

'''simple docstring'''

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


lowerCAmelCase_     =      logging.get_logger(__name__)

lowerCAmelCase_     =      {
    "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}







class lowerCamelCase   (    __lowerCAmelCase				):
				snake_case_							     = '''blenderbot'''
				snake_case_							     = ['''past_key_values''']
				snake_case_							     = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

				def __init__(       self,						lowercase_=8008,						lowercase_=128,						lowercase_=2,						lowercase_=10240,						lowercase_=32,						lowercase_=24,						lowercase_=10240,						lowercase_=32,						lowercase_=0.0,						lowercase_=0.0,						lowercase_=True,						lowercase_=True,						lowercase_="gelu",						lowercase_=2560,						lowercase_=0.1,						lowercase_=0.0,						lowercase_=0.0,						lowercase_=0.02,						lowercase_=1,						lowercase_=False,						lowercase_=0,						lowercase_=1,						lowercase_=2,						lowercase_=3,						lowercase_=2,						**lowercase_,						)    ->							Any:
					snake_case							=					vocab_size
					snake_case							=					max_position_embeddings
					snake_case							=					d_model
					snake_case							=					encoder_ffn_dim
					snake_case							=					encoder_layers
					snake_case							=					encoder_attention_heads
					snake_case							=					decoder_ffn_dim
					snake_case							=					decoder_layers
					snake_case							=					decoder_attention_heads
					snake_case							=					dropout
					snake_case							=					attention_dropout
					snake_case							=					activation_dropout
					snake_case							=					activation_function
					snake_case							=					init_std
					snake_case							=					encoder_layerdrop
					snake_case							=					decoder_layerdrop
					snake_case							=					use_cache
					snake_case							=					encoder_layers
					snake_case							=					scale_embedding  # scale factor will be sqrt(d_model) if True

					super().__init__(
					    pad_token_id=lowercase_,						bos_token_id=lowercase_,						eos_token_id=lowercase_,						is_encoder_decoder=lowercase_,						decoder_start_token_id=lowercase_,						encoder_no_repeat_ngram_size=lowercase_,						forced_eos_token_id=lowercase_,						**lowercase_,						)







class lowerCamelCase   (    __lowerCAmelCase				):

				@property
				def 					_lowerCamelCase    (       self )    ->							Mapping[str, Mapping[int, str]]:
					if self.task in ["default", "seq2seq-lm"]:
						snake_case							=					OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						    ] )
						if self.use_past:
							snake_case							=					{0: 'batch'}
							snake_case							=					{0: 'batch', 1: 'past_decoder_sequence + sequence'}
						else:
							snake_case							=					{0: 'batch', 1: 'decoder_sequence'}
							snake_case							=					{0: 'batch', 1: 'decoder_sequence'}
						if self.use_past:
							self.fill_with_past_key_values_(lowercase_,						direction='inputs' )
					elif self.task == "causal-lm":
						snake_case							=					OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						    ] )
						if self.use_past:
							snake_case			,			snake_case							=					self.num_layers
							for i in range(lowercase_ ):
								snake_case							=					{0: 'batch', 2: 'past_sequence + sequence'}
								snake_case							=					{0: 'batch', 2: 'past_sequence + sequence'}
					else:
						snake_case							=					OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
						        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
						    ] )

					return common_inputs

				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
				def 					_lowerCamelCase    (       self )    ->							Mapping[str, Mapping[int, str]]:
					if self.task in ["default", "seq2seq-lm"]:
						snake_case							=					super().outputs
					else:
						snake_case							=					super(lowercase_,						self ).outputs
						if self.use_past:
							snake_case			,			snake_case							=					self.num_layers
							for i in range(lowercase_ ):
								snake_case							=					{0: 'batch', 2: 'past_sequence + sequence'}
								snake_case							=					{0: 'batch', 2: 'past_sequence + sequence'}
					return common_outputs

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ = -1,						lowercase_ = -1,						lowercase_ = False,						lowercase_ = None,						)    ->							Mapping[str, Any]:
					snake_case							=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_ )
					# Generate decoder inputs
					snake_case							=					seq_length if not self.use_past else 1
					snake_case							=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_ )
					snake_case							=					{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
					snake_case							=					dict(**lowercase_,						**lowercase_ )

