import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       int ,     __UpperCamelCase    :       Any	)							->					Tuple:







  """simple docstring"""





  assert isinstance(_A ,     _A	)
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("""keep_in_memory""" ,     [False, True]	)
def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       Any	)							->					int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      tmp_path / """cache"""
  SCREAMING_SNAKE_CASE__										=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE__										=      JsonDatasetReader(_A ,     cache_dir=_A ,     keep_in_memory=_A	).read()
  _check_json_dataset(_A ,     _A	)



@pytest.mark.parametrize(
    """features""" ,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ] ,     )
def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       List[Any]	)							->					Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      tmp_path / """cache"""
  SCREAMING_SNAKE_CASE__										=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  SCREAMING_SNAKE_CASE__										=      features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE__										=      (
      Features({feature: Value(_A	) for feature, dtype in features.items()}	) if features is not None else None
  )
  SCREAMING_SNAKE_CASE__										=      JsonDatasetReader(_A ,     features=_A ,     cache_dir=_A	).read()
  _check_json_dataset(_A ,     _A	)



@pytest.mark.parametrize(
    """features""" ,     [
        None,
        {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""},
    ] ,     )
def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       int ,     __UpperCamelCase    :       Any ,     __UpperCamelCase    :       List[str]	)							->					Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      tmp_path / """cache"""
  SCREAMING_SNAKE_CASE__										=      {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""}
  SCREAMING_SNAKE_CASE__										=      features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE__										=      (
      Features({feature: Value(_A	) for feature, dtype in features.items()}	) if features is not None else None
  )
  SCREAMING_SNAKE_CASE__										=      JsonDatasetReader(_A ,     features=_A ,     cache_dir=_A	).read()
  assert isinstance(_A ,     _A	)
  assert dataset.num_rows == 2
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_3", "col_1", "col_2"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Optional[Any]	)							->					List[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      {"""col_2""": """int64""", """col_3""": """float64""", """col_1""": """string"""}
  SCREAMING_SNAKE_CASE__										=      features.copy()
  SCREAMING_SNAKE_CASE__										=      (
      Features({feature: Value(_A	) for feature, dtype in features.items()}	) if features is not None else None
  )
  SCREAMING_SNAKE_CASE__										=      tmp_path / """cache"""
  SCREAMING_SNAKE_CASE__										=      JsonDatasetReader(_A ,     features=_A ,     cache_dir=_A	).read()
  assert isinstance(_A ,     _A	)
  assert dataset.num_rows == 2
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_2", "col_3", "col_1"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("""split""" ,     [None, NamedSplit("""train"""	), """train""", """test"""]	)
def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       List[str]	)							->					Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      tmp_path / """cache"""
  SCREAMING_SNAKE_CASE__										=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  SCREAMING_SNAKE_CASE__										=      JsonDatasetReader(_A ,     cache_dir=_A ,     split=_A	).read()
  _check_json_dataset(_A ,     _A	)
  assert dataset.split == split if split else "train"



@pytest.mark.parametrize("""path_type""" ,     [str, list]	)
def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       str	)							->					Optional[int]:







  """simple docstring"""





  if issubclass(_A ,     _A	):
    SCREAMING_SNAKE_CASE__										=      jsonl_path
  elif issubclass(_A ,     _A	):
    SCREAMING_SNAKE_CASE__										=      [jsonl_path]
  SCREAMING_SNAKE_CASE__										=      tmp_path / """cache"""
  SCREAMING_SNAKE_CASE__										=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  SCREAMING_SNAKE_CASE__										=      JsonDatasetReader(_A ,     cache_dir=_A	).read()
  _check_json_dataset(_A ,     _A	)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Dict ,     __UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       int=("train",)	)							->					List[Any]:







  """simple docstring"""





  assert isinstance(_A ,     _A	)
  for split in splits:
    SCREAMING_SNAKE_CASE__										=      dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("""keep_in_memory""" ,     [False, True]	)
def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Any ,     __UpperCamelCase    :       int	)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      tmp_path / """cache"""
  SCREAMING_SNAKE_CASE__										=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE__										=      JsonDatasetReader({"""train""": jsonl_path} ,     cache_dir=_A ,     keep_in_memory=_A	).read()
  _check_json_datasetdict(_A ,     _A	)



@pytest.mark.parametrize(
    """features""" ,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ] ,     )
def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       Optional[int]	)							->					List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      tmp_path / """cache"""
  SCREAMING_SNAKE_CASE__										=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  SCREAMING_SNAKE_CASE__										=      features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE__										=      (
      Features({feature: Value(_A	) for feature, dtype in features.items()}	) if features is not None else None
  )
  SCREAMING_SNAKE_CASE__										=      JsonDatasetReader({"""train""": jsonl_path} ,     features=_A ,     cache_dir=_A	).read()
  _check_json_datasetdict(_A ,     _A	)



@pytest.mark.parametrize("""split""" ,     [None, NamedSplit("""train"""	), """train""", """test"""]	)
def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any ,     __UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       Tuple	)							->					Dict:







  """simple docstring"""





  if split:
    SCREAMING_SNAKE_CASE__										=      {split: jsonl_path}
  else:
    SCREAMING_SNAKE_CASE__										=      """train"""
    SCREAMING_SNAKE_CASE__										=      {"""train""": jsonl_path, """test""": jsonl_path}
  SCREAMING_SNAKE_CASE__										=      tmp_path / """cache"""
  SCREAMING_SNAKE_CASE__										=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  SCREAMING_SNAKE_CASE__										=      JsonDatasetReader(_A ,     cache_dir=_A	).read()
  _check_json_datasetdict(_A ,     _A ,     splits=list(path.keys()	)	)
  assert all(dataset[split].split == split for split in path.keys()	)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       int	)							->					Union[str, Any]:







  """simple docstring"""





  return json.load(_A	)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       int	)							->					Optional[int]:







  """simple docstring"""





  return [json.loads(_A	) for line in buffer]







class __snake_case				:




   @pytest.mark.parametrize("""lines, load_json_function"""       ,	[(True, load_json_lines), (False, load_json)]      )
   def 	__a						(    self			:				Any       ,	_lowercase			:				int       ,	_lowercase			:				List[str]       ,	_lowercase			:				Dict      ):



     """simple docstring"""






     with io.BytesIO() as buffer:
       JsonDatasetWriter(UpperCamelCase__       ,	UpperCamelCase__       ,	lines=UpperCamelCase__      ).write()
       buffer.seek(0      )
       SCREAMING_SNAKE_CASE__										=      load_json_function(UpperCamelCase__      )
     assert isinstance(UpperCamelCase__       ,	UpperCamelCase__      )
     assert isinstance(exported_content[0]       ,	UpperCamelCase__      )
     assert len(UpperCamelCase__      ) == 10




   @pytest.mark.parametrize(
       """orient, container, keys, len_at"""       ,	[
           ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
           ("""split""", dict, {"""columns""", """data"""}, """data"""),
           ("""index""", dict, set("""0123456789"""      ), None),
           ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
           ("""values""", list, None, None),
           ("""table""", dict, {"""schema""", """data"""}, """data"""),
       ]       ,	)
   def 	__a						(    self			:				List[str]       ,	_lowercase			:				Dict       ,	_lowercase			:				int       ,	_lowercase			:				List[str]       ,	_lowercase			:				int       ,	_lowercase			:				Optional[Any]      ):



     """simple docstring"""






     with io.BytesIO() as buffer:
       JsonDatasetWriter(UpperCamelCase__       ,	UpperCamelCase__       ,	lines=UpperCamelCase__       ,	orient=UpperCamelCase__      ).write()
       buffer.seek(0      )
       SCREAMING_SNAKE_CASE__										=      load_json(UpperCamelCase__      )
     assert isinstance(UpperCamelCase__       ,	UpperCamelCase__      )
     if keys:
       if container is dict:
         assert exported_content.keys() == keys
       else:
         assert exported_content[0].keys() == keys
     else:
       assert not hasattr(UpperCamelCase__       ,	"""keys"""      ) and not hasattr(exported_content[0]       ,	"""keys"""      )
     if len_at:
       assert len(exported_content[len_at]      ) == 10
     else:
       assert len(UpperCamelCase__      ) == 10




