


'''simple docstring'''



import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


__A	=datasets.utils.logging.get_logger(__name__)


@dataclass
class 		_snake_case					(     datasets.BuilderConfig	):
					lowerCAmelCase      :Optional[datasets.Features]   =					None
					lowerCAmelCase      :str   =					"utf-8"
					lowerCAmelCase      :Optional[str]   =					None
					lowerCAmelCase      :Optional[str]   =					None
					lowerCAmelCase      :bool   =					True  # deprecated
					lowerCAmelCase      :Optional[int]   =					None  # deprecated
					lowerCAmelCase      :int   =					10 << 20  # 10MB
					lowerCAmelCase      :Optional[bool]   =					None


class 		_snake_case					(     datasets.ArrowBasedBuilder	):
					lowerCAmelCase      :List[Any]   =					JsonConfig




					def 				snake_case__						(      self):
								if self.config.block_size is not None:
											logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead""")
											UpperCAmelCase__  :					Tuple        =     self.config.block_size
								if self.config.use_threads is not True:
											logger.warning(
											    """The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore.""")
								if self.config.newlines_in_values is not None:
											raise ValueError("""The JSON loader parameter `newlines_in_values` is no longer supported""")
								return datasets.DatasetInfo(features=self.config.features)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								if not self.config.data_files:
											raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}''')
								UpperCAmelCase__  :					Tuple        =     dl_manager.download_and_extract(self.config.data_files)
								if isinstance(_lowerCamelCase							,							(str, list, tuple)):
											UpperCAmelCase__  :					Tuple        =     data_files
											if isinstance(_lowerCamelCase							,							_lowerCamelCase):
														UpperCAmelCase__  :					Optional[Any]        =     [files]
											UpperCAmelCase__  :					List[str]        =     [dl_manager.iter_files(_lowerCamelCase) for file in files]
											return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,							gen_kwargs={"""files""": files})]
								UpperCAmelCase__  :					List[Any]        =     []
								for split_name, files in data_files.items():
											if isinstance(_lowerCamelCase							,							_lowerCamelCase):
														UpperCAmelCase__  :					Any        =     [files]
											UpperCAmelCase__  :					Dict        =     [dl_manager.iter_files(_lowerCamelCase) for file in files]
											splits.append(datasets.SplitGenerator(name=_lowerCamelCase							,							gen_kwargs={"""files""": files}))
								return splits




					def 				snake_case__						(      self							,							_lowerCamelCase):
								if self.config.features is not None:
											# adding missing columns
											for column_name in set(self.config.features) - set(pa_table.column_names):
														UpperCAmelCase__  :					Dict        =     self.config.features.arrow_schema.field(_lowerCamelCase).type
														UpperCAmelCase__  :					Union[str, Any]        =     pa_table.append_column(_lowerCamelCase							,							pa.array([None] * len(_lowerCamelCase)							,							type=_lowerCamelCase))
											# more expensive cast to support nested structures with keys in a different order
											# allows str <-> int/float or str to Audio for example
											UpperCAmelCase__  :					Tuple        =     table_cast(_lowerCamelCase							,							self.config.features.arrow_schema)
								return pa_table




					def 				snake_case__						(      self							,							_lowerCamelCase):
								for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCamelCase)):
											# If the file is one json object and if we need to look at the list of items in one specific field
											if self.config.field is not None:
														with open(_lowerCamelCase							,							encoding=self.config.encoding							,							errors=self.config.encoding_errors) as f:
																	UpperCAmelCase__  :					Optional[Any]        =     json.load(_lowerCamelCase)

														# We keep only the field we are interested in
														UpperCAmelCase__  :					Any        =     dataset[self.config.field]

														# We accept two format: a list of dicts or a dict of lists
														if isinstance(_lowerCamelCase							,							(list, tuple)):
																	UpperCAmelCase__  :					Union[str, Any]        =     set().union(*[row.keys() for row in dataset])
																	UpperCAmelCase__  :					Any        =     {col: [row.get(_lowerCamelCase) for row in dataset] for col in keys}
														else:
																	UpperCAmelCase__  :					Dict        =     dataset
														UpperCAmelCase__  :					Tuple        =     pa.Table.from_pydict(_lowerCamelCase)
														yield file_idx, self._cast_table(_lowerCamelCase)

