





import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
	from transformers import SpeechaTextFeatureExtractor

_lowerCamelCase 					=      random.Random()






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int       , UpperCamelCase__:				Union[str, Any]=1.0       , UpperCamelCase__:				str=None       , UpperCamelCase__:				int=None     ):
		if rng is None:
				SCREAMING_SNAKE_CASE__								=   global_rng

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

		return values




@require_torch
@require_torchaudio
class 	UpperCamelCase_							(  unittest.TestCase     ):


							def __init__(				self     :Optional[Any]				,					__A     :str				,					__A     :int=7				,					__A     :Optional[Any]=400				,					__A     :Tuple=2000				,					__A     :Any=24				,					__A     :Optional[Any]=24				,					__A     :List[str]=0.0				,					__A     :Optional[int]=1_6000				,					__A     :Dict=True				,					__A     :List[Any]=True				,					) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   parent
									SCREAMING_SNAKE_CASE__								=   batch_size
									SCREAMING_SNAKE_CASE__								=   min_seq_length
									SCREAMING_SNAKE_CASE__								=   max_seq_length
									SCREAMING_SNAKE_CASE__								=   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									SCREAMING_SNAKE_CASE__								=   feature_size
									SCREAMING_SNAKE_CASE__								=   num_mel_bins
									SCREAMING_SNAKE_CASE__								=   padding_value
									SCREAMING_SNAKE_CASE__								=   sampling_rate
									SCREAMING_SNAKE_CASE__								=   return_attention_mask
									SCREAMING_SNAKE_CASE__								=   do_normalize


							def   _snake_case	(				self     :Tuple		) -> Optional[int]:



									"""simple docstring"""



									return {
									    "feature_size": self.feature_size,
									    "num_mel_bins": self.num_mel_bins,
									    "padding_value": self.padding_value,
									    "sampling_rate": self.sampling_rate,
									    "return_attention_mask": self.return_attention_mask,
									    "do_normalize": self.do_normalize,
									}


							def   _snake_case	(				self     :List[str]				,					__A     :str=False				,					__A     :Any=False		) -> Any:



									"""simple docstring"""



									def _flatten(__A     :Tuple		):
											return list(itertools.chain(*__A		)		)

									if equal_length:
											SCREAMING_SNAKE_CASE__								=   [floats_list((self.max_seq_length, self.feature_size)		) for _ in range(self.batch_size		)]
									else:
											# make sure that inputs increase in size
											SCREAMING_SNAKE_CASE__								=   [
											    floats_list((x, self.feature_size)		)
											    for x in range(self.min_seq_length				,					self.max_seq_length				,					self.seq_length_diff		)
											]
									if numpify:
											SCREAMING_SNAKE_CASE__								=   [np.asarray(__A		) for x in speech_inputs]
									return speech_inputs




@require_torch
@require_torchaudio
class 	UpperCamelCase_							(  UpperCamelCase__					,	unittest.TestCase     ):
							lowerCamelCase_							     =       SpeechaTextFeatureExtractor if is_speech_available() else None


							def   _snake_case	(				self     :Tuple		) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   SpeechaTextFeatureExtractionTester(self		)


							def   _snake_case	(				self     :Dict				,					__A     :Union[str, Any]		) -> Union[str, Any]:



									"""simple docstring"""



									self.assertTrue(np.all(np.mean(__A				,					axis=0		) < 1E-3		)		)
									self.assertTrue(np.all(np.abs(np.var(__A				,					axis=0		) - 1		) < 1E-3		)		)


							def   _snake_case	(				self     :Optional[Any]		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									# create three inputs of length 800, 1000, and 1200
									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in range(800				,					1400				,					200		)]
									SCREAMING_SNAKE_CASE__								=   [np.asarray(__A		) for speech_input in speech_inputs]

									# Test feature size
									SCREAMING_SNAKE_CASE__								=   feature_extractor(__A				,					padding=__A				,					return_tensors="""np"""		).input_features
									self.assertTrue(input_features.ndim == 3		)
									self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size		)

									# Test not batched input
									SCREAMING_SNAKE_CASE__								=   feature_extractor(speech_inputs[0]				,					return_tensors="""np"""		).input_features
									SCREAMING_SNAKE_CASE__								=   feature_extractor(np_speech_inputs[0]				,					return_tensors="""np"""		).input_features
									self.assertTrue(np.allclose(__A				,					__A				,					atol=1E-3		)		)

									# Test batched
									SCREAMING_SNAKE_CASE__								=   feature_extractor(__A				,					return_tensors="""np"""		).input_features
									SCREAMING_SNAKE_CASE__								=   feature_extractor(__A				,					return_tensors="""np"""		).input_features
									for enc_seq_a, enc_seq_a in zip(__A				,					__A		):
											self.assertTrue(np.allclose(__A				,					__A				,					atol=1E-3		)		)

