
'''simple docstring'''







import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput







@dataclass
class 		snake_case_       (						a__						):



   """simple docstring"""





   __lowerCAmelCase	:							float
   __lowerCAmelCase	:							Optional[float]								=None
   __lowerCAmelCase	:							Optional[float]								=None







class 		snake_case_       (						unittest.TestCase						):



   """simple docstring"""





   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      ModelOutputTest(a=30)
          self.assertEqual(x.a			,   30)
          self.assertIsNone(x.b)
          self.assertIsNone(x.c)
          with self.assertRaises(_lowerCamelCase):
                 lowerCamelCase__				   =      x.d







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      ModelOutputTest(a=30			,   b=10)
          self.assertEqual(x[0]			,   30)
          self.assertEqual(x[1]			,   10)
          self.assertEqual(x[:2]			,   (30, 10))
          self.assertEqual(x[:]			,   (30, 10))

          lowerCamelCase__				   =      ModelOutputTest(a=30			,   c=10)
          self.assertEqual(x[0]			,   30)
          self.assertEqual(x[1]			,   10)
          self.assertEqual(x[:2]			,   (30, 10))
          self.assertEqual(x[:]			,   (30, 10))







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      ModelOutputTest(a=30			,   b=10)
          self.assertEqual(x["a"]			,   30)
          self.assertEqual(x["b"]			,   10)
          with self.assertRaises(_lowerCamelCase):
                 lowerCamelCase__				   =      x['''c''']

          lowerCamelCase__				   =      ModelOutputTest(a=30			,   c=10)
          self.assertEqual(x["a"]			,   30)
          self.assertEqual(x["c"]			,   10)
          with self.assertRaises(_lowerCamelCase):
                 lowerCamelCase__				   =      x['''b''']







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      ModelOutputTest(a=30)
          self.assertEqual(list(x.keys())			,   ["a"])
          self.assertEqual(list(x.values())			,   [30])
          self.assertEqual(list(x.items())			,   [("a", 30)])
          self.assertEqual(list(_lowerCamelCase)			,   ["a"])

          lowerCamelCase__				   =      ModelOutputTest(a=30			,   b=10)
          self.assertEqual(list(x.keys())			,   ["a", "b"])
          self.assertEqual(list(x.values())			,   [30, 10])
          self.assertEqual(list(x.items())			,   [("a", 30), ("b", 10)])
          self.assertEqual(list(_lowerCamelCase)			,   ["a", "b"])

          lowerCamelCase__				   =      ModelOutputTest(a=30			,   c=10)
          self.assertEqual(list(x.keys())			,   ["a", "c"])
          self.assertEqual(list(x.values())			,   [30, 10])
          self.assertEqual(list(x.items())			,   [("a", 30), ("c", 10)])
          self.assertEqual(list(_lowerCamelCase)			,   ["a", "c"])

          with self.assertRaises(_lowerCamelCase):
                 lowerCamelCase__				   =      x.update({"d": 20})
          with self.assertRaises(_lowerCamelCase):
                 del x["a"]
          with self.assertRaises(_lowerCamelCase):
                 lowerCamelCase__				   =      x.pop("a")
          with self.assertRaises(_lowerCamelCase):
                 lowerCamelCase__				   =      x.setdefault("d"			,   32)







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      ModelOutputTest(a=30)
          lowerCamelCase__				   =      10
          self.assertEqual(x.a			,   10)
          self.assertEqual(x["a"]			,   10)







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      ModelOutputTest(a=30)
          lowerCamelCase__				   =      10
          self.assertEqual(x.a			,   10)
          self.assertEqual(x["a"]			,   10)







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      ModelOutputTest({"a": 30, "b": 10})
          self.assertEqual(list(x.keys())			,   ["a", "b"])
          self.assertEqual(x.a			,   30)
          self.assertEqual(x.b			,   10)







