




from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      ="EncodecFeatureExtractor"
						lowerCAmelCase__			      =("T5Tokenizer", "T5TokenizerFast")



						def __init__(			self	:   Optional[int]					,							snake_case__	:   List[Any]					,							snake_case__	:   int  ):

								"""simple docstring"""

								super().__init__(snake_case__					,							snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							self.feature_extractor
								SCREAMING_SNAKE_CASE_					      =							False



						def 	__a					(			self	:   int					,							snake_case__	:   Optional[int]=None					,							snake_case__	:   Optional[Any]=None					,							snake_case__	:   Tuple=True  ):

								"""simple docstring"""

								return self.tokenizer.get_decoder_prompt_ids(task=snake_case__					,							language=snake_case__					,							no_timestamps=snake_case__  )



						def __call__(			self	:   Tuple					,							*snake_case__	:   Optional[int]					,							**snake_case__	:   Optional[int]  ):

								"""simple docstring"""

								if self._in_target_context_manager:
										return self.current_processor(*snake_case__					,							**snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							kwargs.pop('audio'					,							snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							kwargs.pop('sampling_rate'					,							snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							kwargs.pop('text'					,							snake_case__  )
								if len(snake_case__  ) > 0:
										SCREAMING_SNAKE_CASE_					      =							args[0]
										SCREAMING_SNAKE_CASE_					      =							args[1:]

								if audio is None and text is None:
										raise ValueError('You need to specify either an `audio` or `text` input to process.'  )

								if text is not None:
										SCREAMING_SNAKE_CASE_					      =							self.tokenizer(snake_case__					,							**snake_case__  )

								if audio is not None:
										SCREAMING_SNAKE_CASE_					      =							self.feature_extractor(snake_case__					,							*snake_case__					,							sampling_rate=snake_case__					,							**snake_case__  )

								if audio is None:
										return inputs

								elif text is None:
										return audio_inputs

								else:
										SCREAMING_SNAKE_CASE_					      =							audio_inputs['input_values']
										if "padding_mask" in audio_inputs:
												SCREAMING_SNAKE_CASE_					      =							audio_inputs['padding_mask']
										return inputs



						def 	__a					(			self	:   Any					,							*snake_case__	:   int					,							**snake_case__	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							kwargs.pop('audio'					,							snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							kwargs.pop('padding_mask'					,							snake_case__  )

								if len(snake_case__  ) > 0:
										SCREAMING_SNAKE_CASE_					      =							args[0]
										SCREAMING_SNAKE_CASE_					      =							args[1:]

								if audio_values is not None:
										return self._decode_audio(snake_case__					,							padding_mask=snake_case__  )
								else:
										return self.tokenizer.batch_decode(*snake_case__					,							**snake_case__  )



						def 	__a					(			self	:   List[str]					,							*snake_case__	:   List[Any]					,							**snake_case__	:   int  ):

								"""simple docstring"""

								return self.tokenizer.decode(*snake_case__					,							**snake_case__  )






						def 	__a					(			self	:   Optional[Any]					,							snake_case__	:   int					,							snake_case__	:   Optional = None  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							to_numpy(snake_case__  )
								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							audio_values.shape

								if padding_mask is None:
										return list(snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							to_numpy(snake_case__  )

								# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
								# token (so that the generated audio values are **not** treated as padded tokens)
								SCREAMING_SNAKE_CASE_					      =							seq_len - padding_mask.shape[-1]
								SCREAMING_SNAKE_CASE_					      =							1 - self.feature_extractor.padding_value
								SCREAMING_SNAKE_CASE_					      =							np.pad(snake_case__					,							((0, 0), (0, difference))					,							'constant'					,							constant_values=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							audio_values.tolist()
								for i in range(snake_case__  ):
										SCREAMING_SNAKE_CASE_					      =							np.asarray(audio_values[i]  )[
										    padding_mask[i][None, :] != self.feature_extractor.padding_value
										]
										SCREAMING_SNAKE_CASE_					      =							sliced_audio.reshape(snake_case__					,							-1  )

								return audio_values





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
						from ...onnx.config import PatchingSpec
						from ...tokenization_utils_base import PreTrainedTokenizerBase


