




'''simple docstring'''






import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

lowercase	:      Optional[Any]				=       logging.get_logger(__name__)




def 		a_( lowerCamelCase   ,				lowerCamelCase = True      )				->Optional[Any]:






       '''simple docstring'''




       lowercase  :		List[Any]						=      {}
       for name in model.attn_processors.keys():
              lowercase  :		Optional[int]						=      name.endswith('''attn2.processor'''      ) and not (
                  name.startswith('''transformer_in'''      ) or '''temp_attentions''' in name.split('''.'''      )
              )
              lowercase  :		Optional[Any]						=      model.config.cross_attention_dim if has_cross_attention else None
              if name.startswith('''mid_block'''      ):
                     lowercase  :		int						=      model.config.block_out_channels[-1]
              elif name.startswith('''up_blocks'''      ):
                     lowercase  :		List[Any]						=      int(name[len('''up_blocks.'''      )]      )
                     lowercase  :		Optional[Any]						=      list(reversed(model.config.block_out_channels      )      )[block_id]
              elif name.startswith('''down_blocks'''      ):
                     lowercase  :		Dict						=      int(name[len('''down_blocks.'''      )]      )
                     lowercase  :		Any						=      model.config.block_out_channels[block_id]
              elif name.startswith('''transformer_in'''      ):
                     # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
                     lowercase  :		str						=      8 * model.config.attention_head_dim

              lowercase  :		str						=      LoRAAttnProcessor(hidden_size=lowerCamelCase   ,				cross_attention_dim=lowerCamelCase      )
              lowercase  :		Union[str, Any]						=      lora_attn_procs[name].to(model.device      )

              if mock_weights:
                     # add 1 to weights to mock trained weights
                     with torch.no_grad():
                            lora_attn_procs[name].to_q_lora.up.weight += 1
                            lora_attn_procs[name].to_k_lora.up.weight += 1
                            lora_attn_procs[name].to_v_lora.up.weight += 1
                            lora_attn_procs[name].to_out_lora.up.weight += 1

       return lora_attn_procs





@skip_mps
class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_		,     lowerCamelCase_		,     unittest.TestCase				):
 __snake_case	:     Optional[Any]			   =      UNetaDConditionModel
 __snake_case	:     Dict			   =      "sample"



 @property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		str						=      4
        lowercase  :		List[Any]						=      4
        lowercase  :		Optional[int]						=      4
        lowercase  :		List[str]						=      (32, 32)

        lowercase  :		Union[str, Any]						=      floats_tensor((batch_size, num_channels, num_frames) + sizes ).to(A )
        lowercase  :		List[str]						=      torch.tensor([10] ).to(A )
        lowercase  :		Union[str, Any]						=      floats_tensor((batch_size, 4, 32) ).to(A )

        return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}



 @property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        return (4, 4, 32, 32)



 @property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        return (4, 4, 32, 32)



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Tuple						=      {
            '''block_out_channels''': (32, 64),
            '''down_block_types''': (
                '''CrossAttnDownBlock3D''',
                '''DownBlock3D''',
            ),
            '''up_block_types''': ('''UpBlock3D''', '''CrossAttnUpBlock3D'''),
            '''cross_attention_dim''': 32,
            '''attention_head_dim''': 8,
            '''out_channels''': 4,
            '''in_channels''': 4,
            '''layers_per_block''': 1,
            '''sample_size''': 32,
        }
        lowercase  :		List[Any]						=      self.dummy_input
        return init_dict, inputs_dict



 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()   ,		reason='''XFormers attention is only available with CUDA and `xformers` installed'''   ,		)
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Union[str, Any]						=      self.prepare_init_args_and_inputs_for_common()
        lowercase  :		List[str]						=      self.model_class(**A )

        model.enable_xformers_memory_efficient_attention()

        assert (
            model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
            == "XFormersAttnProcessor"
        ), "xformers is not enabled"



