import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
      from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

UpperCamelCase													=		logging.get_logger(__name__)

def     lowercase_       (    _lowerCamelCase							: bytes	,			_lowerCamelCase							: int):
    lowercase__				:     Optional[Any]					      =       f'''{sampling_rate}'''
    lowercase__				:     Dict					      =       "1"
    lowercase__				:     List[str]					      =       "f32le"
    lowercase__				:     int					      =       [
        "ffmpeg",
        "-i",
        "pipe:0",
        "-ac",
        ac,
        "-ar",
        ar,
        "-f",
        format_for_conversion,
        "-hide_banner",
        "-loglevel",
        "quiet",
        "pipe:1",
    ]

    try:
        lowercase__				:     Tuple					      =       subprocess.Popen(_lowerCamelCase	,			stdin=subprocess.PIPE	,			stdout=subprocess.PIPE)
    except FileNotFoundError:
        raise ValueError("ffmpeg was not found but is required to load audio files from filename")
    lowercase__				:     int					      =       ffmpeg_process.communicate(_lowerCamelCase)
    lowercase__				:     Tuple					      =       output_stream[0]

    lowercase__				:     Dict					      =       np.frombuffer(_lowerCamelCase	,			np.floataa)
    if audio.shape[0] == 0:
        raise ValueError("Malformed soundfile")
    return audio



@add_end_docstrings(__A )
class 						snake_case_ (	__A ):






   def __init__(   self       :  str			,		*lowercase_       :  Tuple			,		**lowercase_       :  Union[str, Any]						)      ->		Optional[Any]:
       # Default, might be overriden by the model.config.
       lowercase__				:     Union[str, Any]					      =       5
       super().__init__(*lowercase_			,		**lowercase_						)

       if self.framework != "pt":
           raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''						)

       self.check_model_type(lowercase_						)






   def __call__(   self       :  Dict			,		lowercase_       :  Union[np.ndarray, bytes, str]			,		**lowercase_       :  Optional[Any]			,		)      ->		List[str]:
       return super().__call__(lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  int			,		lowercase_       :  List[Any]=None			,		**lowercase_       :  Dict						)      ->		Optional[int]:
       # No parameters on this pipeline right now
       lowercase__				:     Any					      =       {}
       if top_k is not None:
           if top_k > self.model.config.num_labels:
               lowercase__				:     List[Any]					      =       self.model.config.num_labels
           lowercase__				:     str					      =       top_k
       return {}, {}, postprocess_params






   def    __UpperCamelCase  (   self       :  Tuple			,		lowercase_       :  Dict						)      ->		List[str]:
       if isinstance(lowercase_			,		lowercase_						):
           if inputs.startswith("http://"						) or inputs.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
               lowercase__				:     Dict					      =       requests.get(lowercase_						).content
           else:
               with open(lowercase_			,		"rb"						) as f:
                   lowercase__				:     Union[str, Any]					      =       f.read()

       if isinstance(lowercase_			,		lowercase_						):
           lowercase__				:     Union[str, Any]					      =       ffmpeg_read(lowercase_			,		self.feature_extractor.sampling_rate						)

       if isinstance(lowercase_			,		lowercase_						):
           # Accepting `"array"` which is the key defined in `datasets` for
           # better integration
           if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
               raise ValueError(
                   "When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a "
                   "\"raw\" key containing the numpy array representing the audio and a \"sampling_rate\" key, "
                   "containing the sampling_rate associated with that array"						)

           lowercase__				:     Optional[int]					      =       inputs.pop("raw"			,		lowercase_						)
           if _inputs is None:
               # Remove path which will not be used from `datasets`.
               inputs.pop("path"			,		lowercase_						)
               lowercase__				:     Any					      =       inputs.pop("array"			,		lowercase_						)
           lowercase__				:     Optional[Any]					      =       inputs.pop("sampling_rate"						)
           lowercase__				:     Dict					      =       _inputs
           if in_sampling_rate != self.feature_extractor.sampling_rate:
               import torch

               if is_torchaudio_available():
                   from torchaudio import functional as F
               else:
                   raise ImportError(
                       "torchaudio is required to resample audio samples in AudioClassificationPipeline. "
                       "The torchaudio package can be installed through: `pip install torchaudio`."						)

               lowercase__				:     List[str]					      =       F.resample(
                   torch.from_numpy(lowercase_						)			,		lowercase_			,		self.feature_extractor.sampling_rate						).numpy()

