

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		(	__magic_name__):
  SCREAMING_SNAKE_CASE     :   Any							=	None
  SCREAMING_SNAKE_CASE     :   Optional[Any]							=	None

  @property
  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        return self.feat_extract_tester.prepare_feat_extract_dict()

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	str     							=  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 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  self.feat_extract_tester.prepare_inputs_for_common()
        SCREAMING_SNAKE_CASE_					:	List[str]     							=  self.feature_extraction_class(**self.feat_extract_dict       )
        SCREAMING_SNAKE_CASE_					:	Any     							=  feat_extract.model_input_names[0]

        SCREAMING_SNAKE_CASE_					:	List[Any]     							=  BatchFeature({input_name: speech_inputs}       )

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

        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowerCAmelCase       )
        SCREAMING_SNAKE_CASE_					:	Any     							=  BatchFeature({input_name: speech_inputs}			,       tensor_type='np'       )

        SCREAMING_SNAKE_CASE_					:	str     							=  processed_features[input_name]

        if len(batch_features_input.shape       ) < 3:
              SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  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 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	Dict     							=  self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowerCAmelCase       )
        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  self.feature_extraction_class(**self.feat_extract_dict       )
        SCREAMING_SNAKE_CASE_					:	Any     							=  feat_extract.model_input_names[0]

        SCREAMING_SNAKE_CASE_					:	Any     							=  BatchFeature({input_name: speech_inputs}			,       tensor_type='pt'       )

        SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  processed_features[input_name]

        if len(batch_features_input.shape       ) < 3:
              SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  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 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowerCAmelCase       )
        SCREAMING_SNAKE_CASE_					:	List[Any]     							=  self.feature_extraction_class(**self.feat_extract_dict       )
        SCREAMING_SNAKE_CASE_					:	List[str]     							=  feat_extract.model_input_names[0]

        SCREAMING_SNAKE_CASE_					:	str     							=  BatchFeature({input_name: speech_inputs}			,       tensor_type='tf'       )

        SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  processed_features[input_name]

        if len(batch_features_input.shape       ) < 3:
              SCREAMING_SNAKE_CASE_					:	Dict     							=  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 			UpperCAmelCase     (					self			,       _SCREAMING_SNAKE_CASE=False       ):





        """simple docstring"""







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

        def _inputs_are_equal(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE       ):
              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

        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  self.feature_extraction_class(**self.feat_extract_dict       )
        SCREAMING_SNAKE_CASE_					:	List[str]     							=  self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowerCAmelCase       )
        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  feat_extract.model_input_names[0]

        SCREAMING_SNAKE_CASE_					:	Any     							=  BatchFeature({input_name: speech_inputs}       )

        SCREAMING_SNAKE_CASE_					:	Tuple     							=  self.feat_extract_tester.seq_length_diff
        SCREAMING_SNAKE_CASE_					:	List[str]     							=  self.feat_extract_tester.max_seq_length + pad_diff
        SCREAMING_SNAKE_CASE_					:	Tuple     							=  self.feat_extract_tester.min_seq_length
        SCREAMING_SNAKE_CASE_					:	Tuple     							=  self.feat_extract_tester.batch_size
        SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  self.feat_extract_tester.feature_size

        # test padding for List[int] + numpy
        SCREAMING_SNAKE_CASE_					:	str     							=  feat_extract.pad(__lowerCAmelCase			,       padding=__lowerCAmelCase       )
        SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  input_a[input_name]

        SCREAMING_SNAKE_CASE_					:	int     							=  feat_extract.pad(__lowerCAmelCase			,       padding='longest'       )
        SCREAMING_SNAKE_CASE_					:	int     							=  input_a[input_name]

        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  feat_extract.pad(__lowerCAmelCase			,       padding='max_length'			,       max_length=len(speech_inputs[-1]       )       )
        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  input_a[input_name]

        SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  feat_extract.pad(__lowerCAmelCase			,       padding='longest'			,       return_tensors='np'       )
        SCREAMING_SNAKE_CASE_					:	Tuple     							=  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]

