
'''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

A           =   logging.get_logger(__name__)
def   snake_case_       (							_a     :							bytes ,					_a     :							int			):
      snake_case_						       =							F'''{sampling_rate}'''
      snake_case_						       =							"1"
      snake_case_						       =							"f32le"
      snake_case_						       =							[
          "ffmpeg",
          "-i",
          "pipe:0",
          "-ac",
          ac,
          "-ar",
          ar,
          "-f",
          format_for_conversion,
          "-hide_banner",
          "-loglevel",
          "quiet",
          "pipe:1",
      ]

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

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




@add_end_docstrings(UpperCamelCase       )
class  lowerCAmelCase__   (       UpperCamelCase       ):



  '''simple docstring'''






  def __init__(self  : List[Any]	, *lowerCAmelCase_  : Tuple	, **lowerCAmelCase_  : Union[str, Any]    )   ->       Optional[Any]:
        # Default, might be overriden by the model.config.
        snake_case_						       =							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  : Any	, lowerCAmelCase_  : Union[np.ndarray, bytes, str]	, **lowerCAmelCase_  : Optional[Any]	, )   ->       List[str]:
        return super().__call__(lowerCAmelCase_	, **lowerCAmelCase_    )

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

  def 		_lowerCamelCase	(self  : Optional[Any]	, lowerCAmelCase_  : Optional[int]    )   ->       Optional[Any]:
        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
                    snake_case_						       =							requests.get(lowerCAmelCase_    ).content
              else:
                    with open(lowerCAmelCase_	, "rb"    ) as f:
                          snake_case_						       =							f.read()

        if isinstance(lowerCAmelCase_	, lowerCAmelCase_    ):
              snake_case_						       =							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"    )

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

                    snake_case_						       =							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"    )

        snake_case_						       =							self.feature_extractor(
            lowerCAmelCase_	, sampling_rate=self.feature_extractor.sampling_rate	, return_tensors="pt"    )
        return processed

  def 		_lowerCamelCase	(self  : int	, lowerCAmelCase_  : int    )   ->       int:
        snake_case_						       =							self.model(**lowerCAmelCase_    )
        return model_outputs


  def 		_lowerCamelCase	(self  : Any	, lowerCAmelCase_  : Tuple	, lowerCAmelCase_  : Any=5    )   ->       Optional[Any]:
        snake_case_						       =							model_outputs.logits[0].softmax(-1    )
        snake_case_       ,	snake_case_						       =							probs.topk(lowerCAmelCase_    )

        snake_case_						       =							scores.tolist()
        snake_case_						       =							ids.tolist()

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

        return labels



'''simple docstring'''





import json
import os
import pickle
import subprocess
from hashlib import mda
from pathlib import Path
from tempfile import gettempdir
from textwrap import dedent
from types import CodeType, FunctionType
from unittest import TestCase
from unittest.mock import patch

import pytest
from multiprocess import Pool
from packaging import version

import datasets
from datasets.fingerprint import Hasher, fingerprint_transform
from datasets.table import InMemoryTable

from .utils import (
    require_regex,
    require_spacy,
    require_spacy_model,
    require_tiktoken,
    require_torch,
    require_transformers,
)



class  lowerCAmelCase__   :



  '''simple docstring'''






  def __init__(self  : int	, lowerCAmelCase_  : Optional[Any]    )   ->       Tuple:
        snake_case_						       =							foo


  def __call__(self  : List[Any]    )   ->       Optional[int]:
        return self.foo



class  lowerCAmelCase__   (       datasets.Dataset       ):



  '''simple docstring'''






  @fingerprint_transform(inplace=lowerCAmelCase_    )
  def 		_lowerCamelCase	(self  : Tuple	, lowerCAmelCase_  : Any	, *lowerCAmelCase_  : Union[str, Any]	, **lowerCAmelCase_  : Any    )   ->       str:
        return DatasetChild(self.data	, fingerprint=lowerCAmelCase_    )


