






"""simple docstring"""





import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest




class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 __UpperCAmelCase			:      Union[str, Any]     	=			(DDIMScheduler,)
 __UpperCAmelCase			:      str     	=			(("eta", 0.0), ("num_inference_steps", 5_0))


 def       _lowercase				(    self							:    Tuple,      **UpperCAmelCase__							:    Optional[int]			):
    __lowercase			=		{
        "num_train_timesteps": 1_0_0_0,
        "beta_start": 0.0_001,
        "beta_end": 0.02,
        "beta_schedule": "linear",
        "clip_sample": True,
    }

    config.update(**A_			)
    return config


 def       _lowercase				(    self							:    Optional[Any],      **UpperCAmelCase__							:    Any			):
    __lowercase			=		self.scheduler_classes[0]
    __lowercase			=		self.get_scheduler_config(**A_			)
    __lowercase			=		scheduler_class(**A_			)

    __lowercase	,__lowercase			=		1_0, 0.0

    __lowercase			=		self.dummy_model()
    __lowercase			=		self.dummy_sample_deter

    scheduler.set_timesteps(A_			)

    for t in scheduler.timesteps:
       __lowercase			=		model(A_,      A_			)
       __lowercase			=		scheduler.step(A_,      A_,      A_,      A_			).prev_sample

    return sample


 def       _lowercase				(    self							:    List[Any]			):
    for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=A_			)


 def       _lowercase				(    self							:    Tuple			):
    for steps_offset in [0, 1]:
       self.check_over_configs(steps_offset=A_			)

    __lowercase			=		self.scheduler_classes[0]
    __lowercase			=		self.get_scheduler_config(steps_offset=1			)
    __lowercase			=		scheduler_class(**A_			)
    scheduler.set_timesteps(5			)
    assert torch.equal(scheduler.timesteps,      torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]			)			)


 def       _lowercase				(    self							:    Union[str, Any]			):
    for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1],      [0.002, 0.02, 0.2, 2]			):
       self.check_over_configs(beta_start=A_,      beta_end=A_			)


 def       _lowercase				(    self							:    int			):
    for schedule in ["linear", "squaredcos_cap_v2"]:
       self.check_over_configs(beta_schedule=A_			)


 def       _lowercase				(    self							:    Any			):
    for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=A_			)


 def       _lowercase				(    self							:    int			):
    for clip_sample in [True, False]:
       self.check_over_configs(clip_sample=A_			)


 def       _lowercase				(    self							:    int			):
    for timestep_spacing in ["trailing", "leading"]:
       self.check_over_configs(timestep_spacing=A_			)


 def       _lowercase				(    self							:    int			):
    for rescale_betas_zero_snr in [True, False]:
       self.check_over_configs(rescale_betas_zero_snr=A_			)


 def       _lowercase				(    self							:    List[str]			):
    self.check_over_configs(thresholding=A_			)
    for threshold in [0.5, 1.0, 2.0]:
       for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(
              thresholding=A_,      prediction_type=A_,      sample_max_value=A_,      )


 def       _lowercase				(    self							:    int			):
    for t in [1, 1_0, 4_9]:
       self.check_over_forward(time_step=A_			)


 def       _lowercase				(    self							:    str			):
    for t, num_inference_steps in zip([1, 1_0, 5_0],      [1_0, 5_0, 5_0_0]			):
       self.check_over_forward(time_step=A_,      num_inference_steps=A_			)


 def       _lowercase				(    self							:    Optional[int]			):
    for t, eta in zip([1, 1_0, 4_9],      [0.0, 0.5, 1.0]			):
       self.check_over_forward(time_step=A_,      eta=A_			)


 def       _lowercase				(    self							:    str			):
    __lowercase			=		self.scheduler_classes[0]
    __lowercase			=		self.get_scheduler_config()
    __lowercase			=		scheduler_class(**A_			)

    assert torch.sum(torch.abs(scheduler._get_variance(0,      0			) - 0.0			)			) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(4_2_0,      4_0_0			) - 0.14_771			)			) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(9_8_0,      9_6_0			) - 0.32_460			)			) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(0,      0			) - 0.0			)			) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(4_8_7,      4_8_6			) - 0.00_979			)			) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(9_9_9,      9_9_8			) - 0.02			)			) < 1E-5


 def       _lowercase				(    self							:    List[Any]			):
    __lowercase			=		self.full_loop()

    __lowercase			=		torch.sum(torch.abs(A_			)			)
    __lowercase			=		torch.mean(torch.abs(A_			)			)

    assert abs(result_sum.item() - 172.0_067			) < 1E-2
    assert abs(result_mean.item() - 0.223_967			) < 1E-3


 def       _lowercase				(    self							:    Tuple			):
    __lowercase			=		self.full_loop(prediction_type="v_prediction"			)

