




"""simple docstring"""

import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets



@contextmanager
def   UpperCamelCase__     (		):
		def filter_log(lowercase__  :     Optional[Any]   ):
				return False if "This IS expected if you are initializing" in record.msg else True

		snake_case		:						List[str]								=				datasets.utils.logging.get_logger("transformers.modeling_utils"   )
		logger.addFilter(lowercase__   )
		try:
				yield
		finally:
				logger.removeFilter(lowercase__   )


__A 		=			"\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n"

__A 		=			"\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project's README at https://github.com/Tiiiger/bert_score#readme for more information.\n"

__A 		=			"\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. 'en').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bertscore = datasets.load_metric(\"bertscore\")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang=\"en\")\n    >>> print([round(v, 2) for v in results[\"f1\"]])\n    [1.0, 1.0]\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,  _KWARGS_DESCRIPTION			)
class 			lowerCamelCase__    (  datasets.Metric			):


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					return datasets.MetricInfo(
					    description=_DESCRIPTION				, citation=_CITATION				, homepage="https://github.com/Tiiiger/bert_score"				, 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/Tiiiger/bert_score"]				, reference_urls=[
					        "https://github.com/Tiiiger/bert_score",
					        "https://arxiv.org/abs/1904.09675",
					    ]				, )


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=False				, SCREAMING_SNAKE_CASE=False				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=64				, SCREAMING_SNAKE_CASE=4				, SCREAMING_SNAKE_CASE=False				, SCREAMING_SNAKE_CASE=False				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=False				, ):



					"""simple docstring"""







					snake_case		:						Any								=				bert_score.utils.get_hash
					snake_case		:						List[str]								=				bert_score.BERTScorer

					if version.parse(bert_score.__version__							) >= version.parse("0.3.10"							):
							snake_case		:						List[str]								=				functools.partial(SCREAMING_SNAKE_CASE				, use_fast_tokenizer=SCREAMING_SNAKE_CASE							)
							snake_case		:						List[str]								=				functools.partial(SCREAMING_SNAKE_CASE				, use_fast_tokenizer=SCREAMING_SNAKE_CASE							)
					elif use_fast_tokenizer:
							raise ImportWarning(
							    "To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"
							    "You can install it with `pip install \"bert-score>=0.3.10\"`."							)

					if model_type is None:
							assert lang is not None, "either lang or model_type should be specified"
							snake_case		:						Optional[Any]								=				bert_score.utils.langamodel[lang.lower()]

					if num_layers is None:
							snake_case		:						List[str]								=				bert_score.utils.modelalayers[model_type]

					snake_case		:						List[Any]								=				get_hash(
					    model=SCREAMING_SNAKE_CASE				, num_layers=SCREAMING_SNAKE_CASE				, idf=SCREAMING_SNAKE_CASE				, rescale_with_baseline=SCREAMING_SNAKE_CASE				, use_custom_baseline=baseline_path is not None				, )

					with filter_logging_context():
							if not hasattr(self				, "cached_bertscorer"							) or self.cached_bertscorer.hash != hashcode:
									snake_case		:						Union[str, Any]								=				scorer(
									    model_type=SCREAMING_SNAKE_CASE				, num_layers=SCREAMING_SNAKE_CASE				, batch_size=SCREAMING_SNAKE_CASE				, nthreads=SCREAMING_SNAKE_CASE				, all_layers=SCREAMING_SNAKE_CASE				, idf=SCREAMING_SNAKE_CASE				, device=SCREAMING_SNAKE_CASE				, lang=SCREAMING_SNAKE_CASE				, rescale_with_baseline=SCREAMING_SNAKE_CASE				, baseline_path=SCREAMING_SNAKE_CASE				, )

					((snake_case)			,				(snake_case)			,				(snake_case))		:						Dict								=				self.cached_bertscorer.score(
					    cands=SCREAMING_SNAKE_CASE				, refs=SCREAMING_SNAKE_CASE				, verbose=SCREAMING_SNAKE_CASE				, batch_size=SCREAMING_SNAKE_CASE				, )
					snake_case		:						Tuple								=				{
					    "precision": P.tolist(),
					    "recall": R.tolist(),
					    "f1": F.tolist(),
					    "hashcode": hashcode,
					}
					return output_dict


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if references is not None:
							snake_case		:						List[Any]								=				[[ref] if isinstance(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							) else ref for ref in references]
					super().add_batch(predictions=SCREAMING_SNAKE_CASE				, references=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)



			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if isinstance(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
							snake_case		:						Tuple								=				[reference]
					super().add(prediction=SCREAMING_SNAKE_CASE				, reference=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)







"""simple docstring"""

import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger
class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      int								=     """config.json"""


			@register_to_config
			def __init__(     self				, SCREAMING_SNAKE_CASE=2				, SCREAMING_SNAKE_CASE=5				, SCREAMING_SNAKE_CASE=(2, 5)				, SCREAMING_SNAKE_CASE="for diffusion"				, SCREAMING_SNAKE_CASE=[1, 3]				, ):



					"""simple docstring"""







					pass







class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      Dict								=     """config.json"""


			@register_to_config
			def __init__(     self				, SCREAMING_SNAKE_CASE=2				, SCREAMING_SNAKE_CASE=5				, SCREAMING_SNAKE_CASE=(2, 5)				, SCREAMING_SNAKE_CASE="for diffusion"				, SCREAMING_SNAKE_CASE=[1, 3]				, ):



					"""simple docstring"""







					pass







class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      Union[str, Any]								=     """config.json"""


