



import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger

def      A_ (		__a       :			Tuple	,						__a       :			Optional[Any]						):

				"""simple docstring"""
				a__			 =						copy.deepcopy(__a						)
				a__			 =						sorted(__a						)
				a__			 =						[bisect.bisect_right(__a	,						__a						) for y in x]
				return quantized

def      A_ (		__a       :			List[str]	,						__a       :			Optional[int]=0						):

				"""simple docstring"""
				a__			 =						np.arange(start=3	,						stop=__a	,						step=4						).tolist() if k > 0 else [10]
				a__			 =						_quantize(__a	,						__a						)
				# count number of elements per group
				a__			 =						np.unique(__a	,						return_counts=__a						)[1]
				a__			 =						[0] + bins + [np.inf]
				logger.info("""Using {} as bins for aspect lengths quantization""".format(__a						)						)
				logger.info("""Count of instances per bin: {}""".format(__a						)						)
				return groups




class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def __init__(							self					,					a_					,					a_					,					a_						):
											if not isinstance(a_					,					a_						):
															raise ValueError(
															    """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(a_						)						)
											a__			 =						sampler
											a__			 =						group_ids
											a__			 =						batch_size


							def __iter__(							self						):
											a__			 =						defaultdict(a_						)
											a__			 =						defaultdict(a_						)

											a__			 =						0
											for idx in self.sampler:
															a__			 =						self.group_ids[idx]
															buffer_per_group[group_id].append(a_						)
															samples_per_group[group_id].append(a_						)
															if len(buffer_per_group[group_id]						) == self.batch_size:
																			yield buffer_per_group[group_id]  # TODO
																			num_batches += 1
																			del buffer_per_group[group_id]
															assert len(buffer_per_group[group_id]						) < self.batch_size

											# now we have run out of elements that satisfy
											# the group criteria, let's return the remaining
											# elements so that the size of the sampler is
											# deterministic
											a__			 =						len(self						)
											a__			 =						expected_num_batches - num_batches
											if num_remaining > 0:
															# for the remaining batches, group the batches by similar lengths
															a__			 =						[]
															for group_id, idxs in sorted(buffer_per_group.items()					,					key=lambda a_						: x[0]						):
																			batch_idx.extend(a_						)
																			if len(a_						) >= self.batch_size:
																							yield batch_idx[: self.batch_size]
																							a__			 =						batch_idx[self.batch_size :]
																							num_remaining -= 1
															if len(a_						) > 0:
																			yield batch_idx
																			num_remaining -= 1
											assert num_remaining == 0







							def __len__(							self						):
											return (len(self.sampler						) + self.batch_size - 1) // self.batch_size






import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
	from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

	from .test_feature_extraction_speechta import floats_list


UpperCAmelCase								=   get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")

@require_torch
class 	__snake_case (				unittest.TestCase):







							'''simple docstring'''






							def _a (							self						):
											a__			 =						tempfile.mkdtemp()

											a__			 =						SpeechTaTokenizer(a_						)
											tokenizer.save_pretrained(self.tmpdirname						)

											a__			 =						{
											    """feature_size""": 1,
											    """padding_value""": 0.0,
											    """sampling_rate""": 16_000,
											    """do_normalize""": False,
											    """num_mel_bins""": 80,
											    """hop_length""": 16,
											    """win_length""": 64,
											    """win_function""": """hann_window""",
											    """fmin""": 80,
											    """fmax""": 7_600,
											    """mel_floor""": 1E-1_0,
											    """reduction_factor""": 2,
											    """return_attention_mask""": True,
											}

											a__			 =						os.path.join(self.tmpdirname					,					a_						)
											with open(self.feature_extraction_file					,					"""w"""					,					encoding="""utf-8"""						) as fp:
															fp.write(json.dumps(a_						) + """\n"""						)


							def _a (							self					,					**a_						):
											return SpeechTaTokenizer.from_pretrained(self.tmpdirname					,					**a_						)


