





'''simple docstring'''







import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


_lowercase       						=			'http://www.mocksite.com/file1.txt'
_lowercase       						=			'"text": ["foo", "foo"]'
_lowercase       						=			'6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'
class 			_lowercase						:
     _UpperCAmelCase										=					200
     _UpperCAmelCase										=					{'''Content-Length''': '''100'''}
     _UpperCAmelCase										=					{}







     def     UpperCamelCase			( self    ,   **A__    ) ->	str:
      return [bytes(A__    ,   '''utf-8'''    )]




def    __UpperCamelCase	(	*a    : List[str]					,   **a    : List[str]     ) ->List[Any]:
 return MockResponse()

@pytest.mark.parametrize('''urls_type'''					,   [str, list, dict]     )
def    __UpperCamelCase	(	a    : Tuple					,   a    : int					,   a    : List[str]     ) ->Optional[Any]:
 import requests

 monkeypatch.setattr(a					,   '''request'''					,   a     )

 snake_case				=			URL
 if issubclass(a					,   a     ):
  snake_case				=			url
 elif issubclass(a					,   a     ):
  snake_case				=			[url]
 elif issubclass(a					,   a     ):
  snake_case				=			{'''train''': url}
 snake_case				=			'''dummy'''
 snake_case				=			'''downloads'''
 snake_case				=			tmp_path
 snake_case				=			DownloadConfig(
     cache_dir=os.path.join(a					,   a     )					,   use_etag=a					,   )
 snake_case				=			DownloadManager(dataset_name=a					,   download_config=a     )
 snake_case				=			dl_manager.download(a     )
 snake_case				=			urls
 for downloaded_paths in [downloaded_paths]:
  if isinstance(a					,   a     ):
   snake_case				=			[downloaded_paths]
   snake_case				=			[urls]
  elif isinstance(a					,   a     ):
   assert "train" in downloaded_paths.keys()
   snake_case				=			downloaded_paths.values()
   snake_case				=			urls.values()
  assert downloaded_paths
  for downloaded_path, input_url in zip(a					,   a     ):
   assert downloaded_path == dl_manager.downloaded_paths[input_url]
   snake_case				=			Path(a     )
   snake_case				=			downloaded_path.parts
   assert parts[-1] == HASH
   assert parts[-2] == cache_subdir
   assert downloaded_path.exists()
   snake_case				=			downloaded_path.read_text()
   assert content == CONTENT
   snake_case				=			downloaded_path.with_suffix('''.json'''     )
   assert metadata_downloaded_path.exists()
   snake_case				=			json.loads(metadata_downloaded_path.read_text()     )
   assert metadata_content == {"url": URL, "etag": None}

@pytest.mark.parametrize('''paths_type'''					,   [str, list, dict]     )
def    __UpperCamelCase	(	a    : List[str]					,   a    : Optional[Any]					,   a    : Optional[int]     ) ->Tuple:
 snake_case				=			str(a     )
 if issubclass(a					,   a     ):
  snake_case				=			filename
 elif issubclass(a					,   a     ):
  snake_case				=			[filename]
 elif issubclass(a					,   a     ):
  snake_case				=			{'''train''': filename}
 snake_case				=			'''dummy'''
 snake_case				=			xz_file.parent
 snake_case				=			'''extracted'''
 snake_case				=			DownloadConfig(
     cache_dir=a					,   use_etag=a					,   )
 snake_case				=			DownloadManager(dataset_name=a					,   download_config=a     )
 snake_case				=			dl_manager.extract(a     )
 snake_case				=			paths
 for extracted_paths in [extracted_paths]:
  if isinstance(a					,   a     ):
   snake_case				=			[extracted_paths]
   snake_case				=			[paths]
  elif isinstance(a					,   a     ):
   assert "train" in extracted_paths.keys()
   snake_case				=			extracted_paths.values()
   snake_case				=			paths.values()
  assert extracted_paths
  for extracted_path, input_path in zip(a					,   a     ):
   assert extracted_path == dl_manager.extracted_paths[input_path]
   snake_case				=			Path(a     )
   snake_case				=			extracted_path.parts
   assert parts[-1] == hash_url_to_filename(a					,   etag=a     )
   assert parts[-2] == extracted_subdir
   assert extracted_path.exists()
   snake_case				=			extracted_path.read_text()
   snake_case				=			text_file.read_text()
   assert extracted_file_content == expected_file_content

def    __UpperCamelCase	(	a    : Any					,   a    : Dict     ) ->Tuple:
 assert path.endswith('''.jsonl'''     )
 for num_items, line in enumerate(a					,   start=1     ):
  snake_case				=			json.loads(line.decode('''utf-8'''     )     )
  assert item.keys() == {"col_1", "col_2", "col_3"}
 assert num_items == 4

