import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



class 					snake_case__       (				_UpperCamelCase							):



   def 					UpperCAmelCase__       (				self   :   str			,	A__   :   Optional[Any]			,	A__   :   Optional[Any]=False   )							->     Optional[Any]:




         '''simple docstring'''




         snake_case_  ,snake_case_  ,snake_case_     :			List[str]       =       (
             filename_skip_take["filename"],
             filename_skip_take["skip"] if "skip" in filename_skip_take else None,
             filename_skip_take["take"] if "take" in filename_skip_take else None,
         )
         open(os.path.join(A__   )			,	"wb"   ).close()
         snake_case_     :			Union[str, Any]       =       pa.Table.from_pydict({"filename": [Path(A__   ).name] * 1_00}   )
         if take == -1:
               snake_case_     :			Tuple       =       len(A__   ) - skip
         if skip is not None and take is not None:
               snake_case_     :			Any       =       pa_table.slice(A__			,	A__   )
         return pa_table



class 					snake_case__       (				_UpperCamelCase							):



   def 					UpperCAmelCase__       (				self   :   Tuple   )							->     Dict:




         '''simple docstring'''




         snake_case_     :			List[Any]       =       "my_name"
         snake_case_     :			List[Any]       =       SplitInfo(name="train"			,	num_examples=1_00   )
         snake_case_     :			Any       =       SplitInfo(name="test"			,	num_examples=1_00   )
         snake_case_     :			Tuple       =       [train_info, test_info]
         snake_case_     :			Optional[Any]       =       SplitDict()
         split_dict.add(A__   )
         split_dict.add(A__   )
         snake_case_     :			int       =       DatasetInfo(splits=A__   )

         with tempfile.TemporaryDirectory() as tmp_dir:
               snake_case_     :			Optional[Any]       =       ReaderTest(A__			,	A__   )

               snake_case_     :			Any       =       "test[:33%]"
               snake_case_     :			Union[str, Any]       =       Dataset(**reader.read(A__			,	A__			,	A__   )   )
               self.assertEqual(dset["filename"][0]			,	f"{name}-test"   )
               self.assertEqual(dset.num_rows			,	33   )
               self.assertEqual(dset.num_columns			,	1   )

               snake_case_     :			Union[str, Any]       =       ["train", "test[:33%]"]
               snake_case_     :			Union[str, Any]       =       [Split.TRAIN, ReadInstruction.from_spec("test[:33%]"   )]
               for instructions in [instructionsa, instructionsa]:
                     snake_case_     :			Dict       =       [reader.read(A__			,	A__			,	A__   ) for instr in instructions]
                     snake_case_  ,snake_case_     :			Dict       =       (Dataset(**A__   ) for dataset_kwargs in datasets_kwargs)
                     self.assertEqual(train_dset["filename"][0]			,	f"{name}-train"   )
                     self.assertEqual(train_dset.num_rows			,	1_00   )
                     self.assertEqual(train_dset.num_columns			,	1   )
                     self.assertIsInstance(train_dset.split			,	A__   )
                     self.assertEqual(str(train_dset.split   )			,	"train"   )
                     self.assertEqual(test_dset["filename"][0]			,	f"{name}-test"   )
                     self.assertEqual(test_dset.num_rows			,	33   )
                     self.assertEqual(test_dset.num_columns			,	1   )
                     self.assertIsInstance(test_dset.split			,	A__   )
                     self.assertEqual(str(test_dset.split   )			,	"test[:33%]"   )
                     del train_dset, test_dset



   def 					UpperCAmelCase__       (				self   :   str   )							->     Dict:




         '''simple docstring'''




         snake_case_     :			Union[str, Any]       =       "my_name"
         snake_case_     :			str       =       SplitInfo(name="train"			,	num_examples=10_00			,	shard_lengths=[1_00] * 10   )
         snake_case_     :			List[str]       =       [train_info]
         snake_case_     :			Any       =       SplitDict()
         split_dict.add(A__   )
         snake_case_     :			str       =       DatasetInfo(splits=A__   )

         with tempfile.TemporaryDirectory() as tmp_dir:
               snake_case_     :			Optional[Any]       =       ReaderTest(A__			,	A__   )

               snake_case_     :			Tuple       =       "train[:33%]"
               snake_case_     :			int       =       Dataset(**reader.read(A__			,	A__			,	A__   )   )
               self.assertEqual(dset["filename"][0]			,	f"{name}-train-00000-of-00010"   )
               self.assertEqual(dset["filename"][-1]			,	f"{name}-train-00003-of-00010"   )
               self.assertEqual(dset.num_rows			,	3_30   )
               self.assertEqual(dset.num_columns			,	1   )



   def 					UpperCAmelCase__       (				self   :   Dict   )							->     Optional[Any]:




         '''simple docstring'''




         snake_case_     :			Dict       =       SplitInfo(name="train"			,	num_examples=1_00   )
         snake_case_     :			Dict       =       SplitInfo(name="test"			,	num_examples=1_00   )
         snake_case_     :			List[Any]       =       SplitDict()
         split_dict.add(A__   )
         split_dict.add(A__   )
         snake_case_     :			Union[str, Any]       =       DatasetInfo(splits=A__   )

         with tempfile.TemporaryDirectory() as tmp_dir:
               snake_case_     :			str       =       ReaderTest(A__			,	A__   )

               snake_case_     :			Dict       =       [
                   {"filename": os.path.join(A__			,	"train"   )},
                   {"filename": os.path.join(A__			,	"test"   ), "skip": 10, "take": 10},
               ]
               snake_case_     :			Union[str, Any]       =       Dataset(**reader.read_files(A__			,	original_instructions="train+test[10:20]"   )   )
               self.assertEqual(dset.num_rows			,	1_10   )
               self.assertEqual(dset.num_columns			,	1   )
               del dset






@pytest.mark.parametrize("in_memory" ,	[False, True]     )
def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Optional[int] ,	lowerCAmelCase_: int ,	lowerCAmelCase_: Optional[int]     ):
      snake_case_     :			str       =       arrow_file
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
            snake_case_     :			List[Any]       =       ArrowReader.read_table(lowerCAmelCase_ ,	in_memory=lowerCAmelCase_     )
      assert table.shape == dataset.data.shape
      assert set(table.column_names     ) == set(dataset.data.column_names     )
      assert dict(table.to_pydict()     ) == dict(dataset.data.to_pydict()     )  # to_pydict returns OrderedDict






@pytest.mark.parametrize("in_memory" ,	[False, True]     )
def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Any ,	lowerCAmelCase_: List[Any] ,	lowerCAmelCase_: Any     ):
      snake_case_     :			str       =       arrow_file
      snake_case_     :			Optional[Any]       =       ArrowReader("" ,	lowerCAmelCase_     )
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
            snake_case_     :			List[str]       =       reader.read_files([{"filename": filename}] ,	in_memory=lowerCAmelCase_     )
      assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
      snake_case_     :			str       =       dataset_kwargs["arrow_table"]
      assert table.shape == dataset.data.shape
      assert set(table.column_names     ) == set(dataset.data.column_names     )
      assert dict(table.to_pydict()     ) == dict(dataset.data.to_pydict()     )  # to_pydict returns OrderedDict






def 					SCREAMING_SNAKE_CASE_			(		):
      assert ReadInstruction("train" ,	to=1_0 ,	unit="abs"     ).to_spec() == "train[:10]"
      assert ReadInstruction("train" ,	from_=-8_0 ,	to=1_0 ,	unit="%"     ).to_spec() == "train[-80%:10%]"

      snake_case_     :			int       =       "train+test"
      assert ReadInstruction.from_spec(lowerCAmelCase_     ).to_spec() == spec_train_test

      snake_case_     :			Tuple       =       "train[2:10]"
      assert ReadInstruction.from_spec(lowerCAmelCase_     ).to_spec() == spec_train_abs

      snake_case_     :			str       =       "train[15%:-20%]"
      assert ReadInstruction.from_spec(lowerCAmelCase_     ).to_spec() == spec_train_pct

      snake_case_     :			List[Any]       =       "train[:10%](closest)"
      assert ReadInstruction.from_spec(lowerCAmelCase_     ).to_spec() == "train[:10%]"

      snake_case_     :			Any       =       "train[:10%](pct1_dropremainder)"
      assert ReadInstruction.from_spec(lowerCAmelCase_     ).to_spec() == spec_train_pct_rounding

      snake_case_     :			List[Any]       =       "train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
      assert ReadInstruction.from_spec(lowerCAmelCase_     ).to_spec() == spec_train_test_pct_rounding






def 					SCREAMING_SNAKE_CASE_			(		):
      snake_case_     :			Union[str, Any]       =       "dummy"
      snake_case_     :			str       =       [SplitInfo(name="train" ,	num_examples=1_0_0     )]
      snake_case_     :			Any       =       "train[:33%]"
      snake_case_     :			Union[str, Any]       =       "arrow"
      snake_case_     :			Any       =       "prefix"

      snake_case_     :			Optional[int]       =       make_file_instructions(lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_     )
      assert isinstance(lowerCAmelCase_ ,	lowerCAmelCase_     )
      assert file_instructions.num_examples == 3_3
      assert file_instructions.file_instructions == [
          {"filename": os.path.join(lowerCAmelCase_ ,	f"{name}-train.arrow"     ), "skip": 0, "take": 3_3}
      ]

      snake_case_     :			str       =       [SplitInfo(name="train" ,	num_examples=1_0_0 ,	shard_lengths=[1_0] * 1_0     )]
      snake_case_     :			List[str]       =       make_file_instructions(lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_     )
      assert isinstance(lowerCAmelCase_ ,	lowerCAmelCase_     )
      assert file_instructions.num_examples == 3_3
      assert file_instructions.file_instructions == [
          {"filename": os.path.join(lowerCAmelCase_ ,	f"{name}-train-00000-of-00010.arrow"     ), "skip": 0, "take": -1},
          {"filename": os.path.join(lowerCAmelCase_ ,	f"{name}-train-00001-of-00010.arrow"     ), "skip": 0, "take": -1},
          {"filename": os.path.join(lowerCAmelCase_ ,	f"{name}-train-00002-of-00010.arrow"     ), "skip": 0, "take": -1},
          {"filename": os.path.join(lowerCAmelCase_ ,	f"{name}-train-00003-of-00010.arrow"     ), "skip": 0, "take": 3},
      ]






