
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	(      lowercase_   )		->	List[Any]:


		'''simple docstring'''






		lowercase__  :   Dict    		=      tmp_path / """file.csv"""
		lowercase__  :   Union[str, Any]    		=      textwrap.dedent(
		    """\
        header1,header2
        1,2
        10,20
        """   )
		with open(lowercase_ , """w"""   ) as f:
				f.write(lowercase_   )
		return str(lowercase_   )

@pytest.fixture
def 		UpperCamelCase	(      lowercase_   )		->	Optional[int]:


		'''simple docstring'''






		lowercase__  :   List[Any]    		=      tmp_path / """malformed_file.csv"""
		lowercase__  :   Any    		=      textwrap.dedent(
		    """\
        header1,header2
        1,2
        10,20,
        """   )
		with open(lowercase_ , """w"""   ) as f:
				f.write(lowercase_   )
		return str(lowercase_   )

@pytest.fixture
def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Any:


		'''simple docstring'''






		lowercase__  :   Optional[Any]    		=      tmp_path / """csv_with_image.csv"""
		lowercase__  :   Optional[int]    		=      textwrap.dedent(
		    F'\\n        image\n        {image_file}\n        '   )
		with open(lowercase_ , """w"""   ) as f:
				f.write(lowercase_   )
		return str(lowercase_   )

@pytest.fixture
def 		UpperCamelCase	(      lowercase_   )		->	List[str]:


		'''simple docstring'''






		lowercase__  :   List[str]    		=      tmp_path / """csv_with_label.csv"""
		lowercase__  :   Union[str, Any]    		=      textwrap.dedent(
		    """\
        label
        good
        bad
        good
        """   )
		with open(lowercase_ , """w"""   ) as f:
				f.write(lowercase_   )
		return str(lowercase_   )

@pytest.fixture
def 		UpperCamelCase	(      lowercase_   )		->	Tuple:


		'''simple docstring'''






		lowercase__  :   Tuple    		=      tmp_path / """csv_with_int_list.csv"""
		lowercase__  :   Dict    		=      textwrap.dedent(
		    """\
        int_list
        1 2 3
        4 5 6
        7 8 9
        """   )
		with open(lowercase_ , """w"""   ) as f:
				f.write(lowercase_   )
		return str(lowercase_   )

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	Any:


		'''simple docstring'''






		lowercase__  :   str    		=      Csv()
		lowercase__  :   Union[str, Any]    		=      csv._generate_tables([[csv_file, malformed_csv_file]]   )
		with pytest.raises(lowercase_ , 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(lowercase_   ) in record.message
		    for record in caplog.records   )

@require_pil
def 		UpperCamelCase	(      lowercase_   )		->	Tuple:


		'''simple docstring'''






		with open(lowercase_ , encoding="""utf-8"""   ) as f:
				lowercase__  :   Optional[Any]    		=      f.read().splitlines()[1]
		lowercase__  :   Optional[int]    		=      Csv(encoding="""utf-8""" , features=Features({"""image""": Image()}   )   )
		lowercase__  :   List[Any]    		=      csv._generate_tables([[csv_file_with_image]]   )
		lowercase__  :   Dict    		=      pa.concat_tables([table for _, table in generator]   )
		assert pa_table.schema.field("""image"""   ).type == Image()()
		lowercase__  :   Tuple    		=      pa_table.to_pydict()["""image"""]
		assert generated_content == [{"path": image_file, "bytes": None}]

def 		UpperCamelCase	(      lowercase_   )		->	Union[str, Any]:


		'''simple docstring'''






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



def 		UpperCamelCase	(      lowercase_   )		->	str:


		'''simple docstring'''






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








from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     torch.FloatTensor
 __lowerCAmelCase  :     torch.FloatTensor
 __lowerCAmelCase  :     Optional[torch.FloatTensor]        =			None



class       _snake_case   (	UpperCAmelCase_  ,						UpperCAmelCase_							):
 __lowerCAmelCase  :     Union[str, Any]        =			2


 @register_to_config
 def __init__(					self					,  SCREAMING_SNAKE_CASE_ = 0.0_2					,  SCREAMING_SNAKE_CASE_ = 1_00					,  SCREAMING_SNAKE_CASE_ = 1.0_0_7					,  SCREAMING_SNAKE_CASE_ = 80					,  SCREAMING_SNAKE_CASE_ = 0.0_5					,  SCREAMING_SNAKE_CASE_ = 50					,  ):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      sigma_max

   # setable values
   lowercase__  :   int    		=      None
   lowercase__  :   np.IntTensor    		=      None
   lowercase__  :   torch.FloatTensor    		=      None  # sigma(t_i)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   return sample


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   lowercase__  :   Dict    		=      num_inference_steps
   lowercase__  :   List[Any]    		=      np.arange(0					,  self.num_inference_steps)[::-1].copy()
   lowercase__  :   Optional[Any]    		=      torch.from_numpy(SCREAMING_SNAKE_CASE_).to(SCREAMING_SNAKE_CASE_)
   lowercase__  :   str    		=      [
       (
           self.config.sigma_max**2
           * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
       )
       for i in self.timesteps
   ]
   lowercase__  :   int    		=      torch.tensor(SCREAMING_SNAKE_CASE_					,  dtype=torch.floataa					,  device=SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   if self.config.s_min <= sigma <= self.config.s_max:
     lowercase__  :   Optional[Any]    		=      min(self.config.s_churn / self.num_inference_steps					,  2**0.5 - 1)
   else:
     lowercase__  :   Tuple    		=      0

   # sample eps ~ N(0, S_noise^2 * I)
   lowercase__  :   Dict    		=      self.config.s_noise * randn_tensor(sample.shape					,  generator=SCREAMING_SNAKE_CASE_).to(sample.device)
   lowercase__  :   int    		=      sigma + gamma * sigma
   lowercase__  :   Optional[int]    		=      sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

   return sample_hat, sigma_hat


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = True					,  ):


   '''simple docstring'''




   lowercase__  :   str    		=      sample_hat + sigma_hat * model_output
   lowercase__  :   str    		=      (sample_hat - pred_original_sample) / sigma_hat
   lowercase__  :   Optional[Any]    		=      sample_hat + (sigma_prev - sigma_hat) * derivative

   if not return_dict:
     return (sample_prev, derivative)

   return KarrasVeOutput(
       prev_sample=SCREAMING_SNAKE_CASE_					,  derivative=SCREAMING_SNAKE_CASE_					,  pred_original_sample=SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = True					,  ):


   '''simple docstring'''




   lowercase__  :   str    		=      sample_prev + sigma_prev * model_output
   lowercase__  :   Dict    		=      (sample_prev - pred_original_sample) / sigma_prev
   lowercase__  :   Optional[int]    		=      sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

   if not return_dict:
     return (sample_prev, derivative)

   return KarrasVeOutput(
       prev_sample=SCREAMING_SNAKE_CASE_					,  derivative=SCREAMING_SNAKE_CASE_					,  pred_original_sample=SCREAMING_SNAKE_CASE_)



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   raise NotImplementedError()