					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							=					common_inputs['input_ids'].shape
						snake_case							=					common_inputs['decoder_input_ids'].shape[1]
						snake_case			,			snake_case							=					self.num_attention_heads
						snake_case							=					(
						    batch,
						    num_encoder_attention_heads,
						    encoder_seq_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)
						snake_case							=					decoder_seq_length
						snake_case							=					(
						    batch,
						    num_decoder_attention_heads,
						    decoder_past_length,
						    self._config.hidden_size // num_decoder_attention_heads,
						)
						snake_case							=					torch.cat(
						    [common_inputs['decoder_attention_mask'], torch.ones(lowercase_,						lowercase_ )],						dim=1 )
						snake_case							=					[]
						snake_case			,			snake_case							=					self.num_layers

						for _ in range(lowercase_ ):
							common_inputs["past_key_values"].append(
							    (
							        torch.zeros(lowercase_ ),
							        torch.zeros(lowercase_ ),
							        torch.zeros(lowercase_ ),
							        torch.zeros(lowercase_ ),
							    ) )
					return common_inputs

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ = -1,						lowercase_ = -1,						lowercase_ = False,						lowercase_ = None,						)    ->							Mapping[str, Any]:
					snake_case							=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_ )

					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							=					common_inputs['input_ids'].shape
						snake_case							=					seqlen
						snake_case			,			snake_case							=					self.num_layers
						snake_case			,			snake_case							=					self.num_attention_heads
						snake_case							=					(
						    batch,
						    num_encoder_attention_heads,
						    past_key_values_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)
						snake_case							=					common_inputs['attention_mask'].dtype
						snake_case							=					torch.cat(
						    [common_inputs['attention_mask'], torch.ones(lowercase_,						lowercase_,						dtype=lowercase_ )],						dim=1 )
						snake_case							=					[
						    (torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ )
						]
					return common_inputs

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ = -1,						lowercase_ = -1,						lowercase_ = False,						lowercase_ = None,						)    ->							Mapping[str, Any]:
					# Copied from OnnxConfig.generate_dummy_inputs
					# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					snake_case							=					compute_effective_axis_dimension(
					    lowercase_,						fixed_dimension=OnnxConfig.default_fixed_batch,						num_token_to_add=0 )

					# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
					snake_case							=					tokenizer.num_special_tokens_to_add(lowercase_ )
					snake_case							=					compute_effective_axis_dimension(
					    lowercase_,						fixed_dimension=OnnxConfig.default_fixed_sequence,						num_token_to_add=lowercase_ )

					# Generate dummy inputs according to compute batch and sequence
					snake_case							=					[' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
					snake_case							=					dict(tokenizer(lowercase_,						return_tensors=lowercase_ ) )
					return common_inputs

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ = -1,						lowercase_ = -1,						lowercase_ = False,						lowercase_ = None,						)    ->							Mapping[str, Any]:
					if self.task in ["default", "seq2seq-lm"]:
						snake_case							=					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
						    lowercase_,						batch_size=lowercase_,						seq_length=lowercase_,						is_pair=lowercase_,						framework=lowercase_ )

					elif self.task == "causal-lm":
						snake_case							=					self._generate_dummy_inputs_for_causal_lm(
						    lowercase_,						batch_size=lowercase_,						seq_length=lowercase_,						is_pair=lowercase_,						framework=lowercase_ )
					else:
						snake_case							=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    lowercase_,						batch_size=lowercase_,						seq_length=lowercase_,						is_pair=lowercase_,						framework=lowercase_ )

					return common_inputs

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_ )    ->							List[str]:
					if self.task in ["default", "seq2seq-lm"]:
						snake_case							=					super()._flatten_past_key_values_(lowercase_,						lowercase_,						lowercase_,						lowercase_ )
					else:
						snake_case							=					super(lowercase_,						self )._flatten_past_key_values_(
						    lowercase_,						lowercase_,						lowercase_,						lowercase_ )