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      ModelOutputTest([("a", 30), ("b", 10)])
          self.assertEqual(list(x.keys())			,   ["a", "b"])
          self.assertEqual(x.a			,   30)
          self.assertEqual(x.b			,   10)

          with self.assertRaises(_lowerCamelCase):
                 lowerCamelCase__				   =      ModelOutputTest([("a", 30), (10, 10)])

          lowerCamelCase__				   =      ModelOutputTest(a=(30, 30))
          self.assertEqual(list(x.keys())			,   ["a"])
          self.assertEqual(x.a			,   (30, 30))



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
      import tensorflow as tf






class     a_       (			TensorFormatter[Mapping, 'tf.Tensor', Mapping]							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase=None				,       **_lowerCamelCase   )					->int:
           super().__init__(features=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Any				      = tf_tensor_kwargs
           import tensorflow as tf  # noqa: import tf at initialization






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Tuple:
           import tensorflow as tf

           if isinstance(_lowerCamelCase				,       _lowerCamelCase   ) and column:
               if all(
                   isinstance(_lowerCamelCase				,       tf.Tensor   ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column   ):
                   return tf.stack(_lowerCamelCase   )
               elif all(
                   isinstance(_lowerCamelCase				,       (tf.Tensor, tf.RaggedTensor)   ) and x.ndim == 1 and x.dtype == column[0].dtype
                   for x in column   ):
                   # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                   return tf.ragged.stack(_lowerCamelCase   )

           return column






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Union[str, Any]:
           import tensorflow as tf

           if value is None:
               return value

           SCREAMING_SNAKE_CASE				:				int				      = {}

           if isinstance(_lowerCamelCase				,       (np.number, np.ndarray)   ) and np.issubdtype(value.dtype				,       np.integer   ):
               SCREAMING_SNAKE_CASE				:				List[Any]				      = {'''dtype''': tf.intaa}
           elif isinstance(_lowerCamelCase				,       (np.number, np.ndarray)   ) and np.issubdtype(value.dtype				,       np.floating   ):
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = {'''dtype''': tf.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
               import PIL.Image

               if isinstance(_lowerCamelCase				,       PIL.Image.Image   ):
                   SCREAMING_SNAKE_CASE				:				Any				      = np.asarray(_lowerCamelCase   )

           return tf.convert_to_tensor(_lowerCamelCase				,       **{**default_dtype, **self.tf_tensor_kwargs}   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Tuple:
           import tensorflow as tf

           # support for torch, tf, jax etc.
           if config.TORCH_AVAILABLE and "torch" in sys.modules:
               import torch

               if isinstance(_lowerCamelCase				,       torch.Tensor   ):
                   return self._tensorize(data_struct.detach().cpu().numpy()[()]   )
           if hasattr(_lowerCamelCase				,       '''__array__'''   ) and not isinstance(_lowerCamelCase				,       tf.Tensor   ):
               SCREAMING_SNAKE_CASE				:				int				      = data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(_lowerCamelCase				,       np.ndarray   ):
               if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(_lowerCamelCase   ) for substruct in data_struct]   )
           elif isinstance(_lowerCamelCase				,       (list, tuple)   ):
               return self._consolidate([self.recursive_tensorize(_lowerCamelCase   ) for substruct in data_struct]   )
           return self._tensorize(_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Optional[int]:
           return map_nested(self._recursive_tensorize				,       _lowerCamelCase				,       map_list=_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Mapping:
           SCREAMING_SNAKE_CASE				:				str				      = self.numpy_arrow_extractor().extract_row(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = self.python_features_decoder.decode_row(_lowerCamelCase   )
           return self.recursive_tensorize(_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->"tf.Tensor":
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = self.numpy_arrow_extractor().extract_column(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				str				      = self.python_features_decoder.decode_column(_lowerCamelCase				,       pa_table.column_names[0]   )
           SCREAMING_SNAKE_CASE				:				str				      = self.recursive_tensorize(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				int				      = self._consolidate(_lowerCamelCase   )
           return column






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Mapping:
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.numpy_arrow_extractor().extract_batch(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Dict				      = self.python_features_decoder.decode_batch(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				int				      = self.recursive_tensorize(_lowerCamelCase   )
           for column_name in batch:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self._consolidate(batch[column_name]   )
           return batch