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Tuple						=      self.prepare_init_args_and_inputs_for_common()

        lowercase  :		int						=      32

        lowercase  :		Tuple						=      self.model_class(**A )
        model.to(A )
        model.eval()

        with torch.no_grad():
               lowercase  :		List[str]						=      model(**A )

               if isinstance(A   ,		A ):
                      lowercase  :		str						=      output.sample

        self.assertIsNotNone(A )
        lowercase  :		List[Any]						=      inputs_dict['''sample'''].shape
        self.assertEqual(output.shape   ,		A   ,		'''Input and output shapes do not match''' )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Tuple						=      self.prepare_init_args_and_inputs_for_common()
        lowercase  :		Dict						=      self.model_class(**A )
        model.to(A )
        model.eval()

        with torch.no_grad():
               # Warmup pass when using mps (see #372)
               if torch_device == "mps" and isinstance(A   ,		A ):
                      model(**self.dummy_input )

               lowercase  :		str						=      model(**A )
               if isinstance(A   ,		A ):
                      lowercase  :		Optional[Any]						=      first.sample

               lowercase  :		int						=      model(**A )
               if isinstance(A   ,		A ):
                      lowercase  :		int						=      second.sample

        lowercase  :		Dict						=      first.cpu().numpy()
        lowercase  :		Optional[Any]						=      second.cpu().numpy()
        lowercase  :		Dict						=      out_a[~np.isnan(A )]
        lowercase  :		Any						=      out_a[~np.isnan(A )]
        lowercase  :		Optional[Any]						=      np.amax(np.abs(out_a - out_a ) )
        self.assertLessEqual(A   ,		1E-5 )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		List[Any]						=      self.prepare_init_args_and_inputs_for_common()

        lowercase  :		Any						=      8

        lowercase  :		Optional[int]						=      self.model_class(**A )
        model.to(A )
        model.eval()

        model.set_attention_slice('''auto''' )
        with torch.no_grad():
               lowercase  :		Dict						=      model(**A )
        assert output is not None

        model.set_attention_slice('''max''' )
        with torch.no_grad():
               lowercase  :		List[str]						=      model(**A )
        assert output is not None

        model.set_attention_slice(2 )
        with torch.no_grad():
               lowercase  :		Optional[Any]						=      model(**A )
        assert output is not None



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Tuple						=      self.prepare_init_args_and_inputs_for_common()

        lowercase  :		str						=      8

        lowercase  :		Any						=      self.model_class(**A )
        model.to(A )

        with torch.no_grad():
               lowercase  :		Optional[int]						=      model(**A ).sample

        lowercase  :		Optional[int]						=      create_lora_layers(A )

        # make sure we can set a list of attention processors
        model.set_attn_processor(A )
        model.to(A )

        # test that attn processors can be set to itself
        model.set_attn_processor(model.attn_processors )

        with torch.no_grad():
               lowercase  :		Tuple						=      model(**A   ,		cross_attention_kwargs={'''scale''': 0.0} ).sample
               lowercase  :		Optional[Any]						=      model(**A   ,		cross_attention_kwargs={'''scale''': 0.5} ).sample
               lowercase  :		Tuple						=      model(**A   ,		cross_attention_kwargs={'''scale''': 0.5} ).sample

        assert (samplea - samplea).abs().max() < 3E-3
        assert (samplea - samplea).abs().max() < 3E-3

        # sample 2 and sample 3 should be different
        assert (samplea - samplea).abs().max() > 3E-3



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Union[str, Any]						=      self.prepare_init_args_and_inputs_for_common()

        lowercase  :		int						=      8

        torch.manual_seed(0 )
        lowercase  :		int						=      self.model_class(**A )
        model.to(A )

        with torch.no_grad():
               lowercase  :		List[Any]						=      model(**A ).sample

        lowercase  :		int						=      create_lora_layers(A )
        model.set_attn_processor(A )

        with torch.no_grad():
               lowercase  :		int						=      model(**A   ,		cross_attention_kwargs={'''scale''': 0.5} ).sample

