



import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class 				__A					( A_      ):





			def __init__(self							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = False							,    __magic_name__ = False							,    __magic_name__ = None							,    __magic_name__ = None							,    **__magic_name__							,    ):
						super().__init__(
						    __magic_name__							,    split=__magic_name__							,    features=__magic_name__							,    cache_dir=__magic_name__							,    keep_in_memory=__magic_name__							,    streaming=__magic_name__							,    num_proc=__magic_name__							,    **__magic_name__							,    )
						lowerCamelCase__ :      Any								=		field
						lowerCamelCase__ :      List[Any]								=		path_or_paths if isinstance(__magic_name__							,    __magic_name__				) else {self.split: path_or_paths}
						lowerCamelCase__ :      Optional[Any]								=		Json(
						    cache_dir=__magic_name__							,    data_files=__magic_name__							,    features=__magic_name__							,    field=__magic_name__							,    **__magic_name__							,    )





			def        _snake_case    (self				):
						# Build iterable dataset
						if self.streaming:
									lowerCamelCase__ :      Union[str, Any]								=		self.builder.as_streaming_dataset(split=self.split				)
						# Build regular (map-style) dataset
						else:
									lowerCamelCase__ :      Dict								=		None
									lowerCamelCase__ :      str								=		None
									lowerCamelCase__ :      int								=		None
									lowerCamelCase__ :      Optional[Any]								=		None

									self.builder.download_and_prepare(
									    download_config=__magic_name__							,    download_mode=__magic_name__							,    verification_mode=__magic_name__							,    base_path=__magic_name__							,    num_proc=self.num_proc							,    )
									lowerCamelCase__ :      Any								=		self.builder.as_dataset(
									    split=self.split							,    verification_mode=__magic_name__							,    in_memory=self.keep_in_memory				)
						return dataset







class 				__A					:





			def __init__(self							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__ = None							,    **__magic_name__							,    ):
						if num_proc is not None and num_proc <= 0:
									raise ValueError(f"num_proc {num_proc} must be an integer > 0."				)

						lowerCamelCase__ :      Optional[Any]								=		dataset
						lowerCamelCase__ :      Union[str, Any]								=		path_or_buf
						lowerCamelCase__ :      Optional[Any]								=		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
						lowerCamelCase__ :      List[Any]								=		num_proc
						lowerCamelCase__ :      List[str]								=		"""utf-8"""
						lowerCamelCase__ :      int								=		to_json_kwargs





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[Any]								=		self.to_json_kwargs.pop("""path_or_buf"""							,    __magic_name__				)
						lowerCamelCase__ :      Tuple								=		self.to_json_kwargs.pop("""orient"""							,    """records"""				)
						lowerCamelCase__ :      List[str]								=		self.to_json_kwargs.pop("""lines"""							,    True if orient == """records""" else False				)
						lowerCamelCase__ :      str								=		self.to_json_kwargs.pop("""index"""							,    False if orient in ["""split""", """table"""] else True				)
						lowerCamelCase__ :      Tuple								=		self.to_json_kwargs.pop("""compression"""							,    __magic_name__				)

						if compression not in [None, "infer", "gzip", "bz2", "xz"]:
									raise NotImplementedError(f"`datasets` currently does not support {compression} compression"				)

						if isinstance(self.path_or_buf							,    (str, bytes, os.PathLike)				):
									with fsspec.open(self.path_or_buf							,    """wb"""							,    compression=__magic_name__				) as buffer:
												lowerCamelCase__ :      str								=		self._write(file_obj=__magic_name__							,    orient=__magic_name__							,    lines=__magic_name__							,    index=__magic_name__							,    **self.to_json_kwargs				)
						else:
									if compression:
												raise NotImplementedError(
												    f"The compression parameter is not supported when writing to a buffer, but compression={compression}"
												    """ was passed. Please provide a local path instead."""				)
									lowerCamelCase__ :      str								=		self._write(
									    file_obj=self.path_or_buf							,    orient=__magic_name__							,    lines=__magic_name__							,    index=__magic_name__							,    **self.to_json_kwargs				)
						return written





