'''simple docstring'''




import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
						import PIL.Image

						from .features import FeatureType


_lowercase						: Optional[List[str]]						=						None
_lowercase						: Any						=						"<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
_lowercase						: Optional[Any]						=						[
    np.dtype("|b1"),
    np.dtype("|u1"),
    np.dtype("<u2"),
    np.dtype(">u2"),
    np.dtype("<i2"),
    np.dtype(">i2"),
    np.dtype("<u4"),
    np.dtype(">u4"),
    np.dtype("<i4"),
    np.dtype(">i4"),
    np.dtype("<f4"),
    np.dtype(">f4"),
    np.dtype("<f8"),
    np.dtype(">f8"),
]





@dataclass
class     __magic_name__						:
			UpperCamelCase__				=       True
			UpperCamelCase__				=       None
			# Automatically constructed
			UpperCamelCase__				=       "PIL.Image.Image"
			UpperCamelCase__				=       pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()})
			UpperCamelCase__				=       field(default='''Image''',     init=_UpperCAmelCase,     repr=_UpperCAmelCase)







			def __call__(	self				:  List[str]    ):
										return self.pa_type







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]				,			lowercase_				:  Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]    ):
										if config.PIL_AVAILABLE:
																	import PIL.Image
										else:
																	raise ImportError("""To support encoding images, please install 'Pillow'."""    )

										if isinstance(lowercase_				,			lowercase_    ):
																	lowercase_					:			Union[str, Any]										=       np.array(lowercase_    )

										if isinstance(lowercase_				,			lowercase_    ):
																	return {"path": value, "bytes": None}
										elif isinstance(lowercase_				,			lowercase_    ):
																	return {"path": None, "bytes": value}
										elif isinstance(lowercase_				,			np.ndarray    ):
																	# convert the image array to PNG/TIFF bytes
																	return encode_np_array(lowercase_    )
										elif isinstance(lowercase_				,			PIL.Image.Image    ):
																	# convert the PIL image to bytes (default format is PNG/TIFF)
																	return encode_pil_image(lowercase_    )
										elif value.get("""path"""    ) is not None and os.path.isfile(value["""path"""]    ):
																	# we set "bytes": None to not duplicate the data if they're already available locally
																	return {"bytes": None, "path": value.get("""path"""    )}
										elif value.get("""bytes"""    ) is not None or value.get("""path"""    ) is not None:
																	# store the image bytes, and path is used to infer the image format using the file extension
																	return {"bytes": value.get("""bytes"""    ), "path": value.get("""path"""    )}
										else:
																	raise ValueError(
																	    f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  dict				,			lowercase_				:  int=None    ):
										if not self.decode:
																	raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead."""    )

										if config.PIL_AVAILABLE:
																	import PIL.Image
										else:
																	raise ImportError("""To support decoding images, please install 'Pillow'."""    )

										if token_per_repo_id is None:
																	lowercase_					:			Optional[int]										=       {}

										lowercase_						,						lowercase_					:			List[Any]										=       value["""path"""], value["""bytes"""]
										if bytes_ is None:
																	if path is None:
																								raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.'''    )
																	else:
																								if is_local_path(lowercase_    ):
																															lowercase_					:			int										=       PIL.Image.open(lowercase_    )
																								else:
																															lowercase_					:			Optional[int]										=       path.split("""::"""    )[-1]
																															try:
																																						lowercase_					:			str										=       string_to_dict(lowercase_				,			config.HUB_DATASETS_URL    )["""repo_id"""]
																																						lowercase_					:			Any										=       token_per_repo_id.get(lowercase_    )
																															except ValueError:
																																						lowercase_					:			Union[str, Any]										=       None
																															with xopen(lowercase_				,			"""rb"""				,			use_auth_token=lowercase_    ) as f:
																																						lowercase_					:			Union[str, Any]										=       BytesIO(f.read()    )
																															lowercase_					:			int										=       PIL.Image.open(bytes_    )
										else:
																	lowercase_					:			Union[str, Any]										=       PIL.Image.open(BytesIO(bytes_    )    )
										image.load()  # to avoid "Too many open files" errors
										return image







