

import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


_UpperCAmelCase		:   str										=       datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
					import pyspark







@dataclass
class  lowercase				(       datasets.BuilderConfig			):
				__SCREAMING_SNAKE_CASE					: Optional[datasets.Features]       			=			None





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							):







							'''simple docstring'''






							import pyspark

							def generate_fn():
														snake_case_							=    df.select('*'   ,							pyspark.sql.functions.spark_partition_id().alias('part_id'      )      )
														for partition_id in partition_order:
																					snake_case_							=    df_with_partition_id.select('*'      ).where(F'''part_id = {partition_id}'''      ).drop('part_id'      )
																					snake_case_							=    partition_df.collect()
																					snake_case_							=    0
																					for row in rows:
																												yield F'''{partition_id}_{row_id}''', row.asDict()
																												row_id += 1

							return generate_fn







class  lowercase				(       _BaseExamplesIterable			):




				def __init__(					self      ,   snake_case      ,   snake_case=None      ,   ):
											snake_case_							=    df
											snake_case_							=    partition_order or range(self.df.rdd.getNumPartitions()       )
											snake_case_							=    _generate_iterable_examples(self.df      ,   self.partition_order       )




				def __iter__(					self       ):
											yield from self.generate_examples_fn()




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    list(range(self.df.rdd.getNumPartitions()       )       )
											generator.shuffle(snake_case       )
											return SparkExamplesIterable(self.df      ,   partition_order=snake_case       )




				def     a							(					self      ,   snake_case      ,   snake_case       ):
											snake_case_							=    self.split_shard_indices_by_worker(snake_case      ,   snake_case       )
											return SparkExamplesIterable(self.df      ,   partition_order=snake_case       )




				@property
				def     a							(					self       ):
											return len(self.partition_order       )







class  lowercase				(       datasets.DatasetBuilder			):
				__SCREAMING_SNAKE_CASE					: Dict       			=			SparkConfig




				def __init__(					self      ,   snake_case      ,   snake_case = None      ,   snake_case = None      ,   **snake_case      ,   ):
											import pyspark

											snake_case_							=    pyspark.sql.SparkSession.builder.getOrCreate()
											snake_case_							=    df
											snake_case_							=    working_dir

											super().__init__(
											    cache_dir=snake_case      ,   config_name=str(self.df.semanticHash()       )      ,   **snake_case      ,   )




				def     a							(					self       ):
											# Returns the path of the created file.
											def create_cache_and_write_probe(snake_case       ):
																		# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
																		# already exist.
																		os.makedirs(self._cache_dir      ,   exist_ok=snake_case       )
																		snake_case_							=    os.path.join(self._cache_dir      ,   'fs_test' + uuid.uuida().hex       )
																		# Opening the file in append mode will create a new file unless it already exists, in which case it will not
																		# change the file contents.
																		open(snake_case      ,   'a'       )
																		return [probe_file]

											if self._spark.conf.get('spark.master'      ,   ''       ).startswith('local'       ):
																		return

											# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
											# accessible to the driver.
											# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
											if self._cache_dir:
																		snake_case_							=    (
																		    self._spark.sparkContext.parallelize(range(1       )      ,   1       ).mapPartitions(snake_case       ).collect()
																		)
																		if os.path.isfile(probe[0]       ):
																									return

											raise ValueError(
											    'When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir'       )




				def     a							(					self       ):
											return datasets.DatasetInfo(features=self.config.features       )




				def     a							(					self      ,   snake_case       ):
											return [datasets.SplitGenerator(name=datasets.Split.TRAIN       )]




				def     a							(					self      ,   snake_case       ):
											import pyspark

											def get_arrow_batch_size(snake_case       ):
																		for batch in it:
																									yield pa.RecordBatch.from_pydict({'batch_bytes': [batch.nbytes]}       )

											snake_case_							=    self.df.count()
											snake_case_							=    df_num_rows if df_num_rows <= 100 else 100
											# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
											snake_case_							=    (
											    self.df.limit(snake_case       )
											    .repartition(1       )
											    .mapInArrow(snake_case      ,   'batch_bytes: long'       )
											    .agg(pyspark.sql.functions.sum('batch_bytes'       ).alias('sample_bytes'       )       )
											    .collect()[0]
											    .sample_bytes
											    / sample_num_rows
											)
											snake_case_							=    approx_bytes_per_row * df_num_rows
											if approx_total_size > max_shard_size:
																		# Make sure there is at least one row per partition.
																		snake_case_							=    min(snake_case      ,   int(approx_total_size / max_shard_size       )       )
																		snake_case_							=    self.df.repartition(snake_case       )