        SCREAMING_SNAKE_CASE_					:	Dict     							=  feat_extract.pad(
            __lowerCAmelCase			,       padding='max_length'			,       max_length=__lowerCAmelCase			,       return_tensors='np'       )
        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  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
        SCREAMING_SNAKE_CASE_					:	List[Any]     							=  feat_extract.pad(__lowerCAmelCase			,       pad_to_multiple_of=10       )
        SCREAMING_SNAKE_CASE_					:	Dict     							=  input_a[input_name]

        SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  feat_extract.pad(__lowerCAmelCase			,       padding='longest'			,       pad_to_multiple_of=10       )
        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  input_a[input_name]

        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  feat_extract.pad(
            __lowerCAmelCase			,       padding='max_length'			,       pad_to_multiple_of=10			,       max_length=__lowerCAmelCase       )
        SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  input_a[input_name]

        SCREAMING_SNAKE_CASE_					:	int     							=  feat_extract.pad(
            __lowerCAmelCase			,       padding='max_length'			,       pad_to_multiple_of=10			,       max_length=__lowerCAmelCase			,       return_tensors='np'			,       )
        SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  input_a[input_name]

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

        SCREAMING_SNAKE_CASE_					:	Dict     							=  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
        SCREAMING_SNAKE_CASE_					:	Any     							=  (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 			UpperCAmelCase     (					self			,       _SCREAMING_SNAKE_CASE=False       ):





        """simple docstring"""







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

        def _inputs_are_equal(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE       ):
              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

        SCREAMING_SNAKE_CASE_					:	Tuple     							=  self.feature_extraction_class(**self.feat_extract_dict       )
        SCREAMING_SNAKE_CASE_					:	int     							=  self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowerCAmelCase       )
        SCREAMING_SNAKE_CASE_					:	Tuple     							=  feat_extract.model_input_names[0]

        SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  BatchFeature({input_name: speech_inputs}       )

        # truncate to smallest
        SCREAMING_SNAKE_CASE_					:	List[Any]     							=  feat_extract.pad(
            __lowerCAmelCase			,       padding='max_length'			,       max_length=len(speech_inputs[0]       )			,       truncation=__lowerCAmelCase       )
        SCREAMING_SNAKE_CASE_					:	Dict     							=  input_a[input_name]

        SCREAMING_SNAKE_CASE_					:	List[str]     							=  feat_extract.pad(__lowerCAmelCase			,       padding='max_length'			,       max_length=len(speech_inputs[0]       )       )
        SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  input_a[input_name]

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

        # truncate to smallest with np
        SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  feat_extract.pad(
            __lowerCAmelCase			,       padding='max_length'			,       max_length=len(speech_inputs[0]       )			,       return_tensors='np'			,       truncation=__lowerCAmelCase			,       )
        SCREAMING_SNAKE_CASE_					:	Tuple     							=  input_a[input_name]

        SCREAMING_SNAKE_CASE_					:	List[Any]     							=  feat_extract.pad(
            __lowerCAmelCase			,       padding='max_length'			,       max_length=len(speech_inputs[0]       )			,       return_tensors='np'       )
        SCREAMING_SNAKE_CASE_					:	Any     							=  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
        SCREAMING_SNAKE_CASE_					:	str     							=  feat_extract.pad(
            __lowerCAmelCase			,       padding='max_length'			,       max_length=len(speech_inputs[1]       )			,       truncation=__lowerCAmelCase			,       return_tensors='np'			,       )
        SCREAMING_SNAKE_CASE_					:	Dict     							=  input_a[input_name]

        SCREAMING_SNAKE_CASE_					:	Dict     							=  feat_extract.pad(
            __lowerCAmelCase			,       padding='max_length'			,       max_length=len(speech_inputs[1]       )			,       truncation=__lowerCAmelCase       )
        SCREAMING_SNAKE_CASE_					:	List[str]     							=  input_a[input_name]

        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  feat_extract.pad(
            __lowerCAmelCase			,       padding='max_length'			,       max_length=len(speech_inputs[1]       )			,       return_tensors='np'       )
        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  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
        SCREAMING_SNAKE_CASE_					:	Any     							=  12
        SCREAMING_SNAKE_CASE_					:	List[Any]     							=  feat_extract.pad(
            __lowerCAmelCase			,       padding='max_length'			,       max_length=len(speech_inputs[0]       )			,       pad_to_multiple_of=__lowerCAmelCase			,       truncation=__lowerCAmelCase			,       )
        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  input_a[input_name]

