



"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

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


__a      =				logging.get_logger(__name__)

__a      =				{
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			List[Any]						=     """gptj"""
					_A		:			Union[str, Any]						=     {
					    """max_position_embeddings""": """n_positions""",
					    """hidden_size""": """n_embd""",
					    """num_attention_heads""": """n_head""",
					    """num_hidden_layers""": """n_layer""",
					}






					def __init__(       self:							int						,     snake_case:							int=50_400						,     snake_case:							Optional[Any]=2_048						,     snake_case:							Any=4_096						,     snake_case:							Dict=28						,     snake_case:							Union[str, Any]=16						,     snake_case:							Optional[int]=64						,     snake_case:							List[Any]=None						,     snake_case:							List[str]="gelu_new"						,     snake_case:							Dict=0.0						,     snake_case:							Union[str, Any]=0.0						,     snake_case:							List[Any]=0.0						,     snake_case:							List[Any]=1E-5						,     snake_case:							Any=0.0_2						,     snake_case:							Union[str, Any]=True						,     snake_case:							int=50_256						,     snake_case:							int=50_256						,     snake_case:							List[Any]=False						,     **snake_case:							List[str]						,     )   ->	Optional[Any]:
											snake_case_ :Optional[Any]						=			vocab_size
											snake_case_ :List[Any]						=			n_positions
											snake_case_ :List[str]						=			n_embd
											snake_case_ :List[str]						=			n_layer
											snake_case_ :int						=			n_head
											snake_case_ :int						=			n_inner
											snake_case_ :List[str]						=			rotary_dim
											snake_case_ :Optional[Any]						=			activation_function
											snake_case_ :int						=			resid_pdrop
											snake_case_ :List[str]						=			embd_pdrop
											snake_case_ :str						=			attn_pdrop
											snake_case_ :Union[str, Any]						=			layer_norm_epsilon
											snake_case_ :Optional[Any]						=			initializer_range
											snake_case_ :Any						=			use_cache

											snake_case_ :Tuple						=			bos_token_id
											snake_case_ :Any						=			eos_token_id

											super().__init__(
											    bos_token_id=snake_case						,     eos_token_id=snake_case						,     tie_word_embeddings=snake_case						,     **snake_case						)

class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					def __init__(       self:							int						,     snake_case:							PretrainedConfig						,     snake_case:							str = "default"						,     snake_case:							List[PatchingSpec] = None						,     snake_case:							bool = False						,     )   ->	Any:
											super().__init__(snake_case						,     task=snake_case						,     patching_specs=snake_case						,     use_past=snake_case						)
											if not getattr(self._config						,     """pad_token_id"""						,     snake_case						):
																	# TODO: how to do that better?
																	snake_case_ :Optional[Any]						=			0






					@property
					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Mapping[str, Mapping[int, str]]:
											snake_case_ :Tuple						=			OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}						)
											if self.use_past:
																	self.fill_with_past_key_values_(snake_case						,     direction="""inputs"""						)
																	snake_case_ :Optional[Any]						=			{0: """batch""", 1: """past_sequence + sequence"""}
											else:
																	snake_case_ :Tuple						=			{0: """batch""", 1: """sequence"""}

											return common_inputs






					@property
					def    lowerCAmelCase_				(       self:							Tuple						)   ->	int:
											return self._config.n_layer






					@property
					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	int:
											return self._config.n_head






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							PreTrainedTokenizer						,     snake_case:							int = -1						,     snake_case:							int = -1						,     snake_case:							bool = False						,     snake_case:							Optional[TensorType] = None						,     )   ->	Mapping[str, Any]:
											snake_case_ :Tuple						=			super(snake_case						,     self						).generate_dummy_inputs(
											    snake_case						,     batch_size=snake_case						,     seq_length=snake_case						,     is_pair=snake_case						,     framework=snake_case						)

											# We need to order the input in the way they appears in the forward()
											snake_case_ :int						=			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

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

											snake_case_ :Dict						=			common_inputs["""attention_mask"""]
											if self.use_past:
																	snake_case_ :Optional[int]						=			ordered_inputs["""attention_mask"""].dtype
																	snake_case_ :List[str]						=			torch.cat(
																	    [ordered_inputs["""attention_mask"""], torch.ones(snake_case						,     snake_case						,     dtype=snake_case						)]						,     dim=1						)

											return ordered_inputs






					@property
					def    lowerCAmelCase_				(       self:							List[str]						)   ->	int:
											return 13

'''simple docstring'''




import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


__lowercase          =  logging.get_logger(__name__)






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Union[str, Any]      =       ['''input_features''']



			def __init__(   self       ,    __lowerCAmelCase=80       ,    __lowerCAmelCase=16000       ,    __lowerCAmelCase=160       ,    __lowerCAmelCase=30       ,    __lowerCAmelCase=400       ,    __lowerCAmelCase=0.0       ,    __lowerCAmelCase=False       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					super().__init__(
					    feature_size=__lowerCAmelCase       ,    sampling_rate=__lowerCAmelCase       ,    padding_value=__lowerCAmelCase       ,    return_attention_mask=__lowerCAmelCase       ,    **__lowerCAmelCase       ,    )
					lowerCAmelCase   					=  n_fft
					lowerCAmelCase   					=  hop_length
					lowerCAmelCase   					=  chunk_length
					lowerCAmelCase   					=  chunk_length * sampling_rate
					lowerCAmelCase   					=  self.n_samples // hop_length
					lowerCAmelCase   					=  sampling_rate
					lowerCAmelCase   					=  mel_filter_bank(
					    num_frequency_bins=1 + n_fft // 2       ,    num_mel_filters=__lowerCAmelCase       ,    min_frequency=0.0       ,    max_frequency=8000.0       ,    sampling_rate=__lowerCAmelCase       ,    norm="""slaney"""       ,    mel_scale="""slaney"""       ,    )



