"""simple docstring"""






import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin





class 					_A		(			lowerCAmelCase      ):
      # to overwrite at feature extractactor specific tests
      snake_case__     :  str				    =      None
      snake_case__     :  Optional[int]				    =      None

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self.feat_extract_tester.prepare_feat_extract_dict()

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.feature_extraction_class(**self.feat_extract_dict		)
            self.assertTrue(hasattr(__lowerCAmelCase						,			"""feature_size"""		)		)
            self.assertTrue(hasattr(__lowerCAmelCase						,			"""sampling_rate"""		)		)
            self.assertTrue(hasattr(__lowerCAmelCase						,			"""padding_value"""		)		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.feat_extract_tester.prepare_inputs_for_common()
            lowercase								=      self.feature_extraction_class(**self.feat_extract_dict		)
            lowercase								=      feat_extract.model_input_names[0]

            lowercase								=      BatchFeature({input_name: speech_inputs}		)

            self.assertTrue(all(len(__lowerCAmelCase		) == len(__lowerCAmelCase		) for x, y in zip(__lowerCAmelCase						,			processed_features[input_name]		)		)		)

            lowercase								=      self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowerCAmelCase		)
            lowercase								=      BatchFeature({input_name: speech_inputs}						,			tensor_type="""np"""		)

            lowercase								=      processed_features[input_name]

            if len(batch_features_input.shape		) < 3:
                  lowercase								=      batch_features_input[:, :, None]

            self.assertTrue(
                batch_features_input.shape
                == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)

      @require_torch
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowerCAmelCase		)
            lowercase								=      self.feature_extraction_class(**self.feat_extract_dict		)
            lowercase								=      feat_extract.model_input_names[0]

            lowercase								=      BatchFeature({input_name: speech_inputs}						,			tensor_type="""pt"""		)

            lowercase								=      processed_features[input_name]

            if len(batch_features_input.shape		) < 3:
                  lowercase								=      batch_features_input[:, :, None]

            self.assertTrue(
                batch_features_input.shape
                == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)

      @require_tf
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowerCAmelCase		)
            lowercase								=      self.feature_extraction_class(**self.feat_extract_dict		)
            lowercase								=      feat_extract.model_input_names[0]

            lowercase								=      BatchFeature({input_name: speech_inputs}						,			tensor_type="""tf"""		)

            lowercase								=      processed_features[input_name]

            if len(batch_features_input.shape		) < 3:
                  lowercase								=      batch_features_input[:, :, None]

            self.assertTrue(
                batch_features_input.shape
                == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)

      def 							A__			(		self						,			__lowerCAmelCase=False		):







            """simple docstring"""



            def _inputs_have_equal_length(__lowerCAmelCase		):
                  lowercase								=      len(input[0]		)
                  for input_slice in input[1:]:
                        if len(__lowerCAmelCase		) != length:
                              return False
                  return True

            def _inputs_are_equal(__lowerCAmelCase						,			__lowerCAmelCase		):
                  if len(__lowerCAmelCase		) != len(__lowerCAmelCase		):
                        return False

                  for input_slice_a, input_slice_a in zip(__lowerCAmelCase						,			__lowerCAmelCase		):
                        if not np.allclose(np.asarray(__lowerCAmelCase		)						,			np.asarray(__lowerCAmelCase		)						,			atol=1E-3		):
                              return False
                  return True

            lowercase								=      self.feature_extraction_class(**self.feat_extract_dict		)
            lowercase								=      self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowerCAmelCase		)
            lowercase								=      feat_extract.model_input_names[0]

            lowercase								=      BatchFeature({input_name: speech_inputs}		)

            lowercase								=      self.feat_extract_tester.seq_length_diff
            lowercase								=      self.feat_extract_tester.max_seq_length + pad_diff
            lowercase								=      self.feat_extract_tester.min_seq_length
            lowercase								=      self.feat_extract_tester.batch_size
            lowercase								=      self.feat_extract_tester.feature_size

            # test padding for List[int] + numpy
            lowercase								=      feat_extract.pad(__lowerCAmelCase						,			padding=__lowerCAmelCase		)
            lowercase								=      input_a[input_name]

            lowercase								=      feat_extract.pad(__lowerCAmelCase						,			padding="""longest"""		)
            lowercase								=      input_a[input_name]

            lowercase								=      feat_extract.pad(__lowerCAmelCase						,			padding="""max_length"""						,			max_length=len(speech_inputs[-1]		)		)
            lowercase								=      input_a[input_name]

            lowercase								=      feat_extract.pad(__lowerCAmelCase						,			padding="""longest"""						,			return_tensors="""np"""		)
            lowercase								=      input_a[input_name]

