

"""simple docstring"""

import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Tuple:
 return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__					,					lowercase__="attention"   )			->						Dict:
 lowerCAmelCase__   :				Optional[Any] 			=				np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]   )
 lowerCAmelCase__   :				List[Any] 			=				k_tmp.reshape(k_tmp.shape[0]					,					k_tmp.shape[1] * k_tmp.shape[2]   )
 lowerCAmelCase__   :				str 			=				np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]   )
 lowerCAmelCase__   :				Tuple 			=				o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]					,					o_tmp.shape[2]   )
 lowerCAmelCase__   :				Optional[int] 			=				np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]   )
 lowerCAmelCase__   :				List[str] 			=				q_tmp.reshape(q_tmp.shape[0]					,					q_tmp.shape[1] * q_tmp.shape[2]   )
 lowerCAmelCase__   :				List[Any] 			=				np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]   )
 lowerCAmelCase__   :				Dict 			=				v_tmp.reshape(v_tmp.shape[0]					,					v_tmp.shape[1] * v_tmp.shape[2]   )
 return k, o, q, v



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__					,					lowercase__=False   )			->						Tuple:
 if split_mlp_wi:
  lowerCAmelCase__   :				Any 			=				params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
  lowerCAmelCase__   :				Tuple 			=				params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
  lowerCAmelCase__   :				Dict 			=				(wi_a, wi_a)
 else:
  lowerCAmelCase__   :				Tuple 			=				params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

 lowerCAmelCase__   :				List[str] 			=				params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
 return wi, wo



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__					,					lowercase__   )			->						Dict:
 return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]



def       SCREAMING_SNAKE_CASE							( lowercase__					,					*, lowercase__					,					lowercase__					,					lowercase__ = False   )			->						Optional[Any]:
 lowerCAmelCase__   :				Tuple 			=				traverse_util.flatten_dict(variables["target"]   )
 lowerCAmelCase__   :				Dict 			=				{"/".join(lowercase__   ): v for k, v in old.items()}

 # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
 lowerCAmelCase__   :				List[str] 			=				"encoder/encoder/mlp/wi_0/kernel" in old
 print("Split MLP:"					,					lowercase__   )

 lowerCAmelCase__   :				Union[str, Any] 			=				collections.OrderedDict()

 # Shared embeddings.
 lowerCAmelCase__   :				Any 			=				old["token_embedder/embedding"]

 # Encoder.
 for i in range(lowercase__   ):
  # Block i, layer 0 (Self Attention).
  lowerCAmelCase__   :				Dict 			=				tax_layer_norm_lookup(lowercase__					,					lowercase__					,					"encoder"					,					"pre_attention_layer_norm"   )
  lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				Optional[Any] 			=				tax_attention_lookup(lowercase__					,					lowercase__					,					"encoder"					,					"attention"   )
  lowerCAmelCase__   :				List[str] 			=				layer_norm
  lowerCAmelCase__   :				Optional[Any] 			=				k.T
  lowerCAmelCase__   :				List[Any] 			=				o.T
  lowerCAmelCase__   :				Optional[Any] 			=				q.T
  lowerCAmelCase__   :				Any 			=				v.T

  # Block i, layer 1 (MLP).
  lowerCAmelCase__   :				int 			=				tax_layer_norm_lookup(lowercase__					,					lowercase__					,					"encoder"					,					"pre_mlp_layer_norm"   )
  lowerCAmelCase__			,						lowerCAmelCase__   :				List[Any] 			=				tax_mlp_lookup(lowercase__					,					lowercase__					,					"encoder"					,					lowercase__   )
  lowerCAmelCase__   :				Any 			=				layer_norm
  if split_mlp_wi:
   lowerCAmelCase__   :				str 			=				wi[0].T
   lowerCAmelCase__   :				Any 			=				wi[1].T
  else:
   lowerCAmelCase__   :				List[Any] 			=				wi.T
  lowerCAmelCase__   :				Optional[int] 			=				wo.T
  if scalable_attention:
   # convert the rel_embedding of each layer
   lowerCAmelCase__   :				str 			=				tax_relpos_bias_lookup(
       lowercase__					,					lowercase__					,					"encoder"   ).T

 lowerCAmelCase__   :				int 			=				old["encoder/encoder_norm/scale"]

 if not scalable_attention:
  lowerCAmelCase__   :				List[str] 			=				tax_relpos_bias_lookup(
      lowercase__					,					0					,					"encoder"   ).T
  lowerCAmelCase__   :				Any 			=				tax_relpos_bias_lookup(
      lowercase__					,					0					,					"decoder"   ).T