  @fingerprint_transform(inplace=lowerCAmelCase_    )
  def 		_lowerCamelCase	(self  : str	, lowerCAmelCase_  : Optional[Any]	, *lowerCAmelCase_  : List[Any]	, **lowerCAmelCase_  : Any    )   ->       List[str]:
        return DatasetChild(self.data	, fingerprint=lowerCAmelCase_    )



class  lowerCAmelCase__   :



  '''simple docstring'''






  def __init__(self  : int	, lowerCAmelCase_  : List[str]    )   ->       Dict:
        snake_case_						       =							callable

  def __call__(self  : List[str]	, *lowerCAmelCase_  : Any	, **lowerCAmelCase_  : Tuple    )   ->       Dict:
        if self.callable is not None:
              return self.callable(*lowerCAmelCase_	, **lowerCAmelCase_    )


  def __getstate__(self  : List[str]    )   ->       Tuple:
        raise pickle.PicklingError()



class  lowerCAmelCase__   (       UpperCamelCase       ):



  '''simple docstring'''






  @require_transformers
  @pytest.mark.integration
  def 		_lowerCamelCase	(self  : str    )   ->       str:
        from transformers import AutoTokenizer

        def encode(lowerCAmelCase_  : str    ):
              return tokenizer(lowerCAmelCase_    )

        # TODO: add hash consistency tests across sessions
        snake_case_						       =							AutoTokenizer.from_pretrained("bert-base-uncased"    )
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lambda lowerCAmelCase_    : tokenizer(lowerCAmelCase_    )    )    ).hexdigest()
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        snake_case_						       =							AutoTokenizer.from_pretrained("bert-base-cased"    )
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lambda lowerCAmelCase_    : tokenizer(lowerCAmelCase_    )    )    ).hexdigest()
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        snake_case_						       =							AutoTokenizer.from_pretrained("bert-base-uncased"    )
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lambda lowerCAmelCase_    : tokenizer(lowerCAmelCase_    )    )    ).hexdigest()
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
        self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
        self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
        self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )

  @require_transformers
  @pytest.mark.integration
  def 		_lowerCamelCase	(self  : Optional[Any]    )   ->       Optional[int]:
        from transformers import AutoTokenizer

        snake_case_						       =							AutoTokenizer.from_pretrained("gpt2"    )
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        tokenizer("Hello world !"    )  # call once to change the tokenizer's cache
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )


  @require_regex
  def 		_lowerCamelCase	(self  : Union[str, Any]    )   ->       Optional[int]:
        import regex

        snake_case_						       =							regex.Regex("foo"    )
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        snake_case_						       =							regex.Regex("bar"    )
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        snake_case_						       =							regex.Regex("foo"    )
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
        self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )



class  lowerCAmelCase__   (       UpperCamelCase       ):



  '''simple docstring'''






  def 		_lowerCamelCase	(self  : Dict    )   ->       Optional[Any]:
        def func():
              return foo

        snake_case_						       =							[0]
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        snake_case_						       =							[1]
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        snake_case_						       =							[0]
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
        self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )

  def 		_lowerCamelCase	(self  : str    )   ->       Tuple:
        def func():
              pass

        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()

        def func():
              pass

        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )

  def 		_lowerCamelCase	(self  : List[Any]    )   ->       Union[str, Any]:
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(Foo([0]    )    )    ).hexdigest()
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(Foo([1]    )    )    ).hexdigest()
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(Foo([0]    )    )    ).hexdigest()
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
        self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )

  def 		_lowerCamelCase	(self  : Union[str, Any]    )   ->       int:
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(Foo([0]    ).__call__    )    ).hexdigest()
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(Foo([1]    ).__call__    )    ).hexdigest()
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(Foo([0]    ).__call__    )    ).hexdigest()
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
        self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )

  def 		_lowerCamelCase	(self  : int    )   ->       Optional[Any]:
        snake_case_						       =							(
            "co_argcount",
            "co_kwonlyargcount",
            "co_nlocals",
            "co_stacksize",
            "co_flags",
            "co_code",
            "co_consts",
            "co_names",
            "co_varnames",
            "co_filename",
            "co_name",
            "co_firstlineno",
            "co_lnotab",
            "co_freevars",
            "co_cellvars",
        )

