from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


__lowercase											=      [
    '''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
    ''' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'''
    ''' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.''',
    '''The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'''
    ''' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'''
    ''' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'''
    ''' body.''',
    '''Amnesty International releases its annual report on the death penalty. The report catalogs the use of'''
    ''' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'''
    ''' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'''
    ''' punishment.''',
]

__lowercase											=      [
    '''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'''
    ''' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'''
    ''' had informed his Lufthansa training school of an episode of severe depression, airline says .''',
    '''Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'''
    ''' Israel and the United States opposed the move, which could open the door to war crimes investigations against'''
    ''' Israelis .''',
    '''Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'''
    ''' death . Organization claims that governments around the world are using the threat of terrorism to advance'''
    ''' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'''
    ''' sentences up by 28% .''',
]



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	calculate_rouge(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    bootstrap_aggregation=SCREAMING_SNAKE_CASE						,    rouge_keys=['''rouge2''', '''rougeL''']			)
    assert isinstance(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Union[str, Any]    						=	calculate_rouge(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    bootstrap_aggregation=SCREAMING_SNAKE_CASE						,    rouge_keys=['''rouge2''']			)
    assert (
        pd.DataFrame(no_aggregation['''rouge2''']			).fmeasure.mean()
        == pd.DataFrame(no_aggregation_just_ra['''rouge2''']			).fmeasure.mean()
    )



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :str    						=	'''rougeLsum'''
    __UpperCamelCase  :List[Any]    						=	calculate_rouge(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    newline_sep=SCREAMING_SNAKE_CASE						,    rouge_keys=[k]			)[k]
    __UpperCamelCase  :Any    						=	calculate_rouge(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    newline_sep=SCREAMING_SNAKE_CASE						,    rouge_keys=[k]			)[k]
    assert score > score_no_sep



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :List[Any]    						=	['''rouge1''', '''rouge2''', '''rougeL''']
    __UpperCamelCase  :int    						=	calculate_rouge(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    newline_sep=SCREAMING_SNAKE_CASE						,    rouge_keys=SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :str    						=	calculate_rouge(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    newline_sep=SCREAMING_SNAKE_CASE						,    rouge_keys=SCREAMING_SNAKE_CASE			)
    assert score_sep == score_no_sep



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	[
        '''Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.''',
        '''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .''',
    ]
    __UpperCamelCase  :Union[str, Any]    						=	[
        '''Margot Frank, died in 1945, a month earlier than previously thought.''',
        '''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'''
        ''' the final seconds on board Flight 9525.''',
    ]
    assert calculate_rouge(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    newline_sep=SCREAMING_SNAKE_CASE			) == calculate_rouge(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    newline_sep=SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Any    						=	[
        '''" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" '''
    ]
    __UpperCamelCase  :Optional[Any]    						=	[
        ''' Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'''
    ]

    __UpperCamelCase  :Union[str, Any]    						=	calculate_rouge(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    rouge_keys=['''rougeLsum''']						,    newline_sep=SCREAMING_SNAKE_CASE			)['''rougeLsum''']
    __UpperCamelCase  :Union[str, Any]    						=	calculate_rouge(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    rouge_keys=['''rougeLsum''']			)['''rougeLsum''']
    assert new_score > prev_score



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :List[Any]    						=	Path('''examples/seq2seq/test_data/wmt_en_ro'''			)
    __UpperCamelCase  :List[Any]    						=	calculate_rouge_path(data_dir.joinpath('''test.source'''			)						,    data_dir.joinpath('''test.target'''			)			)
    assert isinstance(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Any    						=	calculate_rouge_path(
        data_dir.joinpath('''test.source'''			)						,    data_dir.joinpath('''test.target'''			)						,    bootstrap_aggregation=SCREAMING_SNAKE_CASE			)
    assert isinstance(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
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 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		Union[str, Any]      			=   None
    a__			:		Tuple      			=   None







    @property
    def     UpperCamelCase__     (			self) ->			Any:
        return self.feat_extract_tester.prepare_feat_extract_dict()







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :str    						=	self.feature_extraction_class(**self.feat_extract_dict)
        self.assertTrue(hasattr(__lowercase				,				'''feature_size'''))
        self.assertTrue(hasattr(__lowercase				,				'''sampling_rate'''))
        self.assertTrue(hasattr(__lowercase				,				'''padding_value'''))







    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :str    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :Optional[int]    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Optional[Any]    						=	feat_extract.model_input_names[0]