									# Test 2-D numpy arrays are batched.
									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in (800, 800, 800)]
									SCREAMING_SNAKE_CASE__								=   np.asarray(__A		)
									SCREAMING_SNAKE_CASE__								=   feature_extractor(__A				,					return_tensors="""np"""		).input_features
									SCREAMING_SNAKE_CASE__								=   feature_extractor(__A				,					return_tensors="""np"""		).input_features
									for enc_seq_a, enc_seq_a in zip(__A				,					__A		):
											self.assertTrue(np.allclose(__A				,					__A				,					atol=1E-3		)		)


							def   _snake_case	(				self     :Dict		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in range(800				,					1400				,					200		)]

									SCREAMING_SNAKE_CASE__								=   ["""longest""", """max_length""", """do_not_pad"""]
									SCREAMING_SNAKE_CASE__								=   [None, 16, None]
									for max_length, padding in zip(__A				,					__A		):
											SCREAMING_SNAKE_CASE__								=   feature_extractor(
											    __A				,					padding=__A				,					max_length=__A				,					return_attention_mask=__A		)
											SCREAMING_SNAKE_CASE__								=   inputs.input_features
											SCREAMING_SNAKE_CASE__								=   inputs.attention_mask
											SCREAMING_SNAKE_CASE__								=   [np.sum(__A		) for x in attention_mask]

											self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]		)
											self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]		)
											self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]		)


							def   _snake_case	(				self     :Tuple		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in range(800				,					1400				,					200		)]

									SCREAMING_SNAKE_CASE__								=   ["""longest""", """max_length""", """do_not_pad"""]
									SCREAMING_SNAKE_CASE__								=   [None, 16, None]
									for max_length, padding in zip(__A				,					__A		):
											SCREAMING_SNAKE_CASE__								=   feature_extractor(
											    __A				,					max_length=__A				,					padding=__A				,					return_tensors="""np"""				,					return_attention_mask=__A		)
											SCREAMING_SNAKE_CASE__								=   inputs.input_features
											SCREAMING_SNAKE_CASE__								=   inputs.attention_mask
											SCREAMING_SNAKE_CASE__								=   [np.sum(__A		) for x in attention_mask]

											self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]		)
											self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6		)
											self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]		)
											self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6		)
											self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]		)


							def   _snake_case	(				self     :Union[str, Any]		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in range(800				,					1400				,					200		)]
									SCREAMING_SNAKE_CASE__								=   feature_extractor(
									    __A				,					padding="""max_length"""				,					max_length=4				,					truncation=__A				,					return_tensors="""np"""				,					return_attention_mask=__A				,					)
									SCREAMING_SNAKE_CASE__								=   inputs.input_features
									SCREAMING_SNAKE_CASE__								=   inputs.attention_mask
									SCREAMING_SNAKE_CASE__								=   np.sum(attention_mask == 1				,					axis=1		)

									self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
									self._check_zero_mean_unit_variance(input_features[1]		)
									self._check_zero_mean_unit_variance(input_features[2]		)


							def   _snake_case	(				self     :Optional[Any]		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in range(800				,					1400				,					200		)]
									SCREAMING_SNAKE_CASE__								=   feature_extractor(
									    __A				,					padding="""longest"""				,					max_length=4				,					truncation=__A				,					return_tensors="""np"""				,					return_attention_mask=__A				,					)
									SCREAMING_SNAKE_CASE__								=   inputs.input_features
									SCREAMING_SNAKE_CASE__								=   inputs.attention_mask
									SCREAMING_SNAKE_CASE__								=   np.sum(attention_mask == 1				,					axis=1		)

									self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
									self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]		)
									self._check_zero_mean_unit_variance(input_features[2]		)

									# make sure that if max_length < longest -> then pad to max_length
									self.assertEqual(input_features.shape				,					(3, 4, 24)		)

									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in range(800				,					1400				,					200		)]
									SCREAMING_SNAKE_CASE__								=   feature_extractor(
									    __A				,					padding="""longest"""				,					max_length=16				,					truncation=__A				,					return_tensors="""np"""				,					return_attention_mask=__A				,					)
									SCREAMING_SNAKE_CASE__								=   inputs.input_features
									SCREAMING_SNAKE_CASE__								=   inputs.attention_mask
									SCREAMING_SNAKE_CASE__								=   np.sum(attention_mask == 1				,					axis=1		)

									self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
									self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]		)
									self._check_zero_mean_unit_variance(input_features[2]		)

									# make sure that if max_length < longest -> then pad to max_length
									self.assertEqual(input_features.shape				,					(3, 6, 24)		)


							def   _snake_case	(				self     :Any		) -> Union[str, Any]:



									"""simple docstring"""



									import torch

									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									SCREAMING_SNAKE_CASE__								=   np.random.rand(100				,					32		).astype(np.floataa		)
									SCREAMING_SNAKE_CASE__								=   np_speech_inputs.tolist()