            # max_length parameter has to be provided when setting `padding="max_length"`
            with self.assertRaises(__lowerCAmelCase		):
                  feat_extract.pad(__lowerCAmelCase						,			padding="""max_length"""		)[input_name]

            lowercase								=      feat_extract.pad(
                __lowerCAmelCase						,			padding="""max_length"""						,			max_length=__lowerCAmelCase						,			return_tensors="""np"""		)
            lowercase								=      input_a[input_name]

            self.assertFalse(_inputs_have_equal_length(__lowerCAmelCase		)		)
            self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase		)		)
            self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase		)		)
            self.assertTrue(_inputs_are_equal(__lowerCAmelCase						,			__lowerCAmelCase		)		)
            self.assertTrue(len(input_a[0]		) == pad_min_length		)
            self.assertTrue(len(input_a[1]		) == pad_min_length + pad_diff		)
            self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]		))		)
            self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)		)

            if feature_size > 1:
                  self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size		)

            # test padding for `pad_to_multiple_of` for List[int] + numpy
            lowercase								=      feat_extract.pad(__lowerCAmelCase						,			pad_to_multiple_of=10		)
            lowercase								=      input_a[input_name]

            lowercase								=      feat_extract.pad(__lowerCAmelCase						,			padding="""longest"""						,			pad_to_multiple_of=10		)
            lowercase								=      input_a[input_name]

            lowercase								=      feat_extract.pad(
                __lowerCAmelCase						,			padding="""max_length"""						,			pad_to_multiple_of=10						,			max_length=__lowerCAmelCase		)
            lowercase								=      input_a[input_name]

            lowercase								=      feat_extract.pad(
                __lowerCAmelCase						,			padding="""max_length"""						,			pad_to_multiple_of=10						,			max_length=__lowerCAmelCase						,			return_tensors="""np"""						,			)
            lowercase								=      input_a[input_name]

            self.assertTrue(all(len(__lowerCAmelCase		) % 10 == 0 for x in input_a		)		)
            self.assertTrue(_inputs_are_equal(__lowerCAmelCase						,			__lowerCAmelCase		)		)

            lowercase								=      pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
            self.assertTrue(all(len(__lowerCAmelCase		) == expected_mult_pad_length for x in input_a		)		)
            self.assertEqual(input_a.shape[:2]						,			(batch_size, expected_mult_pad_length)		)

            if feature_size > 1:
                  self.assertTrue(input_a.shape[2] == feature_size		)

            # Check padding value is correct
            lowercase								=      (np.ones(self.feat_extract_tester.feature_size		) * feat_extract.padding_value).sum()
            self.assertTrue(
                abs(np.asarray(input_a[0]		)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)		)
                < 1E-3		)
            self.assertTrue(
                abs(
                    np.asarray(input_a[1]		)[pad_min_length + pad_diff :].sum()
                    - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)		)
                < 1E-3		)
            self.assertTrue(
                abs(
                    np.asarray(input_a[2]		)[pad_min_length + 2 * pad_diff :].sum()
                    - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)		)
                < 1E-3		)
            self.assertTrue(
                abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)		) < 1E-3		)
            self.assertTrue(
                abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)		)
                < 1E-3		)

      def 							A__			(		self						,			__lowerCAmelCase=False		):







            """simple docstring"""



            def _inputs_have_equal_length(__lowerCAmelCase		):
                  lowercase								=      len(input[0]		)
                  for input_slice in input[1:]:
                        if len(__lowerCAmelCase		) != length:
                              return False
                  return True

            def _inputs_are_equal(__lowerCAmelCase						,			__lowerCAmelCase		):
                  if len(__lowerCAmelCase		) != len(__lowerCAmelCase		):
                        return False

                  for input_slice_a, input_slice_a in zip(__lowerCAmelCase						,			__lowerCAmelCase		):
                        if not np.allclose(np.asarray(__lowerCAmelCase		)						,			np.asarray(__lowerCAmelCase		)						,			atol=1E-3		):
                              return False
                  return True

            lowercase								=      self.feature_extraction_class(**self.feat_extract_dict		)
            lowercase								=      self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowerCAmelCase		)
            lowercase								=      feat_extract.model_input_names[0]

            lowercase								=      BatchFeature({input_name: speech_inputs}		)