    __lowercase			=		torch.sum(torch.abs(A_			)			)
    __lowercase			=		torch.mean(torch.abs(A_			)			)

    assert abs(result_sum.item() - 52.5_302			) < 1E-2
    assert abs(result_mean.item() - 0.0_684			) < 1E-3


 def       _lowercase				(    self							:    int			):
    # We specify different beta, so that the first alpha is 0.99
    __lowercase			=		self.full_loop(set_alpha_to_one=A_,      beta_start=0.01			)
    __lowercase			=		torch.sum(torch.abs(A_			)			)
    __lowercase			=		torch.mean(torch.abs(A_			)			)

    assert abs(result_sum.item() - 149.8_295			) < 1E-2
    assert abs(result_mean.item() - 0.1_951			) < 1E-3



 def       _lowercase				(    self							:    int			):
    # We specify different beta, so that the first alpha is 0.99
    __lowercase			=		self.full_loop(set_alpha_to_one=A_,      beta_start=0.01			)
    __lowercase			=		torch.sum(torch.abs(A_			)			)
    __lowercase			=		torch.mean(torch.abs(A_			)			)

    assert abs(result_sum.item() - 149.0_784			) < 1E-2
    assert abs(result_mean.item() - 0.1_941			) < 1E-3







"""simple docstring"""





import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Value
from datasets.packaged_modules.json.json import Json




@pytest.fixture
def 						_A						(						UpperCamelCase_  : str)			->		Tuple:
   '''simple docstring'''


   __lowercase			=		tmp_path / "file.jsonl"
   __lowercase			=		textwrap.dedent(
       "\\n        {\"col_1\": -1}\n        {\"col_1\": 1, \"col_2\": 2}\n        {\"col_1\": 10, \"col_2\": 20}\n        ")
   with open(UpperCamelCase_,    "w") as f:
      f.write(UpperCamelCase_)
   return str(UpperCamelCase_)




@pytest.fixture
def 						_A						(						UpperCamelCase_  : str)			->		Any:
   '''simple docstring'''


   __lowercase			=		tmp_path / "file_utf16_encoded.jsonl"
   __lowercase			=		textwrap.dedent(
       "\\n        {\"col_1\": -1}\n        {\"col_1\": 1, \"col_2\": 2}\n        {\"col_1\": 10, \"col_2\": 20}\n        ")
   with open(UpperCamelCase_,    "w",    encoding="utf-16") as f:
      f.write(UpperCamelCase_)
   return str(UpperCamelCase_)




@pytest.fixture
def 						_A						(						UpperCamelCase_  : Optional[int])			->		Optional[Any]:
   '''simple docstring'''


   __lowercase			=		tmp_path / "file_with_list_of_dicts.json"
   __lowercase			=		textwrap.dedent(
       "\\n        [\n            {\"col_1\": -1},\n            {\"col_1\": 1, \"col_2\": 2},\n            {\"col_1\": 10, \"col_2\": 20}\n        ]\n        ")
   with open(UpperCamelCase_,    "w") as f:
      f.write(UpperCamelCase_)
   return str(UpperCamelCase_)




@pytest.fixture
def 						_A						(						UpperCamelCase_  : str)			->		int:
   '''simple docstring'''


   __lowercase			=		tmp_path / "file_with_list_of_dicts_field.json"
   __lowercase			=		textwrap.dedent(
       "\\n        {\n            \"field1\": 1,\n            \"field2\": \"aabb\",\n            \"field3\": [\n                {\"col_1\": -1},\n                {\"col_1\": 1, \"col_2\": 2},\n                {\"col_1\": 10, \"col_2\": 20}\n            ]\n        }\n        ")
   with open(UpperCamelCase_,    "w") as f:
      f.write(UpperCamelCase_)
   return str(UpperCamelCase_)




@pytest.mark.parametrize(
    "file_fixture, config_kwargs",    [
        ("jsonl_file", {}),
        ("jsonl_file_utf16_encoded", {"encoding": "utf-16"}),
        ("json_file_with_list_of_dicts", {}),
        ("json_file_with_list_of_dicts_field", {"field": "field3"}),
    ],    )
def 						_A						(						UpperCamelCase_  : Any,    UpperCamelCase_  : List[Any],    UpperCamelCase_  : Optional[Any])			->		List[Any]:
   '''simple docstring'''


   __lowercase			=		Json(**UpperCamelCase_)
   __lowercase			=		json._generate_tables([[request.getfixturevalue(UpperCamelCase_)]])
   __lowercase			=		pa.concat_tables([table for _, table in generator])
   assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20]}




@pytest.mark.parametrize(
    "file_fixture, config_kwargs",    [
        (
            "jsonl_file",
            {"features": Features({"col_1": Value("int64"), "col_2": Value("int64"), "missing_col": Value("string")})},
        ),
        (
            "json_file_with_list_of_dicts",
            {"features": Features({"col_1": Value("int64"), "col_2": Value("int64"), "missing_col": Value("string")})},
        ),
        (
            "json_file_with_list_of_dicts_field",
            {
                "field": "field3",
                "features": Features(
                    {"col_1": Value("int64"), "col_2": Value("int64"), "missing_col": Value("string")}),
            },
        ),
    ],    )
def 						_A						(						UpperCamelCase_  : Optional[int],    UpperCamelCase_  : int,    UpperCamelCase_  : str)			->		Optional[int]:
   '''simple docstring'''


   __lowercase			=		Json(**UpperCamelCase_)
   __lowercase			=		json._generate_tables([[request.getfixturevalue(UpperCamelCase_)]])
   __lowercase			=		pa.concat_tables([table for _, table in generator])
   assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20], "missing_col": [None, None, None]}