@pytest.mark.parametrize('''archive_jsonl'''					,   ['''tar_jsonl_path''', '''zip_jsonl_path''']     )
def    __UpperCamelCase	(	a    : Dict					,   a    : Tuple     ) ->List[Any]:
 snake_case				=			request.getfixturevalue(a     )
 snake_case				=			DownloadManager()
 for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(a     )					,   start=1     ):
  _test_jsonl(a					,   a     )
 assert num_jsonl == 2

@pytest.mark.parametrize('''archive_nested_jsonl'''					,   ['''tar_nested_jsonl_path''', '''zip_nested_jsonl_path''']     )
def    __UpperCamelCase	(	a    : Tuple					,   a    : Union[str, Any]     ) ->Any:
 snake_case				=			request.getfixturevalue(a     )
 snake_case				=			DownloadManager()
 for num_tar, (path, file) in enumerate(dl_manager.iter_archive(a     )					,   start=1     ):
  for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(a     )					,   start=1     ):
   _test_jsonl(a					,   a     )
 assert num_tar == 1
 assert num_jsonl == 2






def    __UpperCamelCase	(	a    : List[str]     ) ->Optional[Any]:
 snake_case				=			DownloadManager()
 for num_file, file in enumerate(dl_manager.iter_files(a     )					,   start=1     ):
  assert os.path.basename(a     ) == ("test.txt" if num_file == 1 else "train.txt")
 assert num_file == 2








'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_lowercase       						=			logging.get_logger(__name__)
class 			_lowercase						(       __a					):







     def __init__( self    ,   A__    ,   A__    ,   A__    ,   **A__    ) ->	Union[str, Any]:
      snake_case				=			feature_size
      snake_case				=			sampling_rate
      snake_case				=			padding_value

      snake_case				=			kwargs.pop('''padding_side'''    ,   '''right'''    )
      snake_case				=			kwargs.pop('''return_attention_mask'''    ,   A__    )

      super().__init__(**A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = True    ,   A__ = None    ,   A__ = False    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   ) ->	BatchFeature:
      # If we have a list of dicts, let's convert it in a dict of lists
      # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
      if isinstance(A__    ,   (list, tuple)    ) and isinstance(processed_features[0]    ,   (dict, BatchFeature)    ):
       snake_case				=			{
           key: [example[key] for example in processed_features] for key in processed_features[0].keys()
       }

      # The model's main input name, usually `input_values`, has be passed for padding
      if self.model_input_names[0] not in processed_features:
       raise ValueError(
           '''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
           F""" to this method that includes {self.model_input_names[0]}, but you provided"""
           F""" {list(processed_features.keys()    )}"""    )

      snake_case				=			processed_features[self.model_input_names[0]]
      snake_case				=			(
          return_attention_mask if return_attention_mask is not None else self.return_attention_mask
      )

      if len(A__    ) == 0:
       if return_attention_mask:
        snake_case				=			[]
       return processed_features

      # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
      # and rebuild them afterwards if no return_tensors is specified
      # Note that we lose the specific device the tensor may be on for PyTorch

      snake_case				=			required_input[0]
      if isinstance(A__    ,   (list, tuple)    ):
       # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
       snake_case				=			0
       while len(required_input[index]    ) == 0:
        index += 1
       if index < len(A__    ):
        snake_case				=			required_input[index][0]

      if return_tensors is None:
       if is_tf_tensor(A__    ):
        snake_case				=			'''tf'''
       elif is_torch_tensor(A__    ):
        snake_case				=			'''pt'''
       elif isinstance(A__    ,   (int, float, list, tuple, np.ndarray)    ):
        snake_case				=			'''np'''
       else:
        raise ValueError(
            F"""type of {first_element} unknown: {type(A__    )}. """
            '''Should be one of a python, numpy, pytorch or tensorflow object.'''    )

      for key, value in processed_features.items():
       if isinstance(value[0]    ,   (int, float)    ):
        snake_case				=			to_numpy(A__    )
       else:
        snake_case				=			[to_numpy(A__    ) for v in value]

