


from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


A	:		List[str]    =       logging.get_logger(__name__)



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self   :						int				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						float				, **_UpperCAmelCase   :						Optional[int]		)						->       int:




							"""simple docstring"""


							lowercase__					      =		feature_size
							lowercase__					      =		sampling_rate
							lowercase__					      =		padding_value

							lowercase__					      =		kwargs.pop("""padding_side"""				, """right"""		)
							lowercase__					      =		kwargs.pop("""return_attention_mask"""				, _UpperCAmelCase		)

							super().__init__(**_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						Union[
					        BatchFeature,
					        List[BatchFeature],
					        Dict[str, BatchFeature],
					        Dict[str, List[BatchFeature]],
					        List[Dict[str, BatchFeature]],
					    ]				, _UpperCAmelCase   :						Union[bool, str, PaddingStrategy] = True				, _UpperCAmelCase   :						Optional[int] = None				, _UpperCAmelCase   :						bool = False				, _UpperCAmelCase   :						Optional[int] = None				, _UpperCAmelCase   :						Optional[bool] = None				, _UpperCAmelCase   :						Optional[Union[str, TensorType]] = None				, )						->       BatchFeature:




							"""simple docstring"""


							if isinstance(_UpperCAmelCase				, (list, tuple)		) and isinstance(processed_features[0]				, (dict, BatchFeature)		):
									lowercase__					      =		{
									    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
									}

							# The model's main input name, usually `input_values`, has be passed for padding
							if self.model_input_names[0] not in processed_features:
									raise ValueError(
									    """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
									    f''' to this method that includes {self.model_input_names[0]}, but you provided'''
									    f''' {list(processed_features.keys()		)}'''		)

							lowercase__					      =		processed_features[self.model_input_names[0]]
							lowercase__					      =		(
							    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
							)

							if len(_UpperCAmelCase		) == 0:
									if return_attention_mask:
											lowercase__					      =		[]
									return processed_features

							# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
							# and rebuild them afterwards if no return_tensors is specified
							# Note that we lose the specific device the tensor may be on for PyTorch

							lowercase__					      =		required_input[0]
							if isinstance(_UpperCAmelCase				, (list, tuple)		):
									# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
									lowercase__					      =		0
									while len(required_input[index]		) == 0:
											index += 1
									if index < len(_UpperCAmelCase		):
											lowercase__					      =		required_input[index][0]

							if return_tensors is None:
									if is_tf_tensor(_UpperCAmelCase		):
											lowercase__					      =		"""tf"""
									elif is_torch_tensor(_UpperCAmelCase		):
											lowercase__					      =		"""pt"""
									elif isinstance(_UpperCAmelCase				, (int, float, list, tuple, np.ndarray)		):
											lowercase__					      =		"""np"""
									else:
											raise ValueError(
											    f'''type of {first_element} unknown: {type(_UpperCAmelCase		)}. '''
											    """Should be one of a python, numpy, pytorch or tensorflow object."""		)

							for key, value in processed_features.items():
									if isinstance(value[0]				, (int, float)		):
											lowercase__					      =		to_numpy(_UpperCAmelCase		)
									else:
											lowercase__					      =		[to_numpy(_UpperCAmelCase		) for v in value]

        # Convert padding_strategy in PaddingStrategy
							lowercase__					      =		self._get_padding_strategies(padding=_UpperCAmelCase				, max_length=_UpperCAmelCase		)

							lowercase__					      =		processed_features[self.model_input_names[0]]

							lowercase__					      =		len(_UpperCAmelCase		)
							if not all(len(_UpperCAmelCase		) == batch_size for v in processed_features.values()		):
									raise ValueError("""Some items in the output dictionary have a different batch size than others."""		)

							lowercase__					      =		[]
							for i in range(_UpperCAmelCase		):
									lowercase__					      =		{k: v[i] for k, v in processed_features.items()}
									# truncation
									lowercase__					      =		self._truncate(
									    _UpperCAmelCase				, max_length=_UpperCAmelCase				, pad_to_multiple_of=_UpperCAmelCase				, truncation=_UpperCAmelCase				, )
									truncated_inputs.append(_UpperCAmelCase		)

							if padding_strategy == PaddingStrategy.LONGEST:
									# make sure that `max_length` cannot be longer than the longest truncated length
									lowercase__					      =		max(len(input_slice[self.model_input_names[0]]		) for input_slice in truncated_inputs		)
									lowercase__					      =		PaddingStrategy.MAX_LENGTH

							lowercase__					      =		{}
							for i in range(_UpperCAmelCase		):
									# padding
									lowercase__					      =		self._pad(
									    truncated_inputs[i]				, max_length=_UpperCAmelCase				, padding_strategy=_UpperCAmelCase				, pad_to_multiple_of=_UpperCAmelCase				, return_attention_mask=_UpperCAmelCase				, )

									for key, value in outputs.items():
											if key not in batch_outputs:
													lowercase__					      =		[]
											if value.dtype is np.dtype(np.floataa		):
													lowercase__					      =		value.astype(np.floataa		)
											batch_outputs[key].append(_UpperCAmelCase		)

							return BatchFeature(_UpperCAmelCase				, tensor_type=_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Any				, _UpperCAmelCase   :						Union[Dict[str, np.ndarray], BatchFeature]				, _UpperCAmelCase   :						Optional[int] = None				, _UpperCAmelCase   :						PaddingStrategy = PaddingStrategy.DO_NOT_PAD				, _UpperCAmelCase   :						Optional[int] = None				, _UpperCAmelCase   :						Optional[bool] = None				, )						->       dict:




							"""simple docstring"""


							lowercase__					      =		processed_features[self.model_input_names[0]]

							if padding_strategy == PaddingStrategy.LONGEST:
									lowercase__					      =		len(_UpperCAmelCase		)

							if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
									lowercase__					      =		((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

							lowercase__					      =		padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_UpperCAmelCase		) < max_length

							if return_attention_mask and "attention_mask" not in processed_features:
									lowercase__					      =		np.ones(len(_UpperCAmelCase		)				, dtype=np.intaa		)

							if needs_to_be_padded:
									lowercase__					      =		max_length - len(_UpperCAmelCase		)
									if self.padding_side == "right":
											if return_attention_mask:
													lowercase__					      =		np.pad(
													    processed_features["""attention_mask"""]				, (0, difference)		)
											lowercase__					      =		((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
											lowercase__					      =		np.pad(
											    _UpperCAmelCase				, _UpperCAmelCase				, """constant"""				, constant_values=self.padding_value		)
									elif self.padding_side == "left":
											if return_attention_mask:
													lowercase__					      =		np.pad(
													    processed_features["""attention_mask"""]				, (difference, 0)		)
											lowercase__					      =		((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
											lowercase__					      =		np.pad(
											    _UpperCAmelCase				, _UpperCAmelCase				, """constant"""				, constant_values=self.padding_value		)
									else:
											raise ValueError("""Invalid padding strategy:""" + str(self.padding_side		)		)

							return processed_features

					def   lowerCamelCase__					(self   :						int				, _UpperCAmelCase   :						Union[Dict[str, np.ndarray], BatchFeature]				, _UpperCAmelCase   :						Optional[int] = None				, _UpperCAmelCase   :						Optional[int] = None				, _UpperCAmelCase   :						Optional[bool] = None				, )						->       Dict:




							"""simple docstring"""


							if not truncation:
									return processed_features
							elif truncation and max_length is None:
									raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""		)

							lowercase__					      =		processed_features[self.model_input_names[0]]

							# find `max_length` that fits `pad_to_multiple_of`
							if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
									lowercase__					      =		((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

							lowercase__					      =		len(_UpperCAmelCase		) > max_length

							if needs_to_be_truncated:
									lowercase__					      =		processed_features[self.model_input_names[0]][:max_length]
									if "attention_mask" in processed_features:
											lowercase__					      =		processed_features["""attention_mask"""][:max_length]

							return processed_features


					def   lowerCamelCase__					(self   :						List[str]				, _UpperCAmelCase   :						Union[str, Any]=False				, _UpperCAmelCase   :						str=None		)						->       Optional[Any]:




							"""simple docstring"""


							if padding is not False:
									if padding is True:
											lowercase__					      =		PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
									elif not isinstance(_UpperCAmelCase				, _UpperCAmelCase		):
											lowercase__					      =		PaddingStrategy(_UpperCAmelCase		)
									elif isinstance(_UpperCAmelCase				, _UpperCAmelCase		):
											lowercase__					      =		padding
							else:
									lowercase__					      =		PaddingStrategy.DO_NOT_PAD

							# Set max length if needed
							if max_length is None:
									if padding_strategy == PaddingStrategy.MAX_LENGTH:
											raise ValueError(
											    f'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''		)

        # Test if we have a padding value
							if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
									raise ValueError(
									    """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
									    """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""		)

							return padding_strategy





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
	import numpy as np
	import tensorflow as tf

	from transformers import TFXLMRobertaModel



@require_tf
@require_sentencepiece
@require_tokenizers
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					@slow
					def   lowerCamelCase__					(self   :						Any		)						->       Union[str, Any]:




							"""simple docstring"""


							lowercase__					      =		TFXLMRobertaModel.from_pretrained("""jplu/tf-xlm-roberta-base"""		)

							lowercase__					      =		{
							    """input_ids""": tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]]				, dtype=tf.intaa		),  # "My dog is cute"
							    """attention_mask""": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]				, dtype=tf.intaa		),
							}

							lowercase__					      =		model(_UpperCAmelCase		)["""last_hidden_state"""]
							lowercase__					      =		tf.TensorShape((1, 6, 768)		)
							self.assertEqual(output.shape				, _UpperCAmelCase		)
							# compare the actual values for a slice.
							lowercase__					      =		tf.convert_to_tensor(
							    [
							        [
							            [0.0_681_762, 0.10_894_451, 0.06_772_504],
							            [-0.06_423_668, 0.02_366_615, 0.04_329_344],
							            [-0.06_057_295, 0.09_974_135, -0.00_070_584],
							        ]
							    ]				, dtype=tf.floataa				, )

							self.assertTrue(np.allclose(output[:, :3, :3].numpy()				, expected_slice.numpy()				, atol=1E-4		)		)