			@register_to_config
			def __init__(     self				, SCREAMING_SNAKE_CASE=2				, SCREAMING_SNAKE_CASE=5				, SCREAMING_SNAKE_CASE=(2, 5)				, SCREAMING_SNAKE_CASE="for diffusion"				, SCREAMING_SNAKE_CASE=[1, 3]				, SCREAMING_SNAKE_CASE=[1, 3]				, ):



					"""simple docstring"""







					pass







class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      Tuple								=     """config.json"""


			@register_to_config
			def __init__(     self				, SCREAMING_SNAKE_CASE=2				, SCREAMING_SNAKE_CASE=5				, SCREAMING_SNAKE_CASE=(2, 5)				, SCREAMING_SNAKE_CASE="for diffusion"				, SCREAMING_SNAKE_CASE=[1, 5]				, SCREAMING_SNAKE_CASE=[5, 4]				, ):



					"""simple docstring"""







					pass







class 			lowerCamelCase__    (  unittest.TestCase			):


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					with self.assertRaises(SCREAMING_SNAKE_CASE							):
							ConfigMixin.load_config("dummy_path"							)


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Dict								=				SampleObject()
					snake_case		:						Dict								=				obj.config
					assert config["a"] == 2
					assert config["b"] == 5
					assert config["c"] == (2, 5)
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]

					# init ignore private arguments
					snake_case		:						Dict								=				SampleObject(_name_or_path="lalala"							)
					snake_case		:						List[Any]								=				obj.config
					assert config["a"] == 2
					assert config["b"] == 5
					assert config["c"] == (2, 5)
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]

					# can override default
					snake_case		:						str								=				SampleObject(c=6							)
					snake_case		:						Any								=				obj.config
					assert config["a"] == 2
					assert config["b"] == 5
					assert config["c"] == 6
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]

					# can use positional arguments.
					snake_case		:						int								=				SampleObject(1				, c=6							)
					snake_case		:						Dict								=				obj.config
					assert config["a"] == 1
					assert config["b"] == 5
					assert config["c"] == 6
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						str								=				SampleObject()
					snake_case		:						List[str]								=				obj.config

					assert config["a"] == 2
					assert config["b"] == 5
					assert config["c"] == (2, 5)
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]

					with tempfile.TemporaryDirectory() as tmpdirname:
							obj.save_config(SCREAMING_SNAKE_CASE							)
							snake_case		:						Union[str, Any]								=				SampleObject.from_config(SampleObject.load_config(SCREAMING_SNAKE_CASE							)							)
							snake_case		:						Union[str, Any]								=				new_obj.config

					# unfreeze configs
					snake_case		:						List[str]								=				dict(SCREAMING_SNAKE_CASE							)
					snake_case		:						Union[str, Any]								=				dict(SCREAMING_SNAKE_CASE							)

					assert config.pop("c"							) == (2, 5)  # instantiated as tuple
					assert new_config.pop("c"							) == [2, 5]  # saved & loaded as list because of json
					config.pop("_use_default_values"							)
					assert config == new_config


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						List[Any]								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						Any								=				DDIMScheduler.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"							)

					assert ddim.__class__ == DDIMScheduler
					# no warning should be thrown
					assert cap_logger.out == ""


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						int								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						Optional[int]								=				EulerDiscreteScheduler.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"							)

					assert euler.__class__ == EulerDiscreteScheduler
					# no warning should be thrown
					assert cap_logger.out == ""


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Any								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						Union[str, Any]								=				EulerAncestralDiscreteScheduler.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"							)

					assert euler.__class__ == EulerAncestralDiscreteScheduler
					# no warning should be thrown
					assert cap_logger.out == ""


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Union[str, Any]								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						List[Any]								=				PNDMScheduler.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"							)

					assert pndm.__class__ == PNDMScheduler
					# no warning should be thrown
					assert cap_logger.out == ""


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						str								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						List[Any]								=				DDPMScheduler.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"				, prediction_type="sample"				, beta_end=8				, )

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger_a:
							snake_case		:						Optional[int]								=				DDPMScheduler.from_pretrained("google/ddpm-celebahq-256"				, beta_start=88							)

					assert ddpm.__class__ == DDPMScheduler
					assert ddpm.config.prediction_type == "sample"
					assert ddpm.config.beta_end == 8
					assert ddpm_a.config.beta_start == 88

					# no warning should be thrown
					assert cap_logger.out == ""
					assert cap_logger_a.out == ""


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Dict								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						Union[str, Any]								=				DPMSolverMultistepScheduler.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"							)

					assert dpm.__class__ == DPMSolverMultistepScheduler
					# no warning should be thrown
					assert cap_logger.out == ""



			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				SampleObject()

					snake_case		:						str								=				{k: v for k, v in config.config.items() if not k.startswith("_"							)}

					# make sure that default config has all keys in `_use_default_values`
					assert set(config_dict.keys()							) == set(config.config._use_default_values							)

					with tempfile.TemporaryDirectory() as tmpdirname:
							config.save_config(SCREAMING_SNAKE_CASE							)

							# now loading it with SampleObject2 should put f into `_use_default_values`
							snake_case		:						Optional[int]								=				SampleObjecta.from_config(SCREAMING_SNAKE_CASE							)

							assert "f" in config._use_default_values
							assert config.f == [1, 3]

					# now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
					# **BECAUSE** it is part of `config._use_default_values`
					snake_case		:						Tuple								=				SampleObjecta.from_config(config.config							)
					assert new_config.f == [5, 4]

					config.config._use_default_values.pop()
					snake_case		:						List[Any]								=				SampleObjecta.from_config(config.config							)
					assert new_config_a.f == [1, 3]

					# Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
					assert new_config_a.e == [1, 3]

