



'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowercase      			=	logging.get_logger(__name__)

lowercase      			=	{
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}






class    __lowerCamelCase							( _SCREAMING_SNAKE_CASE   ):







	'''simple docstring'''
	snake_case__		:   Any       =				'''gpt_neo'''
	snake_case__		:   Dict       =				['''past_key_values''']
	snake_case__		:   str       =				{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}






	def __init__(      self      ,     a__=50257      ,     a__=2048      ,     a__=2048      ,     a__=24      ,     a__=[[["global", "local"], 12]]      ,     a__=16      ,     a__=None      ,     a__=256      ,     a__="gelu_new"      ,     a__=0.0      ,     a__=0.0      ,     a__=0.0      ,     a__=0.1      ,     a__=1e-5      ,     a__=0.02      ,     a__=True      ,     a__=50256      ,     a__=50256      ,     **a__      ,     ):
						__SCREAMING_SNAKE_CASE				:    List[str]						    =							vocab_size
						__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							max_position_embeddings
						__SCREAMING_SNAKE_CASE				:    Dict						    =							hidden_size
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							num_layers
						__SCREAMING_SNAKE_CASE				:    str						    =							num_heads
						__SCREAMING_SNAKE_CASE				:    int						    =							intermediate_size
						__SCREAMING_SNAKE_CASE				:    List[str]						    =							window_size
						__SCREAMING_SNAKE_CASE				:    Dict						    =							activation_function
						__SCREAMING_SNAKE_CASE				:    str						    =							resid_dropout
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							embed_dropout
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							attention_dropout
						__SCREAMING_SNAKE_CASE				:    Dict						    =							classifier_dropout
						__SCREAMING_SNAKE_CASE				:    str						    =							layer_norm_epsilon
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							initializer_range
						__SCREAMING_SNAKE_CASE				:    Any						    =							use_cache

						__SCREAMING_SNAKE_CASE				:    Tuple						    =							bos_token_id
						__SCREAMING_SNAKE_CASE				:    Any						    =							eos_token_id

						__SCREAMING_SNAKE_CASE				:    List[str]						    =							attention_types
						__SCREAMING_SNAKE_CASE				:    Any						    =							self.expand_attention_types_params(_lowercase					)

						if len(self.attention_layers					) != self.num_layers:
											raise ValueError(
											    "Configuration for convolutional module is incorrect. "
											    "It is required that `len(config.attention_layers)` == `config.num_layers` "
											    f'but is `len(config.attention_layers) = {len(self.attention_layers					)}`, '
											    f'`config.num_layers = {self.num_layers}`. '
											    "`config.attention_layers` is prepared using `config.attention_types`. "
											    "Please verify the value of `config.attention_types` argument."					)

						super().__init__(bos_token_id=_lowercase      ,     eos_token_id=_lowercase      ,     **_lowercase					)






	@staticmethod
	def  a_					(      a__					):
						__SCREAMING_SNAKE_CASE				:    Any						    =							[]
						for item in attention_types:
											for _ in range(item[1]					):
																attentions.extend(item[0]					)
						return attentions



def        __A   (							_SCREAMING_SNAKE_CASE     :  Tuple					,   _SCREAMING_SNAKE_CASE     :  Optional[Any]					,   _SCREAMING_SNAKE_CASE     :  List[str]					,   _SCREAMING_SNAKE_CASE     :  int	):







					"""simple docstring"""






					import torch

					__SCREAMING_SNAKE_CASE				:    Dict						    =							input.size()
					__SCREAMING_SNAKE_CASE				:    Tuple						    =							len(__lowerCamelCase	)
					__SCREAMING_SNAKE_CASE				:    List[str]						    =							shape[dimension]

					__SCREAMING_SNAKE_CASE				:    Dict						    =							torch.arange(0					,   __lowerCamelCase					,   __lowerCamelCase	)
					__SCREAMING_SNAKE_CASE				:    List[Any]						    =							torch.div(sizedim - size					,   __lowerCamelCase					,   rounding_mode="floor"	) + 1
					__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							torch.arange(__lowerCamelCase	) + low_indices[:min_length][:, None]

