import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


_SCREAMING_SNAKE_CASE:						Optional[int]    	=       '''\
@inproceedings{ramshaw-marcus-1995-text,
    title = "Text Chunking using Transformation-Based Learning",
    author = "Ramshaw, Lance  and
      Marcus, Mitch",
    booktitle = "Third Workshop on Very Large Corpora",
    year = "1995",
    url = "https://www.aclweb.org/anthology/W95-0107",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
'''

_SCREAMING_SNAKE_CASE:						Any    	=       '''\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
'''

_SCREAMING_SNAKE_CASE:						str    	=       '''
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode="strict". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        "warn". "warn" acts as 0, but the warning is raised.

Returns:
    \'scores\': dict. Summary of the scores for overall and per type
        Overall:
            \'accuracy\': accuracy,
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure,
        Per type:
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> seqeval = datasets.load_metric("seqeval")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']
    >>> print(results["overall_f1"])
    0.5
    >>> print(results["PER"]["f1"])
    1.0
'''

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






		"""simple docstring"""







		def 				_a				(			self    :				Optional[int]   ):






					"""simple docstring"""
					return datasets.MetricInfo(
					    description=_DESCRIPTION  ,					citation=_CITATION  ,					homepage="https://github.com/chakki-works/seqeval"  ,					inputs_description=_KWARGS_DESCRIPTION  ,					features=datasets.Features(
					        {
					            "predictions": datasets.Sequence(datasets.Value("string"  ,					id="label"   )  ,					id="sequence"   ),
					            "references": datasets.Sequence(datasets.Value("string"  ,					id="label"   )  ,					id="sequence"   ),
					        }   )  ,					codebase_urls=["https://github.com/chakki-works/seqeval"]  ,					reference_urls=["https://github.com/chakki-works/seqeval"]  ,					)







		def 				_a				(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				Any  ,					__SCREAMING_SNAKE_CASE    :				bool = False  ,					__SCREAMING_SNAKE_CASE    :				Optional[str] = None  ,					__SCREAMING_SNAKE_CASE    :				Optional[str] = None  ,					__SCREAMING_SNAKE_CASE    :				Optional[List[int]] = None  ,					__SCREAMING_SNAKE_CASE    :				Union[str, int] = "warn"  ,					):






					"""simple docstring"""
					if scheme is not None:
								try:
											_snake_case              =      importlib.import_module("seqeval.scheme"   )
											_snake_case              =      getattr(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
								except AttributeError:
											raise ValueError(F'Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'   )
					_snake_case              =      classification_report(
					    y_true=__SCREAMING_SNAKE_CASE  ,					y_pred=__SCREAMING_SNAKE_CASE  ,					suffix=__SCREAMING_SNAKE_CASE  ,					output_dict=__SCREAMING_SNAKE_CASE  ,					scheme=__SCREAMING_SNAKE_CASE  ,					mode=__SCREAMING_SNAKE_CASE  ,					sample_weight=__SCREAMING_SNAKE_CASE  ,					zero_division=__SCREAMING_SNAKE_CASE  ,					)
					report.pop("macro avg"   )
					report.pop("weighted avg"   )
					_snake_case              =      report.pop("micro avg"   )

					_snake_case              =      {
					    type_name: {
					        "precision": score["precision"],
					        "recall": score["recall"],
					        "f1": score["f1-score"],
					        "number": score["support"],
					    }
					    for type_name, score in report.items()
					}
					_snake_case              =      overall_score["precision"]
					_snake_case              =      overall_score["recall"]
					_snake_case              =      overall_score["f1-score"]
					_snake_case              =      accuracy_score(y_true=__SCREAMING_SNAKE_CASE  ,					y_pred=__SCREAMING_SNAKE_CASE   )

					return scores


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:						List[str]    	=       logging.get_logger(__name__)  # pylint: disable=invalid-name


_SCREAMING_SNAKE_CASE:						Dict    	=       '''lfs-multipart-upload'''

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		@staticmethod
		def 				_a				(			__SCREAMING_SNAKE_CASE    :				ArgumentParser   ):






					"""simple docstring"""
					_snake_case              =      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 __SCREAMING_SNAKE_CASE   : LfsEnableCommand(__SCREAMING_SNAKE_CASE   )   )

					_snake_case              =      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 __SCREAMING_SNAKE_CASE   : LfsUploadCommand(__SCREAMING_SNAKE_CASE   )   )

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def __init__(			self    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      args







		def 				_a				(			self    :				Optional[int]   ):






					"""simple docstring"""
					warnings.warn(
					    "Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."   )
					_snake_case              =      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    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      json.dumps(lowerCamelCase_  ) + "\n"
			sys.stdout.write(lowerCamelCase_  )
			sys.stdout.flush()






def    UpperCAmelCase__(						):



			"""simple docstring"""





			_snake_case              =      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 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def __init__(			self    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					_snake_case              =      filepath
					_snake_case              =      seek_from
					_snake_case              =      read_limit
					_snake_case              =      0



		def __enter__(			self    :				Optional[int]   ):






					"""simple docstring"""
					_snake_case              =      open(self.filepath  ,					"rb"   )
					self.f.seek(self.seek_from   )
					return self



		def __len__(			self    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      os.fstat(self.f.fileno()   ).st_size
					return min(self.read_limit  ,					total_length - self.seek_from   )



		def 				_a				(			self    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				Any=-1   ):






					"""simple docstring"""
					if self.n_seen >= self.read_limit:
								return b""
					_snake_case              =      self.read_limit - self.n_seen
					_snake_case              =      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   ):






					"""simple docstring"""
					yield self.read(n=4 * 1_024 * 1_024   )







		def __exit__(			self    :				Dict  ,					*__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					self.f.close()

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def __init__(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					_snake_case              =      args







		def 				_a				(			self    :				Optional[int]   ):






					"""simple docstring"""
					# 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.
					_snake_case              =      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:
								_snake_case              =      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   )

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

								_snake_case              =      []
								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:
														_snake_case              =      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.

								_snake_case              =      requests.post(
								    __SCREAMING_SNAKE_CASE  ,					json={
								        "oid": oid,
								        "parts": parts,
								    }  ,					)
								r.raise_for_status()

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

