


"""simple docstring"""




import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil

@pytest.fixture
def        lowercase							(		lowerCAmelCase__ :     Optional[Any]			) ->      Optional[int]:
		__a           =			tmp_path / '''file.csv'''
		__a           =			textwrap.dedent(
		    '''\
        header1,header2
        1,2
        10,20
        '''			)
		with open(lowerCAmelCase__    ,					'''w'''			) as f:
				f.write(lowerCAmelCase__			)
		return str(lowerCAmelCase__			)

@pytest.fixture
def        lowercase							(		lowerCAmelCase__ :     str			) ->      Any:
		__a           =			tmp_path / '''malformed_file.csv'''
		__a           =			textwrap.dedent(
		    '''\
        header1,header2
        1,2
        10,20,
        '''			)
		with open(lowerCAmelCase__    ,					'''w'''			) as f:
				f.write(lowerCAmelCase__			)
		return str(lowerCAmelCase__			)

@pytest.fixture
def        lowercase							(		lowerCAmelCase__ :     Optional[int]    ,					lowerCAmelCase__ :     Any			) ->      List[str]:
		__a           =			tmp_path / '''csv_with_image.csv'''
		__a           =			textwrap.dedent(
		    f'''\
        image
        {image_file}
        '''			)
		with open(lowerCAmelCase__    ,					'''w'''			) as f:
				f.write(lowerCAmelCase__			)
		return str(lowerCAmelCase__			)

@pytest.fixture
def        lowercase							(		lowerCAmelCase__ :     Dict			) ->      Union[str, Any]:
		__a           =			tmp_path / '''csv_with_label.csv'''
		__a           =			textwrap.dedent(
		    '''\
        label
        good
        bad
        good
        '''			)
		with open(lowerCAmelCase__    ,					'''w'''			) as f:
				f.write(lowerCAmelCase__			)
		return str(lowerCAmelCase__			)

@pytest.fixture
def        lowercase							(		lowerCAmelCase__ :     Optional[int]			) ->      List[str]:
		__a           =			tmp_path / '''csv_with_int_list.csv'''
		__a           =			textwrap.dedent(
		    '''\
        int_list
        1 2 3
        4 5 6
        7 8 9
        '''			)
		with open(lowerCAmelCase__    ,					'''w'''			) as f:
				f.write(lowerCAmelCase__			)
		return str(lowerCAmelCase__			)

def        lowercase							(		lowerCAmelCase__ :     Optional[int]    ,					lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     Any			) ->      Tuple:
		__a           =			Csv()
		__a           =			csv._generate_tables([[csv_file, malformed_csv_file]]			)
		with pytest.raises(lowerCAmelCase__    ,					match='''Error tokenizing data'''			):
				for _ in generator:
						pass
		assert any(
		    record.levelname == '''ERROR'''
		    and '''Failed to read file''' in record.message
		    and os.path.basename(lowerCAmelCase__			) in record.message
		    for record in caplog.records			)

@require_pil
def        lowercase							(		lowerCAmelCase__ :     Optional[int]			) ->      Union[str, Any]:
		with open(lowerCAmelCase__    ,					encoding='''utf-8'''			) as f:
				__a           =			f.read().splitlines()[1]
		__a           =			Csv(encoding='''utf-8'''    ,					features=Features({'''image''': Image()}			)			)
		__a           =			csv._generate_tables([[csv_file_with_image]]			)
		__a           =			pa.concat_tables([table for _, table in generator]			)
		assert pa_table.schema.field('''image'''			).type == Image()()
		__a           =			pa_table.to_pydict()['''image''']
		assert generated_content == [{"path": image_file, "bytes": None}]

