from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


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






@add_end_docstrings(UpperCamelCase_   )
class 				snake_case__     (   UpperCamelCase_   ):


 def __init__(				self  ,						**lowerCamelCase		):
        super().__init__(**_a		)

        if self.framework != "pt":
               raise ValueError(F"The {self.__class__} is only available in PyTorch."		)
        # No specific FOR_XXX available yet


 def __call__(				self  ,						lowerCamelCase  ,						**lowerCamelCase		):
        return super().__call__(_a  ,						**_a		)


 def 		a__ (				self  ,						**lowerCamelCase		):
        __a     				=  {}
        if "candidate_labels" in kwargs:
               __a     				=  kwargs["""candidate_labels"""]
        if "hypothesis_template" in kwargs:
               __a     				=  kwargs["""hypothesis_template"""]

        return preprocess_params, {}, {}


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=None  ,						lowerCamelCase="This is a sound of {}."		):


        if isinstance(_a  ,						_a		):
               if audio.startswith("http://"		) or audio.startswith("https://"		):
                      # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                      # like http_huggingface_co.png
                      __a     				=  requests.get(_a		).content
               else:
                      with open(_a  ,						"rb"		) as f:
                             __a     				=  f.read()

        if isinstance(_a  ,						_a		):
               __a     				=  ffmpeg_read(_a  ,						self.feature_extractor.sampling_rate		)

        if not isinstance(_a  ,						np.ndarray		):
               raise ValueError("We expect a numpy ndarray as input"		)
        if len(audio.shape		) != 1:
               raise ValueError("We expect a single channel audio input for ZeroShotAudioClassificationPipeline"		)

        __a     				=  self.feature_extractor(
            [audio]  ,						sampling_rate=self.feature_extractor.sampling_rate  ,						return_tensors="pt"		)
        __a     				=  candidate_labels
        __a     				=  [hypothesis_template.format(_a		) for x in candidate_labels]
        __a     				=  self.tokenizer(_a  ,						return_tensors=self.framework  ,						padding=_a		)
        __a     				=  [text_inputs]
        return inputs


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  model_inputs.pop("candidate_labels"		)
        __a     				=  model_inputs.pop("text_inputs"		)
        if isinstance(text_inputs[0]  ,						_a		):
               __a     				=  text_inputs[0]
        else:
               # Batching case.
               __a     				=  text_inputs[0][0]

        __a     				=  self.model(**_a  ,						**_a		)

        __a     				=  {
            """candidate_labels""": candidate_labels,
            """logits""": outputs.logits_per_audio,
        }
        return model_outputs






 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  model_outputs.pop("candidate_labels"		)
        __a     				=  model_outputs["""logits"""][0]

        if self.framework == "pt":
               __a     				=  logits.softmax(dim=0		)
               __a     				=  probs.tolist()
        else:
               raise ValueError("`tf` framework not supported."		)

        __a     				=  [
            {"""score""": score, """label""": candidate_label}
            for score, candidate_label in sorted(zip(_a  ,						_a		)  ,						key=lambda lowerCamelCase		: -x[0]		)
        ]
        return result

"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_barthez import BarthezTokenizer
else:
   SCREAMING_SNAKE_CASE__:Optional[Any]			=	None

SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:int			=	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__:Dict			=	{
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

SCREAMING_SNAKE_CASE__:List[str]			=	"""▁"""






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			str           =		VOCAB_FILES_NAMES
 _snake_case   :			Optional[Any]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			str           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			Dict           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			str           =		BarthezTokenizer


 def __init__(				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase="<pad>"  ,						lowerCamelCase="<mask>"  ,						**lowerCamelCase  ,						):
        # Mask token behave like a normal word, i.e. include the space before it
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else mask_token

        super().__init__(
            lowerCamelCase  ,						tokenizer_file=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						unk_token=lowerCamelCase  ,						sep_token=lowerCamelCase  ,						cls_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						mask_token=lowerCamelCase  ,						**lowerCamelCase  ,						)

        __a     				=  vocab_file
        __a     				=  False if not self.vocab_file else True


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):

        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __a     				=  [self.cls_token_id]
        __a     				=  [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __a     				=  [self.sep_token_id]
        __a     				=  [self.cls_token_id]

        if token_ids_a is None:
               return len(cls + token_ids_a + sep		) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."		)

        if not os.path.isdir(lowerCamelCase		):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
               return
        __a     				=  os.path.join(
            lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

        if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCamelCase		):
               copyfile(self.vocab_file  ,						lowerCamelCase		)

        return (out_vocab_file,)
