




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


_UpperCAmelCase	:						Dict			=     logging.get_logger(__name__)




class    __lowerCAmelCase       (							lowerCAmelCase):
       _a 					=						['''input_values''', '''padding_mask''']






       def __init__(   self:					List[Any]       ,					_lowerCAmelCase:					int = 1       ,					_lowerCAmelCase:					int = 2_40_00       ,					_lowerCAmelCase:					float = 0.0       ,					_lowerCAmelCase:					float = None       ,					_lowerCAmelCase:					float = None       ,					**_lowerCAmelCase:					Union[str, Any]       ,					):
         super().__init__(feature_size=_lowerCAmelCase       ,					sampling_rate=_lowerCAmelCase       ,					padding_value=_lowerCAmelCase       ,					**_lowerCAmelCase				)
         lowercase    :int      	=						chunk_length_s
         lowercase    :Tuple      	=						overlap






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         if self.chunk_length_s is None:
           return None
         else:
           return int(self.chunk_length_s * self.sampling_rate				)






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         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:					Union[str, Any]       ,					_lowerCAmelCase:					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,					_lowerCAmelCase:					Optional[Union[bool, str, PaddingStrategy]] = None       ,					_lowerCAmelCase:					Optional[bool] = False       ,					_lowerCAmelCase:					Optional[int] = None       ,					_lowerCAmelCase:					Optional[Union[str, TensorType]] = None       ,					_lowerCAmelCase:					Optional[int] = None       ,					):
         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
           lowercase    :List[Any]      	=						True

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

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

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

         # verify inputs are valid
         for idx, example in enumerate(_lowerCAmelCase				):
           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"				)

         lowercase    :Dict      	=						None
         lowercase    :Tuple      	=						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:
             lowercase    :Dict      	=						min(array.shape[0] for array in raw_audio				)
             lowercase    :int      	=						int(np.floor(max_length / self.chunk_stride				)				)
             lowercase    :int      	=						(nb_step - 1) * self.chunk_stride + self.chunk_length
           elif padding:
             lowercase    :str      	=						max(array.shape[0] for array in raw_audio				)
             lowercase    :List[Any]      	=						int(np.ceil(max_length / self.chunk_stride				)				)
             lowercase    :List[str]      	=						(nb_step - 1) * self.chunk_stride + self.chunk_length
             lowercase    :Optional[int]      	=						"max_length"
           else:
             lowercase    :List[Any]      	=						input_values

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

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

         lowercase    :List[Any]      	=						input_values
         if return_tensors is not None:
           lowercase    :str      	=						padded_inputs.convert_to_tensors(_lowerCAmelCase				)

         return padded_inputs









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


_UpperCAmelCase	:						Optional[int]			=     logging.get_logger(__name__)

_UpperCAmelCase	:						List[Any]			=     {
    "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):
       _a 					=						'''gptj'''
       _a 					=						{
           '''max_position_embeddings''': '''n_positions''',
           '''hidden_size''': '''n_embd''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }






       def __init__(   self:					List[str]       ,					_lowerCAmelCase:					Union[str, Any]=5_04_00       ,					_lowerCAmelCase:					Optional[int]=20_48       ,					_lowerCAmelCase:					Dict=40_96       ,					_lowerCAmelCase:					str=28       ,					_lowerCAmelCase:					Dict=16       ,					_lowerCAmelCase:					str=64       ,					_lowerCAmelCase:					Dict=None       ,					_lowerCAmelCase:					Optional[int]="gelu_new"       ,					_lowerCAmelCase:					Optional[int]=0.0       ,					_lowerCAmelCase:					List[str]=0.0       ,					_lowerCAmelCase:					str=0.0       ,					_lowerCAmelCase:					Any=1e-5       ,					_lowerCAmelCase:					str=0.02       ,					_lowerCAmelCase:					str=True       ,					_lowerCAmelCase:					Any=5_02_56       ,					_lowerCAmelCase:					Union[str, Any]=5_02_56       ,					_lowerCAmelCase:					Optional[int]=False       ,					**_lowerCAmelCase:					int       ,					):
         lowercase    :str      	=						vocab_size
         lowercase    :Union[str, Any]      	=						n_positions
         lowercase    :Optional[int]      	=						n_embd
         lowercase    :int      	=						n_layer
         lowercase    :Tuple      	=						n_head
         lowercase    :Optional[Any]      	=						n_inner
         lowercase    :List[Any]      	=						rotary_dim
         lowercase    :List[Any]      	=						activation_function
         lowercase    :Tuple      	=						resid_pdrop
         lowercase    :Optional[int]      	=						embd_pdrop
         lowercase    :Any      	=						attn_pdrop
         lowercase    :Optional[int]      	=						layer_norm_epsilon
         lowercase    :str      	=						initializer_range
         lowercase    :Optional[int]      	=						use_cache

         lowercase    :Dict      	=						bos_token_id
         lowercase    :List[Any]      	=						eos_token_id

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







class    __lowerCAmelCase       (							lowerCAmelCase):






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






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         lowercase    :Optional[int]      	=						OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}				)
         if self.use_past:
           self.fill_with_past_key_values_(_lowerCAmelCase       ,					direction="inputs"				)
           lowercase    :Any      	=						{0: "batch", 1: "past_sequence + sequence"}
         else:
           lowercase    :Optional[int]      	=						{0: "batch", 1: "sequence"}

         return common_inputs






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         return self._config.n_layer






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         return self._config.n_head






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					PreTrainedTokenizer       ,					_lowerCAmelCase:					int = -1       ,					_lowerCAmelCase:					int = -1       ,					_lowerCAmelCase:					bool = False       ,					_lowerCAmelCase:					Optional[TensorType] = None       ,					):
         lowercase    :Union[str, Any]      	=						super(_lowerCAmelCase       ,					self				).generate_dummy_inputs(
             _lowerCAmelCase       ,					batch_size=_lowerCAmelCase       ,					seq_length=_lowerCAmelCase       ,					is_pair=_lowerCAmelCase       ,					framework=_lowerCAmelCase				)

         # We need to order the input in the way they appears in the forward()
         lowercase    :Optional[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

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

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

         return ordered_inputs






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         return 13



