

'''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 							_UpperCamelCase				(   lowerCAmelCase__:					List[Any]   )      -> Optional[Any]:
							SCREAMING_SNAKE_CASE_				=				tmp_path / 'file.csv'
							SCREAMING_SNAKE_CASE_				=				textwrap.dedent(
							    '\\n        header1,header2\n        1,2\n        10,20\n        '   )
							with open(lowerCAmelCase__		,'w'   ) as f:
														f.write(lowerCAmelCase__   )
							return str(lowerCAmelCase__   )




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




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




@pytest.fixture
def 							_UpperCamelCase				(   lowerCAmelCase__:					Optional[Any]   )      -> Tuple:
							SCREAMING_SNAKE_CASE_				=				tmp_path / 'csv_with_label.csv'
							SCREAMING_SNAKE_CASE_				=				textwrap.dedent(
							    '\\n        label\n        good\n        bad\n        good\n        '   )
							with open(lowerCAmelCase__		,'w'   ) as f:
														f.write(lowerCAmelCase__   )
							return str(lowerCAmelCase__   )




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




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




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




def 							_UpperCamelCase				(   lowerCAmelCase__:					List[str]   )      -> str:
							SCREAMING_SNAKE_CASE_				=				Csv(encoding='utf-8'		,sep=','		,converters={'int_list': lambda lowerCAmelCase__   : [int(lowerCAmelCase__   ) for i in x.split()]}   )
							SCREAMING_SNAKE_CASE_				=				csv._generate_tables([[csv_file_with_int_list]]   )
							SCREAMING_SNAKE_CASE_				=				pa.concat_tables([table for _, table in generator]   )
							assert pa.types.is_list(pa_table.schema.field('int_list'   ).type   )
							SCREAMING_SNAKE_CASE_				=				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    snake_case     :






				"""simple docstring"""
				# setable values
				_a    	=     None
				_a    	=     None
				_a    	=     None  # sigma(t_i)



				@classmethod
				def   a__					(    cls       )     ->							Dict:
											return cls()






@dataclass
class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     42
				_a    	=     42
				_a    	=     42






class    snake_case     (      lowercase_,						lowercase_		):






				"""simple docstring"""



				@property
				def   a__					(    self       )     ->							int:
											return True



				@register_to_config
				def __init__(    self,     _lowercase = 0.02,     _lowercase = 100,     _lowercase = 1.007,     _lowercase = 80,     _lowercase = 0.05,     _lowercase = 50,     )     ->							List[Any]:
											pass



				def   a__					(    self       )     ->							List[Any]:
											return KarrasVeSchedulerState.create()



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase = ()       )     ->							KarrasVeSchedulerState:
											SCREAMING_SNAKE_CASE_				=				jnp.arange(0,     _lowercase       )[::-1].copy()
											SCREAMING_SNAKE_CASE_				=				[
											    (
											        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=_lowercase,     schedule=jnp.array(_lowercase,     dtype=jnp.floataa       ),     timesteps=_lowercase,     )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     )     ->							Tuple[jnp.ndarray, float]:
											if self.config.s_min <= sigma <= self.config.s_max:
																		SCREAMING_SNAKE_CASE_				=				min(self.config.s_churn / state.num_inference_steps,     2**0.5 - 1       )
											else:
																		SCREAMING_SNAKE_CASE_				=				0

											# sample eps ~ N(0, S_noise^2 * I)
											SCREAMING_SNAKE_CASE_				=				random.split(_lowercase,     num=1       )
											SCREAMING_SNAKE_CASE_				=				self.config.s_noise * random.normal(key=_lowercase,     shape=sample.shape       )
											SCREAMING_SNAKE_CASE_				=				sigma + gamma * sigma
											SCREAMING_SNAKE_CASE_				=				sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

											return sample_hat, sigma_hat



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase = True,     )     ->							Union[FlaxKarrasVeOutput, Tuple]:
											SCREAMING_SNAKE_CASE_				=				sample_hat + sigma_hat * model_output
											SCREAMING_SNAKE_CASE_				=				(sample_hat - pred_original_sample) / sigma_hat
											SCREAMING_SNAKE_CASE_				=				sample_hat + (sigma_prev - sigma_hat) * derivative

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

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



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase = True,     )     ->							Union[FlaxKarrasVeOutput, Tuple]:
											SCREAMING_SNAKE_CASE_				=				sample_prev + sigma_prev * model_output
											SCREAMING_SNAKE_CASE_				=				(sample_prev - pred_original_sample) / sigma_prev
											SCREAMING_SNAKE_CASE_				=				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=_lowercase,     derivative=_lowercase,     state=_lowercase       )





				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase       )     ->							List[Any]:
											raise NotImplementedError()