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   ):
											import pyspark

											snake_case_							=    ParquetWriter if file_format == 'parquet' else ArrowWriter
											snake_case_							=    os.path.join(self._working_dir      ,   os.path.basename(snake_case       )       ) if self._working_dir else fpath
											snake_case_							=    file_format == 'parquet'

											# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
											# pickling the SparkContext.
											snake_case_							=    self.config.features
											snake_case_							=    self._writer_batch_size
											snake_case_							=    self._fs.storage_options

											def write_arrow(snake_case       ):
																		# Within the same SparkContext, no two task attempts will share the same attempt ID.
																		snake_case_							=    pyspark.TaskContext().taskAttemptId()
																		snake_case_							=    next(snake_case      ,   snake_case       )
																		if first_batch is None:
																									# Some partitions might not receive any data.
																									return pa.RecordBatch.from_arrays(
																									    [[task_id], [0], [0]]      ,   names=['task_id', 'num_examples', 'num_bytes']      ,   )
																		snake_case_							=    0
																		snake_case_							=    writer_class(
																		    features=snake_case      ,   path=working_fpath.replace('SSSSS'      ,   F'''{shard_id:05d}'''       ).replace('TTTTT'      ,   F'''{task_id:05d}'''       )      ,   writer_batch_size=snake_case      ,   storage_options=snake_case      ,   embed_local_files=snake_case      ,   )
																		snake_case_							=    pa.Table.from_batches([first_batch]       )
																		writer.write_table(snake_case       )
																		for batch in it:
																									if max_shard_size is not None and writer._num_bytes >= max_shard_size:
																																snake_case_				,    snake_case_							=    writer.finalize()
																																writer.close()
																																yield pa.RecordBatch.from_arrays(
																																    [[task_id], [num_examples], [num_bytes]]      ,   names=['task_id', 'num_examples', 'num_bytes']      ,   )
																																shard_id += 1
																																snake_case_							=    writer_class(
																																    features=writer._features      ,   path=working_fpath.replace('SSSSS'      ,   F'''{shard_id:05d}'''       ).replace('TTTTT'      ,   F'''{task_id:05d}'''       )      ,   writer_batch_size=snake_case      ,   storage_options=snake_case      ,   embed_local_files=snake_case      ,   )
																									snake_case_							=    pa.Table.from_batches([batch]       )
																									writer.write_table(snake_case       )

																		if writer._num_bytes > 0:
																									snake_case_				,    snake_case_							=    writer.finalize()
																									writer.close()
																									yield pa.RecordBatch.from_arrays(
																									    [[task_id], [num_examples], [num_bytes]]      ,   names=['task_id', 'num_examples', 'num_bytes']      ,   )

																		if working_fpath != fpath:
																									for file in os.listdir(os.path.dirname(snake_case       )       ):
																																snake_case_							=    os.path.join(os.path.dirname(snake_case       )      ,   os.path.basename(snake_case       )       )
																																shutil.move(snake_case      ,   snake_case       )

											snake_case_							=    (
											    self.df.mapInArrow(snake_case      ,   'task_id: long, num_examples: long, num_bytes: long'       )
											    .groupBy('task_id'       )
											    .agg(
											        pyspark.sql.functions.sum('num_examples'       ).alias('total_num_examples'       )      ,   pyspark.sql.functions.sum('num_bytes'       ).alias('total_num_bytes'       )      ,   pyspark.sql.functions.count('num_bytes'       ).alias('num_shards'       )      ,   pyspark.sql.functions.collect_list('num_examples'       ).alias('shard_lengths'       )      ,   )
											    .collect()
											)
											for row in stats:
																		yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)




				def     a							(					self      ,   snake_case      ,   snake_case = "arrow"      ,   snake_case = None      ,   snake_case = None      ,   **snake_case      ,   ):
											self._validate_cache_dir()

											snake_case_							=    convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE       )
											self._repartition_df_if_needed(snake_case       )
											snake_case_							=    not is_remote_filesystem(self._fs       )
											snake_case_							=    os.path.join if is_local else posixpath.join