        with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_attn_procs(A )
               self.assertTrue(os.path.isfile(os.path.join(A   ,		'''pytorch_lora_weights.bin''' ) ) )
               torch.manual_seed(0 )
               lowercase  :		Optional[Any]						=      self.model_class(**A )
               new_model.to(A )
               new_model.load_attn_procs(A )

        with torch.no_grad():
               lowercase  :		List[str]						=      new_model(**A   ,		cross_attention_kwargs={'''scale''': 0.5} ).sample

        assert (sample - new_sample).abs().max() < 1E-3

        # LoRA and no LoRA should NOT be the same
        assert (sample - old_sample).abs().max() > 1E-4



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Tuple						=      self.prepare_init_args_and_inputs_for_common()

        lowercase  :		Tuple						=      8

        torch.manual_seed(0 )
        lowercase  :		List[str]						=      self.model_class(**A )
        model.to(A )

        with torch.no_grad():
               lowercase  :		Any						=      model(**A ).sample

        lowercase  :		Tuple						=      create_lora_layers(A )
        model.set_attn_processor(A )

        with torch.no_grad():
               lowercase  :		Tuple						=      model(**A   ,		cross_attention_kwargs={'''scale''': 0.5} ).sample

        with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_attn_procs(A   ,		safe_serialization=A )
               self.assertTrue(os.path.isfile(os.path.join(A   ,		'''pytorch_lora_weights.safetensors''' ) ) )
               torch.manual_seed(0 )
               lowercase  :		Optional[Any]						=      self.model_class(**A )
               new_model.to(A )
               new_model.load_attn_procs(A )

        with torch.no_grad():
               lowercase  :		List[Any]						=      new_model(**A   ,		cross_attention_kwargs={'''scale''': 0.5} ).sample

        assert (sample - new_sample).abs().max() < 3E-3

        # LoRA and no LoRA should NOT be the same
        assert (sample - old_sample).abs().max() > 1E-4



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		List[Any]						=      self.prepare_init_args_and_inputs_for_common()

        lowercase  :		Any						=      8

        torch.manual_seed(0 )
        lowercase  :		Optional[Any]						=      self.model_class(**A )
        model.to(A )

        lowercase  :		Optional[Any]						=      create_lora_layers(A   ,		mock_weights=A )
        model.set_attn_processor(A )
        # Saving as torch, properly reloads with directly filename
        with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_attn_procs(A )
               self.assertTrue(os.path.isfile(os.path.join(A   ,		'''pytorch_lora_weights.bin''' ) ) )
               torch.manual_seed(0 )
               lowercase  :		Optional[int]						=      self.model_class(**A )
               new_model.to(A )
               new_model.load_attn_procs(A   ,		weight_name='''pytorch_lora_weights.bin''' )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Dict						=      self.prepare_init_args_and_inputs_for_common()

        lowercase  :		List[Any]						=      8

        torch.manual_seed(0 )
        lowercase  :		Union[str, Any]						=      self.model_class(**A )
        model.to(A )

        lowercase  :		int						=      create_lora_layers(A   ,		mock_weights=A )
        model.set_attn_processor(A )
        # Saving as torch, properly reloads with directly filename
        with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_attn_procs(A )
               self.assertTrue(os.path.isfile(os.path.join(A   ,		'''pytorch_lora_weights.bin''' ) ) )
               torch.manual_seed(0 )
               lowercase  :		Tuple						=      self.model_class(**A )
               new_model.to(A )
               with self.assertRaises(A ) as e:
                      new_model.load_attn_procs(A   ,		use_safetensors=A )
               self.assertIn('''Error no file named pytorch_lora_weights.safetensors'''   ,		str(e.exception ) )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Any						=      self.prepare_init_args_and_inputs_for_common()

        lowercase  :		List[Any]						=      8

        torch.manual_seed(0 )
        lowercase  :		Union[str, Any]						=      self.model_class(**A )
        model.to(A )

        with torch.no_grad():
               lowercase  :		Tuple						=      model(**A ).sample

        lowercase  :		int						=      create_lora_layers(A )
        model.set_attn_processor(A )

        with torch.no_grad():
               lowercase  :		Any						=      model(**A   ,		cross_attention_kwargs={'''scale''': 0.0} ).sample

        model.set_attn_processor(AttnProcessor() )

        with torch.no_grad():
               lowercase  :		str						=      model(**A ).sample

        assert (sample - new_sample).abs().max() < 1E-4
        assert (sample - old_sample).abs().max() < 3E-3



