






'''simple docstring'''

import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType






class 				a_						:

  def __init__(      self			,		_SCREAMING_SNAKE_CASE=2			,		_SCREAMING_SNAKE_CASE=3			,		_SCREAMING_SNAKE_CASE=64			,		_SCREAMING_SNAKE_CASE=None      )  ->      str:



        """simple docstring"""






        UpperCamelCase				  =					np.random.default_rng(_SCREAMING_SNAKE_CASE      )
        UpperCamelCase				  =					length
        UpperCamelCase				  =					rng.normal(size=(length,)      ).astype(np.floataa      )
        UpperCamelCase				  =					a * self.x + b + rng.normal(scale=0.1			,		size=(length,)      ).astype(np.floataa      )

  def __len__(      self      )  ->      str:



        """simple docstring"""






        return self.length


  def __getitem__(      self			,		_SCREAMING_SNAKE_CASE      )  ->      Optional[int]:



        """simple docstring"""






        return {"x": self.x[i], "y": self.y[i]}






class 				a_						(			torch.nn.Module				):

  def __init__(      self			,		_SCREAMING_SNAKE_CASE=0			,		_SCREAMING_SNAKE_CASE=0			,		_SCREAMING_SNAKE_CASE=False      )  ->      Optional[int]:



        """simple docstring"""






        super().__init__()
        UpperCamelCase				  =					torch.nn.Parameter(torch.tensor([2, 3]      ).float()      )
        UpperCamelCase				  =					torch.nn.Parameter(torch.tensor([2, 3]      ).float()      )
        UpperCamelCase				  =					True


  def   A__						(      self			,		_SCREAMING_SNAKE_CASE=None      )  ->      List[Any]:



        """simple docstring"""






        if self.first_batch:
              print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"      )
              UpperCamelCase				  =					False
        return x * self.a[0] + self.b[0]






class 				a_						(			torch.nn.Module				):

  def __init__(      self			,		_SCREAMING_SNAKE_CASE=0			,		_SCREAMING_SNAKE_CASE=0			,		_SCREAMING_SNAKE_CASE=False      )  ->      Optional[Any]:



        """simple docstring"""






        super().__init__()
        UpperCamelCase				  =					torch.nn.Parameter(torch.tensor(_SCREAMING_SNAKE_CASE      ).float()      )
        UpperCamelCase				  =					torch.nn.Parameter(torch.tensor(_SCREAMING_SNAKE_CASE      ).float()      )
        UpperCamelCase				  =					True


  def   A__						(      self			,		_SCREAMING_SNAKE_CASE=None      )  ->      Dict:



        """simple docstring"""






        if self.first_batch:
              print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"      )
              UpperCamelCase				  =					False
        return x * self.a + self.b







def     lowercase__ (      __UpperCamelCase		,						__UpperCamelCase = 16    )-> List[str]:
      from datasets import load_dataset
      from transformers import AutoTokenizer

      UpperCamelCase				  =					AutoTokenizer.from_pretrained("""bert-base-cased"""    )
      UpperCamelCase				  =					{'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
      UpperCamelCase				  =					load_dataset("""csv"""		,						data_files=lowercase__    )
      UpperCamelCase				  =					datasets['train'].unique("""label"""    )

      UpperCamelCase				  =					{v: i for i, v in enumerate(lowercase__    )}

      def tokenize_function(__UpperCamelCase    ):
            # max_length=None => use the model max length (it's actually the default)
            UpperCamelCase				  =					tokenizer(
                examples["""sentence1"""]		,						examples["""sentence2"""]		,						truncation=lowercase__		,						max_length=lowercase__		,						padding="""max_length"""    )
            if "label" in examples:
                  UpperCamelCase				  =					[label_to_id[l] for l in examples['label']]
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      UpperCamelCase				  =					datasets.map(
          lowercase__		,						batched=lowercase__		,						remove_columns=["""sentence1""", """sentence2""", """label"""]		,						)

      def collate_fn(__UpperCamelCase    ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(lowercase__		,						padding="""max_length"""		,						max_length=128		,						return_tensors="""pt"""    )
            return tokenizer.pad(lowercase__		,						padding="""longest"""		,						return_tensors="""pt"""    )

      # Instantiate dataloaders.
      UpperCamelCase				  =					DataLoader(tokenized_datasets["""train"""]		,						shuffle=lowercase__		,						collate_fn=lowercase__		,						batch_size=2    )
      UpperCamelCase				  =					DataLoader(tokenized_datasets["""validation"""]		,						shuffle=lowercase__		,						collate_fn=lowercase__		,						batch_size=1    )

      return train_dataloader, eval_dataloader






"""simple docstring"""






# Imports
import numpy as np






class lowerCAmelCase__	:






			'''simple docstring'''



