
"""simple docstring"""

import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
					import librosa  # noqa: E402

					_A				=					True
					_A				=					''
except Exception as e:
					_A				=					False
					_A				=					(
					    F"""Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."""
					)


from PIL import Image  # noqa: E402






class 			_lowercase  (							__UpperCAmelCase							,			__UpperCAmelCase						):
	lowercase_								=      'mel_config.json'




	@register_to_config
	def __init__(					self						,					UpperCAmelCase_ = 256						,					UpperCAmelCase_ = 256						,					UpperCAmelCase_ = 22050						,					UpperCAmelCase_ = 2048						,					UpperCAmelCase_ = 512						,					UpperCAmelCase_ = 80						,					UpperCAmelCase_ = 32						,					)						->				Union[str, Any]:
			lowerCamelCase	:     Optional[int]            =       hop_length
			lowerCamelCase	:     Tuple            =       sample_rate
			lowerCamelCase	:     Any            =       n_fft
			lowerCamelCase	:     int            =       top_db
			lowerCamelCase	:     List[str]            =       n_iter
			self.set_resolution(UpperCAmelCase_						,					UpperCAmelCase_   )
			lowerCamelCase	:     List[Any]            =       None

			if not _librosa_can_be_imported:
					raise ValueError(_import_error   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				Union[str, Any]:
			lowerCamelCase	:     Optional[int]            =       x_res
			lowerCamelCase	:     List[Any]            =       y_res
			lowerCamelCase	:     Optional[Any]            =       self.y_res
			lowerCamelCase	:     Dict            =       self.x_res * self.hop_length - 1




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = None   )						->				List[Any]:
			if audio_file is not None:
					lowerCamelCase      ,       lowerCamelCase	:     Optional[int]            =       librosa.load(UpperCAmelCase_						,					mono=UpperCAmelCase_						,					sr=self.sr   )
			else:
					lowerCamelCase	:     Tuple            =       raw_audio

			# Pad with silence if necessary.
			if len(self.audio   ) < self.x_res * self.hop_length:
					lowerCamelCase	:     int            =       np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio   ),)   )]   )




	def      _UpperCamelCase       (					self   )						->				int:
			return len(self.audio   ) // self.slice_size




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_ = 0   )						->				np.ndarray:
			return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]




	def      _UpperCamelCase       (					self   )						->				int:
			return self.sr




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				Image.Image:
			lowerCamelCase	:     Dict            =       librosa.feature.melspectrogram(
			    y=self.get_audio_slice(UpperCAmelCase_   )						,					sr=self.sr						,					n_fft=self.n_fft						,					hop_length=self.hop_length						,					n_mels=self.n_mels   )
			lowerCamelCase	:     int            =       librosa.power_to_db(UpperCAmelCase_						,					ref=np.max						,					top_db=self.top_db   )
			lowerCamelCase	:     str            =       (((log_S + self.top_db) * 255 / self.top_db).clip(0						,					255   ) + 0.5).astype(np.uinta   )
			lowerCamelCase	:     List[Any]            =       Image.fromarray(UpperCAmelCase_   )
			return image




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				np.ndarray:
			lowerCamelCase	:     Union[str, Any]            =       np.frombuffer(image.tobytes()						,					dtype='uint8'   ).reshape((image.height, image.width)   )
			lowerCamelCase	:     Union[str, Any]            =       bytedata.astype('float'   ) * self.top_db / 255 - self.top_db
			lowerCamelCase	:     List[str]            =       librosa.db_to_power(UpperCAmelCase_   )
			lowerCamelCase	:     List[Any]            =       librosa.feature.inverse.mel_to_audio(
			    UpperCAmelCase_						,					sr=self.sr						,					n_fft=self.n_fft						,					hop_length=self.hop_length						,					n_iter=self.n_iter   )
			return audio








"""simple docstring"""

import os
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ... import AutoTokenizer
from ...utils import logging


_A				=					'block_records.npy'


_A				=					logging.get_logger(__name__)
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		import tensorflow.compat.va as tf

		lowerCamelCase	:     Union[str, Any]            =       tf.data.TFRecordDataset(a_,		buffer_size=512 * 1024 * 1024     )
		lowerCamelCase	:     Union[str, Any]            =       blocks_dataset.batch(a_,		drop_remainder=a_     )
		lowerCamelCase	:     Union[str, Any]            =       next(blocks_dataset.take(1     ).as_numpy_iterator()     )

		return np_record






class 			_lowercase  :




	def __init__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_=2						,					UpperCAmelCase_=1000						,					UpperCAmelCase_=100						,					UpperCAmelCase_=100000						,					)						->				List[Any]:
			from scann.scann_ops.py.scann_ops_pybind import builder as Builder

			lowerCamelCase	:     int            =       Builder(db=UpperCAmelCase_						,					num_neighbors=UpperCAmelCase_						,					distance_measure='dot_product'   )
			lowerCamelCase	:     Optional[int]            =       builder.tree(
			    num_leaves=UpperCAmelCase_						,					num_leaves_to_search=UpperCAmelCase_						,					training_sample_size=UpperCAmelCase_   )
			lowerCamelCase	:     Union[str, Any]            =       builder.score_ah(dimensions_per_block=UpperCAmelCase_   )

			lowerCamelCase	:     List[Any]            =       builder.build()