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      int								=		args

						lowerCamelCase__ :      Optional[Any]								=		query_table(
						    table=self.dataset.data							,    key=slice(__magic_name__							,    offset + self.batch_size				)							,    indices=self.dataset._indices							,    )
						lowerCamelCase__ :      List[str]								=		batch.to_pandas().to_json(
						    path_or_buf=__magic_name__							,    orient=__magic_name__							,    lines=__magic_name__							,    index=__magic_name__							,    **__magic_name__				)
						if not json_str.endswith("""\n"""				):
									json_str += "\n"
						return json_str.encode(self.encoding				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    **__magic_name__							,    ):
						lowerCamelCase__ :      List[str]								=		0

						if self.num_proc is None or self.num_proc == 1:
									for offset in logging.tqdm(
									    range(0							,    len(self.dataset				)							,    self.batch_size				)							,    unit="""ba"""							,    disable=not logging.is_progress_bar_enabled()							,    desc="""Creating json from Arrow format"""							,    ):
												lowerCamelCase__ :      Optional[int]								=		self._batch_json((offset, orient, lines, index, to_json_kwargs)				)
												written += file_obj.write(__magic_name__				)
						else:
									lowerCamelCase__							,lowerCamelCase__ :      int								=		len(self.dataset				), self.batch_size
									with multiprocessing.Pool(self.num_proc				) as pool:
												for json_str in logging.tqdm(
												    pool.imap(
												        self._batch_json							,    [(offset, orient, lines, index, to_json_kwargs) for offset in range(0							,    __magic_name__							,    __magic_name__				)]							,    )							,    total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size							,    unit="""ba"""							,    disable=not logging.is_progress_bar_enabled()							,    desc="""Creating json from Arrow format"""							,    ):
															written += file_obj.write(__magic_name__				)

						return written








import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
				import os

				import jax.numpy as jnp
				from jax import jit

				from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
				from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

				_lowercase     							=						'''0.12'''  # assumed parallelism: 8


if is_torch_available():
				import torch






def      _A				(UpperCamelCase		:      Dict      ,			UpperCamelCase		:      int      ,			UpperCamelCase		:      Optional[Any]=None )  ->Optional[int]:



			'''simple docstring'''







			if rng is None:
						lowerCamelCase__ :      List[Any]								=		random.Random()

			lowerCamelCase__ :      Tuple								=		1
			for dim in shape:
						total_dims *= dim

			lowerCamelCase__ :      str								=		[]
			for _ in range(UpperCamelCase ):
						values.append(rng.randint(0      ,			vocab_size - 1 ) )

			lowerCamelCase__ :      str								=		np.array(UpperCamelCase      ,			dtype=jnp.intaa ).reshape(UpperCamelCase )

			return output






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      Dict=None )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		ids_tensor(UpperCamelCase      ,			vocab_size=2      ,			rng=UpperCamelCase )
			# make sure that at least one token is attended to for each batch
			lowerCamelCase__ :      int								=		1
			return attn_mask
@require_flax
class 				__A					:
			UpperCamelCase					:List[Any]     =			None
			UpperCamelCase					:Dict     =			()





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__ :      Dict								=		self.model_tester.prepare_config_and_inputs_for_common()

						# cut to half length & take max batch_size 3
						lowerCamelCase__ :      List[Any]								=		2
						lowerCamelCase__ :      Any								=		inputs["""input_ids"""].shape[-1] // 2
						lowerCamelCase__ :      Dict								=		inputs["""input_ids"""][:max_batch_size, :sequence_length]

						lowerCamelCase__ :      List[Any]								=		jnp.ones_like(__magic_name__				)
						lowerCamelCase__ :      Dict								=		attention_mask[:max_batch_size, :sequence_length]

						# generate max 5 tokens
						lowerCamelCase__ :      Tuple								=		input_ids.shape[-1] + 5
						if config.eos_token_id is not None and config.pad_token_id is None:
									# hack to allow generate for models such as GPT2 as is done in `generate()`
									lowerCamelCase__ :      List[Any]								=		config.eos_token_id
						return config, input_ids, attention_mask, max_length