        SCREAMING_SNAKE_CASE_					:	List[str]     							=  feat_extract.pad(
            __lowerCAmelCase			,       padding='max_length'			,       max_length=len(speech_inputs[0]       )			,       pad_to_multiple_of=__lowerCAmelCase			,       )
        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  input_a[input_name]

        # retrieve expected_length as multiple of pad_to_multiple_of
        SCREAMING_SNAKE_CASE_					:	str     							=  len(speech_inputs[0]       )
        if expected_length % pad_to_multiple_of != 0:
              SCREAMING_SNAKE_CASE_					:	Dict     							=  ((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 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        self._check_padding(numpify=__lowerCAmelCase       )

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        self._check_padding(numpify=__lowerCAmelCase       )

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        self._check_truncation(numpify=__lowerCAmelCase       )

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        self._check_truncation(numpify=__lowerCAmelCase       )

  @require_torch
  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	Any     							=  self.feature_extraction_class(**self.feat_extract_dict       )
        SCREAMING_SNAKE_CASE_					:	List[str]     							=  self.feat_extract_tester.prepare_inputs_for_common()
        SCREAMING_SNAKE_CASE_					:	List[str]     							=  feat_extract.model_input_names[0]

        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  BatchFeature({input_name: speech_inputs}       )

        SCREAMING_SNAKE_CASE_					:	int     							=  feat_extract.pad(__lowerCAmelCase			,       padding='longest'			,       return_tensors='np'       )[input_name]
        SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  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 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  self.feature_extraction_class(**self.feat_extract_dict       )
        SCREAMING_SNAKE_CASE_					:	List[Any]     							=  self.feat_extract_tester.prepare_inputs_for_common()
        SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  feat_extract.model_input_names[0]

        SCREAMING_SNAKE_CASE_					:	Dict     							=  BatchFeature({input_name: speech_inputs}       )

        SCREAMING_SNAKE_CASE_					:	Tuple     							=  feat_extract.pad(__lowerCAmelCase			,       padding='longest'			,       return_tensors='np'       )[input_name]
        SCREAMING_SNAKE_CASE_					:	Dict     							=  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 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	Dict     							=  self.feat_extract_dict
        SCREAMING_SNAKE_CASE_					:	Any     							=  True
        SCREAMING_SNAKE_CASE_					:	Any     							=  self.feature_extraction_class(**__lowerCAmelCase       )
        SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  self.feat_extract_tester.prepare_inputs_for_common()
        SCREAMING_SNAKE_CASE_					:	int     							=  [len(__lowerCAmelCase       ) for x in speech_inputs]
        SCREAMING_SNAKE_CASE_					:	str     							=  feat_extract.model_input_names[0]

        SCREAMING_SNAKE_CASE_					:	str     							=  BatchFeature({input_name: speech_inputs}       )

        SCREAMING_SNAKE_CASE_					:	int     							=  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 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	Any     							=  self.feat_extract_dict
        SCREAMING_SNAKE_CASE_					:	Any     							=  True
        SCREAMING_SNAKE_CASE_					:	str     							=  self.feature_extraction_class(**__lowerCAmelCase       )
        SCREAMING_SNAKE_CASE_					:	int     							=  self.feat_extract_tester.prepare_inputs_for_common()
        SCREAMING_SNAKE_CASE_					:	List[str]     							=  [len(__lowerCAmelCase       ) for x in speech_inputs]
        SCREAMING_SNAKE_CASE_					:	Tuple     							=  feat_extract.model_input_names[0]

        SCREAMING_SNAKE_CASE_					:	List[Any]     							=  BatchFeature({input_name: speech_inputs}       )
        SCREAMING_SNAKE_CASE_					:	List[str]     							=  min(__lowerCAmelCase       )

        SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  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]       )













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A       :   Dict					= {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Dict					= ['''LayoutXLMTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Dict					= ['''LayoutXLMTokenizerFast''']

if TYPE_CHECKING:
					from .processing_layoutxlm import LayoutXLMProcessor

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutxlm import LayoutXLMTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
					import sys

					A       :   int					= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