def        lowercase							(		lowerCAmelCase__ :     List[str]			) ->      Optional[int]:
		with open(lowerCAmelCase__    ,					encoding='''utf-8'''			) as f:
				__a           =			f.read().splitlines()[1:]
		__a           =			Csv(encoding='''utf-8'''    ,					features=Features({'''label''': ClassLabel(names=['''good''', '''bad''']			)}			)			)
		__a           =			csv._generate_tables([[csv_file_with_label]]			)
		__a           =			pa.concat_tables([table for _, table in generator]			)
		assert pa_table.schema.field('''label'''			).type == ClassLabel(names=['''good''', '''bad''']			)()
		__a           =			pa_table.to_pydict()['''label''']
		assert generated_content == [ClassLabel(names=['''good''', '''bad''']			).straint(lowerCAmelCase__			) for label in labels]




def        lowercase							(		lowerCAmelCase__ :     Dict			) ->      str:
		__a           =			Csv(encoding='''utf-8'''    ,					sep=''','''    ,					converters={'''int_list''': lambda lowerCAmelCase__			: [int(lowerCAmelCase__			) for i in x.split()]}			)
		__a           =			csv._generate_tables([[csv_file_with_int_list]]			)
		__a           =			pa.concat_tables([table for _, table in generator]			)
		assert pa.types.is_list(pa_table.schema.field('''int_list'''			).type			)
		__a           =			pa_table.to_pydict()['''int_list''']
		assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]














"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin







@flax.struct.dataclass
class 			UpperCamelCase_       :
	# setable values
	__magic_name__	      =				None
	__magic_name__	      =				None
	__magic_name__	      =				None  # sigma(t_i)





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: List[str]						)						->			Optional[Any]:
				return cls()







@dataclass
class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				42
	__magic_name__	      =				42
	__magic_name__	      =				42







class 			UpperCamelCase_       (__A				,						__A     ):





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			str:
				return True





	@register_to_config
	def __init__(      self					: List[str]	,	lowerCAmelCase_					: float = 0.0_2	,	lowerCAmelCase_					: float = 100	,	lowerCAmelCase_					: float = 1.0_0_7	,	lowerCAmelCase_					: float = 80	,	lowerCAmelCase_					: float = 0.0_5	,	lowerCAmelCase_					: float = 50	,	)						->			Union[str, Any]:
				pass





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			Tuple:
				return KarrasVeSchedulerState.create()





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Tuple = ()						)						->			KarrasVeSchedulerState:
				UpperCAmelCase_     : Dict			     =					jnp.arange(0	,	lowerCAmelCase_						)[::-1].copy()
				UpperCAmelCase_     : Dict			     =					[
				    (
				        self.config.sigma_max**2
				        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
				    )
				    for i in timesteps
				]

				return state.replace(
				    num_inference_steps=lowerCAmelCase_	,	schedule=jnp.array(lowerCAmelCase_	,	dtype=jnp.floataa						)	,	timesteps=lowerCAmelCase_	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: random.KeyArray	,	)						->			Tuple[jnp.ndarray, float]:
				if self.config.s_min <= sigma <= self.config.s_max:
							UpperCAmelCase_     : Any			     =					min(self.config.s_churn / state.num_inference_steps	,	2**0.5 - 1						)
				else:
							UpperCAmelCase_     : Optional[int]			     =					0

				# sample eps ~ N(0, S_noise^2 * I)
				UpperCAmelCase_     : List[Any]			     =					random.split(lowerCAmelCase_	,	num=1						)
				UpperCAmelCase_     : List[str]			     =					self.config.s_noise * random.normal(key=lowerCAmelCase_	,	shape=sample.shape						)
				UpperCAmelCase_     : Optional[Any]			     =					sigma + gamma * sigma
				UpperCAmelCase_     : Any			     =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

				return sample_hat, sigma_hat





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + sigma_hat * model_output
				UpperCAmelCase_     : List[Any]			     =					(sample_hat - pred_original_sample) / sigma_hat
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + (sigma_prev - sigma_hat) * derivative

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : str			     =					sample_prev + sigma_prev * model_output
				UpperCAmelCase_     : Any			     =					(sample_prev - pred_original_sample) / sigma_prev
				UpperCAmelCase_     : int			     =					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Optional[Any]						)						->			Dict:
				raise NotImplementedError()



