






import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


UpperCAmelCase_					:		List[str]           = '''\
@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},
}
'''

UpperCAmelCase_					:		Optional[int]           = '''\
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.
'''

UpperCAmelCase_					:		Optional[Any]           = '''
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 ):





	def 		_A					(						self							:   Optional[Any]      ):
		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							:   Optional[Any]					,    __lowerCamelCase							:   str					,    __lowerCamelCase							:   Dict					,    __lowerCamelCase							:   Optional[int] = False					,    __lowerCamelCase							:   Optional[int] = None					,    __lowerCamelCase							:   Optional[Any] = None					,    __lowerCamelCase							:   int = None					,    __lowerCamelCase							:   Any = "warn"					,    ):
		if scheme is not None:
			try:
				UpperCamelCase	:Tuple		=      importlib.import_module("""seqeval.scheme"""      )
				UpperCamelCase	:str		=      getattr(lowerCAmelCase_					,    lowerCAmelCase_      )
			except AttributeError:
				raise ValueError(F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""      )
		UpperCamelCase	:Optional[int]		=      classification_report(
		    y_true=lowerCAmelCase_					,    y_pred=lowerCAmelCase_					,    suffix=lowerCAmelCase_					,    output_dict=lowerCAmelCase_					,    scheme=lowerCAmelCase_					,    mode=lowerCAmelCase_					,    sample_weight=lowerCAmelCase_					,    zero_division=lowerCAmelCase_					,    )
		report.pop("""macro avg"""      )
		report.pop("""weighted avg"""      )
		UpperCamelCase	:str		=      report.pop("""micro avg"""      )

		UpperCamelCase	:Union[str, Any]		=      {
		    type_name: {
		        """precision""": score["""precision"""],
		        """recall""": score["""recall"""],
		        """f1""": score["""f1-score"""],
		        """number""": score["""support"""],
		    }
		    for type_name, score in report.items()
		}
		UpperCamelCase	:Tuple		=      overall_score["""precision"""]
		UpperCamelCase	:int		=      overall_score["""recall"""]
		UpperCamelCase	:Union[str, Any]		=      overall_score["""f1-score"""]
		UpperCamelCase	:Union[str, Any]		=      accuracy_score(y_true=lowerCAmelCase_					,    y_pred=lowerCAmelCase_      )

		return scores







# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


UpperCAmelCase_   				=  os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
UpperCAmelCase_   				=  os.path.join(hf_cache_home, """accelerate""")
UpperCAmelCase_   				=  os.path.join(cache_dir, """default_config.yaml""")
UpperCAmelCase_   				=  os.path.join(cache_dir, """default_config.yaml""")

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
					UpperCAmelCase_   				=  default_yaml_config_file
else:
					UpperCAmelCase_   				=  default_json_config_file





def     lowerCamelCase__  (  UpperCamelCase__      :							List[str]    )      ->			str:



					'''simple docstring'''




					if config_file is not None:
										if not os.path.isfile(UpperCamelCase__    ):
															raise FileNotFoundError(
															    F'''The passed configuration file `{config_file}` does not exist. '''
															    'Please pass an existing file to `accelerate launch`, or use the the default one '
															    'created through `accelerate config` and run `accelerate launch` '
															    'without the `--config_file` argument.'    )
					else:
										_snake_case						=					default_config_file
					with open(UpperCamelCase__							,     'r'							,     encoding='utf-8'    ) as f:
										if config_file.endswith('.json'    ):
															if (
															    json.load(UpperCamelCase__    ).get('compute_environment'							,     ComputeEnvironment.LOCAL_MACHINE    )
															    == ComputeEnvironment.LOCAL_MACHINE
															):
																				_snake_case						=					ClusterConfig
															else:
																				_snake_case						=					SageMakerConfig
															return config_class.from_json_file(json_file=UpperCamelCase__    )
										else:
															if (
															    yaml.safe_load(UpperCamelCase__    ).get('compute_environment'							,     ComputeEnvironment.LOCAL_MACHINE    )
															    == ComputeEnvironment.LOCAL_MACHINE
															):
																				_snake_case						=					ClusterConfig
															else:
																				_snake_case						=					SageMakerConfig
															return config_class.from_yaml_file(yaml_file=UpperCamelCase__    )
@dataclass
class       UpperCamelCase_		:
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						42




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case						=					self.__dict__
										# For serialization, it's best to convert Enums to strings (or their underlying value type).
										for key, value in result.items():
															if isinstance(lowerCAmelCase_					,    lowerCAmelCase_	):
																				_snake_case						=					value.value
															if isinstance(lowerCAmelCase_					,    lowerCAmelCase_	) and not bool(lowerCAmelCase_	):
																				_snake_case						=					None
										_snake_case						=					{k: v for k, v in result.items() if v is not None}
										return result




