import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch


__SCREAMING_SNAKE_CASE						:   Any         =			random.Random()
def  snake_case						(__lowercase ,      __lowercase=1.0 ,      __lowercase=None ,      __lowercase=None						)	->	int:

						'''simple docstring'''



						if rng is None:
												_snake_case						:					Optional[Any]    =   global_rng

						_snake_case						:					Any    =   []
						for batch_idx in range(shape[0]						):
												values.append([]						)
												for _ in range(shape[1]						):
																		values[-1].append(rng.random() * scale						)

						return values



@require_torch
class        lowercase_   (					unittest.TestCase		):



							def __init__(						self				,    lowercase_				,    lowercase_=7				,    lowercase_=400				,    lowercase_=2_000				,    lowercase_=1				,    lowercase_=0.0				,    lowercase_=16_000				,    lowercase_=True				,    lowercase_=80				,    lowercase_=16				,    lowercase_=64				,    lowercase_="hann_window"				,    lowercase_=80				,    lowercase_=7_600				,    lowercase_=1e-10				,    lowercase_=True				,    ):
													_snake_case						:					int    =   parent
													_snake_case						:					Union[str, Any]    =   batch_size
													_snake_case						:					int    =   min_seq_length
													_snake_case						:					int    =   max_seq_length
													_snake_case						:					Dict    =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
													_snake_case						:					List[Any]    =   feature_size
													_snake_case						:					Union[str, Any]    =   padding_value
													_snake_case						:					Any    =   sampling_rate
													_snake_case						:					List[Any]    =   do_normalize
													_snake_case						:					str    =   num_mel_bins
													_snake_case						:					Any    =   hop_length
													_snake_case						:					Optional[Any]    =   win_length
													_snake_case						:					Optional[int]    =   win_function
													_snake_case						:					Optional[Any]    =   fmin
													_snake_case						:					Union[str, Any]    =   fmax
													_snake_case						:					Optional[int]    =   mel_floor
													_snake_case						:					Optional[Any]    =   return_attention_mask



							def 	UpperCamelCase	(						self    ):
													return {
													    "feature_size": self.feature_size,
													    "padding_value": self.padding_value,
													    "sampling_rate": self.sampling_rate,
													    "do_normalize": self.do_normalize,
													    "num_mel_bins": self.num_mel_bins,
													    "hop_length": self.hop_length,
													    "win_length": self.win_length,
													    "win_function": self.win_function,
													    "fmin": self.fmin,
													    "fmax": self.fmax,
													    "mel_floor": self.mel_floor,
													    "return_attention_mask": self.return_attention_mask,
													}



							def 	UpperCamelCase	(						self				,    lowercase_=False				,    lowercase_=False    ):
													def _flatten(lowercase_    ):
																			return list(itertools.chain(*lowercase_    )    )

													if equal_length:
																			_snake_case						:					List[str]    =   floats_list((self.batch_size, self.max_seq_length)    )
													else:
																			# make sure that inputs increase in size
																			_snake_case						:					str    =   [
																			    _flatten(floats_list((x, self.feature_size)    )    )
																			    for x in range(self.min_seq_length				,    self.max_seq_length				,    self.seq_length_diff    )
																			]

													if numpify:
																			_snake_case						:					List[str]    =   [np.asarray(lowercase_    ) for x in speech_inputs]

													return speech_inputs



							def 	UpperCamelCase	(						self				,    lowercase_=False				,    lowercase_=False    ):
													if equal_length:
																			_snake_case						:					Union[str, Any]    =   [floats_list((self.max_seq_length, self.num_mel_bins)    ) for _ in range(self.batch_size    )]
													else:
																			# make sure that inputs increase in size
																			_snake_case						:					Union[str, Any]    =   [
																			    floats_list((x, self.num_mel_bins)    )
																			    for x in range(self.min_seq_length				,    self.max_seq_length				,    self.seq_length_diff    )
																			]

													if numpify:
																			_snake_case						:					Optional[Any]    =   [np.asarray(lowercase_    ) for x in speech_inputs]

													return speech_inputs




@require_torch
class        lowercase_   (					__snake_case						,			unittest.TestCase		):
							_lowerCamelCase						=		SpeechTaFeatureExtractor



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   SpeechTaFeatureExtractionTester(self    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													self.assertTrue(np.all(np.mean(lowercase_				,    axis=0    ) < 1e-3    )    )
													self.assertTrue(np.all(np.abs(np.var(lowercase_				,    axis=0    ) - 1    ) < 1e-3    )    )



							def 	UpperCamelCase	(						self    ):
													# Tests that all call wrap to encode_plus and batch_encode_plus
													_snake_case						:					int    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
													# create three inputs of length 800, 1000, and 1200
													_snake_case						:					Tuple    =   [floats_list((1, x)    )[0] for x in range(800				,    1_400				,    200    )]
													_snake_case						:					Dict    =   [np.asarray(lowercase_    ) for speech_input in speech_inputs]

													# Test not batched input
													_snake_case						:					Dict    =   feat_extract(speech_inputs[0]				,    return_tensors="np"    ).input_values
													_snake_case						:					List[str]    =   feat_extract(np_speech_inputs[0]				,    return_tensors="np"    ).input_values
													self.assertTrue(np.allclose(lowercase_				,    lowercase_				,    atol=1e-3    )    )

													# Test batched
													_snake_case						:					Dict    =   feat_extract(lowercase_				,    return_tensors="np"    ).input_values
													_snake_case						:					int    =   feat_extract(lowercase_				,    return_tensors="np"    ).input_values
													for enc_seq_a, enc_seq_a in zip(lowercase_				,    lowercase_    ):
																			self.assertTrue(np.allclose(lowercase_				,    lowercase_				,    atol=1e-3    )    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Union[str, Any]    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
													_snake_case						:					Optional[int]    =   [floats_list((1, x)    )[0] for x in range(800				,    1_400				,    200    )]

													_snake_case						:					str    =   ["longest", "max_length", "do_not_pad"]
													_snake_case						:					Any    =   [None, 1_600, None]
													for max_length, padding in zip(lowercase_				,    lowercase_    ):
																			_snake_case						:					List[Any]    =   feat_extract(lowercase_				,    padding=lowercase_				,    max_length=lowercase_				,    return_tensors="np"    )
																			_snake_case						:					Union[str, Any]    =   processed.input_values

																			self._check_zero_mean_unit_variance(input_values[0][:800]    )
																			self.assertTrue(input_values[0][800:].sum() < 1e-6    )
																			self._check_zero_mean_unit_variance(input_values[1][:1_000]    )
																			self.assertTrue(input_values[0][1_000:].sum() < 1e-6    )
																			self._check_zero_mean_unit_variance(input_values[2][:1_200]    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Union[str, Any]    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
													_snake_case						:					List[Any]    =   range(800				,    1_400				,    200    )
													_snake_case						:					Any    =   [floats_list((1, x)    )[0] for x in lengths]

													_snake_case						:					Union[str, Any]    =   ["longest", "max_length", "do_not_pad"]
													_snake_case						:					Optional[Any]    =   [None, 1_600, None]

													for max_length, padding in zip(lowercase_				,    lowercase_    ):
																			_snake_case						:					List[Any]    =   feat_extract(lowercase_				,    max_length=lowercase_				,    padding=lowercase_    )
																			_snake_case						:					Tuple    =   processed.input_values

																			self._check_zero_mean_unit_variance(input_values[0][:800]    )
																			self._check_zero_mean_unit_variance(input_values[1][:1_000]    )
																			self._check_zero_mean_unit_variance(input_values[2][:1_200]    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Union[str, Any]    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
													_snake_case						:					Union[str, Any]    =   [floats_list((1, x)    )[0] for x in range(800				,    1_400				,    200    )]
													_snake_case						:					Tuple    =   feat_extract(
													    lowercase_				,    truncation=lowercase_				,    max_length=1_000				,    padding="max_length"				,    return_tensors="np"    )
													_snake_case						:					str    =   processed.input_values

													self._check_zero_mean_unit_variance(input_values[0, :800]    )
													self._check_zero_mean_unit_variance(input_values[1]    )
													self._check_zero_mean_unit_variance(input_values[2]    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
													_snake_case						:					Optional[int]    =   [floats_list((1, x)    )[0] for x in range(800				,    1_400				,    200    )]
													_snake_case						:					List[Any]    =   feat_extract(
													    lowercase_				,    truncation=lowercase_				,    max_length=1_000				,    padding="longest"				,    return_tensors="np"    )
													_snake_case						:					str    =   processed.input_values

													self._check_zero_mean_unit_variance(input_values[0, :800]    )
													self._check_zero_mean_unit_variance(input_values[1, :1_000]    )
													self._check_zero_mean_unit_variance(input_values[2]    )

													# make sure that if max_length < longest -> then pad to max_length
													self.assertTrue(input_values.shape == (3, 1_000)    )

													_snake_case						:					Optional[Any]    =   [floats_list((1, x)    )[0] for x in range(800				,    1_400				,    200    )]
													_snake_case						:					Tuple    =   feat_extract(
													    lowercase_				,    truncation=lowercase_				,    max_length=2_000				,    padding="longest"				,    return_tensors="np"    )
													_snake_case						:					Dict    =   processed.input_values

													self._check_zero_mean_unit_variance(input_values[0, :800]    )
													self._check_zero_mean_unit_variance(input_values[1, :1_000]    )
													self._check_zero_mean_unit_variance(input_values[2]    )

													# make sure that if max_length > longest -> then pad to longest
													self.assertTrue(input_values.shape == (3, 1_200)    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					int    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
													_snake_case						:					int    =   np.random.rand(100    ).astype(np.floataa    )
													_snake_case						:					Dict    =   np_speech_inputs.tolist()

													for inputs in [py_speech_inputs, np_speech_inputs]:
																			_snake_case						:					List[Any]    =   feature_extractor.pad([{"input_values": inputs}]				,    return_tensors="np"    )
																			self.assertTrue(np_processed.input_values.dtype == np.floataa    )
																			_snake_case						:					str    =   feature_extractor.pad([{"input_values": inputs}]				,    return_tensors="pt"    )
																			self.assertTrue(pt_processed.input_values.dtype == torch.floataa    )



							def 	UpperCamelCase	(						self    ):
													# Tests that all call wrap to encode_plus and batch_encode_plus
													_snake_case						:					Dict    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
													# create three inputs of length 800, 1000, and 1200
													_snake_case						:					Optional[Any]    =   [floats_list((1, x)    )[0] for x in range(800				,    1_400				,    200    )]
													_snake_case						:					Any    =   [np.asarray(lowercase_    ) for speech_input in speech_inputs]

													# Test feature size
													_snake_case						:					str    =   feature_extractor(audio_target=lowercase_				,    padding=lowercase_				,    return_tensors="np"    ).input_values
													self.assertTrue(input_values.ndim == 3    )
													self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins    )

													# Test not batched input
													_snake_case						:					Any    =   feature_extractor(speech_inputs[0]				,    return_tensors="np"    ).input_values
													_snake_case						:					Dict    =   feature_extractor(np_speech_inputs[0]				,    return_tensors="np"    ).input_values
													self.assertTrue(np.allclose(lowercase_				,    lowercase_				,    atol=1e-3    )    )

													# Test batched
													_snake_case						:					Optional[Any]    =   feature_extractor(lowercase_				,    return_tensors="np"    ).input_values
													_snake_case						:					int    =   feature_extractor(lowercase_				,    return_tensors="np"    ).input_values
													for enc_seq_a, enc_seq_a in zip(lowercase_				,    lowercase_    ):
																			self.assertTrue(np.allclose(lowercase_				,    lowercase_				,    atol=1e-3    )    )

													# Test 2-D numpy arrays are batched.
													_snake_case						:					Tuple    =   [floats_list((1, x)    )[0] for x in (800, 800, 800)]
													_snake_case						:					str    =   np.asarray(lowercase_    )
													_snake_case						:					int    =   feature_extractor(lowercase_				,    return_tensors="np"    ).input_values
													_snake_case						:					List[Any]    =   feature_extractor(lowercase_				,    return_tensors="np"    ).input_values
													for enc_seq_a, enc_seq_a in zip(lowercase_				,    lowercase_    ):
																			self.assertTrue(np.allclose(lowercase_				,    lowercase_				,    atol=1e-3    )    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   self.feat_extract_tester.prepare_inputs_for_target()
													_snake_case						:					Any    =   self.feature_extraction_class(**self.feat_extract_dict    )
													_snake_case						:					str    =   feat_extract.model_input_names[0]

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

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

													_snake_case						:					Optional[int]    =   self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase_    )
													_snake_case						:					str    =   BatchFeature({input_name: speech_inputs}				,    tensor_type="np"    )

													_snake_case						:					int    =   processed_features[input_name]

													if len(batch_features_input.shape    ) < 3:
																			_snake_case						:					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.num_mel_bins)    )



							@require_torch
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase_    )
													_snake_case						:					Any    =   self.feature_extraction_class(**self.feat_extract_dict    )
													_snake_case						:					Dict    =   feat_extract.model_input_names[0]

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

													_snake_case						:					Tuple    =   processed_features[input_name]

													if len(batch_features_input.shape    ) < 3:
																			_snake_case						:					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.num_mel_bins)    )



							@require_torch
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					int    =   self.feature_extraction_class(**self.feat_extract_dict    )
													_snake_case						:					List[Any]    =   self.feat_extract_tester.prepare_inputs_for_target()
													_snake_case						:					Tuple    =   feat_extract.model_input_names[0]

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

													_snake_case						:					List[str]    =   feat_extract.num_mel_bins  # hack!

													_snake_case						:					Tuple    =   feat_extract.pad(lowercase_				,    padding="longest"				,    return_tensors="np"    )[input_name]
													_snake_case						:					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    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[Any]    =   self.feat_extract_dict
													_snake_case						:					Dict    =   True
													_snake_case						:					int    =   self.feature_extraction_class(**lowercase_    )
													_snake_case						:					Optional[Any]    =   self.feat_extract_tester.prepare_inputs_for_target()
													_snake_case						:					str    =   [len(lowercase_    ) for x in speech_inputs]
													_snake_case						:					Any    =   feat_extract.model_input_names[0]

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

													_snake_case						:					int    =   feat_extract.num_mel_bins  # hack!

													_snake_case						:					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    ):
													_snake_case						:					List[Any]    =   self.feat_extract_dict
													_snake_case						:					Dict    =   True
													_snake_case						:					Tuple    =   self.feature_extraction_class(**lowercase_    )
													_snake_case						:					Dict    =   self.feat_extract_tester.prepare_inputs_for_target()
													_snake_case						:					str    =   [len(lowercase_    ) for x in speech_inputs]
													_snake_case						:					List[Any]    =   feat_extract.model_input_names[0]

													_snake_case						:					Any    =   BatchFeature({input_name: speech_inputs}    )
													_snake_case						:					List[Any]    =   min(lowercase_    )

													_snake_case						:					Tuple    =   feat_extract.num_mel_bins  # hack!

													_snake_case						:					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]    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													from datasets import load_dataset

													_snake_case						:					Tuple    =   load_dataset("hf-internal-testing/librispeech_asr_dummy"				,    "clean"				,    split="validation"    )
													# automatic decoding with librispeech
													_snake_case						:					Tuple    =   ds.sort("id"    ).select(range(lowercase_    )    )[:num_samples]["audio"]

													return [x["array"] for x in speech_samples]



							def 	UpperCamelCase	(						self    ):
													# fmt: off
													_snake_case						:					Tuple    =   torch.tensor(
													    [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
													     3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
													     2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
													     4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
													     7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
													     4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03]    )
													# fmt: on

													_snake_case						:					int    =   self._load_datasamples(1    )
													_snake_case						:					Union[str, Any]    =   SpeechTaFeatureExtractor()
													_snake_case						:					List[str]    =   feature_extractor(lowercase_				,    return_tensors="pt"    ).input_values
													self.assertEquals(input_values.shape				,    (1, 93_680)    )
													self.assertTrue(torch.allclose(input_values[0, :30]				,    lowercase_				,    atol=1e-6    )    )



							def 	UpperCamelCase	(						self    ):
													# fmt: off
													_snake_case						:					Optional[int]    =   torch.tensor(
													    [-2.6_870, -3.0_104, -3.1_356, -3.5_352, -3.0_044, -3.0_353, -3.4_719, -3.6_777,
													     -3.1_520, -2.9_435, -2.6_553, -2.8_795, -2.9_944, -2.5_921, -3.0_279, -3.0_386,
													     -3.0_864, -3.1_291, -3.2_353, -2.7_444, -2.6_831, -2.7_287, -3.1_761, -3.1_571,
													     -3.2_726, -3.0_582, -3.1_007, -3.4_533, -3.4_695, -3.0_998]    )
													# fmt: on

													_snake_case						:					Any    =   self._load_datasamples(1    )
													_snake_case						:					Optional[int]    =   SpeechTaFeatureExtractor()
													_snake_case						:					List[str]    =   feature_extractor(audio_target=lowercase_				,    return_tensors="pt"    ).input_values
													self.assertEquals(input_values.shape				,    (1, 366, 80)    )
													self.assertTrue(torch.allclose(input_values[0, 0, :30]				,    lowercase_				,    atol=1e-4    )    )
import logging

from transformers import PretrainedConfig


__SCREAMING_SNAKE_CASE						:   Any         =			logging.getLogger(__name__)


__SCREAMING_SNAKE_CASE						:   int         =			{
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'bertabs'



							def __init__(						self				,    lowercase_=30_522				,    lowercase_=512				,    lowercase_=6				,    lowercase_=512				,    lowercase_=8				,    lowercase_=512				,    lowercase_=0.2				,    lowercase_=6				,    lowercase_=768				,    lowercase_=8				,    lowercase_=2_048				,    lowercase_=0.2				,    **lowercase_				,    ):
													super().__init__(**lowercase_    )

													_snake_case						:					List[Any]    =   vocab_size
													_snake_case						:					int    =   max_pos

													_snake_case						:					Tuple    =   enc_layers
													_snake_case						:					Optional[Any]    =   enc_hidden_size
													_snake_case						:					Union[str, Any]    =   enc_heads
													_snake_case						:					str    =   enc_ff_size
													_snake_case						:					Any    =   enc_dropout

													_snake_case						:					Tuple    =   dec_layers
													_snake_case						:					Optional[Any]    =   dec_hidden_size
													_snake_case						:					Dict    =   dec_heads
													_snake_case						:					str    =   dec_ff_size
													_snake_case						:					List[str]    =   dec_dropout