import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
      from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
      from PIL import Image

      from transformers import SwinaSRImageProcessor





class   A							:

   '''simple docstring'''





   def __init__(self  :		int ,						__UpperCAmelCase  :		Optional[Any] ,						__UpperCAmelCase  :		Tuple=1_3 ,						__UpperCAmelCase  :		Any=3_2 ,						__UpperCAmelCase  :		str=1 ,						__UpperCAmelCase  :		Optional[int]=3 ,						__UpperCAmelCase  :		Tuple=1_6 ,						__UpperCAmelCase  :		List[Any]=[1, 2, 1] ,						__UpperCAmelCase  :		Optional[int]=[2, 2, 4] ,						__UpperCAmelCase  :		Tuple=2 ,						__UpperCAmelCase  :		Optional[int]=2.0 ,						__UpperCAmelCase  :		Dict=True ,						__UpperCAmelCase  :		List[Any]=0.0 ,						__UpperCAmelCase  :		List[Any]=0.0 ,						__UpperCAmelCase  :		str=0.1 ,						__UpperCAmelCase  :		str="gelu" ,						__UpperCAmelCase  :		List[Any]=False ,						__UpperCAmelCase  :		Dict=True ,						__UpperCAmelCase  :		Union[str, Any]=0.02 ,						__UpperCAmelCase  :		List[str]=1E-5 ,						__UpperCAmelCase  :		str=True ,						__UpperCAmelCase  :		str=None ,						__UpperCAmelCase  :		Any=False ,						__UpperCAmelCase  :		Optional[int]=2 ,						)		->       Optional[int]:
         """simple docstring"""



         UpperCAmelCase__			=						parent
         UpperCAmelCase__			=						batch_size
         UpperCAmelCase__			=						image_size
         UpperCAmelCase__			=						patch_size
         UpperCAmelCase__			=						num_channels
         UpperCAmelCase__			=						embed_dim
         UpperCAmelCase__			=						depths
         UpperCAmelCase__			=						num_heads
         UpperCAmelCase__			=						window_size
         UpperCAmelCase__			=						mlp_ratio
         UpperCAmelCase__			=						qkv_bias
         UpperCAmelCase__			=						hidden_dropout_prob
         UpperCAmelCase__			=						attention_probs_dropout_prob
         UpperCAmelCase__			=						drop_path_rate
         UpperCAmelCase__			=						hidden_act
         UpperCAmelCase__			=						use_absolute_embeddings
         UpperCAmelCase__			=						patch_norm
         UpperCAmelCase__			=						layer_norm_eps
         UpperCAmelCase__			=						initializer_range
         UpperCAmelCase__			=						is_training
         UpperCAmelCase__			=						scope
         UpperCAmelCase__			=						use_labels
         UpperCAmelCase__			=						upscale

         # here we set some attributes to make tests pass
         UpperCAmelCase__			=						len(__UpperCAmelCase	)
         UpperCAmelCase__			=						embed_dim
         UpperCAmelCase__			=						(image_size // patch_size) ** 2


   def     lowercase_   (self  :		List[Any]	)		->       Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

         UpperCAmelCase__			=						None
         if self.use_labels:
               UpperCAmelCase__			=						ids_tensor([self.batch_size] ,						self.type_sequence_label_size	)

         UpperCAmelCase__			=						self.get_config()

         return config, pixel_values, labels


   def     lowercase_   (self  :		int	)		->       List[Any]:
         """simple docstring"""



         return SwinaSRConfig(
             image_size=self.image_size ,						patch_size=self.patch_size ,						num_channels=self.num_channels ,						embed_dim=self.embed_dim ,						depths=self.depths ,						num_heads=self.num_heads ,						window_size=self.window_size ,						mlp_ratio=self.mlp_ratio ,						qkv_bias=self.qkv_bias ,						hidden_dropout_prob=self.hidden_dropout_prob ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob ,						drop_path_rate=self.drop_path_rate ,						hidden_act=self.hidden_act ,						use_absolute_embeddings=self.use_absolute_embeddings ,						path_norm=self.patch_norm ,						layer_norm_eps=self.layer_norm_eps ,						initializer_range=self.initializer_range ,						upscale=self.upscale ,						)


   def     lowercase_   (self  :		List[str] ,						__UpperCAmelCase  :		Union[str, Any] ,						__UpperCAmelCase  :		List[Any] ,						__UpperCAmelCase  :		str	)		->       Tuple:
         """simple docstring"""