					@classmethod
					def 						lowerCAmelCase      (		cls					,    lowerCAmelCase_=None	)		->				List[Any]:
										_snake_case						=					default_json_config_file if json_file is None else json_file
										with open(lowerCAmelCase_					,    'r'					,    encoding='utf-8'	) as f:
															_snake_case						=					json.load(lowerCAmelCase_	)
										if "compute_environment" not in config_dict:
															_snake_case						=					ComputeEnvironment.LOCAL_MACHINE
										if "mixed_precision" not in config_dict:
															_snake_case						=					'fp16' if ('fp16' in config_dict and config_dict['fp16']) else None
										if "fp16" in config_dict:  # Convert the config to the new format.
															del config_dict["fp16"]
										if "dynamo_backend" in config_dict:  # Convert the config to the new format.
															_snake_case						=					config_dict.pop('dynamo_backend'	)
															_snake_case						=					{} if dynamo_backend == 'NO' else {'dynamo_backend': dynamo_backend}
										if "use_cpu" not in config_dict:
															_snake_case						=					False
										return cls(**lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				List[str]:
										with open(lowerCAmelCase_					,    'w'					,    encoding='utf-8'	) as f:
															_snake_case						=					json.dumps(self.to_dict()					,    indent=2					,    sort_keys=lowerCAmelCase_	) + '\n'
															f.write(lowerCAmelCase_	)




					@classmethod
					def 						lowerCAmelCase      (		cls					,    lowerCAmelCase_=None	)		->				int:
										_snake_case						=					default_yaml_config_file if yaml_file is None else yaml_file
										with open(lowerCAmelCase_					,    'r'					,    encoding='utf-8'	) as f:
															_snake_case						=					yaml.safe_load(lowerCAmelCase_	)
										if "compute_environment" not in config_dict:
															_snake_case						=					ComputeEnvironment.LOCAL_MACHINE

										if "mixed_precision" not in config_dict:
															_snake_case						=					'fp16' if ('fp16' in config_dict and config_dict['fp16']) else None
										if isinstance(config_dict['mixed_precision']					,    lowerCAmelCase_	) and not config_dict["mixed_precision"]:
															_snake_case						=					'no'
										if "fp16" in config_dict:  # Convert the config to the new format.
															del config_dict["fp16"]
										if "dynamo_backend" in config_dict:  # Convert the config to the new format.
															_snake_case						=					config_dict.pop('dynamo_backend'	)
															_snake_case						=					{} if dynamo_backend == 'NO' else {'dynamo_backend': dynamo_backend}
										if "use_cpu" not in config_dict:
															_snake_case						=					False
										return cls(**lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				List[str]:
										with open(lowerCAmelCase_					,    'w'					,    encoding='utf-8'	) as f:
															yaml.safe_dump(self.to_dict()					,    lowerCAmelCase_	)






					def 						lowerCAmelCase      (		self	)		->				Tuple:
										if isinstance(self.compute_environment					,    lowerCAmelCase_	):
															_snake_case						=					ComputeEnvironment(self.compute_environment	)
										if isinstance(self.distributed_type					,    lowerCAmelCase_	):
															if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
																				_snake_case						=					SageMakerDistributedType(self.distributed_type	)
															else:
																				_snake_case						=					DistributedType(self.distributed_type	)
										if self.dynamo_config is None:
															_snake_case						=					{}





@dataclass
class       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						0
					lowerCAmelCase_				=						1
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						"static"
					lowerCAmelCase_				=						False
					lowerCAmelCase_				=						"main"

					# args for deepspeed_plugin
					lowerCAmelCase_				=						None
					# args for fsdp
					lowerCAmelCase_				=						None
					# args for megatron_lm
					lowerCAmelCase_				=						None
					# args for ipex
					lowerCAmelCase_				=						None
					# args for TPU
					lowerCAmelCase_				=						False

					# args for TPU pods
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						False
					lowerCAmelCase_				=						False
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None

					# args for dynamo
					lowerCAmelCase_				=						None




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										if self.deepspeed_config is None:
															_snake_case						=					{}
										if self.fsdp_config is None:
															_snake_case						=					{}
										if self.megatron_lm_config is None:
															_snake_case						=					{}
										if self.ipex_config is None:
															_snake_case						=					{}
										return super().__post_init__()





@dataclass
class       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						"us-east-1"
					lowerCAmelCase_				=						1
					lowerCAmelCase_				=						"all"
					lowerCAmelCase_				=						f"accelerate-sagemaker-{num_machines}"
					lowerCAmelCase_				=						SAGEMAKER_PYTORCH_VERSION
					lowerCAmelCase_				=						SAGEMAKER_TRANSFORMERS_VERSION
					lowerCAmelCase_				=						SAGEMAKER_PYTHON_VERSION
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None