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										from .features import Value

										return (
										    self
										    if self.decode
										    else {
										        "bytes": Value("""binary"""    ),
										        "path": Value("""string"""    ),
										    }
										)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Union[pa.StringArray, pa.StructArray, pa.ListArray]    ):
										if pa.types.is_string(storage.type    ):
																	lowercase_					:			str										=       pa.array([None] * len(lowercase_    )				,			type=pa.binary()    )
																	lowercase_					:			Tuple										=       pa.StructArray.from_arrays([bytes_array, storage]				,			["""bytes""", """path"""]				,			mask=storage.is_null()    )
										elif pa.types.is_binary(storage.type    ):
																	lowercase_					:			Tuple										=       pa.array([None] * len(lowercase_    )				,			type=pa.string()    )
																	lowercase_					:			Tuple										=       pa.StructArray.from_arrays([storage, path_array]				,			["""bytes""", """path"""]				,			mask=storage.is_null()    )
										elif pa.types.is_struct(storage.type    ):
																	if storage.type.get_field_index("""bytes"""    ) >= 0:
																								lowercase_					:			Union[str, Any]										=       storage.field("""bytes"""    )
																	else:
																								lowercase_					:			Any										=       pa.array([None] * len(lowercase_    )				,			type=pa.binary()    )
																	if storage.type.get_field_index("""path"""    ) >= 0:
																								lowercase_					:			Union[str, Any]										=       storage.field("""path"""    )
																	else:
																								lowercase_					:			Dict										=       pa.array([None] * len(lowercase_    )				,			type=pa.string()    )
																	lowercase_					:			Optional[int]										=       pa.StructArray.from_arrays([bytes_array, path_array]				,			["""bytes""", """path"""]				,			mask=storage.is_null()    )
										elif pa.types.is_list(storage.type    ):
																	lowercase_					:			Optional[int]										=       pa.array(
																	    [encode_np_array(np.array(lowercase_    )    )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()]				,			type=pa.binary()				,			)
																	lowercase_					:			Union[str, Any]										=       pa.array([None] * len(lowercase_    )				,			type=pa.string()    )
																	lowercase_					:			Any										=       pa.StructArray.from_arrays(
																	    [bytes_array, path_array]				,			["""bytes""", """path"""]				,			mask=bytes_array.is_null()    )
										return array_cast(lowercase_				,			self.pa_type    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict				,			lowercase_				:  pa.StructArray    ):

										@no_op_if_value_is_null
										def path_to_bytes(lowercase_				:  List[Any]    ):
																	with xopen(lowercase_				,			"""rb"""    ) as f:
																								lowercase_					:			int										=       f.read()
																	return bytes_

										lowercase_					:			Optional[Any]										=       pa.array(
										    [
										        (path_to_bytes(x["""path"""]    ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
										        for x in storage.to_pylist()
										    ]				,			type=pa.binary()				,			)
										lowercase_					:			Dict										=       pa.array(
										    [os.path.basename(lowercase_    ) if path is not None else None for path in storage.field("""path"""    ).to_pylist()]				,			type=pa.string()				,			)
										lowercase_					:			int										=       pa.StructArray.from_arrays([bytes_array, path_array]				,			["""bytes""", """path"""]				,			mask=bytes_array.is_null()    )
										return array_cast(lowercase_				,			self.pa_type    )




def 							lowerCamelCase	(      )			->      List[str]:
							if config.PIL_AVAILABLE:
														import PIL.Image
							else:
														raise ImportError("""To support encoding images, please install 'Pillow'.""" )

							global _IMAGE_COMPRESSION_FORMATS
							if _IMAGE_COMPRESSION_FORMATS is None:
														PIL.Image.init()
														lowercase_					:			Optional[Any]										=       list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
							return _IMAGE_COMPRESSION_FORMATS




def 							lowerCamelCase	(      UpperCAmelCase__       :					"PIL.Image.Image" )			->      bytes:
							lowercase_					:			Any										=       BytesIO()
							if image.format in list_image_compression_formats():
														lowercase_					:			str										=       image.format
							else:
														lowercase_					:			Union[str, Any]										=       """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
							image.save(UpperCAmelCase__      ,      format=UpperCAmelCase__ )
							return buffer.getvalue()




def 							lowerCamelCase	(      UpperCAmelCase__       :					"PIL.Image.Image" )			->      dict:
							if hasattr(UpperCAmelCase__      ,      """filename""" ) and image.filename != "":
														return {"path": image.filename, "bytes": None}
							else:
														return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}




def 							lowerCamelCase	(      UpperCAmelCase__       :					np.ndarray )			->      dict:
							if config.PIL_AVAILABLE:
														import PIL.Image
							else:
														raise ImportError("""To support encoding images, please install 'Pillow'.""" )

							lowercase_					:			Optional[Any]										=       array.dtype
							lowercase_					:			List[Any]										=       dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
							lowercase_					:			int										=       dtype.kind
							lowercase_					:			Optional[int]										=       dtype.itemsize

