






'''simple docstring'''

import re
import string

import numpy as np

import datasets


A:      Optional[int]  				=				"\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"

A:      Tuple  				=				"\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    33.3\n\n"

A:      str  				=				"\n"






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







	"""simple docstring"""



	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							return datasets.MetricInfo(
							    description=_DESCRIPTION  ,      citation=_CITATION  ,      inputs_description=_KWARGS_DESCRIPTION  ,      features=datasets.Features(
							        {
							            'predictions': datasets.Value('string'  ,      id='sequence'       ),
							            'references': datasets.Value('string'  ,      id='sequence'       ),
							        }       )  ,      reference_urls=[]  ,      )







	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase=None  ,      _lowercase=False  ,      _lowercase=False  ,      _lowercase=False  ,      )						->  Any:
							if regexes_to_ignore is not None:
													for s in regexes_to_ignore:
																			lowercase_      :   Any        =			np.array([re.sub(_lowercase  ,      ''  ,      _lowercase       ) for x in predictions]       )
																			lowercase_      :   Union[str, Any]        =			np.array([re.sub(_lowercase  ,      ''  ,      _lowercase       ) for x in references]       )
							else:
													lowercase_      :   int        =			np.asarray(_lowercase       )
													lowercase_      :   str        =			np.asarray(_lowercase       )

							if ignore_case:
													lowercase_      :   Dict        =			np.char.lower(_lowercase       )
													lowercase_      :   Union[str, Any]        =			np.char.lower(_lowercase       )

							if ignore_punctuation:
													lowercase_      :   Union[str, Any]        =			string.punctuation.maketrans(''  ,      ''  ,      string.punctuation       )
													lowercase_      :   List[str]        =			np.char.translate(_lowercase  ,      table=_lowercase       )
													lowercase_      :   List[str]        =			np.char.translate(_lowercase  ,      table=_lowercase       )

							if ignore_numbers:
													lowercase_      :   Optional[Any]        =			string.digits.maketrans(''  ,      ''  ,      string.digits       )
													lowercase_      :   Optional[int]        =			np.char.translate(_lowercase  ,      table=_lowercase       )
													lowercase_      :   Dict        =			np.char.translate(_lowercase  ,      table=_lowercase       )

							lowercase_      :   Union[str, Any]        =			predictions == references

							return {"exact_match": np.mean(_lowercase       ) * 100}








'''simple docstring'''

import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible






class 							__magic_name__     (	unittest.TestCase				):







	"""simple docstring"""



	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							lowercase_      :   Tuple        =			[
							    'safety_checker/pytorch_model.bin',
							    'safety_checker/model.safetensors',
							    'vae/diffusion_pytorch_model.bin',
							    'vae/diffusion_pytorch_model.safetensors',
							    'text_encoder/pytorch_model.bin',
							    'text_encoder/model.safetensors',
							    'unet/diffusion_pytorch_model.bin',
							    'unet/diffusion_pytorch_model.safetensors',
							]
							self.assertTrue(is_safetensors_compatible(_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  Dict:
							lowercase_      :   Tuple        =			[
							    'unet/diffusion_pytorch_model.bin',
							    'unet/diffusion_pytorch_model.safetensors',
							]
							self.assertTrue(is_safetensors_compatible(_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  str:
							lowercase_      :   int        =			[
							    'safety_checker/pytorch_model.bin',
							    'safety_checker/model.safetensors',
							    'vae/diffusion_pytorch_model.bin',
							    'vae/diffusion_pytorch_model.safetensors',
							    'text_encoder/pytorch_model.bin',
							    'text_encoder/model.safetensors',
							    'unet/diffusion_pytorch_model.bin',
							    # Removed: 'unet/diffusion_pytorch_model.safetensors',
							]
							self.assertFalse(is_safetensors_compatible(_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  Any:
							lowercase_      :   Dict        =			[
							    'text_encoder/pytorch_model.bin',
							    'text_encoder/model.safetensors',
							]
							self.assertTrue(is_safetensors_compatible(_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							lowercase_      :   List[Any]        =			[
							    'safety_checker/pytorch_model.bin',
							    'safety_checker/model.safetensors',
							    'vae/diffusion_pytorch_model.bin',
							    'vae/diffusion_pytorch_model.safetensors',
							    'text_encoder/pytorch_model.bin',
							    # Removed: 'text_encoder/model.safetensors',
							    'unet/diffusion_pytorch_model.bin',
							    'unet/diffusion_pytorch_model.safetensors',
							]
							self.assertFalse(is_safetensors_compatible(_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							lowercase_      :   str        =			[
							    'safety_checker/pytorch_model.fp16.bin',
							    'safety_checker/model.fp16.safetensors',
							    'vae/diffusion_pytorch_model.fp16.bin',
							    'vae/diffusion_pytorch_model.fp16.safetensors',
							    'text_encoder/pytorch_model.fp16.bin',
							    'text_encoder/model.fp16.safetensors',
							    'unet/diffusion_pytorch_model.fp16.bin',
							    'unet/diffusion_pytorch_model.fp16.safetensors',
							]
							lowercase_      :   Union[str, Any]        =			'fp16'
							self.assertTrue(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   Optional[int]        =			[
							    'unet/diffusion_pytorch_model.fp16.bin',
							    'unet/diffusion_pytorch_model.fp16.safetensors',
							]
							lowercase_      :   Dict        =			'fp16'
							self.assertTrue(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  int:
							# pass variant but use the non-variant filenames
							lowercase_      :   Optional[int]        =			[
							    'unet/diffusion_pytorch_model.bin',
							    'unet/diffusion_pytorch_model.safetensors',
							]
							lowercase_      :   Dict        =			'fp16'
							self.assertTrue(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							lowercase_      :   int        =			[
							    'safety_checker/pytorch_model.fp16.bin',
							    'safety_checker/model.fp16.safetensors',
							    'vae/diffusion_pytorch_model.fp16.bin',
							    'vae/diffusion_pytorch_model.fp16.safetensors',
							    'text_encoder/pytorch_model.fp16.bin',
							    'text_encoder/model.fp16.safetensors',
							    'unet/diffusion_pytorch_model.fp16.bin',
							    # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
							]
							lowercase_      :   Dict        =			'fp16'
							self.assertFalse(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   str        =			[
							    'text_encoder/pytorch_model.fp16.bin',
							    'text_encoder/model.fp16.safetensors',
							]
							lowercase_      :   str        =			'fp16'
							self.assertTrue(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )
	def        lowerCamelCase__		(							self       )						->  List[str]:
							# pass variant but use the non-variant filenames
							lowercase_      :   List[Any]        =			[
							    'text_encoder/pytorch_model.bin',
							    'text_encoder/model.safetensors',
							]
							lowercase_      :   Dict        =			'fp16'
							self.assertTrue(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )







	def        lowerCamelCase__		(							self       )						->  List[str]:
							lowercase_      :   Union[str, Any]        =			[
							    'safety_checker/pytorch_model.fp16.bin',
							    'safety_checker/model.fp16.safetensors',
							    'vae/diffusion_pytorch_model.fp16.bin',
							    'vae/diffusion_pytorch_model.fp16.safetensors',
							    'text_encoder/pytorch_model.fp16.bin',
							    # 'text_encoder/model.fp16.safetensors',
							    'unet/diffusion_pytorch_model.fp16.bin',
							    'unet/diffusion_pytorch_model.fp16.safetensors',
							]
							lowercase_      :   Dict        =			'fp16'
							self.assertFalse(is_safetensors_compatible(_lowercase  ,      variant=_lowercase       )       )