   @pytest.mark.parametrize("""lines, load_json_function"""       ,	[(True, load_json_lines), (False, load_json)]      )
   def 	__a						(    self			:				Optional[Any]       ,	_lowercase			:				str       ,	_lowercase			:				Optional[Any]       ,	_lowercase			:				Optional[Any]      ):



     """simple docstring"""






     with io.BytesIO() as buffer:
       JsonDatasetWriter(UpperCamelCase__       ,	UpperCamelCase__       ,	lines=UpperCamelCase__       ,	num_proc=2      ).write()
       buffer.seek(0      )
       SCREAMING_SNAKE_CASE__										=      load_json_function(UpperCamelCase__      )
     assert isinstance(UpperCamelCase__       ,	UpperCamelCase__      )
     assert isinstance(exported_content[0]       ,	UpperCamelCase__      )
     assert len(UpperCamelCase__      ) == 10




   @pytest.mark.parametrize(
       """orient, container, keys, len_at"""       ,	[
           ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
           ("""split""", dict, {"""columns""", """data"""}, """data"""),
           ("""index""", dict, set("""0123456789"""      ), None),
           ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
           ("""values""", list, None, None),
           ("""table""", dict, {"""schema""", """data"""}, """data"""),
       ]       ,	)
   def 	__a						(    self			:				Union[str, Any]       ,	_lowercase			:				Optional[Any]       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				Optional[Any]       ,	_lowercase			:				Optional[int]       ,	_lowercase			:				List[Any]      ):



     """simple docstring"""






     with io.BytesIO() as buffer:
       JsonDatasetWriter(UpperCamelCase__       ,	UpperCamelCase__       ,	lines=UpperCamelCase__       ,	orient=UpperCamelCase__       ,	num_proc=2      ).write()
       buffer.seek(0      )
       SCREAMING_SNAKE_CASE__										=      load_json(UpperCamelCase__      )
     assert isinstance(UpperCamelCase__       ,	UpperCamelCase__      )
     if keys:
       if container is dict:
         assert exported_content.keys() == keys
       else:
         assert exported_content[0].keys() == keys
     else:
       assert not hasattr(UpperCamelCase__       ,	"""keys"""      ) and not hasattr(exported_content[0]       ,	"""keys"""      )
     if len_at:
       assert len(exported_content[len_at]      ) == 10
     else:
       assert len(UpperCamelCase__      ) == 10




   def 	__a						(    self			:				Optional[int]       ,	_lowercase			:				List[str]      ):



     """simple docstring"""






     with pytest.raises(UpperCamelCase__      ):
       with io.BytesIO() as buffer:
         JsonDatasetWriter(UpperCamelCase__       ,	UpperCamelCase__       ,	num_proc=0      )




   @pytest.mark.parametrize("""compression, extension"""       ,	[("""gzip""", """gz"""), ("""bz2""", """bz2"""), ("""xz""", """xz""")]      )
   def 	__a						(    self			:				Tuple       ,	_lowercase			:				str       ,	_lowercase			:				List[Any]       ,	_lowercase			:				Tuple       ,	_lowercase			:				Any       ,	_lowercase			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      tmp_path_factory.mktemp("""data"""      ) / f"""test.json.{extension}"""
     SCREAMING_SNAKE_CASE__										=      str(shared_datadir / f"""test_file.json.{extension}"""      )
     JsonDatasetWriter(UpperCamelCase__       ,	UpperCamelCase__       ,	compression=UpperCamelCase__      ).write()

     with fsspec.open(UpperCamelCase__       ,	"""rb"""       ,	compression="""infer"""      ) as f:
       SCREAMING_SNAKE_CASE__										=      f.read()
     with fsspec.open(UpperCamelCase__       ,	"""rb"""       ,	compression="""infer"""      ) as f:
       SCREAMING_SNAKE_CASE__										=      f.read()
     assert exported_content == original_content






