.path_or_buf       ,'''wb+''') as buffer:
																						lowercase						     =						self._write(file_obj=A__       ,batch_size=A__       ,**self.parquet_writer_kwargs)
												else:
																	lowercase						     =						self._write(file_obj=self.path_or_buf       ,batch_size=A__       ,**self.parquet_writer_kwargs)
												return written





							def 			A__				(    self       ,A__       ,A__       ,**A__):
												lowercase						     =						0
												lowercase						     =						parquet_writer_kwargs.pop('''path_or_buf'''       ,A__)
												lowercase						     =						self.dataset.features.arrow_schema

												lowercase						     =						pq.ParquetWriter(A__       ,schema=A__       ,**A__)

												for offset in logging.tqdm(
												    range(0       ,len(self.dataset)       ,A__)       ,unit='''ba'''       ,disable=not logging.is_progress_bar_enabled()       ,desc='''Creating parquet from Arrow format'''       ,):
																	lowercase						     =						query_table(
																	    table=self.dataset._data       ,key=slice(A__       ,offset + batch_size)       ,indices=self.dataset._indices if self.dataset._indices is not None else None       ,)
																	writer.write_table(A__)
																	written += batch.nbytes
												writer.close()
												return written




