




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

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


_lowerCAmelCase    : str		= logging.get_logger(__name__)

_lowerCAmelCase    : str		= {
    "microsoft/layoutlm-base-uncased": (
        "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"
    ),
    "microsoft/layoutlm-large-uncased": (
        "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"
    ),
}
class       _UpperCamelCase   (					lowerCAmelCase		):
      UpperCAmelCase_          =		"""layoutlm"""
      def __init__(      self :List[str]			,   lowerCamelCase :Any=3_0522			,   lowerCamelCase :Dict=768			,   lowerCamelCase :Optional[int]=12			,   lowerCamelCase :List[Any]=12			,   lowerCamelCase :Dict=3072			,   lowerCamelCase :str="gelu"			,   lowerCamelCase :Optional[int]=0.1			,   lowerCamelCase :str=0.1			,   lowerCamelCase :List[str]=512			,   lowerCamelCase :Tuple=2			,   lowerCamelCase :List[str]=0.02			,   lowerCamelCase :str=1e-12			,   lowerCamelCase :str=0			,   lowerCamelCase :Tuple="absolute"			,   lowerCamelCase :Optional[Any]=True			,   lowerCamelCase :Dict=1024			,   **lowerCamelCase :Optional[Any]			,   )  ->      Optional[int]:
       super().__init__(pad_token_id=lowerCamelCase			,   **lowerCamelCase   )
       UpperCAmelCase__							=    vocab_size
       UpperCAmelCase__							=    hidden_size
       UpperCAmelCase__							=    num_hidden_layers
       UpperCAmelCase__							=    num_attention_heads
       UpperCAmelCase__							=    hidden_act
       UpperCAmelCase__							=    intermediate_size
       UpperCAmelCase__							=    hidden_dropout_prob
       UpperCAmelCase__							=    attention_probs_dropout_prob
       UpperCAmelCase__							=    max_position_embeddings
       UpperCAmelCase__							=    type_vocab_size
       UpperCAmelCase__							=    initializer_range
       UpperCAmelCase__							=    layer_norm_eps
       UpperCAmelCase__							=    position_embedding_type
       UpperCAmelCase__							=    use_cache
       UpperCAmelCase__							=    max_ad_position_embeddings



class       _UpperCamelCase   (					lowerCAmelCase		):
      def __init__(      self :str			,   lowerCamelCase :PretrainedConfig			,   lowerCamelCase :str = "default"			,   lowerCamelCase :List[PatchingSpec] = None			,   )  ->      List[str]:
       super().__init__(lowerCamelCase			,   task=lowerCamelCase			,   patching_specs=lowerCamelCase   )
       UpperCAmelCase__							=    config.max_ad_position_embeddings - 1
      @property
      def 						UpperCAmelCase_    (      self :Optional[int]   )  ->      Mapping[str, Mapping[int, str]]:
       return OrderedDict(
           [
               ("input_ids", {0: "batch", 1: "sequence"}),
               ("bbox", {0: "batch", 1: "sequence"}),
               ("attention_mask", {0: "batch", 1: "sequence"}),
               ("token_type_ids", {0: "batch", 1: "sequence"}),
           ]   )







      def 						UpperCAmelCase_    (      self :Optional[Any]			,   lowerCamelCase :PreTrainedTokenizer			,   lowerCamelCase :int = -1			,   lowerCamelCase :int = -1			,   lowerCamelCase :bool = False			,   lowerCamelCase :Optional[TensorType] = None			,   )  ->      Mapping[str, Any]:
       UpperCAmelCase__							=    super().generate_dummy_inputs(
           lowerCamelCase			,   batch_size=lowerCamelCase			,   seq_length=lowerCamelCase			,   is_pair=lowerCamelCase			,   framework=lowerCamelCase   )

       # Generate a dummy bbox
       UpperCAmelCase__							=    [48, 84, 73, 128]

       if not framework == TensorType.PYTORCH:
        raise NotImplementedError("Exporting LayoutLM to ONNX is currently only supported for PyTorch."   )

       if not is_torch_available():
        raise ValueError("Cannot generate dummy inputs without PyTorch installed."   )
       import torch

       UpperCAmelCase__			,			UpperCAmelCase__							=    input_dict["input_ids"].shape
       UpperCAmelCase__							=    torch.tensor([*[box] * seq_length]   ).tile(lowerCamelCase			,   1			,   1   )
       return input_dict