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  spectrogram(
					    __lowerCAmelCase       ,    window_function(self.n_fft       ,    """hann""")       ,    frame_length=self.n_fft       ,    hop_length=self.hop_length       ,    power=2.0       ,    mel_filters=self.mel_filters       ,    log_mel="""log10"""       ,    )
					lowerCAmelCase   					=  log_spec[:, :-1]
					lowerCAmelCase   					=  np.maximum(__lowerCAmelCase       ,    log_spec.max() - 8.0)
					lowerCAmelCase   					=  (log_spec + 4.0) / 4.0
					return log_spec



			@staticmethod
			# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
			def 							a_       (   __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = 0.0):




					"""simple docstring"""


					if attention_mask is not None:
							lowerCAmelCase   					=  np.array(__lowerCAmelCase       ,    np.intaa)
							lowerCAmelCase   					=  []

							for vector, length in zip(__lowerCAmelCase       ,    attention_mask.sum(-1)):
									lowerCAmelCase   					=  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7)
									if length < normed_slice.shape[0]:
											lowerCAmelCase   					=  padding_value

									normed_input_values.append(__lowerCAmelCase)
					else:
							lowerCAmelCase   					=  [(x - x.mean()) / np.sqrt(x.var() + 1E-7) for x in input_values]

					return normed_input_values



			def __call__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = True       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = "max_length"       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    **__lowerCAmelCase       ,    ):




					"""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.__class__.__name__} was trained using a"
									    f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
									    f" was sampled with {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.""")

					lowerCAmelCase   					=  isinstance(__lowerCAmelCase       ,    np.ndarray) and len(raw_speech.shape) > 1
					if is_batched_numpy and len(raw_speech.shape) > 2:
							raise ValueError(f"Only mono-channel audio is supported for input to {self}")
					lowerCAmelCase   					=  is_batched_numpy or (
					    isinstance(__lowerCAmelCase       ,    (list, tuple)) and (isinstance(raw_speech[0]       ,    (np.ndarray, tuple, list)))
					)

					if is_batched:
							lowerCAmelCase   					=  [np.asarray([speech]       ,    dtype=np.floataa).T for speech in raw_speech]
					elif not is_batched and not isinstance(__lowerCAmelCase       ,    np.ndarray):
							lowerCAmelCase   					=  np.asarray(__lowerCAmelCase       ,    dtype=np.floataa)
					elif isinstance(__lowerCAmelCase       ,    np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
							lowerCAmelCase   					=  raw_speech.astype(np.floataa)

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

					lowerCAmelCase   					=  BatchFeature({"""input_features""": raw_speech})

					# convert into correct format for padding

					lowerCAmelCase   					=  self.pad(
					    __lowerCAmelCase       ,    padding=__lowerCAmelCase       ,    max_length=max_length if max_length else self.n_samples       ,    truncation=__lowerCAmelCase       ,    pad_to_multiple_of=__lowerCAmelCase       ,    return_attention_mask=return_attention_mask or do_normalize       ,    )

					# zero-mean and unit-variance normalization
					if do_normalize:
							lowerCAmelCase   					=  self.zero_mean_unit_var_norm(
							    padded_inputs["""input_features"""]       ,    attention_mask=padded_inputs["""attention_mask"""]       ,    padding_value=self.padding_value       ,    )
							lowerCAmelCase   					=  np.stack(padded_inputs["""input_features"""]       ,    axis=0)

					# make sure list is in array format
					lowerCAmelCase   					=  padded_inputs.get("""input_features""").transpose(2       ,    0       ,    1)

					lowerCAmelCase   					=  [self._np_extract_fbank_features(__lowerCAmelCase) for waveform in input_features[0]]

					if isinstance(input_features[0]       ,    __lowerCAmelCase):
							lowerCAmelCase   					=  [np.asarray(__lowerCAmelCase       ,    dtype=np.floataa) for feature in input_features]
					else:
							lowerCAmelCase   					=  input_features

					if return_attention_mask:
							# rescale from sample (48000) to feature (3000)
							lowerCAmelCase   					=  padded_inputs["""attention_mask"""][:, :: self.hop_length]

					if return_tensors is not None:
							lowerCAmelCase   					=  padded_inputs.convert_to_tensors(__lowerCAmelCase)

					return padded_inputs







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  copy.deepcopy(self.__dict__)
					lowerCAmelCase   					=  self.__class__.__name__
					if "mel_filters" in output:
							del output["mel_filters"]
					return output