SCREAMING_SNAKE_CASE:     str									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Optional[int]									= {
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      ="longformer"



						def __init__(			self	:   Optional[Any]					,							snake_case__	:   Union[List[int], int] = 5_12					,							snake_case__	:   int = 2					,							snake_case__	:   int = 1					,							snake_case__	:   int = 0					,							snake_case__	:   int = 2					,							snake_case__	:   int = 3_05_22					,							snake_case__	:   int = 7_68					,							snake_case__	:   int = 12					,							snake_case__	:   int = 12					,							snake_case__	:   int = 30_72					,							snake_case__	:   str = "gelu"					,							snake_case__	:   float = 0.1					,							snake_case__	:   float = 0.1					,							snake_case__	:   int = 5_12					,							snake_case__	:   int = 2					,							snake_case__	:   float = 0.02					,							snake_case__	:   float = 1e-12					,							snake_case__	:   bool = False					,							**snake_case__	:   Tuple					,							):

								"""simple docstring"""

								super().__init__(pad_token_id=snake_case__					,							**snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							attention_window
								SCREAMING_SNAKE_CASE_					      =							sep_token_id
								SCREAMING_SNAKE_CASE_					      =							bos_token_id
								SCREAMING_SNAKE_CASE_					      =							eos_token_id
								SCREAMING_SNAKE_CASE_					      =							vocab_size
								SCREAMING_SNAKE_CASE_					      =							hidden_size
								SCREAMING_SNAKE_CASE_					      =							num_hidden_layers
								SCREAMING_SNAKE_CASE_					      =							num_attention_heads
								SCREAMING_SNAKE_CASE_					      =							hidden_act
								SCREAMING_SNAKE_CASE_					      =							intermediate_size
								SCREAMING_SNAKE_CASE_					      =							hidden_dropout_prob
								SCREAMING_SNAKE_CASE_					      =							attention_probs_dropout_prob
								SCREAMING_SNAKE_CASE_					      =							max_position_embeddings
								SCREAMING_SNAKE_CASE_					      =							type_vocab_size
								SCREAMING_SNAKE_CASE_					      =							initializer_range
								SCREAMING_SNAKE_CASE_					      =							layer_norm_eps
								SCREAMING_SNAKE_CASE_					      =							onnx_export



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):



						def __init__(			self	:   Dict					,							snake_case__	:   "PretrainedConfig"					,							snake_case__	:   str = "default"					,							snake_case__	:   "List[PatchingSpec]" = None  ):

								"""simple docstring"""

								super().__init__(snake_case__					,							snake_case__					,							snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							True



						@property
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								if self.task == "multiple-choice":
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch', 1: 'choice', 2: 'sequence'}
								else:
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch', 1: 'sequence'}
								return OrderedDict(
								    [
								        ('input_ids', dynamic_axis),
								        ('attention_mask', dynamic_axis),
								        ('global_attention_mask', dynamic_axis),
								    ]  )



						@property
						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							super().outputs
								if self.task == "default":
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch'}
								return outputs



						@property
						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								return 1e-4



						@property
						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								return max(super().default_onnx_opset					,							14  )






						def 	__a					(			self	:   List[str]					,							snake_case__	:   "PreTrainedTokenizerBase"					,							snake_case__	:   int = -1					,							snake_case__	:   int = -1					,							snake_case__	:   bool = False					,							snake_case__	:   Optional[TensorType] = None					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							super().generate_dummy_inputs(
								    preprocessor=snake_case__					,							batch_size=snake_case__					,							seq_length=snake_case__					,							is_pair=snake_case__					,							framework=snake_case__  )
								import torch

								# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
								# makes the export fail randomly
								SCREAMING_SNAKE_CASE_					      =							torch.zeros_like(inputs['input_ids']  )
								# make every second token global
								SCREAMING_SNAKE_CASE_					      =							1

								return inputs