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   (UnCLIPScheduler,)



		def        SCREAMING_SNAKE_CASE__			(     self,	**UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  {
								    '''num_train_timesteps''': 1000,
								    '''variance_type''': '''fixed_small_log''',
								    '''clip_sample''': True,
								    '''clip_sample_range''': 1.0,
								    '''prediction_type''': '''epsilon''',
								}

								config.update(**UpperCamelCase__   )
								return config



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for timesteps in [1, 5, 100, 1000]:
														self.check_over_configs(num_train_timesteps=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for variance in ["fixed_small_log", "learned_range"]:
														self.check_over_configs(variance_type=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for clip_sample_range in [1, 5, 10, 20]:
														self.check_over_configs(clip_sample_range=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for prediction_type in ["epsilon", "sample"]:
														self.check_over_configs(prediction_type=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for time_step in [0, 500, 999]:
														for prev_timestep in [None, 5, 100, 250, 500, 750]:
																				if prev_timestep is not None and prev_timestep >= time_step:
																										continue

																				self.check_over_forward(time_step=UpperCamelCase__,	prev_timestep=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config(variance_type='''fixed_small_log'''   )
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0000E-10   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_549_625   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.9_994_987   )   ) < 1E-5



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config(variance_type='''learned_range'''   )
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								lowerCAmelCase_      			=  0.5

								assert scheduler._get_variance(1,	predicted_variance=UpperCamelCase__   ) - -10.1_712_790 < 1E-5
								assert scheduler._get_variance(487,	predicted_variance=UpperCamelCase__   ) - -5.7_998_052 < 1E-5
								assert scheduler._get_variance(999,	predicted_variance=UpperCamelCase__   ) - -0.0_010_011 < 1E-5



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config()
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								lowerCAmelCase_      			=  scheduler.timesteps

								lowerCAmelCase_      			=  self.dummy_model()
								lowerCAmelCase_      			=  self.dummy_sample_deter
								lowerCAmelCase_      			=  torch.manual_seed(0   )

								for i, t in enumerate(UpperCamelCase__   ):
														# 1. predict noise residual
														lowerCAmelCase_      			=  model(UpperCamelCase__,	UpperCamelCase__   )

														# 2. predict previous mean of sample x_t-1
														lowerCAmelCase_      			=  scheduler.step(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	generator=UpperCamelCase__   ).prev_sample

														lowerCAmelCase_      			=  pred_prev_sample

								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 252.2_682_495   ) < 1E-2
								assert abs(result_mean.item() - 0.3_284_743   ) < 1E-3



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config()
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								scheduler.set_timesteps(25   )

								lowerCAmelCase_      			=  scheduler.timesteps

								lowerCAmelCase_      			=  self.dummy_model()
								lowerCAmelCase_      			=  self.dummy_sample_deter
								lowerCAmelCase_      			=  torch.manual_seed(0   )

								for i, t in enumerate(UpperCamelCase__   ):
														# 1. predict noise residual
														lowerCAmelCase_      			=  model(UpperCamelCase__,	UpperCamelCase__   )

														if i + 1 == timesteps.shape[0]:
																				lowerCAmelCase_      			=  None
														else:
																				lowerCAmelCase_      			=  timesteps[i + 1]

														# 2. predict previous mean of sample x_t-1
														lowerCAmelCase_      			=  scheduler.step(
														    UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	prev_timestep=UpperCamelCase__,	generator=UpperCamelCase__   ).prev_sample

														lowerCAmelCase_      			=  pred_prev_sample

								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 258.2_044_983   ) < 1E-2
								assert abs(result_mean.item() - 0.3_362_038   ) < 1E-3



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass




