


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  UpperCAmelCase_						(				_a):
       '''simple docstring'''






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=False    ):




              """simple docstring"""




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




class  UpperCAmelCase_						(				_a):
       '''simple docstring'''






       def 			_lowercase (  self    ):




              """simple docstring"""




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

              with tempfile.TemporaryDirectory() as tmp_dir:
                     UpperCamelCase     :						int 						=		ReaderTest(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

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

                     UpperCamelCase     :						Tuple 						=		['''train''', '''test[:33%]''']
                     UpperCamelCase     :						List[str] 						=		[Split.TRAIN, ReadInstruction.from_spec('''test[:33%]'''    )]
                     for instructions in [instructionsa, instructionsa]:
                            UpperCamelCase     :						Union[str, Any] 						=		[reader.read(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ) for instr in instructions]
                            UpperCamelCase						,			UpperCamelCase     :						Any 						=		(Dataset(**__SCREAMING_SNAKE_CASE    ) 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			,       __SCREAMING_SNAKE_CASE    )
                            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			,       __SCREAMING_SNAKE_CASE    )
                            self.assertEqual(str(test_dset.split    )			,       '''test[:33%]'''    )
                            del train_dset, test_dset






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Any 						=		'''my_name'''
              UpperCamelCase     :						Dict 						=		SplitInfo(name='''train'''			,       num_examples=1_000			,       shard_lengths=[100] * 10    )
              UpperCamelCase     :						Tuple 						=		[train_info]
              UpperCamelCase     :						str 						=		SplitDict()
              split_dict.add(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Any 						=		DatasetInfo(splits=__SCREAMING_SNAKE_CASE    )

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

                     UpperCamelCase     :						Union[str, Any] 						=		'''train[:33%]'''
                     UpperCamelCase     :						str 						=		Dataset(**reader.read(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )    )
                     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 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						str 						=		SplitInfo(name='''train'''			,       num_examples=100    )
              UpperCamelCase     :						str 						=		SplitInfo(name='''test'''			,       num_examples=100    )
              UpperCamelCase     :						Any 						=		SplitDict()
              split_dict.add(__SCREAMING_SNAKE_CASE    )
              split_dict.add(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[int] 						=		DatasetInfo(splits=__SCREAMING_SNAKE_CASE    )

              with tempfile.TemporaryDirectory() as tmp_dir:
                     UpperCamelCase     :						Dict 						=		ReaderTest(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

                     UpperCamelCase     :						List[str] 						=		[
                         {'''filename''': os.path.join(__SCREAMING_SNAKE_CASE			,       '''train'''    )},
                         {'''filename''': os.path.join(__SCREAMING_SNAKE_CASE			,       '''test'''    ), '''skip''': 10, '''take''': 10},
                     ]
                     UpperCamelCase     :						List[str] 						=		Dataset(**reader.read_files(__SCREAMING_SNAKE_CASE			,       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						(				SCREAMING_SNAKE_CASE_   :		Any				,      SCREAMING_SNAKE_CASE_   :		List[Any]				,      SCREAMING_SNAKE_CASE_   :		int							):






       """simple docstring"""





       UpperCamelCase     :						Tuple 						=		arrow_file
       with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
              UpperCamelCase     :						Optional[int] 						=		ArrowReader.read_table(SCREAMING_SNAKE_CASE_				,      in_memory=SCREAMING_SNAKE_CASE_							)
       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						(				SCREAMING_SNAKE_CASE_   :		Dict				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]				,      SCREAMING_SNAKE_CASE_   :		List[Any]							):






       """simple docstring"""





       UpperCamelCase     :						Optional[Any] 						=		arrow_file
       UpperCamelCase     :						Optional[Any] 						=		ArrowReader(''''''				,      SCREAMING_SNAKE_CASE_							)
       with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
              UpperCamelCase     :						int 						=		reader.read_files([{'''filename''': filename}]				,      in_memory=SCREAMING_SNAKE_CASE_							)
       assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
       UpperCamelCase     :						Tuple 						=		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						(				):