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				Optional[int]:
			lowerCamelCase      ,       lowerCamelCase	:     List[Any]            =       self.searcher.search_batched(question_projection.detach().cpu()   )
			return retrieved_block_ids.astype('int64'   )






class 			_lowercase  :




	def __init__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				Optional[Any]:
			super().__init__()
			lowerCamelCase	:     Tuple            =       block_records
			lowerCamelCase	:     Any            =       tokenizer




	def __call__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_=None						,					UpperCAmelCase_="pt"   )						->				Tuple:
			lowerCamelCase	:     List[str]            =       np.take(self.block_records						,					indices=UpperCAmelCase_						,					axis=0   )

			lowerCamelCase	:     Any            =       self.tokenizer.decode(question_input_ids[0]						,					skip_special_tokens=UpperCAmelCase_   )

			lowerCamelCase	:     int            =       []
			lowerCamelCase	:     Optional[int]            =       []
			for retrieved_block in retrieved_blocks:
					text.append(UpperCAmelCase_   )
					text_pair.append(retrieved_block.decode()   )

			lowerCamelCase	:     Union[str, Any]            =       self.tokenizer(
			    UpperCAmelCase_						,					UpperCAmelCase_						,					padding=UpperCAmelCase_						,					truncation=UpperCAmelCase_						,					return_special_tokens_mask=UpperCAmelCase_						,					max_length=UpperCAmelCase_   )
			lowerCamelCase	:     str            =       concat_inputs.convert_to_tensors(UpperCAmelCase_   )

			if answer_ids is not None:
					return self.block_has_answer(UpperCAmelCase_						,					UpperCAmelCase_   ) + (concat_inputs_tensors,)
			else:
					return (None, None, None, concat_inputs_tensors)




	@classmethod
	def      _UpperCamelCase       (					cls						,					UpperCAmelCase_						,					*UpperCAmelCase_						,					**UpperCAmelCase_   )						->				Dict:
			if os.path.isdir(UpperCAmelCase_   ):
					lowerCamelCase	:     str            =       os.path.join(UpperCAmelCase_						,					_REALM_BLOCK_RECORDS_FILENAME   )
			else:
					lowerCamelCase	:     Tuple            =       hf_hub_download(
					    repo_id=UpperCAmelCase_						,					filename=_REALM_BLOCK_RECORDS_FILENAME						,					**UpperCAmelCase_   )
			lowerCamelCase	:     Dict            =       np.load(UpperCAmelCase_						,					allow_pickle=UpperCAmelCase_   )

			lowerCamelCase	:     Union[str, Any]            =       AutoTokenizer.from_pretrained(UpperCAmelCase_						,					*UpperCAmelCase_						,					**UpperCAmelCase_   )

			return cls(UpperCAmelCase_						,					UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				Union[str, Any]:
			# save block records
			np.save(os.path.join(UpperCAmelCase_						,					_REALM_BLOCK_RECORDS_FILENAME   )						,					self.block_records   )
			# save tokenizer
			self.tokenizer.save_pretrained(UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				Tuple:
			lowerCamelCase	:     List[Any]            =       []
			lowerCamelCase	:     Any            =       []
			lowerCamelCase	:     int            =       []
			lowerCamelCase	:     Union[str, Any]            =       0

			for input_id in concat_inputs.input_ids:
					lowerCamelCase	:     Optional[Any]            =       input_id.tolist()
					# Check answers between two [SEP] tokens
					lowerCamelCase	:     Tuple            =       input_id_list.index(self.tokenizer.sep_token_id   )
					lowerCamelCase	:     Any            =       first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id   )

					start_pos.append([]   )
					end_pos.append([]   )
					for answer in answer_ids:
							for idx in range(first_sep_idx + 1						,					UpperCAmelCase_   ):
									if answer[0] == input_id_list[idx]:
											if input_id_list[idx : idx + len(UpperCAmelCase_   )] == answer:
													start_pos[-1].append(UpperCAmelCase_   )
													end_pos[-1].append(idx + len(UpperCAmelCase_   ) - 1   )

					if len(start_pos[-1]   ) == 0:
							has_answers.append(UpperCAmelCase_   )
					else:
							has_answers.append(UpperCAmelCase_   )
							if len(start_pos[-1]   ) > max_answers:
									lowerCamelCase	:     List[str]            =       len(start_pos[-1]   )

        # Pad -1 to max_answers
			for start_pos_, end_pos_ in zip(UpperCAmelCase_						,					UpperCAmelCase_   ):
					if len(start_pos_   ) < max_answers:
							lowerCamelCase	:     Optional[int]            =       [-1] * (max_answers - len(start_pos_   ))
							start_pos_ += padded
							end_pos_ += padded
			return has_answers, start_pos, end_pos