            # truncate to smallest
            lowercase								=      feat_extract.pad(
                __lowerCAmelCase						,			padding="""max_length"""						,			max_length=len(speech_inputs[0]		)						,			truncation=__lowerCAmelCase		)
            lowercase								=      input_a[input_name]

            lowercase								=      feat_extract.pad(__lowerCAmelCase						,			padding="""max_length"""						,			max_length=len(speech_inputs[0]		)		)
            lowercase								=      input_a[input_name]

            self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase		)		)
            self.assertFalse(_inputs_have_equal_length(__lowerCAmelCase		)		)

            # truncate to smallest with np
            lowercase								=      feat_extract.pad(
                __lowerCAmelCase						,			padding="""max_length"""						,			max_length=len(speech_inputs[0]		)						,			return_tensors="""np"""						,			truncation=__lowerCAmelCase						,			)
            lowercase								=      input_a[input_name]

            lowercase								=      feat_extract.pad(
                __lowerCAmelCase						,			padding="""max_length"""						,			max_length=len(speech_inputs[0]		)						,			return_tensors="""np"""		)
            lowercase								=      input_a[input_name]

            self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase		)		)
            self.assertTrue(input_a.shape[1] == len(speech_inputs[0]		)		)

            # since truncation forces padding to be smaller than longest input
            # function can't return `np.ndarray`, but has to return list
            self.assertFalse(_inputs_have_equal_length(__lowerCAmelCase		)		)

            # truncate to middle
            lowercase								=      feat_extract.pad(
                __lowerCAmelCase						,			padding="""max_length"""						,			max_length=len(speech_inputs[1]		)						,			truncation=__lowerCAmelCase						,			return_tensors="""np"""						,			)
            lowercase								=      input_a[input_name]

            lowercase								=      feat_extract.pad(
                __lowerCAmelCase						,			padding="""max_length"""						,			max_length=len(speech_inputs[1]		)						,			truncation=__lowerCAmelCase		)
            lowercase								=      input_a[input_name]

            lowercase								=      feat_extract.pad(
                __lowerCAmelCase						,			padding="""max_length"""						,			max_length=len(speech_inputs[1]		)						,			return_tensors="""np"""		)
            lowercase								=      input_a[input_name]

            self.assertTrue(input_a.shape[1] == len(speech_inputs[1]		)		)
            self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase		)		)
            self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase		)		)
            self.assertTrue(_inputs_are_equal(__lowerCAmelCase						,			__lowerCAmelCase		)		)

            # since truncation forces padding to be smaller than longest input
            # function can't return `np.ndarray`, but has to return list
            self.assertFalse(_inputs_have_equal_length(__lowerCAmelCase		)		)
            self.assertTrue(len(input_a[-1]		) == len(speech_inputs[-1]		)		)

            # padding has to be max_length when setting `truncation=True`
            with self.assertRaises(__lowerCAmelCase		):
                  feat_extract.pad(__lowerCAmelCase						,			truncation=__lowerCAmelCase		)[input_name]

            # padding has to be max_length when setting `truncation=True`
            with self.assertRaises(__lowerCAmelCase		):
                  feat_extract.pad(__lowerCAmelCase						,			padding="""longest"""						,			truncation=__lowerCAmelCase		)[input_name]

            # padding has to be max_length when setting `truncation=True`
            with self.assertRaises(__lowerCAmelCase		):
                  feat_extract.pad(__lowerCAmelCase						,			padding="""longest"""						,			truncation=__lowerCAmelCase		)[input_name]

            # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
            with self.assertRaises(__lowerCAmelCase		):
                  feat_extract.pad(__lowerCAmelCase						,			padding="""max_length"""						,			truncation=__lowerCAmelCase		)[input_name]

            # test truncation for `pad_to_multiple_of` for List[int] + numpy
            lowercase								=      12
            lowercase								=      feat_extract.pad(
                __lowerCAmelCase						,			padding="""max_length"""						,			max_length=len(speech_inputs[0]		)						,			pad_to_multiple_of=__lowerCAmelCase						,			truncation=__lowerCAmelCase						,			)
            lowercase								=      input_a[input_name]

            lowercase								=      feat_extract.pad(
                __lowerCAmelCase						,			padding="""max_length"""						,			max_length=len(speech_inputs[0]		)						,			pad_to_multiple_of=__lowerCAmelCase						,			)
            lowercase								=      input_a[input_name]

            # retrieve expected_length as multiple of pad_to_multiple_of
            lowercase								=      len(speech_inputs[0]		)
            if expected_length % pad_to_multiple_of != 0:
                  lowercase								=      ((len(speech_inputs[0]		) // pad_to_multiple_of) + 1) * pad_to_multiple_of

            self.assertTrue(len(input_a[0]		) == expected_length		)
            self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase		)		)
            self.assertFalse(_inputs_have_equal_length(__lowerCAmelCase		)		)

      def 							A__			(		self		):







