
'''simple docstring'''





from typing import List, Optional

import numpy as np

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

class 						a   (				__lowerCamelCase    ):
    _snake_case		    = '''EncodecFeatureExtractor'''
    _snake_case		    = ('''T5Tokenizer''', '''T5TokenizerFast''')




    def __init__(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				str      ):
          super().__init__(a_,    a_      )
          snake_case       :     Optional[Any] =    self.feature_extractor
          snake_case       :     Optional[int] =    False




    def 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				List[str]=None,    SCREAMING_SNAKE_CASE_	:				int=None,    SCREAMING_SNAKE_CASE_	:				Tuple=True      ):
          return self.tokenizer.get_decoder_prompt_ids(task=a_,    language=a_,    no_timestamps=a_      )




    def __call__(							self	:				Tuple,    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				Dict      ):
          # For backward compatibility
          if self._in_target_context_manager:
                return self.current_processor(*a_,    **a_      )

          snake_case       :     str =    kwargs.pop('''audio''',    a_      )
          snake_case       :     List[str] =    kwargs.pop('''sampling_rate''',    a_      )
          snake_case       :     Optional[Any] =    kwargs.pop('''text''',    a_      )
          if len(a_      ) > 0:
                snake_case       :     int =    args[0]
                snake_case       :     str =    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:
                snake_case       :     Dict =    self.tokenizer(a_,    **a_      )

          if audio is not None:
                snake_case       :     Optional[Any] =    self.feature_extractor(a_,    *a_,    sampling_rate=a_,    **a_      )

          if audio is None:
                return inputs

          elif text is None:
                return audio_inputs

          else:
                snake_case       :     Dict =    audio_inputs["input_values"]
                if "padding_mask" in audio_inputs:
                      snake_case       :     int =    audio_inputs["padding_mask"]
                return inputs




    def 	__snake_case							(							self	:				List[Any],    *SCREAMING_SNAKE_CASE_	:				Union[str, Any],    **SCREAMING_SNAKE_CASE_	:				Union[str, Any]      ):
          snake_case       :     Dict =    kwargs.pop('''audio''',    a_      )
          snake_case       :     Optional[Any] =    kwargs.pop('''padding_mask''',    a_      )

          if len(a_      ) > 0:
                snake_case       :     Optional[int] =    args[0]
                snake_case       :     Optional[Any] =    args[1:]

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




    def 	__snake_case							(							self	:				str,    *SCREAMING_SNAKE_CASE_	:				List[str],    **SCREAMING_SNAKE_CASE_	:				int      ):
          return self.tokenizer.decode(*a_,    **a_      )




    def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				List[str] = None      ):
          snake_case       :     Any =    to_numpy(a_      )
          snake_case       :     List[str] =    audio_values.shape

          if padding_mask is None:
                return list(a_      )

          snake_case       :     Tuple =    to_numpy(a_      )

          # 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)
          snake_case       :     List[str] =    seq_len - padding_mask.shape[-1]
          snake_case       :     int =    1 - self.feature_extractor.padding_value
          snake_case       :     List[Any] =    np.pad(a_,    ((0, 0), (0, difference)),    '''constant''',    constant_values=a_      )

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

          return audio_values



'''simple docstring'''





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase    					=   {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

UpperCAmelCase    					=   {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}

class 						a   (				__magic_name__    ):
				_snake_case		    = VOCAB_FILES_NAMES
				_snake_case		    = PRETRAINED_VOCAB_FILES_MAP
				_snake_case		    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		    = ['''input_ids''', '''attention_mask''']
				_snake_case		    = GPTaTokenizer




				def __init__(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=None,    SCREAMING_SNAKE_CASE_	:				Any=None,    SCREAMING_SNAKE_CASE_	:				List[str]=None,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				Optional[int]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				Dict="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=False,    **SCREAMING_SNAKE_CASE_	:				Tuple,    ):
										super().__init__(
										    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    tokenizer_file=SCREAMING_SNAKE_CASE_,    unk_token=SCREAMING_SNAKE_CASE_,    bos_token=SCREAMING_SNAKE_CASE_,    eos_token=SCREAMING_SNAKE_CASE_,    add_prefix_space=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )

										snake_case       :     Optional[Any] =    kwargs.pop('''add_bos_token''',    SCREAMING_SNAKE_CASE_      )

										snake_case       :     Dict =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
										if pre_tok_state.get('''add_prefix_space''',    SCREAMING_SNAKE_CASE_      ) != add_prefix_space:
																snake_case       :     List[Any] =    getattr(SCREAMING_SNAKE_CASE_,    pre_tok_state.pop('''type'''      )      )
																snake_case       :     Dict =    add_prefix_space
																snake_case       :     List[Any] =    pre_tok_class(**SCREAMING_SNAKE_CASE_      )

										snake_case       :     Optional[Any] =    add_prefix_space




				def 	__snake_case							(							self	:				Optional[Any],    *SCREAMING_SNAKE_CASE_	:				Dict,    **SCREAMING_SNAKE_CASE_	:				List[str]      ):
										snake_case       :     str =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )
										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Union[str, Any],    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     int =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )

										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[str] = None      ):
										snake_case       :     List[Any] =    self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,    name=SCREAMING_SNAKE_CASE_      )
										return tuple(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				"Conversation"      ):
										snake_case       :     int =    []
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_,    add_special_tokens=SCREAMING_SNAKE_CASE_      ) + [self.eos_token_id]      )

										if len(SCREAMING_SNAKE_CASE_      ) > self.model_max_length:
																snake_case       :     str =    input_ids[-self.model_max_length :]
										return input_ids