       """simple docstring"""





       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%]"

       UpperCamelCase     :						Dict 						=		'''train+test'''
       assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE_							).to_spec() == spec_train_test

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

       UpperCamelCase     :						Tuple 						=		'''train[15%:-20%]'''
       assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE_							).to_spec() == spec_train_pct

       UpperCamelCase     :						Optional[int] 						=		'''train[:10%](closest)'''
       assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE_							).to_spec() == "train[:10%]"

       UpperCamelCase     :						Optional[int] 						=		'''train[:10%](pct1_dropremainder)'''
       assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE_							).to_spec() == spec_train_pct_rounding

       UpperCamelCase     :						List[str] 						=		'''train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'''
       assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE_							).to_spec() == spec_train_test_pct_rounding


def   a						(				):






       """simple docstring"""





       UpperCamelCase     :						Tuple 						=		'''dummy'''
       UpperCamelCase     :						Tuple 						=		[SplitInfo(name='''train'''				,      num_examples=1_0_0							)]
       UpperCamelCase     :						Any 						=		'''train[:33%]'''
       UpperCamelCase     :						int 						=		'''arrow'''
       UpperCamelCase     :						Optional[int] 						=		'''prefix'''

       UpperCamelCase     :						Tuple 						=		make_file_instructions(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							)
       assert isinstance(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							)
       assert file_instructions.num_examples == 3_3
       assert file_instructions.file_instructions == [
           {"filename": os.path.join(SCREAMING_SNAKE_CASE_				,      F"""{name}-train.arrow"""							), "skip": 0, "take": 3_3}
       ]

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


@pytest.mark.parametrize('''name, expected_exception'''				,      [(None, TypeError), ('''''', ValueError)]							)
def   a						(				SCREAMING_SNAKE_CASE_   :		List[str]				,      SCREAMING_SNAKE_CASE_   :		Dict							):






       """simple docstring"""





       UpperCamelCase     :						Tuple 						=		[SplitInfo(name='''train'''				,      num_examples=1_0_0							)]
       UpperCamelCase     :						Optional[int] 						=		'''train'''
       UpperCamelCase     :						int 						=		'''arrow'''
       UpperCamelCase     :						List[Any] 						=		'''prefix_path'''
       with pytest.raises(SCREAMING_SNAKE_CASE_							):
              UpperCamelCase     :						Any 						=		make_file_instructions(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							)





import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()



class  UpperCAmelCase_						(				unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase   :  Tuple				=       "hf-internal-testing/tiny-stable-diffusion-pipe"
       __UpperCamelCase   :  Optional[Any]				=       1
       __UpperCamelCase   :  str				=       77
       __UpperCamelCase   :  Tuple				=       32
       __UpperCamelCase   :  List[str]				=       4
       __UpperCamelCase   :  Optional[int]				=       64
       __UpperCamelCase   :  Union[str, Any]				=       torch.manual_seed(0)






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE=0.9_999    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[int] 						=		UNetaDConditionModel.from_pretrained(self.model_id			,       subfolder='''unet'''    )
              UpperCamelCase     :						Any 						=		unet.to(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Dict 						=		EMAModel(unet.parameters()			,       decay=__SCREAMING_SNAKE_CASE			,       model_cls=__SCREAMING_SNAKE_CASE			,       model_config=unet.config    )
              return unet, ema_unet






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Tuple 						=		torch.randn(
                  self.batch_size			,       self.num_in_channels			,       self.latent_height			,       self.latent_width			,       generator=self.generator    ).to(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[int] 						=		torch.randint(0			,       1_000			,       size=(self.batch_size,)			,       generator=self.generator    ).to(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						List[Any] 						=		torch.randn(
                  self.batch_size			,       self.prompt_length			,       self.text_encoder_hidden_dim			,       generator=self.generator    ).to(__SCREAMING_SNAKE_CASE    )
              return noisy_latents, timesteps, encoder_hidden_states






