

"""simple docstring"""

import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


SCREAMING_SNAKE_CASE_         =  logging.get_logger(__name__)  # pylint: disable=invalid-name


SCREAMING_SNAKE_CASE_         =  '''lfs-multipart-upload'''
class        _UpperCAmelCase							(						_a							):




      @staticmethod
      def       a_ (							lowercase_							)  ->   List[Any]:
          UpperCAmelCase       =   parser.add_parser(
              'lfs-enable-largefiles'				,    help=(
                  'Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB.'
              )				,    )
          enable_parser.add_argument('path'				,    type=SCREAMING_SNAKE_CASE__				,    help='Local path to repository you want to configure.'							)
          enable_parser.set_defaults(func=lambda lowercase_							: LfsEnableCommand(SCREAMING_SNAKE_CASE__							)							)

          UpperCAmelCase       =   parser.add_parser(
              SCREAMING_SNAKE_CASE__				,    help=(
                  'Deprecated: use `huggingface-cli` instead. '
                  'Command will get called by git-lfs, do not call it directly.'
              )				,    )
          upload_parser.set_defaults(func=lambda lowercase_							: LfsUploadCommand(SCREAMING_SNAKE_CASE__							)							)

class        _UpperCAmelCase							:




      def __init__(							self				,    lowercase_							)  ->   int:
          UpperCAmelCase       =   args




      def       a_ (							self							)  ->   Dict:
          warnings.warn(
              'Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead.'							)
          UpperCAmelCase       =   os.path.abspath(self.args.path							)
          if not os.path.isdir(SCREAMING_SNAKE_CASE__							):
              print('This does not look like a valid git repo.'							)
              exit(1							)
          subprocess.run(
              'git config lfs.customtransfer.multipart.path transformers-cli'.split()				,    check=SCREAMING_SNAKE_CASE__				,    cwd=SCREAMING_SNAKE_CASE__							)
          subprocess.run(
              F"git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}".split()				,    check=SCREAMING_SNAKE_CASE__				,    cwd=SCREAMING_SNAKE_CASE__				,    )
          print('Local repo set up for largefiles'							)




def       lowercase__  (   lowerCAmelCase			:   int	)	-> List[Any]:






    """simple docstring"""




    UpperCAmelCase       =   json.dumps(lowerCAmelCase	) + "\n"
    sys.stdout.write(lowerCAmelCase	)
    sys.stdout.flush()




def       lowercase__  (   )	-> int:






    """simple docstring"""




    UpperCAmelCase       =   json.loads(sys.stdin.readline().strip()	)

    if "terminate" in (msg.get('type'	), msg.get('event'	)):
        # terminate message received
        return None

    if msg.get('event'	) not in ("download", "upload"):
        logger.critical('Received unexpected message'	)
        sys.exit(1	)

    return msg
class        _UpperCAmelCase							(						_a							):




      def __init__(							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   int:
          UpperCAmelCase       =   filepath
          UpperCAmelCase       =   seek_from
          UpperCAmelCase       =   read_limit
          UpperCAmelCase       =   0




      def __enter__(							self							)  ->   Union[str, Any]:
          UpperCAmelCase       =   open(self.filepath				,    'rb'							)
          self.f.seek(self.seek_from							)
          return self




      def __len__(							self							)  ->   Any:
          UpperCAmelCase       =   os.fstat(self.f.fileno()							).st_size
          return min(self.read_limit				,    total_length - self.seek_from							)




      def       a_ (							self				,    lowercase_=-1							)  ->   Union[str, Any]:
          if self.n_seen >= self.read_limit:
              return b""
          UpperCAmelCase       =   self.read_limit - self.n_seen
          UpperCAmelCase       =   self.f.read(remaining_amount if n < 0 else min(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__							)							)
          self.n_seen += len(SCREAMING_SNAKE_CASE__							)
          return data




      def __iter__(							self							)  ->   str:
          yield self.read(n=4 * 1_0_2_4 * 1_0_2_4							)




      def __exit__(							self				,    *lowercase_							)  ->   Union[str, Any]:
          self.f.close()

class        _UpperCAmelCase							:




      def __init__(							self				,    lowercase_							)  ->   Optional[int]:
          UpperCAmelCase       =   args




      def       a_ (							self							)  ->   int:
          # Immediately after invoking a custom transfer process, git-lfs
          # sends initiation data to the process over stdin.
          # This tells the process useful information about the configuration.
          UpperCAmelCase       =   json.loads(sys.stdin.readline().strip()							)
          if not (init_msg.get('event'							) == "init" and init_msg.get('operation'							) == "upload"):
              write_msg({'error': {'code': 3_2, 'message': 'Wrong lfs init operation'}}							)
              sys.exit(1							)