							def _a (							self					,					**a_						):
											return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname					,					**a_						)


							def _a (							self						):
											shutil.rmtree(self.tmpdirname						)


							def _a (							self						):
											a__			 =						self.get_tokenizer()
											a__			 =						self.get_feature_extractor()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

											processor.save_pretrained(self.tmpdirname						)
											a__			 =						SpeechTaProcessor.from_pretrained(self.tmpdirname						)

											self.assertEqual(processor.tokenizer.get_vocab()					,					tokenizer.get_vocab()						)
											self.assertIsInstance(processor.tokenizer					,					a_						)

											self.assertEqual(processor.feature_extractor.to_json_string()					,					feature_extractor.to_json_string()						)
											self.assertIsInstance(processor.feature_extractor					,					a_						)


							def _a (							self						):
											a__			 =						SpeechTaProcessor(tokenizer=self.get_tokenizer()					,					feature_extractor=self.get_feature_extractor()						)
											processor.save_pretrained(self.tmpdirname						)

											a__			 =						self.get_tokenizer(bos_token="""(BOS)"""					,					eos_token="""(EOS)"""						)
											a__			 =						self.get_feature_extractor(do_normalize=a_					,					padding_value=1.0						)

											a__			 =						SpeechTaProcessor.from_pretrained(
											    self.tmpdirname					,					bos_token="""(BOS)"""					,					eos_token="""(EOS)"""					,					do_normalize=a_					,					padding_value=1.0						)

											self.assertEqual(processor.tokenizer.get_vocab()					,					tokenizer_add_kwargs.get_vocab()						)
											self.assertIsInstance(processor.tokenizer					,					a_						)

											self.assertEqual(processor.feature_extractor.to_json_string()					,					feature_extractor_add_kwargs.to_json_string()						)
											self.assertIsInstance(processor.feature_extractor					,					a_						)


							def _a (							self						):
											a__			 =						self.get_feature_extractor()
											a__			 =						self.get_tokenizer()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

											a__			 =						floats_list((3, 1_000)						)

											a__			 =						feature_extractor(audio=a_					,					return_tensors="""np"""						)
											a__			 =						processor(audio=a_					,					return_tensors="""np"""						)

											for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()					,					input_processor[key].sum()					,					delta=1E-2						)


							def _a (							self						):
											a__			 =						self.get_feature_extractor()
											a__			 =						self.get_tokenizer()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

											a__			 =						floats_list((3, 1_000)						)

											a__			 =						feature_extractor(audio_target=a_					,					return_tensors="""np"""						)
											a__			 =						processor(audio_target=a_					,					return_tensors="""np"""						)

											for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()					,					input_processor[key].sum()					,					delta=1E-2						)


							def _a (							self						):
											a__			 =						self.get_feature_extractor()
											a__			 =						self.get_tokenizer()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

											a__			 =						"""This is a test string"""

											a__			 =						processor(text=a_						)
											a__			 =						tokenizer(a_						)

											for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]					,					encoded_processor[key]						)


							def _a (							self						):
											a__			 =						self.get_feature_extractor()
											a__			 =						self.get_tokenizer()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

											a__			 =						"""This is a test string"""

											a__			 =						processor(text_target=a_						)
											a__			 =						tokenizer(a_						)

											for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]					,					encoded_processor[key]						)


							def _a (							self						):
											a__			 =						self.get_feature_extractor()
											a__			 =						self.get_tokenizer()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

											a__			 =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											a__			 =						processor.batch_decode(a_						)
											a__			 =						tokenizer.batch_decode(a_						)

											self.assertListEqual(a_					,					a_						)







							def _a (							self						):
											a__			 =						self.get_feature_extractor()
											a__			 =						self.get_tokenizer()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

											self.assertListEqual(
											    processor.model_input_names					,					feature_extractor.model_input_names					,					msg="""`processor` and `feature_extractor` model input names do not match"""					,					)