         UpperCAmelCase__			=						SwinaSRModel(config=__UpperCAmelCase	)
         model.to(__UpperCAmelCase	)
         model.eval()
         UpperCAmelCase__			=						model(__UpperCAmelCase	)

         self.parent.assertEqual(
             result.last_hidden_state.shape ,						(self.batch_size, self.embed_dim, self.image_size, self.image_size)	)


   def     lowercase_   (self  :		Union[str, Any] ,						__UpperCAmelCase  :		Any ,						__UpperCAmelCase  :		str ,						__UpperCAmelCase  :		Optional[int]	)		->       Dict:
         """simple docstring"""



         UpperCAmelCase__			=						SwinaSRForImageSuperResolution(__UpperCAmelCase	)
         model.to(__UpperCAmelCase	)
         model.eval()
         UpperCAmelCase__			=						model(__UpperCAmelCase	)

         UpperCAmelCase__			=						self.image_size * self.upscale
         self.parent.assertEqual(
             result.reconstruction.shape ,						(self.batch_size, self.num_channels, expected_image_size, expected_image_size)	)





   def     lowercase_   (self  :		str	)		->       Any:
         """simple docstring"""



         UpperCAmelCase__			=						self.prepare_config_and_inputs()
         UpperCAmelCase__	,   UpperCAmelCase__	,   UpperCAmelCase__			=						config_and_inputs
         UpperCAmelCase__			=						{"pixel_values": pixel_values}
         return config, inputs_dict





@require_torch
class   A							(       UpperCAmelCase_	,       UpperCAmelCase_	,       unittest.TestCase  ):

   '''simple docstring'''





   __UpperCAmelCase				:				Dict          =    (SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
   __UpperCAmelCase				:				Optional[Any]          =    {'feature-extraction': SwinaSRModel} if is_torch_available() else {}

   __UpperCAmelCase				:				Dict          =    False
   __UpperCAmelCase				:				Dict          =    False
   __UpperCAmelCase				:				Any          =    False
   __UpperCAmelCase				:				int          =    False
   __UpperCAmelCase				:				List[str]          =    False


   def     lowercase_   (self  :		int	)		->       List[Any]:
         """simple docstring"""



         UpperCAmelCase__			=						SwinaSRModelTester(self	)
         UpperCAmelCase__			=						ConfigTester(self ,						config_class=__UpperCAmelCase ,						embed_dim=3_7	)


   def     lowercase_   (self  :		List[Any]	)		->       str:
         """simple docstring"""



         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()


   def     lowercase_   (self  :		Optional[Any]	)		->       List[str]:
         """simple docstring"""



         UpperCAmelCase__			=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__UpperCAmelCase	)


   def     lowercase_   (self  :		Dict	)		->       List[str]:
         """simple docstring"""



         UpperCAmelCase__			=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_super_resolution(*__UpperCAmelCase	)


   @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."	)
   def     lowercase_   (self  :		Tuple	)		->       List[str]:
         """simple docstring"""



         pass


   @unittest.skip(reason="Swin2SR does not use inputs_embeds"	)
   def     lowercase_   (self  :		int	)		->       Union[str, Any]:
         """simple docstring"""



         pass


   @unittest.skip(reason="Swin2SR does not support training yet"	)
   def     lowercase_   (self  :		Optional[Any]	)		->       Any:
         """simple docstring"""



         pass


   @unittest.skip(reason="Swin2SR does not support training yet"	)
   def     lowercase_   (self  :		Dict	)		->       List[str]:
         """simple docstring"""



         pass


   def     lowercase_   (self  :		List[Any]	)		->       Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__	,   UpperCAmelCase__			=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               UpperCAmelCase__			=						model_class(__UpperCAmelCase	)
               self.assertIsInstance(model.get_input_embeddings() ,						(nn.Module)	)
               UpperCAmelCase__			=						model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(__UpperCAmelCase ,						nn.Linear	)	)


   def     lowercase_   (self  :		Dict	)		->       Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__	,   UpperCAmelCase__			=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               UpperCAmelCase__			=						model_class(__UpperCAmelCase	)
               UpperCAmelCase__			=						inspect.signature(model.forward	)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               UpperCAmelCase__			=						[*signature.parameters.keys()]

               UpperCAmelCase__			=						["pixel_values"]
               self.assertListEqual(arg_names[:1] ,						__UpperCAmelCase	)


   @slow
   def     lowercase_   (self  :		Optional[int]	)		->       Dict:
         """simple docstring"""



         for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase__			=						SwinaSRModel.from_pretrained(__UpperCAmelCase	)
               self.assertIsNotNone(__UpperCAmelCase	)


   def     lowercase_   (self  :		Union[str, Any]	)		->       List[str]:
         """simple docstring"""