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		{}
              for k, param in unet.state_dict().items():
                     UpperCamelCase     :						int 						=		torch.randn_like(__SCREAMING_SNAKE_CASE    ) + (param * torch.randn_like(__SCREAMING_SNAKE_CASE    ))
                     updated_state_dict.update({k: updated_param}    )
              unet.load_state_dict(__SCREAMING_SNAKE_CASE    )
              return unet






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						Optional[Any] 						=		self.get_models()
              # Take the first (hypothetical) EMA step.
              ema_unet.step(unet.parameters()    )
              assert ema_unet.optimization_step == 1

              # Take two more.
              for _ in range(2    ):
                     ema_unet.step(unet.parameters()    )
              assert ema_unet.optimization_step == 3






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						List[Any] 						=		self.get_models()
              # Since the `unet` is not being updated (i.e., backprop'd)
              # there won't be any difference between the `params` of `unet`
              # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
              ema_unet.step(unet.parameters()    )
              UpperCamelCase     :						int 						=		list(unet.parameters()    )
              for s_param, param in zip(ema_unet.shadow_params			,       __SCREAMING_SNAKE_CASE    ):
                     assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              # The above holds true even if we call `ema.step()` multiple times since
              # `unet` params are still not being updated.
              for _ in range(4    ):
                     ema_unet.step(unet.parameters()    )
              for s_param, param in zip(ema_unet.shadow_params			,       __SCREAMING_SNAKE_CASE    ):
                     assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						Any 						=		self.get_models()
              # Here we simulate the parameter updates for `unet`. Since there might
              # be some parameters which are initialized to zero we take extra care to
              # initialize their values to something non-zero before the multiplication.
              UpperCamelCase     :						Union[str, Any] 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )

              # Take the EMA step.
              ema_unet.step(unet_pseudo_updated_step_one.parameters()    )

              # Now the EMA'd parameters won't be equal to the original model parameters.
              UpperCamelCase     :						Any 						=		list(unet_pseudo_updated_step_one.parameters()    )
              for s_param, param in zip(ema_unet.shadow_params			,       __SCREAMING_SNAKE_CASE    ):
                     assert ~torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              # Ensure this is the case when we take multiple EMA steps.
              for _ in range(4    ):
                     ema_unet.step(unet.parameters()    )
              for s_param, param in zip(ema_unet.shadow_params			,       __SCREAMING_SNAKE_CASE    ):
                     assert ~torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						Optional[int] 						=		self.get_models()

              # First backprop + EMA
              UpperCamelCase     :						Dict 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_one.parameters()    )
              UpperCamelCase     :						List[str] 						=		ema_unet.shadow_params

              # Second backprop + EMA
              UpperCamelCase     :						str 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_two.parameters()    )
              UpperCamelCase     :						List[Any] 						=		ema_unet.shadow_params

              for step_one, step_two in zip(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):
                     assert ~torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						int 						=		self.get_models(decay=0.0    )
              UpperCamelCase     :						Optional[int] 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_one.parameters()    )
              UpperCamelCase     :						Union[str, Any] 						=		ema_unet.shadow_params

              UpperCamelCase     :						Any 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_two.parameters()    )
              UpperCamelCase     :						int 						=		ema_unet.shadow_params

              for step_one, step_two in zip(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):
                     assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       @skip_mps
       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						str 						=		self.get_models()
              UpperCamelCase						,			UpperCamelCase						,			UpperCamelCase     :						Union[str, Any] 						=		self.get_dummy_inputs()

              with tempfile.TemporaryDirectory() as tmpdir:
                     ema_unet.save_pretrained(__SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						Optional[int] 						=		UNetaDConditionModel.from_pretrained(__SCREAMING_SNAKE_CASE			,       model_cls=__SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						List[str] 						=		loaded_unet.to(unet.device    )

              # Since no EMA step has been performed the outputs should match.
              UpperCamelCase     :						Optional[Any] 						=		unet(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ).sample
              UpperCamelCase     :						List[str] 						=		loaded_unet(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ).sample

              assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       atol=1e-4    )

