



'''simple docstring'''






# 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 torch

class        SCREAMING_SNAKE_CASE(							TensorFormatter[Mapping, """torch.Tensor""", Mapping]    ):







	"""simple docstring"""






	def __init__(		self    :  str	,    __snake_case    :  Dict=None	,    **__snake_case    :  Optional[int]	)    ->	Optional[int]:
								super().__init__(features=__snake_case	)
								UpperCAmelCase				: Dict											=	torch_tensor_kwargs
								import torch  # noqa import torch at initialization







	def   A			(		self    :  Tuple	,    __snake_case    :  List[str]	)    ->	Dict:
								import torch

								if isinstance(__snake_case	,    __snake_case	) and column:
															if all(
															    isinstance(__snake_case	,    torch.Tensor	) and x.shape == column[0].shape and x.dtype == column[0].dtype
															    for x in column	):
																						return torch.stack(__snake_case	)
								return column







	def   A			(		self    :  List[Any]	,    __snake_case    :  List[Any]	)    ->	Tuple:
								import torch

								if isinstance(__snake_case	,    (str, bytes, type(__snake_case	))	):
															return value
								elif isinstance(__snake_case	,    (np.character, np.ndarray)	) and np.issubdtype(value.dtype	,    np.character	):
															return value.tolist()

								UpperCAmelCase				: Tuple											=	{}

								if isinstance(__snake_case	,    (np.number, np.ndarray)	) and np.issubdtype(value.dtype	,    np.integer	):
															UpperCAmelCase				: Any											=	{'''dtype''': torch.intaa}
								elif isinstance(__snake_case	,    (np.number, np.ndarray)	) and np.issubdtype(value.dtype	,    np.floating	):
															UpperCAmelCase				: Tuple											=	{'''dtype''': torch.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
															import PIL.Image

															if isinstance(__snake_case	,    PIL.Image.Image	):
																						UpperCAmelCase				: List[Any]											=	np.asarray(__snake_case	)
								return torch.tensor(__snake_case	,    **{**default_dtype, **self.torch_tensor_kwargs}	)







	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  int	)    ->	Union[str, Any]:
								import torch

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







	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  dict	)    ->	Any:
								return map_nested(self._recursive_tensorize	,    __snake_case	,    map_list=__snake_case	)







	def   A			(		self    :  str	,    __snake_case    :  pa.Table	)    ->	Mapping:
								UpperCAmelCase				: Any											=	self.numpy_arrow_extractor().extract_row(__snake_case	)
								UpperCAmelCase				: Tuple											=	self.python_features_decoder.decode_row(__snake_case	)
								return self.recursive_tensorize(__snake_case	)







	def   A			(		self    :  List[Any]	,    __snake_case    :  pa.Table	)    ->	"torch.Tensor":
								UpperCAmelCase				: Any											=	self.numpy_arrow_extractor().extract_column(__snake_case	)
								UpperCAmelCase				: Any											=	self.python_features_decoder.decode_column(__snake_case	,    pa_table.column_names[0]	)
								UpperCAmelCase				: Optional[Any]											=	self.recursive_tensorize(__snake_case	)
								UpperCAmelCase				: Tuple											=	self._consolidate(__snake_case	)
								return column







	def   A			(		self    :  int	,    __snake_case    :  pa.Table	)    ->	Mapping:
								UpperCAmelCase				: List[Any]											=	self.numpy_arrow_extractor().extract_batch(__snake_case	)
								UpperCAmelCase				: Any											=	self.python_features_decoder.decode_batch(__snake_case	)
								UpperCAmelCase				: Dict											=	self.recursive_tensorize(__snake_case	)
								for column_name in batch:
															UpperCAmelCase				: Union[str, Any]											=	self._consolidate(batch[column_name]	)
								return batch










'''simple docstring'''






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
						import torch

@require_torch
@require_optimum
@slow
class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def   A			(		self    :  Union[str, Any]	)    ->	Any:
								UpperCAmelCase				: Optional[int]											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: int											=	AutoTokenizer.from_pretrained(__snake_case	)
								UpperCAmelCase				: Tuple											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: Optional[Any]											=	tokenizer('''This is me'''	,    return_tensors='''pt'''	)

								UpperCAmelCase				: Union[str, Any]											=	model.to_bettertransformer()

								self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

								UpperCAmelCase				: Dict											=	model.generate(**__snake_case	)

								UpperCAmelCase				: Union[str, Any]											=	model.reverse_bettertransformer()

								self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

								with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_pretrained(__snake_case	)

															UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

															self.assertFalse(
															    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()	)	)

															UpperCAmelCase				: Dict											=	model_reloaded.generate(**__snake_case	)
															self.assertTrue(torch.allclose(__snake_case	,    __snake_case	)	)







	def   A			(		self    :  Optional[int]	)    ->	List[Any]:
								UpperCAmelCase				: Dict											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: List[Any]											=	model.to_bettertransformer()

								with tempfile.TemporaryDirectory() as tmpdirname:
															with self.assertRaises(__snake_case	):
																						model.save_pretrained(__snake_case	)

															UpperCAmelCase				: Any											=	model.reverse_bettertransformer()
															model.save_pretrained(__snake_case	)





