

'''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


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


lowercase__            =     "lfs-multipart-upload"




class 			A_					(							_snake_case				):






     '''simple docstring'''



     @staticmethod
     def   UpperCAmelCase_			(						lowercase_					:	ArgumentParser			)		->      Dict:
      UpperCAmelCase			:      Optional[Any]		   =					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=lowercase_	,     help='Local path to repository you want to configure.'			)
      enable_parser.set_defaults(func=lambda lowercase_			: LfsEnableCommand(lowercase_			)			)

      UpperCAmelCase			:      List[Any]		   =					parser.add_parser(
          lowercase_	,     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(lowercase_			)			)




class 			A_					:






     '''simple docstring'''



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




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




def  UpperCamelCase(	UpperCAmelCase_      ):
 UpperCAmelCase			:      Optional[int]		   =					json.dumps(UpperCAmelCase_      ) + '\n'
 sys.stdout.write(UpperCAmelCase_      )
 sys.stdout.flush()




def  UpperCamelCase(	):
 UpperCAmelCase			:      Union[str, Any]		   =					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 			A_					(							_snake_case				):






     '''simple docstring'''



     def __init__(						self					:	int	,     lowercase_					:	str	,     lowercase_					:	int	,     lowercase_					:	int			)		->      List[str]:
      UpperCAmelCase			:      Dict		   =					filepath
      UpperCAmelCase			:      List[Any]		   =					seek_from
      UpperCAmelCase			:      Optional[Any]		   =					read_limit
      UpperCAmelCase			:      List[str]		   =					0


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


     def __len__(						self					:	Union[str, Any]			)		->      Tuple:
      UpperCAmelCase			:      Optional[int]		   =					os.fstat(self.f.fileno()			).st_size
      return min(self.read_limit	,     total_length - self.seek_from			)


     def   UpperCAmelCase_			(						self					:	List[str]	,     lowercase_					:	Tuple=-1			)		->      Tuple:
      if self.n_seen >= self.read_limit:
       return b""
      UpperCAmelCase			:      int		   =					self.read_limit - self.n_seen
      UpperCAmelCase			:      Dict		   =					self.f.read(remaining_amount if n < 0 else min(lowercase_	,     lowercase_			)			)
      self.n_seen += len(lowercase_			)
      return data


     def __iter__(						self					:	Optional[int]			)		->      Union[str, Any]:
      yield self.read(n=4 * 1_024 * 1_024			)




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




class 			A_					:






     '''simple docstring'''



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




     def   UpperCAmelCase_			(						self					:	str			)		->      Any:
      # 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			:      List[str]		   =					json.loads(sys.stdin.readline().strip()			)
      if not (init_msg.get('event'			) == "init" and init_msg.get('operation'			) == "upload"):
       write_msg({'error': {'code': 32, '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			:      List[str]		   =					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			:      Union[str, Any]		   =					msg['oid']
       UpperCAmelCase			:      int		   =					msg['path']
       UpperCAmelCase			:      Any		   =					msg['action']['href']
       UpperCAmelCase			:      Optional[Any]		   =					msg['action']['header']
       UpperCAmelCase			:      Tuple		   =					int(header.pop('chunk_size'			)			)
       UpperCAmelCase			:      List[str]		   =					list(header.values()			)

       UpperCAmelCase			:      List[Any]		   =					[]
       for i, presigned_url in enumerate(lowercase_			):
        with FileSlice(lowercase_	,     seek_from=i * chunk_size	,     read_limit=lowercase_			) as data:
         UpperCAmelCase			:      Dict		   =					requests.put(lowercase_	,     data=lowercase_			)
         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			:      Tuple		   =					requests.post(
           lowercase_	,     json={
               'oid': oid,
               'parts': parts,
           }	,     )
       r.raise_for_status()

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







'''simple docstring'''

from sklearn.metrics import accuracy_score

import datasets


lowercase__            =     "\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


lowercase__            =     "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


lowercase__            =     "\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,  _KWARGS_DESCRIPTION				)
class 			A_					(							datasets.Metric				):






     '''simple docstring'''



     def   UpperCAmelCase_			(						self					:	int			)		->      str:
      return datasets.MetricInfo(
          description=_DESCRIPTION	,     citation=_CITATION	,     inputs_description=_KWARGS_DESCRIPTION	,     features=datasets.Features(
              {
                  'predictions': datasets.Sequence(datasets.Value('int32'			)			),
                  'references': datasets.Sequence(datasets.Value('int32'			)			),
              }
              if self.config_name == 'multilabel'
              else {
                  'predictions': datasets.Value('int32'			),
                  'references': datasets.Value('int32'			),
              }			)	,     reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']	,     )




     def   UpperCAmelCase_			(						self					:	str	,     lowercase_					:	Union[str, Any]	,     lowercase_					:	Any	,     lowercase_					:	Tuple=True	,     lowercase_					:	Union[str, Any]=None			)		->      Dict:
      return {
          "accuracy": float(
              accuracy_score(lowercase_	,     lowercase_	,     normalize=lowercase_	,     sample_weight=lowercase_			)			)
      }