					__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							[slice(__lowerCamelCase	)] * rank
					__SCREAMING_SNAKE_CASE				:    str						    =							indices
					__SCREAMING_SNAKE_CASE				:    Dict						    =							input[s]

					__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							list(range(0					,   rank + 1	)	)
					perm.append(perm.pop(dimension + 1	)	)

					return sliced.permute(__lowerCamelCase	)



def        __A   (							_SCREAMING_SNAKE_CASE     :  Optional[Any]					,   _SCREAMING_SNAKE_CASE     :  List[Any]	):







					"""simple docstring"""






					import torch

					__SCREAMING_SNAKE_CASE				:    List[Any]						    =							torch.arange(1					,   __lowerCamelCase	)
					__SCREAMING_SNAKE_CASE				:    Dict						    =							torch.remainder(__lowerCamelCase					,   __lowerCamelCase	)
					__SCREAMING_SNAKE_CASE				:    Dict						    =							remainders == 0
					__SCREAMING_SNAKE_CASE				:    Tuple						    =							candidates[divisor_indices]
					__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							torch.max(__lowerCamelCase	)
					return largest_divisor, torch.div(__lowerCamelCase					,   __lowerCamelCase					,   rounding_mode="floor"	)






class    __lowerCamelCase							( _SCREAMING_SNAKE_CASE   ):







	'''simple docstring'''






	@property
	def  a_					(      self					):
						__SCREAMING_SNAKE_CASE				:    str						    =							OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}					)
						if self.use_past:
											self.fill_with_past_key_values_(_lowercase      ,     direction="inputs"					)
											__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							{0: "batch", 1: "past_sequence + sequence"}
						else:
											__SCREAMING_SNAKE_CASE				:    Tuple						    =							{0: "batch", 1: "sequence"}

						return common_inputs






	@property
	def  a_					(      self					):
						return self._config.num_heads






	def  a_					(      self      ,     a__      ,     a__ = -1      ,     a__ = -1      ,     a__ = False      ,     a__ = None      ,     ):
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							super(_lowercase      ,     self					).generate_dummy_inputs(
						    _lowercase      ,     batch_size=_lowercase      ,     seq_length=_lowercase      ,     is_pair=_lowercase      ,     framework=_lowercase					)

						# We need to order the input in the way they appears in the forward()
						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							OrderedDict({"input_ids": common_inputs["input_ids"]}					)

						# Need to add the past_keys
						if self.use_past:
											if not is_torch_available():
																raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."					)
											else:
																import torch

																__SCREAMING_SNAKE_CASE    ,       __SCREAMING_SNAKE_CASE				:    List[str]						    =							common_inputs["input_ids"].shape
																# Not using the same length for past_key_values
																__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							seqlen + 2
																__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							(
																    batch,
																    self.num_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // self.num_attention_heads,
																)
																__SCREAMING_SNAKE_CASE				:    int						    =							[
																    (torch.zeros(_lowercase					), torch.zeros(_lowercase					)) for _ in range(self.num_layers					)
																]

						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							common_inputs["attention_mask"]
						if self.use_past:
											__SCREAMING_SNAKE_CASE				:    Any						    =							ordered_inputs["attention_mask"].dtype
											__SCREAMING_SNAKE_CASE				:    Any						    =							torch.cat(
											    [ordered_inputs["attention_mask"], torch.ones(_lowercase      ,     _lowercase      ,     dtype=_lowercase					)]      ,     dim=1					)

						return ordered_inputs






	@property
	def  a_					(      self					):
						return 13




'''simple docstring'''



from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowercase          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: List[str]										=  ['''input_values''', '''padding_mask''']




						def __init__(   self       ,  _lowercase = 1       ,  _lowercase = 24_000       ,  _lowercase = 0.0       ,  _lowercase = None       ,  _lowercase = None       ,  **_lowercase       ,  ):