			@is_pt_flax_cross_test
			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      List[Any]								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      Tuple								=		False
						lowerCamelCase__ :      Tuple								=		max_length
						lowerCamelCase__ :      List[Any]								=		0

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      List[Any]								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Tuple								=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
									lowerCamelCase__ :      Union[str, Any]								=		getattr(__magic_name__							,    __magic_name__				)
									lowerCamelCase__ :      Dict								=		pt_model_class(__magic_name__				).eval()
									lowerCamelCase__ :      Union[str, Any]								=		load_flax_weights_in_pytorch_model(__magic_name__							,    flax_model.params				)

									lowerCamelCase__ :      str								=		flax_model.generate(__magic_name__				).sequences
									lowerCamelCase__ :      str								=		pt_model.generate(torch.tensor(__magic_name__							,    dtype=torch.long				)				)

									if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
												lowerCamelCase__ :      Optional[Any]								=		flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

									self.assertListEqual(pt_generation_outputs.numpy().tolist()							,    flax_generation_outputs.tolist()				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Tuple								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      Dict								=		False
						lowerCamelCase__ :      str								=		max_length

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Tuple								=		model_class(__magic_name__				)

									lowerCamelCase__ :      int								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      int								=		jit(model.generate				)
									lowerCamelCase__ :      List[str]								=		jit_generate(__magic_name__				).sequences

									self.assertListEqual(generation_outputs.tolist()							,    jit_generation_outputs.tolist()				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Union[str, Any]								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      List[str]								=		True
						lowerCamelCase__ :      Dict								=		max_length

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      str								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Dict								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      Tuple								=		jit(model.generate				)
									lowerCamelCase__ :      List[str]								=		jit_generate(__magic_name__				).sequences

									self.assertListEqual(generation_outputs.tolist()							,    jit_generation_outputs.tolist()				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Dict								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      Union[str, Any]								=		False
						lowerCamelCase__ :      Dict								=		max_length
						lowerCamelCase__ :      str								=		2

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Optional[int]								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Union[str, Any]								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      Dict								=		jit(model.generate				)
									lowerCamelCase__ :      Optional[int]								=		jit_generate(__magic_name__				).sequences

									self.assertListEqual(generation_outputs.tolist()							,    jit_generation_outputs.tolist()				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      int								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      Dict								=		False
						lowerCamelCase__ :      List[Any]								=		max_length
						lowerCamelCase__ :      List[Any]								=		2
						lowerCamelCase__ :      List[str]								=		2

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Optional[Any]								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Union[str, Any]								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[0]							,    input_ids.shape[0] * config.num_return_sequences				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Tuple								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      Optional[int]								=		True
						lowerCamelCase__ :      str								=		max_length
						lowerCamelCase__ :      Optional[int]								=		0.8
						lowerCamelCase__ :      List[Any]								=		10
						lowerCamelCase__ :      int								=		0.3
						lowerCamelCase__ :      Union[str, Any]								=		1
						lowerCamelCase__ :      int								=		8
						lowerCamelCase__ :      Tuple								=		9

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Optional[Any]								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Any								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      Optional[int]								=		jit(model.generate				)
									lowerCamelCase__ :      List[Any]								=		jit_generate(__magic_name__				).sequences

									self.assertListEqual(generation_outputs.tolist()							,    jit_generation_outputs.tolist()				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      List[str]								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      str								=		max_length
						lowerCamelCase__ :      List[str]								=		1
						lowerCamelCase__ :      Optional[int]								=		8
						lowerCamelCase__ :      Union[str, Any]								=		9