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

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

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

        __UpperCamelCase  :Any    						=	processed_features[input_name]

        if len(batch_features_input.shape) < 3:
            __UpperCamelCase  :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) ->			int:
        __UpperCamelCase  :Dict    						=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase)
        __UpperCamelCase  :Tuple    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :str    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Optional[Any]    						=	BatchFeature({input_name: speech_inputs}				,				tensor_type='''pt''')

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

        if len(batch_features_input.shape) < 3:
            __UpperCamelCase  :str    						=	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) ->			Optional[Any]:
        __UpperCamelCase  :str    						=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase)
        __UpperCamelCase  :str    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Union[str, Any]    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :Optional[Any]    						=	BatchFeature({input_name: speech_inputs}				,				tensor_type='''tf''')

        __UpperCamelCase  :int    						=	processed_features[input_name]

        if len(batch_features_input.shape) < 3:
            __UpperCamelCase  :List[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))







    def     UpperCamelCase__     (			self				,				__lowercase=False) ->			Dict:
        def _inputs_have_equal_length(__lowercase):
            __UpperCamelCase  :List[str]    						=	len(input[0])
            for input_slice in input[1:]:
                if len(__lowercase) != length:
                    return False
            return True

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

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

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

        __UpperCamelCase  :Optional[Any]    						=	BatchFeature({input_name: speech_inputs})

        __UpperCamelCase  :Optional[Any]    						=	self.feat_extract_tester.seq_length_diff
        __UpperCamelCase  :Union[str, Any]    						=	self.feat_extract_tester.max_seq_length + pad_diff
        __UpperCamelCase  :Tuple    						=	self.feat_extract_tester.min_seq_length
        __UpperCamelCase  :Optional[int]    						=	self.feat_extract_tester.batch_size
        __UpperCamelCase  :Any    						=	self.feat_extract_tester.feature_size

        # test padding for List[int] + numpy
        __UpperCamelCase  :List[Any]    						=	feat_extract.pad(__lowercase				,				padding=__lowercase)
        __UpperCamelCase  :Tuple    						=	input_a[input_name]

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

        __UpperCamelCase  :Optional[Any]    						=	feat_extract.pad(__lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[-1]))
        __UpperCamelCase  :Dict    						=	input_a[input_name]

        __UpperCamelCase  :List[Any]    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				return_tensors='''np''')
        __UpperCamelCase  :Optional[int]    						=	input_a[input_name]

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

        __UpperCamelCase  :Any    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=__lowercase				,				return_tensors='''np''')
        __UpperCamelCase  :Tuple    						=	input_a[input_name]

        self.assertFalse(_inputs_have_equal_length(__lowercase))
        self.assertTrue(_inputs_have_equal_length(__lowercase))
        self.assertTrue(_inputs_have_equal_length(__lowercase))
        self.assertTrue(_inputs_are_equal(__lowercase				,				__lowercase))
        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
        __UpperCamelCase  :int    						=	feat_extract.pad(__lowercase				,				pad_to_multiple_of=10)
        __UpperCamelCase  :Tuple    						=	input_a[input_name]

        __UpperCamelCase  :Optional[int]    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				pad_to_multiple_of=10)
        __UpperCamelCase  :Tuple    						=	input_a[input_name]

        __UpperCamelCase  :str    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				pad_to_multiple_of=10				,				max_length=__lowercase)
        __UpperCamelCase  :Any    						=	input_a[input_name]

        __UpperCamelCase  :List[str]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				pad_to_multiple_of=10				,				max_length=__lowercase				,				return_tensors='''np'''				,				)
        __UpperCamelCase  :List[str]    						=	input_a[input_name]

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

        __UpperCamelCase  :str    						=	pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
        self.assertTrue(all(len(__lowercase) == 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
        __UpperCamelCase  :Optional[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				,				__lowercase=False) ->			Dict:
        def _inputs_have_equal_length(__lowercase):
            __UpperCamelCase  :Dict    						=	len(input[0])
            for input_slice in input[1:]:
                if len(__lowercase) != length:
                    return False
            return True

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

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

        __UpperCamelCase  :str    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Union[str, Any]    						=	self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowercase)
        __UpperCamelCase  :Tuple    						=	feat_extract.model_input_names[0]

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

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

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

        self.assertTrue(_inputs_have_equal_length(__lowercase))
        self.assertFalse(_inputs_have_equal_length(__lowercase))

        # truncate to smallest with np
        __UpperCamelCase  :Union[str, Any]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				return_tensors='''np'''				,				truncation=__lowercase				,				)
        __UpperCamelCase  :List[Any]    						=	input_a[input_name]

        __UpperCamelCase  :int    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				return_tensors='''np''')
        __UpperCamelCase  :Optional[Any]    						=	input_a[input_name]

        self.assertTrue(_inputs_have_equal_length(__lowercase))
        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(__lowercase))