											snake_case_							=    '-TTTTT-SSSSS-of-NNNNN'
											snake_case_							=    F'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
											snake_case_							=    path_join(self._output_dir      ,   snake_case       )

											snake_case_							=    0
											snake_case_							=    0
											snake_case_							=    0
											snake_case_							=    []
											snake_case_							=    []

											for task_id, content in self._prepare_split_single(snake_case      ,   snake_case      ,   snake_case       ):
																		(
																		    (
																		    snake_case_
																		)				,    (
																		    snake_case_
																		)				,    (
																		    snake_case_
																		)				,    (
																		    snake_case_
																		)				,    
																		)							=    content
																		if num_bytes > 0:
																									total_num_examples += num_examples
																									total_num_bytes += num_bytes
																									total_shards += num_shards
																									task_id_and_num_shards.append((task_id, num_shards)       )
																									all_shard_lengths.extend(snake_case       )

											snake_case_							=    total_num_examples
											snake_case_							=    total_num_bytes

											# should rename everything at the end
											logger.debug(F'''Renaming {total_shards} shards.'''       )
											if total_shards > 1:
																		snake_case_							=    all_shard_lengths

																		# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
																		# pickling error due to pickling the SparkContext.
																		snake_case_							=    self._fs

																		# use the -SSSSS-of-NNNNN pattern
																		def _rename_shard(
																		    snake_case      ,   snake_case      ,   snake_case      ,   ):
																									rename(
																									    snake_case      ,   fpath.replace('SSSSS'      ,   F'''{shard_id:05d}'''       ).replace('TTTTT'      ,   F'''{task_id:05d}'''       )      ,   fpath.replace('TTTTT-SSSSS'      ,   F'''{global_shard_id:05d}'''       ).replace('NNNNN'      ,   F'''{total_shards:05d}'''       )      ,   )

																		snake_case_							=    []
																		snake_case_							=    0
																		for i in range(len(snake_case       )       ):
																									snake_case_				,    snake_case_							=    task_id_and_num_shards[i]
																									for shard_id in range(snake_case       ):
																																args.append([task_id, shard_id, global_shard_id]       )
																																global_shard_id += 1
																		self._spark.sparkContext.parallelize(snake_case      ,   len(snake_case       )       ).map(lambda snake_case       : _rename_shard(*snake_case       )       ).collect()
											else:
																		# don't use any pattern
																		snake_case_							=    0
																		snake_case_							=    task_id_and_num_shards[0][0]
																		self._rename(
																		    fpath.replace('SSSSS'      ,   F'''{shard_id:05d}'''       ).replace('TTTTT'      ,   F'''{task_id:05d}'''       )      ,   fpath.replace(snake_case      ,   ''       )      ,   )




				def     a							(					self      ,   snake_case      ,   ):
											return SparkExamplesIterable(self.df       )







import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase		:   List[Any]										=       logging.get_logger()







@dataclass
class  lowercase				:
				__SCREAMING_SNAKE_CASE					: nn.Module
				__SCREAMING_SNAKE_CASE					: List[nn.Module]       			=			field(default_factory=lowercase_			)
				__SCREAMING_SNAKE_CASE					: list       			=			field(default_factory=lowercase_			)




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    len(list(m.modules()       )       ) == 1 or isinstance(snake_case      ,   nn.Convad       ) or isinstance(snake_case      ,   nn.BatchNormad       )
											if has_not_submodules:
																		self.traced.append(snake_case       )




				def __call__(					self      ,   snake_case       ):
											for m in self.module.modules():
																		self.handles.append(m.register_forward_hook(self._forward_hook       )       )
											self.module(snake_case       )
											[x.remove() for x in self.handles]
											return self




				@property
				def     a							(					self       ):
											# check the len of the state_dict keys to see if we have learnable params
											return list(filter(lambda snake_case       : len(list(x.state_dict().keys()       )       ) > 0      ,   self.traced       )       )