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


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :Union[str, Any]		) -> Tuple:



									"""simple docstring"""



									from datasets import load_dataset

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

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


							def   _snake_case	(				self     :Dict		) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   np.array([
									    -1.5_7_4_5, -1.7_7_1_3, -1.7_0_2_0, -1.6_0_6_9, -1.2_2_5_0, -1.1_1_0_5, -0.9_0_7_2, -0.8_2_4_1,
									    -1.2_3_1_0, -0.8_0_9_8, -0.3_3_2_0, -0.4_1_0_1, -0.7_9_8_5, -0.4_9_9_6, -0.8_2_1_3, -0.9_1_2_8,
									    -1.0_4_2_0, -1.1_2_8_6, -1.0_4_4_0, -0.7_9_9_9, -0.8_4_0_5, -1.2_2_7_5, -1.5_4_4_3, -1.4_6_2_5,
									]		)
									# fmt: on

									SCREAMING_SNAKE_CASE__								=   self._load_datasamples(1		)
									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									SCREAMING_SNAKE_CASE__								=   feature_extractor(__A				,					return_tensors="""pt"""		).input_features
									self.assertEquals(input_features.shape				,					(1, 584, 24)		)
									self.assertTrue(np.allclose(input_features[0, 0, :30]				,					__A				,					atol=1E-4		)		)
"""simple docstring"""



import os
from collections import deque

import torch
from torch.utils.data import Dataset
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_=""   ,      snake_case_="train"							):







										'''simple docstring'''


										assert os.path.isdir(snake_case_							)

										A__       :     Dict								=			[]
										A__       :     Optional[Any]								=			os.listdir(snake_case_							)
										for story_filename in story_filenames_list:
																	if "summary" in story_filename:
																								continue
																	A__       :     Any								=			os.path.join(snake_case_   ,      snake_case_							)
																	if not os.path.isfile(snake_case_							):
																								continue
																	self.documents.append(snake_case_							)





			def __len__(			self							):







										'''simple docstring'''


										return len(self.documents							)





			def __getitem__(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     List[str]								=			self.documents[idx]
										A__       :     List[Any]								=			document_path.split("""/"""							)[-1]
										with open(snake_case_   ,      encoding="""utf-8"""							) as source:
																	A__       :     Dict								=			source.read()
																	A__				,							A__       :     Dict								=			process_story(snake_case_							)
										return document_name, story_lines, summary_lines





def  _A(		lowerCAmelCase							):
							A__       :     Optional[int]								=			list(filter(lambda lowerCAmelCase							: len(lowerCAmelCase							) != 0			,  [line.strip() for line in raw_story.split("""\n"""							)]							)							)

							# for some unknown reason some lines miss a period, add it
							A__       :     List[str]								=			[_add_missing_period(lowerCAmelCase							) for line in nonempty_lines]

							# gather article lines
							A__       :     Union[str, Any]								=			[]
							A__       :     Optional[Any]								=			deque(lowerCAmelCase							)
							while True:
														try:
																					A__       :     List[str]								=			lines.popleft()
																					if element.startswith("""@highlight"""							):
																												break
																					story_lines.append(lowerCAmelCase							)
														except IndexError:
																					# if "@highlight" is absent from the file we pop
																					# all elements until there is None, raising an exception.
																					return story_lines, []

    # gather summary lines
							A__       :     List[str]								=			list(filter(lambda lowerCAmelCase							: not t.startswith("""@highlight"""							)			,  lowerCAmelCase							)							)

							return story_lines, summary_lines

def  _A(		lowerCAmelCase							):
							A__       :     Tuple								=			[""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
							if line.startswith("""@highlight"""							):
														return line
							if line[-1] in END_TOKENS:
														return line
							return line + "."

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							if len(lowerCAmelCase							) > block_size:
														return sequence[:block_size]
							else:
														sequence.extend([pad_token_id] * (block_size - len(lowerCAmelCase							))							)
														return sequence

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Any								=			torch.ones_like(lowerCAmelCase							)
							A__       :     Any								=			sequence == pad_token_id
							A__       :     Any								=			0
							return mask

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Dict								=			[tokenizer.encode(lowerCAmelCase							) for line in story_lines]
							A__       :     Any								=			[token for sentence in story_lines_token_ids for token in sentence]
							A__       :     List[str]								=			[tokenizer.encode(lowerCAmelCase							) for line in summary_lines]
							A__       :     int								=			[token for sentence in summary_lines_token_ids for token in sentence]

							return story_token_ids, summary_token_ids



def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     str								=			[]
							for sequence in batch:
														A__       :     List[Any]								=			-1
														A__       :     Dict								=			[]
														for s in sequence:
																					if s == separator_token_id:
																												sentence_num += 1
																					embeddings.append(sentence_num % 2							)
														batch_embeddings.append(lowerCAmelCase							)
							return torch.tensor(lowerCAmelCase							)