        # Convert padding_strategy in PaddingStrategy
      snake_case				=			self._get_padding_strategies(padding=A__    ,   max_length=A__    )

      snake_case				=			processed_features[self.model_input_names[0]]

      snake_case				=			len(A__    )
      if not all(len(A__    ) == batch_size for v in processed_features.values()    ):
       raise ValueError('''Some items in the output dictionary have a different batch size than others.'''    )

      snake_case				=			[]
      for i in range(A__    ):
       snake_case				=			{k: v[i] for k, v in processed_features.items()}
       # truncation
       snake_case				=			self._truncate(
           A__    ,   max_length=A__    ,   pad_to_multiple_of=A__    ,   truncation=A__    ,   )
       truncated_inputs.append(A__    )

      if padding_strategy == PaddingStrategy.LONGEST:
       # make sure that `max_length` cannot be longer than the longest truncated length
       snake_case				=			max(len(input_slice[self.model_input_names[0]]    ) for input_slice in truncated_inputs    )
       snake_case				=			PaddingStrategy.MAX_LENGTH

      snake_case				=			{}
      for i in range(A__    ):
       # padding
       snake_case				=			self._pad(
           truncated_inputs[i]    ,   max_length=A__    ,   padding_strategy=A__    ,   pad_to_multiple_of=A__    ,   return_attention_mask=A__    ,   )

       for key, value in outputs.items():
        if key not in batch_outputs:
         snake_case				=			[]
        if value.dtype is np.dtype(np.floataa    ):
         snake_case				=			value.astype(np.floataa    )
        batch_outputs[key].append(A__    )

      return BatchFeature(A__    ,   tensor_type=A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ,   A__ = PaddingStrategy.DO_NOT_PAD    ,   A__ = None    ,   A__ = None    ,   ) ->	dict:
      snake_case				=			processed_features[self.model_input_names[0]]

      if padding_strategy == PaddingStrategy.LONGEST:
       snake_case				=			len(A__    )

      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
       snake_case				=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      snake_case				=			padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A__    ) < max_length

      if return_attention_mask and "attention_mask" not in processed_features:
       snake_case				=			np.ones(len(A__    )    ,   dtype=np.intaa    )

      if needs_to_be_padded:
       snake_case				=			max_length - len(A__    )
       if self.padding_side == "right":
        if return_attention_mask:
         snake_case				=			np.pad(
             processed_features['''attention_mask''']    ,   (0, difference)    )
        snake_case				=			((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
        snake_case				=			np.pad(
            A__    ,   A__    ,   '''constant'''    ,   constant_values=self.padding_value    )
       elif self.padding_side == "left":
        if return_attention_mask:
         snake_case				=			np.pad(
             processed_features['''attention_mask''']    ,   (difference, 0)    )
        snake_case				=			((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
        snake_case				=			np.pad(
            A__    ,   A__    ,   '''constant'''    ,   constant_values=self.padding_value    )
       else:
        raise ValueError('''Invalid padding strategy:''' + str(self.padding_side    )    )

      return processed_features







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   ) ->	Union[str, Any]:
      if not truncation:
       return processed_features
      elif truncation and max_length is None:
       raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.'''    )

      snake_case				=			processed_features[self.model_input_names[0]]

      # find `max_length` that fits `pad_to_multiple_of`
      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
       snake_case				=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      snake_case				=			len(A__    ) > max_length

      if needs_to_be_truncated:
       snake_case				=			processed_features[self.model_input_names[0]][:max_length]
       if "attention_mask" in processed_features:
        snake_case				=			processed_features['''attention_mask'''][:max_length]

      return processed_features







     def     UpperCamelCase			( self    ,   A__=False    ,   A__=None    ) ->	Union[str, Any]:

      # Get padding strategy
      if padding is not False:
       if padding is True:
        snake_case				=			PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
       elif not isinstance(A__    ,   A__    ):
        snake_case				=			PaddingStrategy(A__    )
       elif isinstance(A__    ,   A__    ):
        snake_case				=			padding
      else:
       snake_case				=			PaddingStrategy.DO_NOT_PAD

      # Set max length if needed
      if max_length is None:
       if padding_strategy == PaddingStrategy.MAX_LENGTH:
        raise ValueError(
            F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""    )

        # Test if we have a padding value
      if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
       raise ValueError(
           '''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
           ''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'''    )

      return padding_strategy