				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ )    ->							List[str]:
					if direction not in ["inputs", "outputs"]:
						raise ValueError(F'''direction must either be "inputs" or "outputs", but {direction} was given''' )

					snake_case							=					'past_key_values' if direction == 'inputs' else 'present'
					snake_case			,			snake_case							=					self.num_layers

					snake_case							=					'past_encoder_sequence'
					snake_case							=					'past_decoder_sequence' if direction == 'inputs' else 'past_decoder_sequence + sequence'

					for i in range(lowercase_ ):
						snake_case							=					{0: 'batch', 2: decoder_sequence}
						snake_case							=					{0: 'batch', 2: decoder_sequence}
						snake_case							=					{0: 'batch', 2: encoder_sequence}
						snake_case							=					{0: 'batch', 2: encoder_sequence}













'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

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


__A   :							int     	=						logging.get_logger(__name__)






class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     _snake_case							   =	['input_values', 'attention_mask']






     def __init__(self     :						Optional[int]					,					lowercase__     :						Tuple=1					,					lowercase__     :						Optional[int]=16000					,					lowercase__     :						Any=128					,					lowercase__     :						Optional[Any]=1024					,					lowercase__     :						int=0.0					,					lowercase__     :						List[str]=True					,					lowercase__     :						Dict=-4.2_67_73_93					,					lowercase__     :						Tuple=4.5_68_99_74					,					lowercase__     :						Optional[int]=False					,					**lowercase__     :						Optional[int]					,					)	->							Tuple:
           super().__init__(feature_size=lowercase__					,					sampling_rate=lowercase__					,					padding_value=lowercase__					,					**lowercase__       )
           __A		 =   num_mel_bins
           __A		 =   max_length
           __A		 =   do_normalize
           __A		 =   mean
           __A		 =   std
           __A		 =   return_attention_mask






     def      _UpperCamelCase (self     :						List[Any]					,					lowercase__     :						np.ndarray					,					lowercase__     :						int					,					)	->							np.ndarray:
           __A		 =   torch.from_numpy(lowercase__       ).unsqueeze(0       )
           __A		 =   ta_kaldi.fbank(
               lowercase__					,					htk_compat=lowercase__					,					sample_frequency=self.sampling_rate					,					use_energy=lowercase__					,					window_type="hanning"					,					num_mel_bins=self.num_mel_bins					,					dither=0.0					,					frame_shift=10					,					)

           __A		 =   fbank.shape[0]
           __A		 =   max_length - n_frames

           # pad or truncate, depending on difference
           if difference > 0:
                 __A		 =   torch.nn.ZeroPadad((0, 0, 0, difference)       )
                 __A		 =   pad_module(lowercase__       )
           elif difference < 0:
                 __A		 =   fbank[0:max_length, :]

           __A		 =   fbank.numpy()

           return fbank






     def      _UpperCamelCase (self     :						int					,					lowercase__     :						np.ndarray       )	->							np.ndarray:
           return (input_values - (self.mean)) / (self.std * 2)






     def __call__(self     :						str					,					lowercase__     :						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,					lowercase__     :						Optional[int] = None					,					lowercase__     :						Optional[Union[str, TensorType]] = None					,					**lowercase__     :						List[Any]					,					)	->							BatchFeature:

           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 `raw_speech` 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."       )

           __A		 =   isinstance(lowercase__					,					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}"       )
           __A		 =   is_batched_numpy or (
               isinstance(lowercase__					,					(list, tuple)       ) and (isinstance(raw_speech[0]					,					(np.ndarray, tuple, list)       ))
           )

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

           # always return batch
           if not is_batched:
                 __A		 =   [raw_speech]

           # extract fbank features and pad/truncate to max_length
           __A		 =   [self._extract_fbank_features(lowercase__					,					max_length=self.max_length       ) for waveform in raw_speech]

           # convert into BatchFeature
           __A		 =   BatchFeature({"input_values": features}       )

           # make sure list is in array format
           __A		 =   padded_inputs.get("input_values"       )
           if isinstance(input_values[0]					,					lowercase__       ):
                 __A		 =   [np.asarray(lowercase__					,					dtype=np.floataa       ) for feature in input_values]

           # normalization
           if self.do_normalize:
                 __A		 =   [self.normalize(lowercase__       ) for feature in input_values]

           if return_tensors is not None:
                 __A		 =   padded_inputs.convert_to_tensors(lowercase__       )

           return padded_inputs