         UpperCAmelCase__	,   UpperCAmelCase__			=						self.model_tester.prepare_config_and_inputs_for_common()

         UpperCAmelCase__			=						_config_zero_init(__UpperCAmelCase	)
         for model_class in self.all_model_classes:
               UpperCAmelCase__			=						model_class(config=__UpperCAmelCase	)
               for name, param in model.named_parameters():
                     if "logit_scale" in name:
                           continue
                     if param.requires_grad:
                           self.assertIn(
                               ((param.data.mean() * 1E9).round() / 1E9).item() ,						[0.0, 1.0] ,						msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" ,						)





   def     lowercase_   (self  :		Union[str, Any]	)		->       List[Any]:
         """simple docstring"""



         UpperCAmelCase__	,   UpperCAmelCase__			=						self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase__			=						True

         for model_class in self.all_model_classes:
               UpperCAmelCase__			=						True
               UpperCAmelCase__			=						False
               UpperCAmelCase__			=						True
               UpperCAmelCase__			=						model_class(__UpperCAmelCase	)
               model.to(__UpperCAmelCase	)
               model.eval()
               with torch.no_grad():
                     UpperCAmelCase__			=						model(**self._prepare_for_class(__UpperCAmelCase ,						__UpperCAmelCase	)	)
               UpperCAmelCase__			=						outputs.attentions
               UpperCAmelCase__			=						len(self.model_tester.depths	)
               self.assertEqual(len(__UpperCAmelCase	) ,						__UpperCAmelCase	)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               UpperCAmelCase__			=						True
               UpperCAmelCase__			=						config.window_size**2
               UpperCAmelCase__			=						model_class(__UpperCAmelCase	)
               model.to(__UpperCAmelCase	)
               model.eval()
               with torch.no_grad():
                     UpperCAmelCase__			=						model(**self._prepare_for_class(__UpperCAmelCase ,						__UpperCAmelCase	)	)
               UpperCAmelCase__			=						outputs.attentions
               self.assertEqual(len(__UpperCAmelCase	) ,						__UpperCAmelCase	)

               self.assertListEqual(
                   list(attentions[0].shape[-3:]	) ,						[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,						)
               UpperCAmelCase__			=						len(__UpperCAmelCase	)

               # Check attention is always last and order is fine
               UpperCAmelCase__			=						True
               UpperCAmelCase__			=						True
               UpperCAmelCase__			=						model_class(__UpperCAmelCase	)
               model.to(__UpperCAmelCase	)
               model.eval()
               with torch.no_grad():
                     UpperCAmelCase__			=						model(**self._prepare_for_class(__UpperCAmelCase ,						__UpperCAmelCase	)	)

               self.assertEqual(out_len + 1 ,						len(__UpperCAmelCase	)	)

               UpperCAmelCase__			=						outputs.attentions

               self.assertEqual(len(__UpperCAmelCase	) ,						__UpperCAmelCase	)

               self.assertListEqual(
                   list(self_attentions[0].shape[-3:]	) ,						[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,						)





@require_vision
@require_torch
@slow
class   A							(       unittest.TestCase  ):

   '''simple docstring'''





   def     lowercase_   (self  :		Optional[Any]	)		->       Tuple:
         """simple docstring"""



         UpperCAmelCase__			=						SwinaSRImageProcessor()
         UpperCAmelCase__			=						SwinaSRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64"	).to(__UpperCAmelCase	)

         UpperCAmelCase__			=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
         UpperCAmelCase__			=						processor(images=__UpperCAmelCase ,						return_tensors="pt"	).to(__UpperCAmelCase	)

         # forward pass
         with torch.no_grad():
               UpperCAmelCase__			=						model(**__UpperCAmelCase	)

         # verify the logits
         UpperCAmelCase__			=						torch.Size([1, 3, 9_7_6, 1_2_9_6]	)
         self.assertEqual(outputs.reconstruction.shape ,						__UpperCAmelCase	)
         UpperCAmelCase__			=						torch.tensor(
             [[0.5458, 0.5546, 0.5638], [0.5526, 0.5565, 0.5651], [0.5396, 0.5426, 0.5621]]	).to(__UpperCAmelCase	)
         self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3] ,						__UpperCAmelCase ,						atol=1E-4	)	)