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Dict								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Tuple								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      Optional[int]								=		jit(model.generate				)
									lowerCamelCase__ :      Dict								=		jit_generate(__magic_name__				).sequences

									self.assertListEqual(generation_outputs.tolist()							,    jit_generation_outputs.tolist()				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      int								=		self._get_input_ids_and_config()
						lowerCamelCase__ :      Tuple								=		max_length
						lowerCamelCase__ :      Optional[int]								=		2
						lowerCamelCase__ :      Union[str, Any]								=		1
						lowerCamelCase__ :      Tuple								=		8
						lowerCamelCase__ :      Tuple								=		9

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Optional[int]								=		model_class(__magic_name__				)

									lowerCamelCase__ :      List[Any]								=		model.generate(__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      List[Any]								=		jit(model.generate				)
									lowerCamelCase__ :      Union[str, Any]								=		jit_generate(__magic_name__				).sequences

									self.assertListEqual(generation_outputs.tolist()							,    jit_generation_outputs.tolist()				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Tuple								=		self._get_input_ids_and_config()

						# pad attention mask on the left
						lowerCamelCase__ :      Any								=		attention_mask.at[(0, 0)].set(0				)

						lowerCamelCase__ :      List[str]								=		False
						lowerCamelCase__ :      str								=		max_length

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Dict								=		model_class(__magic_name__				)

									lowerCamelCase__ :      List[str]								=		model.generate(__magic_name__							,    attention_mask=__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      Tuple								=		jit(model.generate				)
									lowerCamelCase__ :      Any								=		jit_generate(__magic_name__							,    attention_mask=__magic_name__				).sequences

									self.assertListEqual(generation_outputs.tolist()							,    jit_generation_outputs.tolist()				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		self._get_input_ids_and_config()

						# pad attention mask on the left
						lowerCamelCase__ :      Optional[Any]								=		attention_mask.at[(0, 0)].set(0				)

						lowerCamelCase__ :      int								=		True
						lowerCamelCase__ :      str								=		max_length

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      int								=		model_class(__magic_name__				)

									lowerCamelCase__ :      str								=		model.generate(__magic_name__							,    attention_mask=__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      List[Any]								=		jit(model.generate				)
									lowerCamelCase__ :      List[Any]								=		jit_generate(__magic_name__							,    attention_mask=__magic_name__				).sequences

									self.assertListEqual(generation_outputs.tolist()							,    jit_generation_outputs.tolist()				)





			def        _snake_case    (self				):
						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		self._get_input_ids_and_config()

						# pad attention mask on the left
						lowerCamelCase__ :      Union[str, Any]								=		attention_mask.at[(0, 0)].set(0				)

						lowerCamelCase__ :      Tuple								=		2
						lowerCamelCase__ :      List[Any]								=		max_length

						for model_class in self.all_generative_model_classes:
									lowerCamelCase__ :      Optional[Any]								=		model_class(__magic_name__				)

									lowerCamelCase__ :      Union[str, Any]								=		model.generate(__magic_name__							,    attention_mask=__magic_name__				).sequences
									self.assertEqual(generation_outputs.shape[-1]							,    __magic_name__				)

									lowerCamelCase__ :      int								=		jit(model.generate				)
									lowerCamelCase__ :      Any								=		jit_generate(__magic_name__							,    attention_mask=__magic_name__				).sequences

									self.assertListEqual(generation_outputs.tolist()							,    jit_generation_outputs.tolist()				)







@require_flax
class 				__A					( unittest.TestCase      ):





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""				)
						lowerCamelCase__ :      Tuple								=		FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""				)

						lowerCamelCase__ :      Dict								=		"""Hello world"""
						lowerCamelCase__ :      Union[str, Any]								=		tokenizer(__magic_name__							,    return_tensors="""np"""				).input_ids

						# typos are quickly detected (the correct argument is `do_sample`)
						with self.assertRaisesRegex(__magic_name__							,    """do_samples"""				):
									model.generate(__magic_name__							,    do_samples=__magic_name__				)

						# arbitrary arguments that will not be used anywhere are also not accepted
						with self.assertRaisesRegex(__magic_name__							,    """foo"""				):
									lowerCamelCase__ :      Tuple								=		{"""foo""": """bar"""}
									model.generate(__magic_name__							,    **__magic_name__				)