 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()   ,		reason='''XFormers attention is only available with CUDA and `xformers` installed'''   ,		)
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Any						=      self.prepare_init_args_and_inputs_for_common()

        lowercase  :		Union[str, Any]						=      4

        torch.manual_seed(0 )
        lowercase  :		Optional[Any]						=      self.model_class(**A )
        model.to(A )
        lowercase  :		Optional[int]						=      create_lora_layers(A )
        model.set_attn_processor(A )

        # default
        with torch.no_grad():
               lowercase  :		int						=      model(**A ).sample

               model.enable_xformers_memory_efficient_attention()
               lowercase  :		Union[str, Any]						=      model(**A ).sample

               model.disable_xformers_memory_efficient_attention()
               lowercase  :		Optional[Any]						=      model(**A ).sample

        assert (sample - on_sample).abs().max() < 1E-4
        assert (sample - off_sample).abs().max() < 1E-4





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		str						=      self.prepare_init_args_and_inputs_for_common()
        lowercase  :		List[str]						=      32

        lowercase  :		Tuple						=      self.model_class(**A )
        model.to(A )
        model.eval()

        with torch.no_grad():
               lowercase  :		List[Any]						=      model(**A )[0]

        model.enable_forward_chunking()
        with torch.no_grad():
               lowercase  :		List[str]						=      model(**A )[0]

        self.assertEqual(output.shape   ,		output_a.shape   ,		'''Shape doesn\'t match''' )
        assert np.abs(output.cpu() - output_a.cpu() ).max() < 1E-2


# (todo: sayakpaul) implement SLOW tests.










'''simple docstring'''






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   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A=False ):






        '''simple docstring'''




        lowercase,     lowercase,     lowercase  :		Union[str, Any]						=      (
            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()
        lowercase  :		List[Any]						=      pa.Table.from_pydict({'''filename''': [Path(A ).name] * 100} )
        if take == -1:
               lowercase  :		int						=      len(A ) - skip
        if skip is not None and take is not None:
               lowercase  :		Optional[Any]						=      pa_table.slice(A   ,		A )
        return pa_table






class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      '''my_name'''
        lowercase  :		Tuple						=      SplitInfo(name='''train'''   ,		num_examples=100 )
        lowercase  :		Union[str, Any]						=      SplitInfo(name='''test'''   ,		num_examples=100 )
        lowercase  :		Any						=      [train_info, test_info]
        lowercase  :		Union[str, Any]						=      SplitDict()
        split_dict.add(A )
        split_dict.add(A )
        lowercase  :		List[str]						=      DatasetInfo(splits=A )

        with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :		Union[str, Any]						=      ReaderTest(A   ,		A )

               lowercase  :		str						=      '''test[:33%]'''
               lowercase  :		List[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 )

               lowercase  :		Dict						=      ['''train''', '''test[:33%]''']
               lowercase  :		List[Any]						=      [Split.TRAIN, ReadInstruction.from_spec('''test[:33%]''' )]
               for instructions in [instructionsa, instructionsa]:
                      lowercase  :		Union[str, Any]						=      [reader.read(A   ,		A   ,		A ) for instr in instructions]
                      lowercase,     lowercase  :		Tuple						=      (Dataset(**A ) for dataset_kwargs in datasets_kwargs)
                      self.assertEqual(train_dset['''filename'''][0]   ,		f"""{name}-train""" )
                      self.assertEqual(train_dset.num_rows   ,		100 )
                      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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		str						=      '''my_name'''
        lowercase  :		Dict						=      SplitInfo(name='''train'''   ,		num_examples=1000   ,		shard_lengths=[100] * 10 )
        lowercase  :		Tuple						=      [train_info]
        lowercase  :		str						=      SplitDict()
        split_dict.add(A )
        lowercase  :		str						=      DatasetInfo(splits=A )

