





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 	lowerCAmelCase__(       unittest.TestCase			):






       '''simple docstring'''


       __snake_case       	=			'hf-internal-testing/tiny-stable-diffusion-pipe'
       __snake_case       	=			1
       __snake_case       	=			7_7
       __snake_case       	=			3_2
       __snake_case       	=			4
       __snake_case       	=			__snake_case       	=			6_4
       __snake_case       	=			torch.manual_seed(0			)




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase=0.9999							)	->   Optional[Any]:
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							UNetaDConditionModel.from_pretrained(self.model_id			,  subfolder="unet"							)
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							unet.to(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Dict  			=							EMAModel(unet.parameters()			,  decay=__lowerCamelCase			,  model_cls=__lowerCamelCase			,  model_config=unet.config							)
            return unet, ema_unet




       def 			UpperCamelCase_	(					self							)	->   Tuple:
            _SCREAMING_SNAKE_CASE				:		int  			=							torch.randn(
                self.batch_size			,  self.num_in_channels			,  self.latent_height			,  self.latent_width			,  generator=self.generator							).to(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							torch.randint(0			,  1_0_0_0			,  size=(self.batch_size,)			,  generator=self.generator							).to(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Dict  			=							torch.randn(
                self.batch_size			,  self.prompt_length			,  self.text_encoder_hidden_dim			,  generator=self.generator							).to(__lowerCamelCase							)
            return noisy_latents, timesteps, encoder_hidden_states




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase							)	->   int:
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							{}
            for k, param in unet.state_dict().items():
                 _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							torch.randn_like(__lowerCamelCase							) + (param * torch.randn_like(__lowerCamelCase							))
                 updated_state_dict.update({k: updated_param}							)
            unet.load_state_dict(__lowerCamelCase							)
            return unet




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		str  			=							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 			UpperCamelCase_	(					self							)	->   Tuple:
            _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		str  			=							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()							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							list(unet.parameters()							)
            for s_param, param in zip(ema_unet.shadow_params			,  __lowerCamelCase							):
                 assert torch.allclose(__lowerCamelCase			,  __lowerCamelCase							)

            # 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			,  __lowerCamelCase							):
                 assert torch.allclose(__lowerCamelCase			,  __lowerCamelCase							)




       def 			UpperCamelCase_	(					self							)	->   List[str]:
            _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		str  			=							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.
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							self.simulate_backprop(__lowerCamelCase							)

            # 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.
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							list(unet_pseudo_updated_step_one.parameters()							)
            for s_param, param in zip(ema_unet.shadow_params			,  __lowerCamelCase							):
                 assert ~torch.allclose(__lowerCamelCase			,  __lowerCamelCase							)

            # 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			,  __lowerCamelCase							):
                 assert ~torch.allclose(__lowerCamelCase			,  __lowerCamelCase							)




       def 			UpperCamelCase_	(					self							)	->   Optional[int]:
            # If we call EMA step after a backpropagation consecutively for two times,
            # the shadow params from those two steps should be different.
            _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		List[Any]  			=							self.get_models()

            # First backprop + EMA
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.simulate_backprop(__lowerCamelCase							)
            ema_unet.step(unet_step_one.parameters()							)
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							ema_unet.shadow_params

            # Second backprop + EMA
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.simulate_backprop(__lowerCamelCase							)
            ema_unet.step(unet_step_two.parameters()							)
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							ema_unet.shadow_params

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




       def 			UpperCamelCase_	(					self							)	->   Union[str, Any]:
            # If there's no decay even if there are backprops, EMA steps
            # won't take any effect i.e., the shadow params would remain the
            # same.
            _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		int  			=							self.get_models(decay=0.0							)
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							self.simulate_backprop(__lowerCamelCase							)
            ema_unet.step(unet_step_one.parameters()							)
            _SCREAMING_SNAKE_CASE				:		Dict  			=							ema_unet.shadow_params

            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.simulate_backprop(__lowerCamelCase							)
            ema_unet.step(unet_step_two.parameters()							)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							ema_unet.shadow_params

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







       @skip_mps
       def 			UpperCamelCase_	(					self							)	->   Optional[Any]:
            _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							self.get_models()
            _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							self.get_dummy_inputs()

            with tempfile.TemporaryDirectory() as tmpdir:
                 ema_unet.save_pretrained(__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							UNetaDConditionModel.from_pretrained(__lowerCamelCase			,  model_cls=__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE				:		str  			=							loaded_unet.to(unet.device							)