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   A							(       UpperCAmelCase_  ):


   def     lowercase_   (self  :		Dict ,						__UpperCAmelCase  :		Optional[int] ,						__UpperCAmelCase  :		List[str]=False	)		->       Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__	,   UpperCAmelCase__	,   UpperCAmelCase__			=						(
             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(__UpperCAmelCase	) ,						"wb"	).close()
         UpperCAmelCase__			=						pa.Table.from_pydict({"filename": [Path(__UpperCAmelCase	).name] * 1_0_0}	)
         if take == -1:
               UpperCAmelCase__			=						len(__UpperCAmelCase	) - skip
         if skip is not None and take is not None:
               UpperCAmelCase__			=						pa_table.slice(__UpperCAmelCase ,						__UpperCAmelCase	)
         return pa_table





class   A							(       UpperCAmelCase_  ):


   def     lowercase_   (self  :		Tuple	)		->       Tuple:
         """simple docstring"""



         UpperCAmelCase__			=						"my_name"
         UpperCAmelCase__			=						SplitInfo(name="train" ,						num_examples=1_0_0	)
         UpperCAmelCase__			=						SplitInfo(name="test" ,						num_examples=1_0_0	)
         UpperCAmelCase__			=						[train_info, test_info]
         UpperCAmelCase__			=						SplitDict()
         split_dict.add(__UpperCAmelCase	)
         split_dict.add(__UpperCAmelCase	)
         UpperCAmelCase__			=						DatasetInfo(splits=__UpperCAmelCase	)

         with tempfile.TemporaryDirectory() as tmp_dir:
               UpperCAmelCase__			=						ReaderTest(__UpperCAmelCase ,						__UpperCAmelCase	)

               UpperCAmelCase__			=						"test[:33%]"
               UpperCAmelCase__			=						Dataset(**reader.read(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase	)	)
               self.assertEqual(dset["filename"][0] ,						f"""{name}-test"""	)
               self.assertEqual(dset.num_rows ,						3_3	)
               self.assertEqual(dset.num_columns ,						1	)

               UpperCAmelCase__			=						["train", "test[:33%]"]
               UpperCAmelCase__			=						[Split.TRAIN, ReadInstruction.from_spec("test[:33%]"	)]
               for instructions in [instructionsa, instructionsa]:
                     UpperCAmelCase__			=						[reader.read(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase	) for instr in instructions]
                     UpperCAmelCase__	,   UpperCAmelCase__			=						(Dataset(**__UpperCAmelCase	) for dataset_kwargs in datasets_kwargs)
                     self.assertEqual(train_dset["filename"][0] ,						f"""{name}-train"""	)
                     self.assertEqual(train_dset.num_rows ,						1_0_0	)
                     self.assertEqual(train_dset.num_columns ,						1	)
                     self.assertIsInstance(train_dset.split ,						__UpperCAmelCase	)
                     self.assertEqual(str(train_dset.split	) ,						"train"	)
                     self.assertEqual(test_dset["filename"][0] ,						f"""{name}-test"""	)
                     self.assertEqual(test_dset.num_rows ,						3_3	)
                     self.assertEqual(test_dset.num_columns ,						1	)
                     self.assertIsInstance(test_dset.split ,						__UpperCAmelCase	)
                     self.assertEqual(str(test_dset.split	) ,						"test[:33%]"	)
                     del train_dset, test_dset


   def     lowercase_   (self  :		Any	)		->       Any:
         """simple docstring"""



         UpperCAmelCase__			=						"my_name"
         UpperCAmelCase__			=						SplitInfo(name="train" ,						num_examples=1_0_0_0 ,						shard_lengths=[1_0_0] * 1_0	)
         UpperCAmelCase__			=						[train_info]
         UpperCAmelCase__			=						SplitDict()
         split_dict.add(__UpperCAmelCase	)
         UpperCAmelCase__			=						DatasetInfo(splits=__UpperCAmelCase	)

         with tempfile.TemporaryDirectory() as tmp_dir:
               UpperCAmelCase__			=						ReaderTest(__UpperCAmelCase ,						__UpperCAmelCase	)

               UpperCAmelCase__			=						"train[:33%]"
               UpperCAmelCase__			=						Dataset(**reader.read(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase	)	)
               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_3_0	)
               self.assertEqual(dset.num_columns ,						1	)





   def     lowercase_   (self  :		Dict	)		->       Dict:
         """simple docstring"""



         UpperCAmelCase__			=						SplitInfo(name="train" ,						num_examples=1_0_0	)
         UpperCAmelCase__			=						SplitInfo(name="test" ,						num_examples=1_0_0	)
         UpperCAmelCase__			=						SplitDict()
         split_dict.add(__UpperCAmelCase	)
         split_dict.add(__UpperCAmelCase	)
         UpperCAmelCase__			=						DatasetInfo(splits=__UpperCAmelCase	)

         with tempfile.TemporaryDirectory() as tmp_dir:
               UpperCAmelCase__			=						ReaderTest(__UpperCAmelCase ,						__UpperCAmelCase	)