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

       lowercase__				:     Optional[Any]					      =       self.feature_extractor(
           lowercase_			,		sampling_rate=self.feature_extractor.sampling_rate			,		return_tensors="pt"						)
       return processed






   def    __UpperCamelCase  (   self       :  Tuple			,		lowercase_       :  Dict						)      ->		Tuple:
       lowercase__				:     str					      =       self.model(**lowercase_						)
       return model_outputs







   def    __UpperCamelCase  (   self       :  Optional[int]			,		lowercase_       :  int			,		lowercase_       :  str=5						)      ->		Optional[int]:
       lowercase__				:     List[str]					      =       model_outputs.logits[0].softmax(-1						)
       lowercase__      ,     lowercase__				:     Union[str, Any]					      =       probs.topk(lowercase_						)

       lowercase__				:     Optional[int]					      =       scores.tolist()
       lowercase__				:     int					      =       ids.tolist()

       lowercase__				:     Optional[int]					      =       [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowercase_			,		lowercase_						)]

       return labels




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 						snake_case_ (	__A ):
   __A			:	Optional[Any]        =       ["image_processor", "tokenizer"]
   __A			:	Tuple        =       "LayoutLMv2ImageProcessor"
   __A			:	Optional[int]        =       ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")






   def __init__(   self       :  Tuple			,		lowercase_       :  List[str]=None			,		lowercase_       :  Dict=None			,		**lowercase_       :  List[str]						)      ->		int:
       lowercase__				:     Optional[Any]					      =       None
       if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."			,		lowercase_			,		)
           lowercase__				:     Any					      =       kwargs.pop("feature_extractor"						)

       lowercase__				:     List[str]					      =       image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
           raise ValueError("You need to specify an `image_processor`."						)
       if tokenizer is None:
           raise ValueError("You need to specify a `tokenizer`."						)

       super().__init__(lowercase_			,		lowercase_						)






   def __call__(   self       :  Union[str, Any]			,		lowercase_       :  Optional[Any]			,		lowercase_       :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,		lowercase_       :  Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None			,		lowercase_       :  Union[List[List[int]], List[List[List[int]]]] = None			,		lowercase_       :  Optional[Union[List[int], List[List[int]]]] = None			,		lowercase_       :  bool = True			,		lowercase_       :  Union[bool, str, PaddingStrategy] = False			,		lowercase_       :  Union[bool, str, TruncationStrategy] = False			,		lowercase_       :  Optional[int] = None			,		lowercase_       :  int = 0			,		lowercase_       :  Optional[int] = None			,		lowercase_       :  Optional[bool] = None			,		lowercase_       :  Optional[bool] = None			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = True			,		lowercase_       :  Optional[Union[str, TensorType]] = None			,		**lowercase_       :  List[str]			,		)      ->		BatchEncoding:
       # verify input
       if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."						)

       if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."						)

       if return_overflowing_tokens is True and return_offsets_mapping is False:
           raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."						)

       # first, apply the image processor
       lowercase__				:     List[Any]					      =       self.image_processor(images=lowercase_			,		return_tensors=lowercase_						)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(lowercase_			,		lowercase_						):
               lowercase__				:     List[Any]					      =       [text]  # add batch dimension (as the image processor always adds a batch dimension)
           lowercase__				:     Union[str, Any]					      =       features["words"]

       lowercase__				:     List[str]					      =       self.tokenizer(
           text=text if text is not None else features["words"]			,		text_pair=text_pair if text_pair is not None else None			,		boxes=boxes if boxes is not None else features["boxes"]			,		word_labels=lowercase_			,		add_special_tokens=lowercase_			,		padding=lowercase_			,		truncation=lowercase_			,		max_length=lowercase_			,		stride=lowercase_			,		pad_to_multiple_of=lowercase_			,		return_token_type_ids=lowercase_			,		return_attention_mask=lowercase_			,		return_overflowing_tokens=lowercase_			,		return_special_tokens_mask=lowercase_			,		return_offsets_mapping=lowercase_			,		return_length=lowercase_			,		verbose=lowercase_			,		return_tensors=lowercase_			,		**lowercase_			,		)

       # add pixel values
       lowercase__				:     Optional[int]					      =       features.pop("pixel_values"						)
       if return_overflowing_tokens is True:
           lowercase__				:     Tuple					      =       self.get_overflowing_images(lowercase_			,		encoded_inputs["overflow_to_sample_mapping"]						)
       lowercase__				:     str					      =       images

       return encoded_inputs






   def    __UpperCamelCase  (   self       :  Tuple			,		lowercase_       :  Optional[int]			,		lowercase_       :  Union[str, Any]						)      ->		Any:
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       lowercase__				:     str					      =       []
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]						)

       if len(lowercase_						) != len(lowercase_						):
           raise ValueError(
               "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
               F''' {len(lowercase_						)} and {len(lowercase_						)}'''						)

       return images_with_overflow






   def    __UpperCamelCase  (   self       :  Any			,		*lowercase_       :  Tuple			,		**lowercase_       :  List[Any]						)      ->		Union[str, Any]:
       return self.tokenizer.batch_decode(*lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  List[Any]			,		*lowercase_       :  Tuple			,		**lowercase_       :  Union[str, Any]						)      ->		List[Any]:
       return self.tokenizer.decode(*lowercase_			,		**lowercase_						)






   @property
   def    __UpperCamelCase  (   self       :  int						)      ->		Optional[Any]:
       return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






   @property
   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		Tuple:
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,		lowercase_			,		)
       return self.image_processor_class







   @property
   def    __UpperCamelCase  (   self       :  Dict						)      ->		List[Any]:
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,		lowercase_			,		)
       return self.image_processor