												"""simple docstring"""



												super().__init__(feature_size=_lowercase       ,  sampling_rate=_lowercase       ,  padding_value=_lowercase       ,  **_lowercase )
												_lowerCAmelCase    		=					chunk_length_s
												_lowerCAmelCase    		=					overlap




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												if self.chunk_length_s is None:
																		return None
												else:
																		return int(self.chunk_length_s * self.sampling_rate )




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												if self.chunk_length_s is None or self.overlap is None:
																		return None
												else:
																		return max(1       ,  int((1.0 - self.overlap) * self.chunk_length ) )




						def __call__(   self       ,  _lowercase       ,  _lowercase = None       ,  _lowercase = False       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  ):

												"""simple docstring"""



												if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																								raise ValueError(
																								    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
																								    F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
																								    F' {self.sampling_rate} and not {sampling_rate}.' )
												else:
																		logger.warning(
																		    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
																		    """Failing to do so can result in silent errors that might be hard to debug.""" )

												if padding and truncation:
																		raise ValueError("""Both padding and truncation were set. Make sure you only set one.""" )
												elif padding is None:
																		# by default let's pad the inputs
																		_lowerCAmelCase    		=					True

												_lowerCAmelCase    		=					bool(
												    isinstance(_lowercase       ,  (list, tuple) ) and (isinstance(raw_audio[0]       ,  (np.ndarray, tuple, list) )) )

												if is_batched:
																		_lowerCAmelCase    		=					[np.asarray(_lowercase       ,  dtype=np.floataa ).T for audio in raw_audio]
												elif not is_batched and not isinstance(_lowercase       ,  np.ndarray ):
																		_lowerCAmelCase    		=					np.asarray(_lowercase       ,  dtype=np.floataa )
												elif isinstance(_lowercase       ,  np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
																		_lowerCAmelCase    		=					raw_audio.astype(np.floataa )

												# always return batch
												if not is_batched:
																		_lowerCAmelCase    		=					[np.asarray(_lowercase ).T]

												# verify inputs are valid
												for idx, example in enumerate(_lowercase ):
																		if example.ndim > 2:
																								raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}' )
																		if self.feature_size == 1 and example.ndim != 1:
																								raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels' )
																		if self.feature_size == 2 and example.shape[-1] != 2:
																								raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels' )

												_lowerCAmelCase    		=					None
												_lowerCAmelCase    		=					BatchFeature({"""input_values""": raw_audio} )
												if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
																		if truncation:
																								_lowerCAmelCase    		=					min(array.shape[0] for array in raw_audio )
																								_lowerCAmelCase    		=					int(np.floor(max_length / self.chunk_stride ) )
																								_lowerCAmelCase    		=					(nb_step - 1) * self.chunk_stride + self.chunk_length
																		elif padding:
																								_lowerCAmelCase    		=					max(array.shape[0] for array in raw_audio )
																								_lowerCAmelCase    		=					int(np.ceil(max_length / self.chunk_stride ) )
																								_lowerCAmelCase    		=					(nb_step - 1) * self.chunk_stride + self.chunk_length
																								_lowerCAmelCase    		=					"""max_length"""
																		else:
																								_lowerCAmelCase    		=					input_values

        # normal padding on batch
												if padded_inputs is None:
																		_lowerCAmelCase    		=					self.pad(
																		    _lowercase       ,  max_length=_lowercase       ,  truncation=_lowercase       ,  padding=_lowercase       ,  return_attention_mask=_lowercase       ,  )
																		if padding:
																								_lowerCAmelCase    		=					padded_inputs.pop("""attention_mask""" )

												_lowerCAmelCase    		=					[]
												for example in padded_inputs.pop("""input_values""" ):
																		if self.feature_size == 1:
																								_lowerCAmelCase    		=					example[..., None]
																		input_values.append(example.T )

												_lowerCAmelCase    		=					input_values
												if return_tensors is not None:
																		_lowerCAmelCase    		=					padded_inputs.convert_to_tensors(_lowercase )

												return padded_inputs