 if not is_encoder_only:
  # Decoder.
  for i in range(lowercase__   ):
   # Block i, layer 0 (Self Attention).
   lowerCAmelCase__   :				int 			=				tax_layer_norm_lookup(lowercase__					,					lowercase__					,					"decoder"					,					"pre_self_attention_layer_norm"   )
   lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				Dict 			=				tax_attention_lookup(lowercase__					,					lowercase__					,					"decoder"					,					"self_attention"   )
   lowerCAmelCase__   :				Dict 			=				layer_norm
   lowerCAmelCase__   :				List[Any] 			=				k.T
   lowerCAmelCase__   :				int 			=				o.T
   lowerCAmelCase__   :				Any 			=				q.T
   lowerCAmelCase__   :				List[Any] 			=				v.T

   # Block i, layer 1 (Cross Attention).
   lowerCAmelCase__   :				Optional[int] 			=				tax_layer_norm_lookup(lowercase__					,					lowercase__					,					"decoder"					,					"pre_cross_attention_layer_norm"   )
   lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				List[Any] 			=				tax_attention_lookup(lowercase__					,					lowercase__					,					"decoder"					,					"encoder_decoder_attention"   )
   lowerCAmelCase__   :				Tuple 			=				layer_norm
   lowerCAmelCase__   :				Optional[Any] 			=				k.T
   lowerCAmelCase__   :				str 			=				o.T
   lowerCAmelCase__   :				Tuple 			=				q.T
   lowerCAmelCase__   :				Union[str, Any] 			=				v.T

   # Block i, layer 2 (MLP).
   lowerCAmelCase__   :				Any 			=				tax_layer_norm_lookup(lowercase__					,					lowercase__					,					"decoder"					,					"pre_mlp_layer_norm"   )
   lowerCAmelCase__			,						lowerCAmelCase__   :				List[str] 			=				tax_mlp_lookup(lowercase__					,					lowercase__					,					"decoder"					,					lowercase__   )
   lowerCAmelCase__   :				Dict 			=				layer_norm
   if split_mlp_wi:
    lowerCAmelCase__   :				Dict 			=				wi[0].T
    lowerCAmelCase__   :				Any 			=				wi[1].T
   else:
    lowerCAmelCase__   :				int 			=				wi.T
   lowerCAmelCase__   :				Any 			=				wo.T

   if scalable_attention:
    # convert the rel_embedding of each layer
    lowerCAmelCase__   :				Optional[int] 			=				tax_relpos_bias_lookup(lowercase__					,					lowercase__					,					"decoder"   ).T

  lowerCAmelCase__   :				str 			=				old["decoder/decoder_norm/scale"]

  # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
  if "decoder/logits_dense/kernel" in old:
   lowerCAmelCase__   :				Any 			=				old["decoder/logits_dense/kernel"].T

 return new



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						str:
 lowerCAmelCase__   :				List[Any] 			=				collections.OrderedDict([(k, torch.from_numpy(v.copy()   )) for (k, v) in converted_params.items()]   )

 # Add what is missing.
 if "encoder.embed_tokens.weight" not in state_dict:
  lowerCAmelCase__   :				Optional[int] 			=				state_dict["shared.weight"]

 if not is_encoder_only:
  if "decoder.embed_tokens.weight" not in state_dict:
   lowerCAmelCase__   :				Any 			=				state_dict["shared.weight"]

  if "lm_head.weight" not in state_dict:  # For old 1.0 models.
   print("Using shared word embeddings as lm_head."   )
   lowerCAmelCase__   :				int 			=				state_dict["shared.weight"]

 return state_dict



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__					,					lowercase__					,					lowercase__   )			->						Optional[int]:
 lowerCAmelCase__   :				Any 			=				checkpoints.load_tax_checkpoint(lowercase__   )
 lowerCAmelCase__   :				str 			=				convert_tax_to_pytorch(
     lowercase__					,					num_layers=config.num_layers					,					is_encoder_only=lowercase__					,					scalable_attention=lowercase__   )
 lowerCAmelCase__   :				str 			=				make_state_dict(lowercase__					,					lowercase__   )
 model.load_state_dict(lowercase__					,					strict=lowercase__   )