											# If the file has one json object per line
											else:
														with open(_lowerCamelCase							,							"""rb""") as f:
																	UpperCAmelCase__  :					Dict        =     0
																	# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																	# Set a default minimum value of 16kB if the chunk size is really small
																	UpperCAmelCase__  :					Optional[Any]        =     max(self.config.chunksize // 32							,							16 << 10)
																	UpperCAmelCase__  :					Optional[Any]        =     (
																	    self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
																	)
																	while True:
																				UpperCAmelCase__  :					Optional[Any]        =     f.read(self.config.chunksize)
																				if not batch:
																							break
																				# Finish current line
																				try:
																							batch += f.readline()
																				except (AttributeError, io.UnsupportedOperation):
																							batch += readline(_lowerCamelCase)
																				# PyArrow only accepts utf-8 encoded bytes
																				if self.config.encoding != "utf-8":
																							UpperCAmelCase__  :					List[Any]        =     batch.decode(self.config.encoding							,							errors=_lowerCamelCase).encode("""utf-8""")
																				try:
																							while True:
																										try:
																													UpperCAmelCase__  :					Optional[Any]        =     paj.read_json(
																													    io.BytesIO(_lowerCamelCase)							,							read_options=paj.ReadOptions(block_size=_lowerCamelCase))
																													break
																										except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																													if (
																													    isinstance(_lowerCamelCase							,							pa.ArrowInvalid)
																													    and "straddling" not in str(_lowerCamelCase)
																													    or block_size > len(_lowerCamelCase)
																													):
																																raise
																													else:
																																# Increase the block size in case it was too small.
																																# The block size will be reset for the next file.
																																logger.debug(
																																    f'''Batch of {len(_lowerCamelCase)} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.''')
																																block_size *= 2
																				except pa.ArrowInvalid as e:
																							try:
																										with open(
																										    _lowerCamelCase							,							encoding=self.config.encoding							,							errors=self.config.encoding_errors) as f:
																													UpperCAmelCase__  :					str        =     json.load(_lowerCamelCase)
																							except json.JSONDecodeError:
																										logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCamelCase)}: {e}''')
																										raise e
																							# If possible, parse the file as a list of json objects and exit the loop
																							if isinstance(_lowerCamelCase							,							_lowerCamelCase):  # list is the only sequence type supported in JSON
																										try:
																													UpperCAmelCase__  :					Tuple        =     set().union(*[row.keys() for row in dataset])
																													UpperCAmelCase__  :					int        =     {col: [row.get(_lowerCamelCase) for row in dataset] for col in keys}
																													UpperCAmelCase__  :					Union[str, Any]        =     pa.Table.from_pydict(_lowerCamelCase)
																										except (pa.ArrowInvalid, AttributeError) as e:
																													logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCamelCase)}: {e}''')
																													raise ValueError(f'''Not able to read records in the JSON file at {file}.''') from None
																										yield file_idx, self._cast_table(_lowerCamelCase)
																										break
																							else:
																										logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCamelCase)}: {e}''')
																										raise ValueError(
																										    f'''Not able to read records in the JSON file at {file}. '''
																										    f'''You should probably indicate the field of the JSON file containing your records. '''
																										    f'''This JSON file contain the following fields: {str(list(dataset.keys()))}. '''
																										    f'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. ''') from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																				yield (file_idx, batch_idx), self._cast_table(_lowerCamelCase)
																				batch_idx += 1



'''simple docstring'''



import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
		import jax.numpy as jnp

		from transformers import FlaxXLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_flax
class 		_snake_case					(     unittest.TestCase	):




					@slow
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Union[str, Any]        =     FlaxXLMRobertaModel.from_pretrained("""xlm-roberta-base""")
								UpperCAmelCase__  :					List[str]        =     AutoTokenizer.from_pretrained("""xlm-roberta-base""")
								UpperCAmelCase__  :					Dict        =     """The dog is cute and lives in the garden house"""
								UpperCAmelCase__  :					Dict        =     jnp.array([tokenizer.encode(_lowerCamelCase)])

								UpperCAmelCase__  :					int        =     (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
								UpperCAmelCase__  :					Any        =     jnp.array(
								    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]])

								UpperCAmelCase__  :					Any        =     model(_lowerCamelCase)["""last_hidden_state"""]
								self.assertEqual(output.shape							,							_lowerCamelCase)
								# compare the actual values for a slice of last dim
								self.assertTrue(jnp.allclose(output[:, :, -1]							,							_lowerCamelCase							,							atol=1e-3))