			def __init__(  self  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ):
							self.set_matricies(red=lowercase  ,      green=lowercase  ,      blue=lowercase  ,      red_edge=lowercase  ,      nir=lowercase  )
			def 					A_    (  self  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ):
							if red is not None:
											_lowerCamelCase     :			Optional[int]    =    red
							if green is not None:
											_lowerCamelCase     :			Optional[Any]    =    green
							if blue is not None:
											_lowerCamelCase     :			Tuple    =    blue
							if red_edge is not None:
											_lowerCamelCase     :			Optional[Any]    =    red_edge
							if nir is not None:
											_lowerCamelCase     :			Union[str, Any]    =    nir
							return True
			def 					A_    (  self  ,      lowercase=""  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ):
							self.set_matricies(red=lowercase  ,      green=lowercase  ,      blue=lowercase  ,      red_edge=lowercase  ,      nir=lowercase  )
							_lowerCamelCase     :			str    =    {
							    'ARVI2': self.arvaa,
							    'CCCI': self.ccci,
							    'CVI': self.cvi,
							    'GLI': self.gli,
							    'NDVI': self.ndvi,
							    'BNDVI': self.bndvi,
							    'redEdgeNDVI': self.red_edge_ndvi,
							    'GNDVI': self.gndvi,
							    'GBNDVI': self.gbndvi,
							    'GRNDVI': self.grndvi,
							    'RBNDVI': self.rbndvi,
							    'PNDVI': self.pndvi,
							    'ATSAVI': self.atsavi,
							    'BWDRVI': self.bwdrvi,
							    'CIgreen': self.ci_green,
							    'CIrededge': self.ci_rededge,
							    'CI': self.ci,
							    'CTVI': self.ctvi,
							    'GDVI': self.gdvi,
							    'EVI': self.evi,
							    'GEMI': self.gemi,
							    'GOSAVI': self.gosavi,
							    'GSAVI': self.gsavi,
							    'Hue': self.hue,
							    'IVI': self.ivi,
							    'IPVI': self.ipvi,
							    'I': self.i,
							    'RVI': self.rvi,
							    'MRVI': self.mrvi,
							    'MSAVI': self.m_savi,
							    'NormG': self.norm_g,
							    'NormNIR': self.norm_nir,
							    'NormR': self.norm_r,
							    'NGRDI': self.ngrdi,
							    'RI': self.ri,
							    'S': self.s,
							    'IF': self._if,
							    'DVI': self.dvi,
							    'TVI': self.tvi,
							    'NDRE': self.ndre,
							}

							try:
											return funcs[index]()
							except KeyError:
											print('Index not in the list!'  )
											return False
			def 					A_    (  self  ):
							return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
			def 					A_    (  self  ):
							return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
							    (self.nir - self.red) / (self.nir + self.red)
							)
			def 					A_    (  self  ):
							return self.nir * (self.red / (self.green**2))
			def 					A_    (  self  ):
							return (2 * self.green - self.red - self.blue) / (
							    2 * self.green + self.red + self.blue
							)
			def 					A_    (  self  ):
							return (self.nir - self.red) / (self.nir + self.red)
			def 					A_    (  self  ):
							return (self.nir - self.blue) / (self.nir + self.blue)
			def 					A_    (  self  ):
							return (self.redEdge - self.red) / (self.redEdge + self.red)
			def 					A_    (  self  ):
							return (self.nir - self.green) / (self.nir + self.green)
			def 					A_    (  self  ):
							return (self.nir - (self.green + self.blue)) / (
							    self.nir + (self.green + self.blue)
							)
			def 					A_    (  self  ):
							return (self.nir - (self.green + self.red)) / (
							    self.nir + (self.green + self.red)
							)
			def 					A_    (  self  ):
							return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
			def 					A_    (  self  ):
							return (self.nir - (self.green + self.red + self.blue)) / (
							    self.nir + (self.green + self.red + self.blue)
							)
			def 					A_    (  self  ,      lowercase=0.08  ,      lowercase=1.22  ,      lowercase=0.03  ):
							return a * (
							    (self.nir - a * self.red - b)
							    / (a * self.nir + self.red - a * b + x * (1 + a**2))
							)
			def 					A_    (  self  ):
							return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
			def 					A_    (  self  ):
							return (self.nir / self.green) - 1
			def 					A_    (  self  ):
							return (self.nir / self.redEdge) - 1
			def 					A_    (  self  ):
							return (self.red - self.blue) / self.red
			def 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    self.ndvi()
							return ((ndvi + 0.5) / (abs(ndvi + 0.5  ))) * (abs(ndvi + 0.5  ) ** (1 / 2))
			def 					A_    (  self  ):
							return self.nir - self.green
			def 					A_    (  self  ):
							return 2.5 * (
							    (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
							)
			def 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
							    self.nir + self.red + 0.5
							)
							return n * (1 - 0.25 * n) - (self.red - 0.1_25) / (1 - self.red)
			def 					A_    (  self  ,      lowercase=0.16  ):
							return (self.nir - self.green) / (self.nir + self.green + y)
			def 					A_    (  self  ,      lowercase=0.5  ):
							return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
			def 					A_    (  self  ):
							return np.arctan(
							    ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)  )
			def 					A_    (  self  ,      lowercase=None  ,      lowercase=None  ):
							return (self.nir - b) / (a * self.red)
			def 					A_    (  self  ):
							return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
			def 					A_    (  self  ):
							return (self.red + self.green + self.blue) / 30.5
			def 					A_    (  self  ):
							return self.nir / self.red
			def 					A_    (  self  ):
							return (self.rvi() - 1) / (self.rvi() + 1)
			def 					A_    (  self  ):
							return (
							    (2 * self.nir + 1)
							    - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
							) / 2
			def 					A_    (  self  ):
							return self.green / (self.nir + self.red + self.green)
			def 					A_    (  self  ):
							return self.nir / (self.nir + self.red + self.green)
			def 					A_    (  self  ):
							return self.red / (self.nir + self.red + self.green)
			def 					A_    (  self  ):
							return (self.green - self.red) / (self.green + self.red)
			def 					A_    (  self  ):
							return (self.red - self.green) / (self.red + self.green)
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    np.max([np.max(self.red  ), np.max(self.green  ), np.max(self.blue  )]  )
							_lowerCamelCase     :			Dict    =    np.min([np.min(self.red  ), np.min(self.green  ), np.min(self.blue  )]  )
							return (max_value - min_value) / max_value
			def 					A_    (  self  ):
							return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
			def 					A_    (  self  ):
							return self.nir / self.red
			def 					A_    (  self  ):
							return (self.ndvi() + 0.5) ** (1 / 2)






			def 					A_    (  self  ):
							return (self.nir - self.redEdge) / (self.nir + self.redEdge)