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

    return flax_key_tuple, flax_tensor






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)	->    Union[str, Any]:
    if "metadata" in layer:
        __lowerCamelCase		=  layer.split('''metadata'''				)
        __lowerCamelCase		=  ''''''.join(split_layer[0]				)[:-1]
        __lowerCamelCase		=  [tuple(('''metadata''' + split_layer[1]).split('''/'''				)				)]
    elif "kvstore" in layer:
        __lowerCamelCase		=  layer.split('''kvstore'''				)
        __lowerCamelCase		=  ''''''.join(split_layer[0]				)[:-1]
        __lowerCamelCase		=  [tuple(('''kvstore''' + split_layer[1]).split('''/'''				)				)]

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

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

    return curr_real_layer_name, split_layer, content






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    Dict:
    __lowerCamelCase		=  rename_keys(UpperCamelCase__				)
    __lowerCamelCase		=  {}
    for k, v in current_block.items():
        __lowerCamelCase		=  v
    __lowerCamelCase		=  new_current_block
    torch.save(UpperCamelCase__				,  UpperCamelCase__				)






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__ = WEIGHTS_NAME				)	->    Tuple:
    __lowerCamelCase		=  convert_file_size_to_int(UpperCamelCase__				)
    __lowerCamelCase		=  []
    __lowerCamelCase		=  {}
    __lowerCamelCase		=  0
    __lowerCamelCase		=  0

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

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

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

        # use the renaming pattern from the small conversion scripts
        __lowerCamelCase	,  __lowerCamelCase		=  rename_base_flax_keys(tuple(key.split('''/'''				)				)				,  UpperCamelCase__				)
        __lowerCamelCase		=  '''/'''.join(UpperCamelCase__				)

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

        __lowerCamelCase		=  raw_weights.to(getattr(UpperCamelCase__				,  UpperCamelCase__				)				)
        current_block_size += weight_size
        total_size += weight_size

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

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

    # Otherwise, let's build the index
    __lowerCamelCase		=  {}
    __lowerCamelCase		=  {}
    for idx, shard in enumerate(UpperCamelCase__				):
        __lowerCamelCase		=  weights_name.replace(
            '''.bin'''				,  f"""-{idx+1:05d}-of-{len(UpperCamelCase__				):05d}.bin"""				)  # len(sharded_state_dicts):05d}
        __lowerCamelCase		=  os.path.join(UpperCamelCase__				,  weights_name.replace('''.bin'''				,  f"""-{idx+1:05d}-of-???.bin"""				)				)
        os.rename(UpperCamelCase__				,  os.path.join(UpperCamelCase__				,  UpperCamelCase__				)				)
        __lowerCamelCase		=  shard
        for key in shard:
            __lowerCamelCase		=  shard_file

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

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

    return metadata, index


if __name__ == "__main__":
  __UpperCAmelCase     							=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.",
  )
  __UpperCAmelCase     							=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 (							)	->    List[Any]:
    from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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






import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def __UpperCamelCase			(       _A ,  _A  ):
						assert isinstance(_A ,  _A  )
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('''keep_in_memory''' ,  [False, True]  )
def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												lowerCAmelCase_      			=  JsonDatasetReader(_A ,  cache_dir=_A ,  keep_in_memory=_A  ).read()
						_check_json_dataset(_A ,  _A  )





@pytest.mark.parametrize(
    '''features''' ,  [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ] ,  )
def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowerCAmelCase_      			=  features.copy() if features else default_expected_features
						lowerCAmelCase_      			=  (
						    Features({feature: Value(_A  ) for feature, dtype in features.items()}  ) if features is not None else None
						)
						lowerCAmelCase_      			=  JsonDatasetReader(_A ,  features=_A ,  cache_dir=_A  ).read()
						_check_json_dataset(_A ,  _A  )