							lowercase_					:			Tuple										=       None

							# Multi-channel array case (only np.dtype("|u1") is allowed)
							if array.shape[2:]:
														lowercase_					:			Dict										=       np.dtype("""|u1""" )
														if dtype_kind not in ["u", "i"]:
																					raise TypeError(
																					    F'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''' )
														if dtype is not dest_dtype:
																					warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
    # Exact match
							elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
														lowercase_					:			Optional[Any]										=       dtype
							else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
														while dtype_itemsize >= 1:
																					lowercase_					:			List[str]										=       dtype_byteorder + dtype_kind + str(UpperCAmelCase__ )
																					lowercase_					:			Dict										=       np.dtype(UpperCAmelCase__ )
																					if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
																												warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
																												break
																					else:
																												dtype_itemsize //= 2

							if dest_dtype is None:
														raise TypeError(
														    F'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''' )
							lowercase_					:			List[str]										=       PIL.Image.fromarray(array.astype(UpperCAmelCase__ ) )
							return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}




def 							lowerCamelCase	(      UpperCAmelCase__       :					Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] )			->      List[dict]:
							if config.PIL_AVAILABLE:
														import PIL.Image
							else:
														raise ImportError("""To support encoding images, please install 'Pillow'.""" )

							if objs:
														lowercase_						,						lowercase_					:			Tuple										=       first_non_null_value(UpperCAmelCase__ )
														if isinstance(UpperCAmelCase__      ,      UpperCAmelCase__ ):
																					return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
														if isinstance(UpperCAmelCase__      ,      np.ndarray ):
																					lowercase_					:			List[str]										=       no_op_if_value_is_null(UpperCAmelCase__ )
																					return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
														elif isinstance(UpperCAmelCase__      ,      PIL.Image.Image ):
																					lowercase_					:			Optional[Any]										=       no_op_if_value_is_null(UpperCAmelCase__ )
																					return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
														else:
																					return objs
							else:
														return objs






'''simple docstring'''




import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


_lowercase						: List[str]						=						"\\n@inproceedings{snover-etal-2006-study,\n    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n    author = \"Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John\",\n    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",\n    month = aug # \" 8-12\",\n    year = \"2006\",\n    address = \"Cambridge, Massachusetts, USA\",\n    publisher = \"Association for Machine Translation in the Americas\",\n    url = \"https://aclanthology.org/2006.amta-papers.25\",\n    pages = \"223--231\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

_lowercase						: Tuple						=						"\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n"

_lowercase						: Optional[int]						=						"\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    'score' (float): TER score (num_edits / sum_ref_lengths * 100)\n    'num_edits' (int): The cumulative number of edits\n    'ref_length' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\",\n        ...                     \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}\n\n    Example 2:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}\n\n    Example 3:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}\n\n    Example 4:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}\n\n    Example 5:\n        >>> predictions = [\"does this sentence match??\",\n        ...                    \"what about this sentence?\",\n        ...                    \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}\n"





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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										if version.parse(scb.__version__    ) < version.parse("""1.4.12"""    ):
																	raise ImportWarning(
																	    """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
																	    """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""    )
										return datasets.MetricInfo(
										    description=_DESCRIPTION				,			citation=_CITATION				,			homepage="""http://www.cs.umd.edu/~snover/tercom/"""				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
										        {
										            """predictions""": datasets.Value("""string"""				,			id="""sequence"""    ),
										            """references""": datasets.Sequence(datasets.Value("""string"""				,			id="""sequence"""    )				,			id="""references"""    ),
										        }    )				,			codebase_urls=["""https://github.com/mjpost/sacreBLEU#ter"""]				,			reference_urls=[
										        """https://github.com/jhclark/tercom""",
										    ]				,			)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  Any				,			lowercase_				:  Tuple				,			lowercase_				:  bool = False				,			lowercase_				:  bool = False				,			lowercase_				:  bool = False				,			lowercase_				:  bool = False				,			):
										lowercase_					:			int										=       len(references[0]    )
										if any(len(lowercase_    ) != references_per_prediction for refs in references    ):
																	raise ValueError("""Sacrebleu requires the same number of references for each prediction"""    )
										lowercase_					:			Optional[int]										=       [[refs[i] for refs in references] for i in range(lowercase_    )]

										lowercase_					:			List[str]										=       TER(
										    normalized=lowercase_				,			no_punct=lowercase_				,			asian_support=lowercase_				,			case_sensitive=lowercase_				,			)
										lowercase_					:			List[str]										=       sb_ter.corpus_score(lowercase_				,			lowercase_    )

										return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}