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

            assert torch.allclose(__lowerCamelCase			,  __lowerCamelCase			,  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 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase=False							)	->   List[Any]:
            _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		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(__lowerCamelCase							)			,  "wb"							).close()
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							pa.Table.from_pydict({"filename": [Path(__lowerCamelCase							).name] * 1_0_0}							)
            if take == -1:
                 _SCREAMING_SNAKE_CASE				:		int  			=							len(__lowerCamelCase							) - skip
            if skip is not None and take is not None:
                 _SCREAMING_SNAKE_CASE				:		List[str]  			=							pa_table.slice(__lowerCamelCase			,  __lowerCamelCase							)
            return pa_table





class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       def 			UpperCamelCase_	(					self							)	->   List[Any]:
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							"my_name"
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							SplitInfo(name="train"			,  num_examples=1_0_0							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							SplitInfo(name="test"			,  num_examples=1_0_0							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[train_info, test_info]
            _SCREAMING_SNAKE_CASE				:		str  			=							SplitDict()
            split_dict.add(__lowerCamelCase							)
            split_dict.add(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		str  			=							DatasetInfo(splits=__lowerCamelCase							)

            with tempfile.TemporaryDirectory() as tmp_dir:
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							ReaderTest(__lowerCamelCase			,  __lowerCamelCase							)

                 _SCREAMING_SNAKE_CASE				:		Dict  			=							"test[:33%]"
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							Dataset(**reader.read(__lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)							)
                 self.assertEqual(dset["filename"][0]			,  F"""{name}-test"""							)
                 self.assertEqual(dset.num_rows			,  3_3							)
                 self.assertEqual(dset.num_columns			,  1							)

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




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							"my_name"
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							SplitInfo(name="train"			,  num_examples=1_0_0_0			,  shard_lengths=[1_0_0] * 1_0							)
            _SCREAMING_SNAKE_CASE				:		str  			=							[train_info]
            _SCREAMING_SNAKE_CASE				:		Dict  			=							SplitDict()
            split_dict.add(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							DatasetInfo(splits=__lowerCamelCase							)

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

                 _SCREAMING_SNAKE_CASE				:		int  			=							"train[:33%]"
                 _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							Dataset(**reader.read(__lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)							)
                 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 			UpperCamelCase_	(					self							)	->   str:
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							SplitInfo(name="train"			,  num_examples=1_0_0							)
            _SCREAMING_SNAKE_CASE				:		Dict  			=							SplitInfo(name="test"			,  num_examples=1_0_0							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							SplitDict()
            split_dict.add(__lowerCamelCase							)
            split_dict.add(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Any  			=							DatasetInfo(splits=__lowerCamelCase							)

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

                 _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[
                     {"filename": os.path.join(__lowerCamelCase			,  "train"							)},
                     {"filename": os.path.join(__lowerCamelCase			,  "test"							), "skip": 1_0, "take": 1_0},
                 ]
                 _SCREAMING_SNAKE_CASE				:		Any  			=							Dataset(**reader.read_files(__lowerCamelCase			,  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__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		Dict  			=							arrow_file
     with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          _SCREAMING_SNAKE_CASE				:		Optional[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 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							arrow_file
     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							ArrowReader("",				__lowerCamelCase		)
     with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          _SCREAMING_SNAKE_CASE				:		Dict  			=							reader.read_files([{"filename": filename}],				in_memory=__lowerCamelCase		)
     assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							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__	():
     assert ReadInstruction("train",				to=10,				unit="abs"		).to_spec() == "train[:10]"
     assert ReadInstruction("train",				from_=-80,				to=10,				unit="%"		).to_spec() == "train[-80%:10%]"

     _SCREAMING_SNAKE_CASE				:		str  			=							"train+test"
     assert ReadInstruction.from_spec(__lowerCamelCase		).to_spec() == spec_train_test

     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							"train[2:10]"
     assert ReadInstruction.from_spec(__lowerCamelCase		).to_spec() == spec_train_abs

     _SCREAMING_SNAKE_CASE				:		List[Any]  			=							"train[15%:-20%]"
     assert ReadInstruction.from_spec(__lowerCamelCase		).to_spec() == spec_train_pct

     _SCREAMING_SNAKE_CASE				:		List[str]  			=							"train[:10%](closest)"
     assert ReadInstruction.from_spec(__lowerCamelCase		).to_spec() == "train[:10%]"

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

     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							"train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
     assert ReadInstruction.from_spec(__lowerCamelCase		).to_spec() == spec_train_test_pct_rounding






def 			lowerCamelCase__	():
     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							"dummy"
     _SCREAMING_SNAKE_CASE				:		List[str]  			=							[SplitInfo(name="train",				num_examples=100		)]
     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							"train[:33%]"
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							"arrow"
     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							"prefix"

     _SCREAMING_SNAKE_CASE				:		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}
     ]

     _SCREAMING_SNAKE_CASE				:		List[str]  			=							[SplitInfo(name="train",				num_examples=100,				shard_lengths=[10] * 10		)]
     _SCREAMING_SNAKE_CASE				:		Tuple  			=							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 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							[SplitInfo(name="train",				num_examples=100		)]
     _SCREAMING_SNAKE_CASE				:		int  			=							"train"
     _SCREAMING_SNAKE_CASE				:		int  			=							"arrow"
     _SCREAMING_SNAKE_CASE				:		Tuple  			=							"prefix_path"
     with pytest.raises(__lowerCamelCase		):
          _SCREAMING_SNAKE_CASE				:		List[str]  			=							make_file_instructions(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		)