@pytest.mark.parametrize(
    '''features''' ,  [
        None,
        {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
    ] ,  )
def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
						lowerCAmelCase_      			=  features.copy() if features else default_expected_features
						lowerCAmelCase_      			=  (
						    Features({feature: Value(_A  ) for feature, dtype in features.items()}  ) if features is not None else None
						)
						lowerCAmelCase_      			=  JsonDatasetReader(_A ,  features=_A ,  cache_dir=_A  ).read()
						assert isinstance(_A ,  _A  )
						assert dataset.num_rows == 2
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_3", "col_1", "col_2"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype





def __UpperCamelCase			(       _A ,  _A  ):
						# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
						lowerCAmelCase_      			=  {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
						lowerCAmelCase_      			=  features.copy()
						lowerCAmelCase_      			=  (
						    Features({feature: Value(_A  ) for feature, dtype in features.items()}  ) if features is not None else None
						)
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  JsonDatasetReader(_A ,  features=_A ,  cache_dir=_A  ).read()
						assert isinstance(_A ,  _A  )
						assert dataset.num_rows == 2
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_2", "col_3", "col_1"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('''split''' ,  [None, NamedSplit('''train'''  ), '''train''', '''test''']  )
def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowerCAmelCase_      			=  JsonDatasetReader(_A ,  cache_dir=_A ,  split=_A  ).read()
						_check_json_dataset(_A ,  _A  )
						assert dataset.split == split if split else "train"





@pytest.mark.parametrize('''path_type''' ,  [str, list]  )
def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						if issubclass(_A ,  _A  ):
												lowerCAmelCase_      			=  jsonl_path
						elif issubclass(_A ,  _A  ):
												lowerCAmelCase_      			=  [jsonl_path]
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowerCAmelCase_      			=  JsonDatasetReader(_A ,  cache_dir=_A  ).read()
						_check_json_dataset(_A ,  _A  )





def __UpperCamelCase			(       _A ,  _A ,  _A=("train",)  ):
						assert isinstance(_A ,  _A  )
						for split in splits:
												lowerCAmelCase_      			=  dataset_dict[split]
												assert dataset.num_rows == 4
												assert dataset.num_columns == 3
												assert dataset.column_names == ["col_1", "col_2", "col_3"]
												for feature, expected_dtype in expected_features.items():
																		assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('''keep_in_memory''' ,  [False, True]  )
def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												lowerCAmelCase_      			=  JsonDatasetReader({'''train''': jsonl_path} ,  cache_dir=_A ,  keep_in_memory=_A  ).read()
						_check_json_datasetdict(_A ,  _A  )





@pytest.mark.parametrize(
    '''features''' ,  [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ] ,  )
def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowerCAmelCase_      			=  features.copy() if features else default_expected_features
						lowerCAmelCase_      			=  (
						    Features({feature: Value(_A  ) for feature, dtype in features.items()}  ) if features is not None else None
						)
						lowerCAmelCase_      			=  JsonDatasetReader({'''train''': jsonl_path} ,  features=_A ,  cache_dir=_A  ).read()
						_check_json_datasetdict(_A ,  _A  )





@pytest.mark.parametrize('''split''' ,  [None, NamedSplit('''train'''  ), '''train''', '''test''']  )
def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						if split:
												lowerCAmelCase_      			=  {split: jsonl_path}
						else:
												lowerCAmelCase_      			=  '''train'''
												lowerCAmelCase_      			=  {'''train''': jsonl_path, '''test''': jsonl_path}
						lowerCAmelCase_      			=  tmp_path / '''cache'''
						lowerCAmelCase_      			=  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						lowerCAmelCase_      			=  JsonDatasetReader(_A ,  cache_dir=_A  ).read()
						_check_json_datasetdict(_A ,  _A ,  splits=list(path.keys()  )  )
						assert all(dataset[split].split == split for split in path.keys()  )





def __UpperCamelCase			(       _A  ):
						return json.load(_A  )





def __UpperCamelCase			(       _A  ):
						return [json.loads(_A  ) for line in buffer]






class  A		:



		@pytest.mark.parametrize('''lines, load_json_function''',	[(True, load_json_lines), (False, load_json)]   )
		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								with io.BytesIO() as buffer:
														JsonDatasetWriter(UpperCamelCase__,	UpperCamelCase__,	lines=UpperCamelCase__   ).write()
														buffer.seek(0   )
														lowerCAmelCase_      			=  load_json_function(UpperCamelCase__   )
								assert isinstance(UpperCamelCase__,	UpperCamelCase__   )
								assert isinstance(exported_content[0],	UpperCamelCase__   )
								assert len(UpperCamelCase__   ) == 10