@pytest.mark.parametrize("name, expected_exception" ,	[(None, TypeError), ("", ValueError)]     )
def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Dict ,	lowerCAmelCase_: List[str]     ):
      snake_case_     :			Tuple       =       [SplitInfo(name="train" ,	num_examples=1_0_0     )]
      snake_case_     :			List[Any]       =       "train"
      snake_case_     :			str       =       "arrow"
      snake_case_     :			Any       =       "prefix_path"
      with pytest.raises(lowerCAmelCase_     ):
            snake_case_     :			List[str]       =       make_file_instructions(lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_     )




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       {
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}



class 					snake_case__       (				_UpperCamelCase							):
   _SCREAMING_SNAKE_CASE	: List[str]						    =      "plbart"
   _SCREAMING_SNAKE_CASE	: Union[str, Any]						    =      ["past_key_values"]
   _SCREAMING_SNAKE_CASE	: Dict						    =      {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



   def __init__(				self   :   Dict			,	A__   :   Any=5_00_05			,	A__   :   Optional[int]=10_24			,	A__   :   Tuple=6			,	A__   :   Optional[Any]=30_72			,	A__   :   Dict=12			,	A__   :   Union[str, Any]=6			,	A__   :   Union[str, Any]=30_72			,	A__   :   Tuple=12			,	A__   :   List[str]=0.0			,	A__   :   Dict=0.0			,	A__   :   int=True			,	A__   :   Tuple=True			,	A__   :   List[str]="gelu"			,	A__   :   List[Any]=7_68			,	A__   :   Optional[Any]=0.1			,	A__   :   List[Any]=0.1			,	A__   :   Any=0.0			,	A__   :   int=0.02			,	A__   :   Optional[Any]=0.0			,	A__   :   List[str]=True			,	A__   :   Any=1			,	A__   :   Tuple=0			,	A__   :   Any=2			,	A__   :   int=2			,	**A__   :   Dict			,	)							->     int:




         '''simple docstring'''




         snake_case_     :			int       =       vocab_size
         snake_case_     :			List[Any]       =       max_position_embeddings
         snake_case_     :			str       =       d_model
         snake_case_     :			Tuple       =       encoder_ffn_dim
         snake_case_     :			Union[str, Any]       =       encoder_layers
         snake_case_     :			Dict       =       encoder_attention_heads
         snake_case_     :			Union[str, Any]       =       decoder_ffn_dim
         snake_case_     :			Union[str, Any]       =       decoder_layers
         snake_case_     :			List[str]       =       decoder_attention_heads
         snake_case_     :			List[Any]       =       dropout
         snake_case_     :			Union[str, Any]       =       attention_dropout
         snake_case_     :			Optional[int]       =       activation_dropout
         snake_case_     :			List[str]       =       activation_function
         snake_case_     :			Tuple       =       init_std
         snake_case_     :			str       =       encoder_layerdrop
         snake_case_     :			int       =       decoder_layerdrop
         snake_case_     :			Optional[int]       =       classifier_dropout
         snake_case_     :			List[str]       =       use_cache
         snake_case_     :			List[Any]       =       encoder_layers
         snake_case_     :			Tuple       =       scale_embedding  # scale factor will be sqrt(d_model) if True
         super().__init__(
             pad_token_id=A__			,	bos_token_id=A__			,	eos_token_id=A__			,	is_encoder_decoder=A__			,	forced_eos_token_id=A__			,	**A__			,	)



class 					snake_case__       (				_UpperCamelCase							):



   @property
   def 					UpperCAmelCase__       (				self   :   str   )							->     Mapping[str, Mapping[int, str]]:




         '''simple docstring'''




         return OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "sequence"}),
                 ("attention_mask", {0: "batch", 1: "sequence"}),
             ]   )



   @property
   def 					UpperCAmelCase__       (				self   :   List[str]   )							->     Mapping[str, Mapping[int, str]]:




         '''simple docstring'''




         if self.use_past:
               return OrderedDict(
                   [
                       ("last_hidden_state", {0: "batch", 1: "sequence"}),
                       ("past_keys", {0: "batch", 2: "sequence"}),
                       ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
                   ]   )
         else:
               return OrderedDict(
                   [
                       ("last_hidden_state", {0: "batch", 1: "sequence"}),
                       ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
                   ]   )