        # truncate to middle
        __UpperCamelCase  :Optional[Any]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[1])				,				truncation=__lowercase				,				return_tensors='''np'''				,				)
        __UpperCamelCase  :Dict    						=	input_a[input_name]

        __UpperCamelCase  :str    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[1])				,				truncation=__lowercase)
        __UpperCamelCase  :Union[str, Any]    						=	input_a[input_name]

        __UpperCamelCase  :Optional[Any]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[1])				,				return_tensors='''np''')
        __UpperCamelCase  :Union[str, Any]    						=	input_a[input_name]

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

        # 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(__lowercase))
        self.assertTrue(len(input_a[-1]) == len(speech_inputs[-1]))

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

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

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

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

        # test truncation for `pad_to_multiple_of` for List[int] + numpy
        __UpperCamelCase  :Dict    						=	12
        __UpperCamelCase  :str    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				pad_to_multiple_of=__lowercase				,				truncation=__lowercase				,				)
        __UpperCamelCase  :List[Any]    						=	input_a[input_name]

        __UpperCamelCase  :Tuple    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				pad_to_multiple_of=__lowercase				,				)
        __UpperCamelCase  :Any    						=	input_a[input_name]

        # retrieve expected_length as multiple of pad_to_multiple_of
        __UpperCamelCase  :Optional[Any]    						=	len(speech_inputs[0])
        if expected_length % pad_to_multiple_of != 0:
            __UpperCamelCase  :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(__lowercase))
        self.assertFalse(_inputs_have_equal_length(__lowercase))







    def     UpperCamelCase__     (			self) ->			Any:
        self._check_padding(numpify=__lowercase)







    def     UpperCamelCase__     (			self) ->			Dict:
        self._check_padding(numpify=__lowercase)







    def     UpperCamelCase__     (			self) ->			Any:
        self._check_truncation(numpify=__lowercase)







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        self._check_truncation(numpify=__lowercase)







    @require_torch
    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :int    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Union[str, Any]    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :str    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :int    						=	BatchFeature({input_name: speech_inputs})

        __UpperCamelCase  :List[str]    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				return_tensors='''np''')[input_name]
        __UpperCamelCase  :List[Any]    						=	feat_extract.pad(__lowercase				,				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) ->			Tuple:
        __UpperCamelCase  :int    						=	self.feature_extraction_class(**self.feat_extract_dict)
        __UpperCamelCase  :Any    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :Any    						=	feat_extract.model_input_names[0]

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

        __UpperCamelCase  :str    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				return_tensors='''np''')[input_name]
        __UpperCamelCase  :Optional[Any]    						=	feat_extract.pad(__lowercase				,				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) ->			str:
        __UpperCamelCase  :List[Any]    						=	self.feat_extract_dict
        __UpperCamelCase  :Dict    						=	True
        __UpperCamelCase  :Dict    						=	self.feature_extraction_class(**__lowercase)
        __UpperCamelCase  :List[Any]    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :Any    						=	[len(__lowercase) for x in speech_inputs]
        __UpperCamelCase  :int    						=	feat_extract.model_input_names[0]

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

        __UpperCamelCase  :Union[str, Any]    						=	feat_extract.pad(__lowercase				,				padding='''longest'''				,				return_tensors='''np''')
        self.assertIn('''attention_mask'''				,				__lowercase)
        self.assertListEqual(list(processed.attention_mask.shape)				,				list(processed[input_name].shape[:2]))
        self.assertListEqual(processed.attention_mask.sum(-1).tolist()				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :Optional[int]    						=	self.feat_extract_dict
        __UpperCamelCase  :Optional[int]    						=	True
        __UpperCamelCase  :Dict    						=	self.feature_extraction_class(**__lowercase)
        __UpperCamelCase  :List[str]    						=	self.feat_extract_tester.prepare_inputs_for_common()
        __UpperCamelCase  :List[Any]    						=	[len(__lowercase) for x in speech_inputs]
        __UpperCamelCase  :List[Any]    						=	feat_extract.model_input_names[0]

        __UpperCamelCase  :int    						=	BatchFeature({input_name: speech_inputs})
        __UpperCamelCase  :Dict    						=	min(__lowercase)

        __UpperCamelCase  :Union[str, Any]    						=	feat_extract.pad(
            __lowercase				,				padding='''max_length'''				,				max_length=__lowercase				,				truncation=__lowercase				,				return_tensors='''np''')
        self.assertIn('''attention_mask'''				,				__lowercase)
        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])