		@pytest.mark.parametrize(
		    '''orient, container, keys, len_at''',	[
		        ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
		        ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
		        ('''index''', dict, set('''0123456789'''   ), None),
		        ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
		        ('''values''', list, None, None),
		        ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
		    ],	)
		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								with io.BytesIO() as buffer:
														JsonDatasetWriter(UpperCamelCase__,	UpperCamelCase__,	lines=UpperCamelCase__,	orient=UpperCamelCase__   ).write()
														buffer.seek(0   )
														lowerCAmelCase_      			=  load_json(UpperCamelCase__   )
								assert isinstance(UpperCamelCase__,	UpperCamelCase__   )
								if keys:
														if container is dict:
																				assert exported_content.keys() == keys
														else:
																				assert exported_content[0].keys() == keys
								else:
														assert not hasattr(UpperCamelCase__,	'''keys'''   ) and not hasattr(exported_content[0],	'''keys'''   )
								if len_at:
														assert len(exported_content[len_at]   ) == 10
								else:
														assert len(UpperCamelCase__   ) == 10



		@pytest.mark.parametrize('''lines, load_json_function''',	[(True, load_json_lines), (False, load_json)]   )
		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								with io.BytesIO() as buffer:
														JsonDatasetWriter(UpperCamelCase__,	UpperCamelCase__,	lines=UpperCamelCase__,	num_proc=2   ).write()
														buffer.seek(0   )
														lowerCAmelCase_      			=  load_json_function(UpperCamelCase__   )
								assert isinstance(UpperCamelCase__,	UpperCamelCase__   )
								assert isinstance(exported_content[0],	UpperCamelCase__   )
								assert len(UpperCamelCase__   ) == 10



		@pytest.mark.parametrize(
		    '''orient, container, keys, len_at''',	[
		        ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
		        ('''split''', dict, {'''columns''', '''data'''}, '''data'''),
		        ('''index''', dict, set('''0123456789'''   ), None),
		        ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
		        ('''values''', list, None, None),
		        ('''table''', dict, {'''schema''', '''data'''}, '''data'''),
		    ],	)
		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								with io.BytesIO() as buffer:
														JsonDatasetWriter(UpperCamelCase__,	UpperCamelCase__,	lines=UpperCamelCase__,	orient=UpperCamelCase__,	num_proc=2   ).write()
														buffer.seek(0   )
														lowerCAmelCase_      			=  load_json(UpperCamelCase__   )
								assert isinstance(UpperCamelCase__,	UpperCamelCase__   )
								if keys:
														if container is dict:
																				assert exported_content.keys() == keys
														else:
																				assert exported_content[0].keys() == keys
								else:
														assert not hasattr(UpperCamelCase__,	'''keys'''   ) and not hasattr(exported_content[0],	'''keys'''   )
								if len_at:
														assert len(exported_content[len_at]   ) == 10
								else:
														assert len(UpperCamelCase__   ) == 10



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								with pytest.raises(UpperCamelCase__   ):
														with io.BytesIO() as buffer:
																				JsonDatasetWriter(UpperCamelCase__,	UpperCamelCase__,	num_proc=0   )







		@pytest.mark.parametrize('''compression, extension''',	[('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')]   )
		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  tmp_path_factory.mktemp('''data'''   ) / f"test.json.{extension}"
								lowerCAmelCase_      			=  str(shared_datadir / f"test_file.json.{extension}"   )
								JsonDatasetWriter(UpperCamelCase__,	UpperCamelCase__,	compression=UpperCamelCase__   ).write()

								with fsspec.open(UpperCamelCase__,	'''rb''',	compression='''infer'''   ) as f:
														lowerCAmelCase_      			=  f.read()
								with fsspec.open(UpperCamelCase__,	'''rb''',	compression='''infer'''   ) as f:
														lowerCAmelCase_      			=  f.read()
								assert exported_content == original_content