        def _create_code_pyaa(*lowerCAmelCase_  : Any    ):
              if hasattr(args[-3]	, "encode"    ):
                    snake_case_						       =							list(lowerCAmelCase_    )
                    snake_case_						       =							args[-3].encode()  # co_lnotab
                    snake_case_						       =							args[-10].encode()  # co_code
              if hasattr(lowerCAmelCase_	, "co_posonlyargcount"    ):
                    if len(lowerCAmelCase_    ) == 16:
                          return CodeType(*lowerCAmelCase_    )
                    elif len(lowerCAmelCase_    ) == 15:
                          return CodeType(args[0]	, 0	, *args[1:]    )
                    return CodeType(args[0]	, 0	, 0	, *args[1:]    )
              elif hasattr(lowerCAmelCase_	, "co_kwonlyargcount"    ):
                    if len(lowerCAmelCase_    ) == 16:
                          return CodeType(args[0]	, *args[2:]    )
                    elif len(lowerCAmelCase_    ) == 15:
                          return CodeType(*lowerCAmelCase_    )
                    return CodeType(args[0]	, 0	, *args[1:]    )
              if len(lowerCAmelCase_    ) == 16:
                    return CodeType(args[0]	, *args[3:]    )
              elif len(lowerCAmelCase_    ) == 15:
                    return CodeType(args[0]	, *args[2:]    )
              return CodeType(*lowerCAmelCase_    )

        def create_ipython_func(lowerCAmelCase_  : Tuple	, lowerCAmelCase_  : Any    ):
              def func():
                    return returned_obj

              snake_case_						       =							func.__code__
              # Use _create_code from dill in order to make it work for different python versions
              if datasets.config.PY_VERSION < version.parse("3.8"    ):
                    snake_case_						       =							_create_code_pyaa(
                        *[getattr(lowerCAmelCase_	, lowerCAmelCase_    ) if k != "co_filename" else co_filename for k in code_args_pyaa]    )
              else:
                    snake_case_						       =							code.replace(co_filename=lowerCAmelCase_    )
              return FunctionType(lowerCAmelCase_	, func.__globals__	, func.__name__	, func.__defaults__	, func.__closure__    )

        snake_case_       ,	snake_case_						       =							"<ipython-input-2-e0383a102aae>", [0]
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(create_ipython_func(lowerCAmelCase_	, lowerCAmelCase_    )    )    ).hexdigest()
        snake_case_       ,	snake_case_						       =							"<ipython-input-2-e0383a102aae>", [1]
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(create_ipython_func(lowerCAmelCase_	, lowerCAmelCase_    )    )    ).hexdigest()
        snake_case_       ,	snake_case_						       =							"<ipython-input-5-713f6613acf3>", [0]
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(create_ipython_func(lowerCAmelCase_	, lowerCAmelCase_    )    )    ).hexdigest()
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
        self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )

        snake_case_       ,	snake_case_						       =							os.path.join(gettempdir()	, "ipykernel_12345"	, "321456789.py"    ), [0]
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(create_ipython_func(lowerCAmelCase_	, lowerCAmelCase_    )    )    ).hexdigest()
        snake_case_       ,	snake_case_						       =							os.path.join(gettempdir()	, "ipykernel_12345"	, "321456789.py"    ), [1]
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(create_ipython_func(lowerCAmelCase_	, lowerCAmelCase_    )    )    ).hexdigest()
        snake_case_       ,	snake_case_						       =							os.path.join(gettempdir()	, "ipykernel_12345"	, "654123987.py"    ), [0]
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(create_ipython_func(lowerCAmelCase_	, lowerCAmelCase_    )    )    ).hexdigest()
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
        self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )


  def 		_lowerCamelCase	(self  : int    )   ->       List[Any]:
        snake_case_       ,	snake_case_						       =							[0], [1]

        def func():
              return foo, bar

        snake_case_						       =							"__main__"