               UpperCAmelCase__			=						[
                   {"filename": os.path.join(__UpperCAmelCase ,						"train"	)},
                   {"filename": os.path.join(__UpperCAmelCase ,						"test"	), "skip": 1_0, "take": 1_0},
               ]
               UpperCAmelCase__			=						Dataset(**reader.read_files(__UpperCAmelCase ,						original_instructions="train+test[10:20]"	)	)
               self.assertEqual(dset.num_rows ,						1_1_0	)
               self.assertEqual(dset.num_columns ,						1	)
               del dset




@pytest.mark.parametrize("in_memory",						[False, True]							)
def      lowerCAmelCase_				(  __A,						__A,						__A							)  ->       List[Any]:







      '''simple docstring'''


      UpperCAmelCase__			=						arrow_file
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
            UpperCAmelCase__			=						ArrowReader.read_table(__A,						in_memory=__A							)
      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      lowerCAmelCase_				(  __A,						__A,						__A							)  ->       Tuple:







      '''simple docstring'''


      UpperCAmelCase__			=						arrow_file
      UpperCAmelCase__			=						ArrowReader("",						__A							)
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
            UpperCAmelCase__			=						reader.read_files([{"filename": filename}],						in_memory=__A							)
      assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
      UpperCAmelCase__			=						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      lowerCAmelCase_				(  )  ->       Any:







      '''simple docstring'''


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

      UpperCAmelCase__			=						"train+test"
      assert ReadInstruction.from_spec(__A							).to_spec() == spec_train_test

      UpperCAmelCase__			=						"train[2:10]"
      assert ReadInstruction.from_spec(__A							).to_spec() == spec_train_abs

      UpperCAmelCase__			=						"train[15%:-20%]"
      assert ReadInstruction.from_spec(__A							).to_spec() == spec_train_pct

      UpperCAmelCase__			=						"train[:10%](closest)"
      assert ReadInstruction.from_spec(__A							).to_spec() == "train[:10%]"

      UpperCAmelCase__			=						"train[:10%](pct1_dropremainder)"
      assert ReadInstruction.from_spec(__A							).to_spec() == spec_train_pct_rounding

      UpperCAmelCase__			=						"train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
      assert ReadInstruction.from_spec(__A							).to_spec() == spec_train_test_pct_rounding




def      lowerCAmelCase_				(  )  ->       int:







      '''simple docstring'''


      UpperCAmelCase__			=						"dummy"
      UpperCAmelCase__			=						[SplitInfo(name="train",						num_examples=100							)]
      UpperCAmelCase__			=						"train[:33%]"
      UpperCAmelCase__			=						"arrow"
      UpperCAmelCase__			=						"prefix"

      UpperCAmelCase__			=						make_file_instructions(__A,						__A,						__A,						__A,						__A							)
      assert isinstance(__A,						__A							)
      assert file_instructions.num_examples == 33
      assert file_instructions.file_instructions == [
          {"filename": os.path.join(__A,						f"""{name}-train.arrow"""							), "skip": 0, "take": 33}
      ]

      UpperCAmelCase__			=						[SplitInfo(name="train",						num_examples=100,						shard_lengths=[10] * 10							)]
      UpperCAmelCase__			=						make_file_instructions(__A,						__A,						__A,						__A,						__A							)
      assert isinstance(__A,						__A							)
      assert file_instructions.num_examples == 33
      assert file_instructions.file_instructions == [
          {"filename": os.path.join(__A,						f"""{name}-train-00000-of-00010.arrow"""							), "skip": 0, "take": -1},
          {"filename": os.path.join(__A,						f"""{name}-train-00001-of-00010.arrow"""							), "skip": 0, "take": -1},
          {"filename": os.path.join(__A,						f"""{name}-train-00002-of-00010.arrow"""							), "skip": 0, "take": -1},
          {"filename": os.path.join(__A,						f"""{name}-train-00003-of-00010.arrow"""							), "skip": 0, "take": 3},
      ]







@pytest.mark.parametrize("name, expected_exception",						[(None, TypeError), ("", ValueError)]							)
def      lowerCAmelCase_				(  __A,						__A							)  ->       Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__			=						[SplitInfo(name="train",						num_examples=100							)]
      UpperCAmelCase__			=						"train"
      UpperCAmelCase__			=						"arrow"
      UpperCAmelCase__			=						"prefix_path"
      with pytest.raises(__A							):
            UpperCAmelCase__			=						make_file_instructions(__A,						__A,						__A,						__A,						__A							)