@dataclass
class  lowercase				:
				__SCREAMING_SNAKE_CASE					: nn.Module
				__SCREAMING_SNAKE_CASE					: nn.Module
				__SCREAMING_SNAKE_CASE					: int       			=			0
				__SCREAMING_SNAKE_CASE					: List       			=			field(default_factory=lowercase_			)
				__SCREAMING_SNAKE_CASE					: List       			=			field(default_factory=lowercase_			)




				def __call__(					self      ,   snake_case       ):
											snake_case_							=    Tracker(self.dest       )(snake_case       ).parametrized
											snake_case_							=    Tracker(self.src       )(snake_case       ).parametrized

											snake_case_							=    list(filter(lambda snake_case       : type(snake_case       ) not in self.src_skip      ,   snake_case       )       )
											snake_case_							=    list(filter(lambda snake_case       : type(snake_case       ) not in self.dest_skip      ,   snake_case       )       )

											if len(snake_case       ) != len(snake_case       ):
																		raise Exception(
																		    F'''Numbers of operations are different. Source module has {len(snake_case       )} operations while'''
																		    F''' destination module has {len(snake_case       )}.'''       )

											for dest_m, src_m in zip(snake_case      ,   snake_case       ):
																		dest_m.load_state_dict(src_m.state_dict()       )
																		if self.verbose == 1:
																									print(F'''Transfered from={src_m} to={dest_m}'''       )





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__ = True      ):







							'''simple docstring'''






							print(F'''Converting {name}...'''      )
							with torch.no_grad():
														snake_case_							=    timm.create_model(UpperCamelCase__   ,							pretrained=UpperCamelCase__      ).eval()
														snake_case_							=    ResNetForImageClassification(UpperCamelCase__      ).eval()
														snake_case_							=    ModuleTransfer(src=UpperCamelCase__   ,							dest=UpperCamelCase__      )
														snake_case_							=    torch.randn((1, 3, 224, 224)      )
														module_transfer(UpperCamelCase__      )

							assert torch.allclose(from_model(UpperCamelCase__      )   ,							our_model(UpperCamelCase__      ).logits      ), "The model logits don't match the original one."

							snake_case_							=    F'''resnet{"-".join(name.split("resnet"      )      )}'''
							print(UpperCamelCase__      )

							if push_to_hub:
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name   ,							commit_message='Add model'   ,							use_temp_dir=UpperCamelCase__   ,							)

														# we can use the convnext one
														snake_case_							=    AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'      )
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name   ,							commit_message='Add image processor'   ,							use_temp_dir=UpperCamelCase__   ,							)

														print(F'''Pushed {checkpoint_name}'''      )





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__ = None   ,							UpperCamelCase__ = True      ):







							'''simple docstring'''






							snake_case_							=    'imagenet-1k-id2label.json'
							snake_case_							=    1000
							snake_case_							=    (1, num_labels)

							snake_case_							=    'huggingface/label-files'
							snake_case_							=    num_labels
							snake_case_							=    json.load(open(hf_hub_download(UpperCamelCase__   ,							UpperCamelCase__   ,							repo_type='dataset'      )   ,							'r'      )      )
							snake_case_							=    {int(UpperCamelCase__      ): v for k, v in idalabel.items()}

							snake_case_							=    idalabel
							snake_case_							=    {v: k for k, v in idalabel.items()}

							snake_case_							=    partial(UpperCamelCase__   ,							num_labels=UpperCamelCase__   ,							idalabel=UpperCamelCase__   ,							labelaid=UpperCamelCase__      )

							snake_case_							=    {
							    'resnet18': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]   ,							hidden_sizes=[64, 128, 256, 512]   ,							layer_type='basic'      ),
							    'resnet26': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							    'resnet34': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]   ,							hidden_sizes=[64, 128, 256, 512]   ,							layer_type='basic'      ),
							    'resnet50': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							    'resnet101': ImageNetPreTrainedConfig(
							        depths=[3, 4, 23, 3]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							    'resnet152': ImageNetPreTrainedConfig(
							        depths=[3, 8, 36, 3]   ,							hidden_sizes=[256, 512, 1024, 2048]   ,							layer_type='bottleneck'      ),
							}

							if model_name:
														convert_weight_and_push(UpperCamelCase__   ,							names_to_config[model_name]   ,							UpperCamelCase__   ,							UpperCamelCase__      )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )
							return config, expected_shape


if __name__ == "__main__":
					_UpperCAmelCase		:   List[str]										=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default=None,
					    type=str,
					    help=(
					        """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
					        """ currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default=None,
					    type=Path,
					    required=True,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument(
					    """--push_to_hub""",
					    default=True,
					    type=bool,
					    required=False,
					    help="""If True, push model and image processor to the hub.""",
					)

					_UpperCAmelCase		:   Optional[Any]										=       parser.parse_args()
					_UpperCAmelCase		:   Path										=       args.pytorch_dump_folder_path
					pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
					convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