          # The transfer process should use the information it needs from the
          # initiation structure, and also perform any one-off setup tasks it
          # needs to do. It should then respond on stdout with a simple empty
          # confirmation structure, as follows:
          write_msg({}							)

          # After the initiation exchange, git-lfs will send any number of
          # transfer requests to the stdin of the transfer process, in a serial sequence.
          while True:
              UpperCAmelCase       =   read_msg()
              if msg is None:
                  # When all transfers have been processed, git-lfs will send
                  # a terminate event to the stdin of the transfer process.
                  # On receiving this message the transfer process should
                  # clean up and terminate. No response is expected.
                  sys.exit(0							)

              UpperCAmelCase       =   msg["oid"]
              UpperCAmelCase       =   msg["path"]
              UpperCAmelCase       =   msg["action"]["href"]
              UpperCAmelCase       =   msg["action"]["header"]
              UpperCAmelCase       =   int(header.pop('chunk_size'							)							)
              UpperCAmelCase       =   list(header.values()							)

              UpperCAmelCase       =   []
              for i, presigned_url in enumerate(SCREAMING_SNAKE_CASE__							):
                  with FileSlice(SCREAMING_SNAKE_CASE__				,    seek_from=i * chunk_size				,    read_limit=SCREAMING_SNAKE_CASE__							) as data:
                      UpperCAmelCase       =   requests.put(SCREAMING_SNAKE_CASE__				,    data=SCREAMING_SNAKE_CASE__							)
                      r.raise_for_status()
                      parts.append(
                          {
                              'etag': r.headers.get('etag'							),
                              'partNumber': i + 1,
                          }							)
                      # In order to support progress reporting while data is uploading / downloading,
                      # the transfer process should post messages to stdout
                      write_msg(
                          {
                              'event': 'progress',
                              'oid': oid,
                              'bytesSoFar': (i + 1) * chunk_size,
                              'bytesSinceLast': chunk_size,
                          }							)
                      # Not precise but that's ok.

              UpperCAmelCase       =   requests.post(
                  SCREAMING_SNAKE_CASE__				,    json={
                      'oid': oid,
                      'parts': parts,
                  }				,    )
              r.raise_for_status()

              write_msg({'event': 'complete', 'oid': oid}							)





"""simple docstring"""

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

SCREAMING_SNAKE_CASE_         =  logging.get_logger(__name__)




def       lowercase__  (   lowerCAmelCase			:   bytes	,			lowerCAmelCase			:   int	)	-> np.array:






    """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(SCREAMING_SNAKE_CASE_							)
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):




      def __init__(							self				,    *lowercase_				,    **lowercase_							)  ->   List[Any]:
          # Default, might be overriden by the model.config.
          UpperCAmelCase       =   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				,    lowercase_				,    **lowercase_				,    )  ->   Tuple:
          return super().__call__(lowercase_				,    **lowercase_							)




      def       a_ (							self				,    lowercase_=None				,    **lowercase_							)  ->   Optional[Any]:
          # 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       a_ (							self				,    lowercase_							)  ->   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
                  UpperCAmelCase       =   requests.get(lowercase_							).content
              else:
                  with open(lowercase_				,    'rb'							) as f:
                      UpperCAmelCase       =   f.read()

          if isinstance(lowercase_				,    lowercase_							):
              UpperCAmelCase       =   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'							)

              UpperCAmelCase       =   inputs.pop('raw'				,    lowercase_							)
              if _inputs is None:
                  # Remove path which will not be used from `datasets`.
                  inputs.pop('path'				,    lowercase_							)
                  UpperCAmelCase       =   inputs.pop('array'				,    lowercase_							)
              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(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'							)

          UpperCAmelCase       =   self.feature_extractor(
              lowercase_				,    sampling_rate=self.feature_extractor.sampling_rate				,    return_tensors='pt'							)
          return processed




      def       a_ (							self				,    lowercase_							)  ->   Any:
          UpperCAmelCase       =   self.model(**lowercase_							)
          return model_outputs




      def       a_ (							self				,    lowercase_				,    lowercase_=5							)  ->   int:
          UpperCAmelCase       =   model_outputs.logits[0].softmax(-1							)
          UpperCAmelCase     ,							UpperCAmelCase       =   probs.topk(lowercase_							)

          UpperCAmelCase       =   scores.tolist()
          UpperCAmelCase       =   ids.tolist()

          UpperCAmelCase       =   [{'score': score, 'label': self.model.config.idalabel[_id]} for score, _id in zip(lowercase_				,    lowercase_							)]

          return labels