        with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :		int						=      ReaderTest(A   ,		A )

               lowercase  :		Dict						=      '''train[:33%]'''
               lowercase  :		Tuple						=      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   ,		330 )
               self.assertEqual(dset.num_columns   ,		1 )





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      SplitInfo(name='''train'''   ,		num_examples=100 )
        lowercase  :		Dict						=      SplitInfo(name='''test'''   ,		num_examples=100 )
        lowercase  :		List[str]						=      SplitDict()
        split_dict.add(A )
        split_dict.add(A )
        lowercase  :		List[Any]						=      DatasetInfo(splits=A )

        with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :		Optional[int]						=      ReaderTest(A   ,		A )

               lowercase  :		Optional[int]						=      [
                   {'''filename''': os.path.join(A   ,		'''train''' )},
                   {'''filename''': os.path.join(A   ,		'''test''' ), '''skip''': 10, '''take''': 10},
               ]
               lowercase  :		int						=      Dataset(**reader.read_files(A   ,		original_instructions='''train+test[10:20]''' ) )
               self.assertEqual(dset.num_rows   ,		110 )
               self.assertEqual(dset.num_columns   ,		1 )
               del dset






@pytest.mark.parametrize('''in_memory'''   ,				[False, True]      )
def 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->Union[str, Any]:






       '''simple docstring'''




       lowercase  :		Any						=      arrow_file
       with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
              lowercase  :		str						=      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 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->Dict:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      arrow_file
       lowercase  :		Union[str, Any]						=      ArrowReader(''''''   ,				lowerCamelCase      )
       with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
              lowercase  :		List[str]						=      reader.read_files([{'''filename''': filename}]   ,				in_memory=lowerCamelCase      )
       assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
       lowercase  :		Optional[int]						=      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 		a_( )				->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%]"

       lowercase  :		Union[str, Any]						=      '''train+test'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == spec_train_test

       lowercase  :		Any						=      '''train[2:10]'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == spec_train_abs

       lowercase  :		List[str]						=      '''train[15%:-20%]'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == spec_train_pct

       lowercase  :		Tuple						=      '''train[:10%](closest)'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == "train[:10%]"

       lowercase  :		Any						=      '''train[:10%](pct1_dropremainder)'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == spec_train_pct_rounding

       lowercase  :		int						=      '''train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == spec_train_test_pct_rounding




def 		a_( )				->str:






       '''simple docstring'''




       lowercase  :		int						=      '''dummy'''
       lowercase  :		int						=      [SplitInfo(name='''train'''   ,				num_examples=100      )]
       lowercase  :		Union[str, Any]						=      '''train[:33%]'''
       lowercase  :		str						=      '''arrow'''
       lowercase  :		int						=      '''prefix'''

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

       lowercase  :		List[str]						=      [SplitInfo(name='''train'''   ,				num_examples=100   ,				shard_lengths=[10] * 10      )]
       lowercase  :		Any						=      make_file_instructions(lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )
       assert isinstance(lowerCamelCase   ,				lowerCamelCase      )
       assert file_instructions.num_examples == 33
       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 		a_( lowerCamelCase   ,				lowerCamelCase      )				->int:






       '''simple docstring'''




       lowercase  :		List[str]						=      [SplitInfo(name='''train'''   ,				num_examples=100      )]
       lowercase  :		List[Any]						=      '''train'''
       lowercase  :		Any						=      '''arrow'''
       lowercase  :		Union[str, Any]						=      '''prefix_path'''
       with pytest.raises(lowerCamelCase      ):
              lowercase  :		List[Any]						=      make_file_instructions(lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )