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__					,					lowercase__ = False					,					lowercase__ = False					,					)			->						Union[str, Any]:
 lowerCAmelCase__   :				Optional[int] 			=				MTaConfig.from_json_file(lowercase__   )
 print(F"""Building PyTorch model from configuration: {config}"""   )
 # Non-v1.1 checkpoints could also use T5Model, but this works for all.
 # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
 if is_encoder_only:
  lowerCAmelCase__   :				int 			=				UMTaEncoderModel(lowercase__   )
 else:
  lowerCAmelCase__   :				Optional[Any] 			=				UMTaForConditionalGeneration(lowercase__   )

 # Load weights from tf checkpoint
 load_tax_weights_in_ta(lowercase__					,					lowercase__					,					lowercase__					,					lowercase__					,					lowercase__   )

 # Save pytorch-model
 print(F"""Save PyTorch model to {pytorch_dump_path}"""   )
 model.save_pretrained(lowercase__   )

 # Verify that we can load the checkpoint.
 model.from_pretrained(lowercase__   )
 print("Done"   )


if __name__ == "__main__":
    _UpperCamelCase		     =    argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
    # Required parameters
    parser.add_argument(
        """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
    )
    parser.add_argument(
        """--config_file""",
        default=None,
        type=str,
        required=True,
        help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
    )
    parser.add_argument(
        """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
    )
    parser.add_argument(
        """--scalable_attention""",
        action="""store_true""",
        help="""Whether the model uses scaled attention (umt5 model)""",
        default=False,
    )
    _UpperCamelCase		     =    parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path,
        args.config_file,
        args.pytorch_dump_path,
        args.is_encoder_only,
        args.scalable_attention,
    )









"""simple docstring"""

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       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Optional[Any]:
 assert isinstance(lowercase__					,					lowercase__   )
 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       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Any:
 lowerCAmelCase__   :				Optional[int] 			=				tmp_path / "cache"
 lowerCAmelCase__   :				Dict 			=				{"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__   :				Tuple 			=				JsonDatasetReader(lowercase__					,					cache_dir=lowercase__					,					keep_in_memory=lowercase__   ).read()
 _check_json_dataset(lowercase__					,					lowercase__   )



@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       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Tuple:
 lowerCAmelCase__   :				Union[str, Any] 			=				tmp_path / "cache"
 lowerCAmelCase__   :				Tuple 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCAmelCase__   :				List[Any] 			=				features.copy() if features else default_expected_features
 lowerCAmelCase__   :				int 			=				(
     Features({feature: Value(lowercase__   ) for feature, dtype in features.items()}   ) if features is not None else None
 )
 lowerCAmelCase__   :				Tuple 			=				JsonDatasetReader(lowercase__					,					features=lowercase__					,					cache_dir=lowercase__   ).read()
 _check_json_dataset(lowercase__					,					lowercase__   )



@pytest.mark.parametrize(
    "features"					,					[
        None,
        {"col_3": "float64", "col_1": "string", "col_2": "int64"},
    ]					,					)
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Optional[Any]:
 lowerCAmelCase__   :				Tuple 			=				tmp_path / "cache"
 lowerCAmelCase__   :				Optional[Any] 			=				{"col_3": "float64", "col_1": "string", "col_2": "int64"}
 lowerCAmelCase__   :				List[str] 			=				features.copy() if features else default_expected_features
 lowerCAmelCase__   :				Dict 			=				(
     Features({feature: Value(lowercase__   ) for feature, dtype in features.items()}   ) if features is not None else None
 )
 lowerCAmelCase__   :				Dict 			=				JsonDatasetReader(lowercase__					,					features=lowercase__					,					cache_dir=lowercase__   ).read()
 assert isinstance(lowercase__					,					lowercase__   )
 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       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Tuple:
 # jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
 lowerCAmelCase__   :				Any 			=				{"col_2": "int64", "col_3": "float64", "col_1": "string"}
 lowerCAmelCase__   :				Union[str, Any] 			=				features.copy()
 lowerCAmelCase__   :				int 			=				(
     Features({feature: Value(lowercase__   ) for feature, dtype in features.items()}   ) if features is not None else None
 )
 lowerCAmelCase__   :				str 			=				tmp_path / "cache"
 lowerCAmelCase__   :				List[str] 			=				JsonDatasetReader(lowercase__					,					features=lowercase__					,					cache_dir=lowercase__   ).read()
 assert isinstance(lowercase__					,					lowercase__   )
 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       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Optional[Any]:
 lowerCAmelCase__   :				Optional[int] 			=				tmp_path / "cache"
 lowerCAmelCase__   :				List[Any] 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCAmelCase__   :				Dict 			=				JsonDatasetReader(lowercase__					,					cache_dir=lowercase__					,					split=lowercase__   ).read()
 _check_json_dataset(lowercase__					,					lowercase__   )
 assert dataset.split == split if split else "train"



@pytest.mark.parametrize("path_type"					,					[str, list]   )
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Optional[Any]:
 if issubclass(lowercase__					,					lowercase__   ):
  lowerCAmelCase__   :				Dict 			=				jsonl_path
 elif issubclass(lowercase__					,					lowercase__   ):
  lowerCAmelCase__   :				Tuple 			=				[jsonl_path]
 lowerCAmelCase__   :				Any 			=				tmp_path / "cache"
 lowerCAmelCase__   :				List[str] 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCAmelCase__   :				int 			=				JsonDatasetReader(lowercase__					,					cache_dir=lowercase__   ).read()
 _check_json_dataset(lowercase__					,					lowercase__   )



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__=("train",)   )			->						List[str]:
 assert isinstance(lowercase__					,					lowercase__   )
 for split in splits:
  lowerCAmelCase__   :				Dict 			=				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       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						List[str]:
 lowerCAmelCase__   :				int 			=				tmp_path / "cache"
 lowerCAmelCase__   :				str 			=				{"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__   :				Dict 			=				JsonDatasetReader({"train": jsonl_path}					,					cache_dir=lowercase__					,					keep_in_memory=lowercase__   ).read()
 _check_json_datasetdict(lowercase__					,					lowercase__   )



@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       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						List[str]:
 lowerCAmelCase__   :				str 			=				tmp_path / "cache"
 lowerCAmelCase__   :				Union[str, Any] 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCAmelCase__   :				str 			=				features.copy() if features else default_expected_features
 lowerCAmelCase__   :				Dict 			=				(
     Features({feature: Value(lowercase__   ) for feature, dtype in features.items()}   ) if features is not None else None
 )
 lowerCAmelCase__   :				List[Any] 			=				JsonDatasetReader({"train": jsonl_path}					,					features=lowercase__					,					cache_dir=lowercase__   ).read()
 _check_json_datasetdict(lowercase__					,					lowercase__   )



@pytest.mark.parametrize("split"					,					[None, NamedSplit("train"   ), "train", "test"]   )
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Dict:
 if split:
  lowerCAmelCase__   :				int 			=				{split: jsonl_path}
 else:
  lowerCAmelCase__   :				str 			=				"train"
  lowerCAmelCase__   :				Tuple 			=				{"train": jsonl_path, "test": jsonl_path}
 lowerCAmelCase__   :				Any 			=				tmp_path / "cache"
 lowerCAmelCase__   :				Dict 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCAmelCase__   :				int 			=				JsonDatasetReader(lowercase__					,					cache_dir=lowercase__   ).read()
 _check_json_datasetdict(lowercase__					,					lowercase__					,					splits=list(path.keys()   )   )
 assert all(dataset[split].split == split for split in path.keys()   )



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						List[Any]:
 return json.load(lowercase__   )



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Optional[int]:
 return [json.loads(lowercase__   ) for line in buffer]






class 			__a					:





       """simple docstring"""


       @pytest.mark.parametrize("lines, load_json_function"			,					[(True, load_json_lines), (False, load_json)]   )
       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        with io.BytesIO() as buffer:
         JsonDatasetWriter(snake_case			,					snake_case			,					lines=snake_case   ).write()
         buffer.seek(0   )
         lowerCAmelCase__   :				int 			=				load_json_function(snake_case   )
        assert isinstance(snake_case			,					snake_case   )
        assert isinstance(exported_content[0]			,					snake_case   )
        assert len(snake_case   ) == 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			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

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






       @pytest.mark.parametrize("lines, load_json_function"			,					[(True, load_json_lines), (False, load_json)]   )
       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

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





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

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






       @pytest.mark.parametrize("compression, extension"			,					[("gzip", "gz"), ("bz2", "bz2"), ("xz", "xz")]   )
       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				tmp_path_factory.mktemp("data"   ) / F"""test.json.{extension}"""
        lowerCAmelCase__   :				str 			=				str(shared_datadir / F"""test_file.json.{extension}"""   )
        JsonDatasetWriter(snake_case			,					snake_case			,					compression=snake_case   ).write()

        with fsspec.open(snake_case			,					"rb"			,					compression="infer"   ) as f:
         lowerCAmelCase__   :				Optional[int] 			=				f.read()
        with fsspec.open(snake_case			,					"rb"			,					compression="infer"   ) as f:
         lowerCAmelCase__   :				Optional[int] 			=				f.read()
        assert exported_content == original_content