            """simple docstring"""



            self._check_padding(numpify=__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            self._check_padding(numpify=__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            self._check_truncation(numpify=__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            self._check_truncation(numpify=__lowerCAmelCase		)

      @require_torch
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.feature_extraction_class(**self.feat_extract_dict		)
            lowercase								=      self.feat_extract_tester.prepare_inputs_for_common()
            lowercase								=      feat_extract.model_input_names[0]

            lowercase								=      BatchFeature({input_name: speech_inputs}		)

            lowercase								=      feat_extract.pad(__lowerCAmelCase						,			padding="""longest"""						,			return_tensors="""np"""		)[input_name]
            lowercase								=      feat_extract.pad(__lowerCAmelCase						,			padding="""longest"""						,			return_tensors="""pt"""		)[input_name]

            self.assertTrue(abs(input_np.astype(np.floataa		).sum() - input_pt.numpy().astype(np.floataa		).sum()		) < 1E-2		)

      @require_tf
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.feature_extraction_class(**self.feat_extract_dict		)
            lowercase								=      self.feat_extract_tester.prepare_inputs_for_common()
            lowercase								=      feat_extract.model_input_names[0]

            lowercase								=      BatchFeature({input_name: speech_inputs}		)

            lowercase								=      feat_extract.pad(__lowerCAmelCase						,			padding="""longest"""						,			return_tensors="""np"""		)[input_name]
            lowercase								=      feat_extract.pad(__lowerCAmelCase						,			padding="""longest"""						,			return_tensors="""tf"""		)[input_name]

            self.assertTrue(abs(input_np.astype(np.floataa		).sum() - input_tf.numpy().astype(np.floataa		).sum()		) < 1E-2		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.feat_extract_dict
            lowercase								=      True
            lowercase								=      self.feature_extraction_class(**__lowerCAmelCase		)
            lowercase								=      self.feat_extract_tester.prepare_inputs_for_common()
            lowercase								=      [len(__lowerCAmelCase		) for x in speech_inputs]
            lowercase								=      feat_extract.model_input_names[0]

            lowercase								=      BatchFeature({input_name: speech_inputs}		)

            lowercase								=      feat_extract.pad(__lowerCAmelCase						,			padding="""longest"""						,			return_tensors="""np"""		)
            self.assertIn("""attention_mask"""						,			__lowerCAmelCase		)
            self.assertListEqual(list(processed.attention_mask.shape		)						,			list(processed[input_name].shape[:2]		)		)
            self.assertListEqual(processed.attention_mask.sum(-1		).tolist()						,			__lowerCAmelCase		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.feat_extract_dict
            lowercase								=      True
            lowercase								=      self.feature_extraction_class(**__lowerCAmelCase		)
            lowercase								=      self.feat_extract_tester.prepare_inputs_for_common()
            lowercase								=      [len(__lowerCAmelCase		) for x in speech_inputs]
            lowercase								=      feat_extract.model_input_names[0]

            lowercase								=      BatchFeature({input_name: speech_inputs}		)
            lowercase								=      min(__lowerCAmelCase		)

            lowercase								=      feat_extract.pad(
                __lowerCAmelCase						,			padding="""max_length"""						,			max_length=__lowerCAmelCase						,			truncation=__lowerCAmelCase						,			return_tensors="""np"""		)
            self.assertIn("""attention_mask"""						,			__lowerCAmelCase		)
            self.assertListEqual(
                list(processed_pad.attention_mask.shape		)						,			[processed_pad[input_name].shape[0], max_length]		)
            self.assertListEqual(
                processed_pad.attention_mask[:, :max_length].sum(-1		).tolist()						,			[max_length for x in speech_inputs]		)




"""simple docstring"""






from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate





@dataclass(frozen=lowerCAmelCase      )
class 					_A		(			lowerCAmelCase      ):
      # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
      snake_case__     :  str				    =      field(default='question-answering-extractive'    ,       metadata={'include_in_asdict_even_if_is_default': True}      )
      snake_case__     :  ClassVar[Features]				    =      Features({'question': Value('string'      ), 'context': Value('string'      )}      )
      snake_case__     :  ClassVar[Features]				    =      Features(
          {
              'answers': Sequence(
                  {
                      'text': Value('string'      ),
                      'answer_start': Value('int32'      ),
                  }      )
          }      )
      snake_case__     :  str				    =      "question"
      snake_case__     :  str				    =      "context"
      snake_case__     :  str				    =      "answers"

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}