        def globalvars_mocka_side_effect(lowerCAmelCase_  : str	, *lowerCAmelCase_  : Tuple	, **lowerCAmelCase_  : List[Any]    ):
              return {"foo": foo, "bar": bar}

        def globalvars_mocka_side_effect(lowerCAmelCase_  : List[str]	, *lowerCAmelCase_  : List[str]	, **lowerCAmelCase_  : Optional[Any]    ):
              return {"bar": bar, "foo": foo}

        with patch("dill.detect.globalvars"	, side_effect=lowerCAmelCase_    ) as globalvars_mocka:
              snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
              self.assertGreater(globalvars_mocka.call_count	, 0    )
        with patch("dill.detect.globalvars"	, side_effect=lowerCAmelCase_    ) as globalvars_mocka:
              snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
              self.assertGreater(globalvars_mocka.call_count	, 0    )
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )



class  lowerCAmelCase__   (       UpperCamelCase       ):



  '''simple docstring'''






  def 		_lowerCamelCase	(self  : Union[str, Any]    )   ->       Tuple:
        snake_case_						       =							Hasher.hash("hello"    )
        snake_case_						       =							Hasher.hash("hello"    )
        snake_case_						       =							Hasher.hash("there"    )
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
        self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )

  def 		_lowerCamelCase	(self  : Any    )   ->       int:
        snake_case_						       =							Hasher.hash(Foo("hello"    )    )
        snake_case_						       =							Hasher.hash(Foo("hello"    )    )
        snake_case_						       =							Hasher.hash(Foo("there"    )    )
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
        self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )

  def 		_lowerCamelCase	(self  : List[Any]    )   ->       List[Any]:
        snake_case_						       =							Hasher()
        for x in ["hello", Foo("hello"    )]:
              hasher.update(lowerCAmelCase_    )
        snake_case_						       =							hasher.hexdigest()
        snake_case_						       =							Hasher()
        for x in ["hello", Foo("hello"    )]:
              hasher.update(lowerCAmelCase_    )
        snake_case_						       =							hasher.hexdigest()
        snake_case_						       =							Hasher()
        for x in ["there", Foo("there"    )]:
              hasher.update(lowerCAmelCase_    )
        snake_case_						       =							hasher.hexdigest()
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
        self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )

  def 		_lowerCamelCase	(self  : str    )   ->       List[Any]:
        with self.assertRaises(pickle.PicklingError    ):
              Hasher.hash(UnpicklableCallable(Foo("hello"    )    )    )

  def 		_lowerCamelCase	(self  : Dict    )   ->       Optional[int]:
        snake_case_						       =							"abc"
        snake_case_						       =							[string, string]  # two strings have the same ids
        snake_case_						       =							[string, string]
        snake_case_						       =							json.loads(f'''["{string}", "{string}"]'''    )  # two strings have different ids
        self.assertIs(obja[0]	, lowerCAmelCase_    )
        self.assertIs(obja[0]	, obja[1]    )
        self.assertIs(obja[0]	, lowerCAmelCase_    )
        self.assertIs(obja[0]	, obja[1]    )
        self.assertIsNot(obja[0]	, lowerCAmelCase_    )
        self.assertIsNot(obja[0]	, obja[1]    )
        snake_case_						       =							Hasher.hash(lowerCAmelCase_    )
        snake_case_						       =							Hasher.hash(lowerCAmelCase_    )
        snake_case_						       =							Hasher.hash(lowerCAmelCase_    )
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )

  @require_tiktoken
  def 		_lowerCamelCase	(self  : Union[str, Any]    )   ->       Union[str, Any]:
        import tiktoken

        snake_case_						       =							tiktoken.get_encoding("gpt2"    )
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        snake_case_						       =							tiktoken.get_encoding("r50k_base"    )
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        snake_case_						       =							tiktoken.get_encoding("gpt2"    )
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
        self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )

  @require_torch
  def 		_lowerCamelCase	(self  : Optional[int]    )   ->       str:
        import torch

        snake_case_						       =							torch.tensor([1.0]    )
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        snake_case_						       =							torch.tensor([2.0]    )
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        snake_case_						       =							torch.tensor([1.0]    )
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
        self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )


  @require_spacy
  @require_spacy_model("en_core_web_sm"    )
  @require_spacy_model("fr_core_news_sm"    )
  @pytest.mark.integration
  def 		_lowerCamelCase	(self  : int    )   ->       Union[str, Any]:
        import spacy

        snake_case_						       =							spacy.load("en_core_web_sm"    )
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        snake_case_						       =							spacy.load("fr_core_news_sm"    )
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        snake_case_						       =							spacy.load("en_core_web_sm"    )
        snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
        self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
        self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )

@pytest.mark.integration
def   snake_case_       (							_a     :							Path			):
      snake_case_						       =							tmp_path / "dir1"
      snake_case_						       =							tmp_path / "dir2"
      dira.mkdir()
      dira.mkdir()
      snake_case_						       =							"script.py"
      snake_case_						       =							dedent(
          "\n    from datasets.fingerprint import Hasher\n    def foo():\n        pass\n    print(Hasher.hash(foo))\n    "			)
      snake_case_						       =							dira / script_filename
      snake_case_						       =							dira / script_filename
      with script_patha.open("w"			) as f:
            f.write(_a			)
      with script_patha.open("w"			) as f:
            f.write(_a			)
      snake_case_						       =							subprocess.check_output(["python", str(_a			)]			)
      snake_case_						       =							subprocess.check_output(["python", str(_a			)]			)
      assert fingerprinta == fingerprinta
def   snake_case_       (							):
      snake_case_						       =							{"a": [0, 1, 2]}
      snake_case_						       =							DatasetChild(InMemoryTable.from_pydict(_a			)			)
      snake_case_						       =							dataset.funca()._fingerprint
      assert expected_fingerprint == dataset.funca()._fingerprint
      assert expected_fingerprint != dataset.funca()._fingerprint

      with Pool(2			) as p:
            assert expected_fingerprint == p.apply_async(dataset.funca			).get()._fingerprint
            assert expected_fingerprint != p.apply_async(dataset.funca			).get()._fingerprint
def   snake_case_       (							):
      snake_case_						       =							{"a": [0, 1, 2]}



      class  lowerCAmelCase__   (       datasets.Dataset       ):



        '''simple docstring'''






        @fingerprint_transform(inplace=lowerCAmelCase_    )
        def 		_lowerCamelCase	(self  : int	, lowerCAmelCase_  : str    )   ->       List[Any]:
              return DummyDatasetChild(self.data	, fingerprint=lowerCAmelCase_    )

      snake_case_						       =							DummyDatasetChild(InMemoryTable.from_pydict(_a			)			).func()



      class  lowerCAmelCase__   (       datasets.Dataset       ):



        '''simple docstring'''






        @fingerprint_transform(inplace=lowerCAmelCase_	, version="1.0.0"    )
        def 		_lowerCamelCase	(self  : Dict	, lowerCAmelCase_  : Any    )   ->       Any:
              return DummyDatasetChild(self.data	, fingerprint=lowerCAmelCase_    )

      snake_case_						       =							DummyDatasetChild(InMemoryTable.from_pydict(_a			)			).func()



      class  lowerCAmelCase__   (       datasets.Dataset       ):



        '''simple docstring'''






        @fingerprint_transform(inplace=lowerCAmelCase_	, version="2.0.0"    )
        def 		_lowerCamelCase	(self  : Union[str, Any]	, lowerCAmelCase_  : Any    )   ->       int:
              return DummyDatasetChild(self.data	, fingerprint=lowerCAmelCase_    )

      snake_case_						       =							DummyDatasetChild(InMemoryTable.from_pydict(_a			)			).func()

      assert len({fingeprint_no_version, fingeprint_a, fingeprint_a}			) == 3




def   snake_case_       (							):
      # AttributeError: module 'dill._dill' has no attribute 'stack'
      snake_case_						       =							Hasher()
      hasher.update(lambda _a			: x			)