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

_lowerCAmelCase    : List[str]		= logging.get_logger(__name__)



def     lowerCAmelCase		(		_lowerCAmelCase				: bytes    ,				_lowerCAmelCase				: int						):




 """simple docstring"""







 UpperCAmelCase__							=    F'''{sampling_rate}'''
 UpperCAmelCase__							=    "1"
 UpperCAmelCase__							=    "f32le"
 UpperCAmelCase__							=    [
     "ffmpeg",
     "-i",
     "pipe:0",
     "-ac",
     ac,
     "-ar",
     ar,
     "-f",
     format_for_conversion,
     "-hide_banner",
     "-loglevel",
     "quiet",
     "pipe:1",
 ]

 try:
  UpperCAmelCase__							=    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"						)
 UpperCAmelCase__							=    ffmpeg_process.communicate(_lowerCAmelCase						)
 UpperCAmelCase__							=    output_stream[0]

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


@add_end_docstrings(lowerCAmelCase		)
class       _UpperCamelCase   (					lowerCAmelCase		):
      def __init__(      self :Any			,   *lowerCamelCase :Any			,   **lowerCamelCase :Optional[Any]   )  ->      Optional[int]:
       # Default, might be overriden by the model.config.
       UpperCAmelCase__							=    5
       super().__init__(*lowerCamelCase			,   **lowerCamelCase   )

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

       self.check_model_type(lowerCamelCase   )
      def __call__(      self :Optional[Any]			,   lowerCamelCase :Union[np.ndarray, bytes, str]			,   **lowerCamelCase :List[Any]			,   )  ->      Tuple:
       return super().__call__(lowerCamelCase			,   **lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :str			,   lowerCamelCase :Dict=None			,   **lowerCamelCase :str   )  ->      int:
       # No parameters on this pipeline right now
       UpperCAmelCase__							=    {}
       if top_k is not None:
        if top_k > self.model.config.num_labels:
         UpperCAmelCase__							=    self.model.config.num_labels
        UpperCAmelCase__							=    top_k
       return {}, {}, postprocess_params
      def 						UpperCAmelCase_    (      self :Optional[Any]			,   lowerCamelCase :Dict   )  ->      Optional[int]:
       if isinstance(lowerCamelCase			,   lowerCamelCase   ):
        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
         UpperCAmelCase__							=    requests.get(lowerCamelCase   ).content
        else:
         with open(lowerCamelCase			,   "rb"   ) as f:
          UpperCAmelCase__							=    f.read()

       if isinstance(lowerCamelCase			,   lowerCamelCase   ):
        UpperCAmelCase__							=    ffmpeg_read(lowerCamelCase			,   self.feature_extractor.sampling_rate   )

       if isinstance(lowerCamelCase			,   lowerCamelCase   ):
        # 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"   )

        UpperCAmelCase__							=    inputs.pop("raw"			,   lowerCamelCase   )
        if _inputs is None:
         # Remove path which will not be used from `datasets`.
         inputs.pop("path"			,   lowerCamelCase   )
         UpperCAmelCase__							=    inputs.pop("array"			,   lowerCamelCase   )
        UpperCAmelCase__							=    inputs.pop("sampling_rate"   )
        UpperCAmelCase__							=    _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`."   )

         UpperCAmelCase__							=    F.resample(
             torch.from_numpy(lowerCamelCase   )			,   lowerCamelCase			,   self.feature_extractor.sampling_rate   ).numpy()

       if not isinstance(lowerCamelCase			,   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"   )

       UpperCAmelCase__							=    self.feature_extractor(
           lowerCamelCase			,   sampling_rate=self.feature_extractor.sampling_rate			,   return_tensors="pt"   )
       return processed
      def 						UpperCAmelCase_    (      self :Tuple			,   lowerCamelCase :Union[str, Any]   )  ->      List[Any]:
       UpperCAmelCase__							=    self.model(**lowerCamelCase   )
       return model_outputs







      def 						UpperCAmelCase_    (      self :List[str]			,   lowerCamelCase :Optional[Any]			,   lowerCamelCase :List[Any]=5   )  ->      Tuple:
       UpperCAmelCase__							=    model_outputs.logits[0].softmax(-1   )
       UpperCAmelCase__			,			UpperCAmelCase__							=    probs.topk(lowerCamelCase   )

       UpperCAmelCase__							=    scores.tolist()
       UpperCAmelCase__							=    ids.tolist()

       UpperCAmelCase__							=    [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase			,   lowerCamelCase   )]

       return labels





