

import os
import sys
import unittest


a_    :						List[Any]    		=      os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
a_    :						Tuple    		=      os.path.join(git_repo_path, "src", "diffusers")




class      UpperCamelCase (     unittest.TestCase   ):

				def UpperCamelCase			(			self :      Union[str, Any]    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			find_backend('    if not is_torch_available():'    )
					self.assertEqual(snake_case__							,    'torch'    )

					# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
					# self.assertEqual(backend_with_underscore, "tensorflow_text")

					SCREAMING_SNAKE_CASE								=			find_backend('    if not (is_torch_available() and is_transformers_available()):'    )
					self.assertEqual(snake_case__							,    'torch_and_transformers'    )

					# double_backend_with_underscore = find_backend(
					#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
					# )
					# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

					SCREAMING_SNAKE_CASE								=			find_backend(
					    '    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'    )
					self.assertEqual(snake_case__							,    'torch_and_transformers_and_onnx'    )

				def UpperCamelCase			(			self :      str    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			read_init()
					# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
					self.assertIn('torch'							,    snake_case__    )
					self.assertIn('torch_and_transformers'							,    snake_case__    )
					self.assertIn('flax_and_transformers'							,    snake_case__    )
					self.assertIn('torch_and_transformers_and_onnx'							,    snake_case__    )

					# Likewise, we can't assert on the exact content of a key
					self.assertIn('UNet2DModel'							,    objects['torch']    )
					self.assertIn('FlaxUNet2DConditionModel'							,    objects['flax']    )
					self.assertIn('StableDiffusionPipeline'							,    objects['torch_and_transformers']    )
					self.assertIn('FlaxStableDiffusionPipeline'							,    objects['flax_and_transformers']    )
					self.assertIn('LMSDiscreteScheduler'							,    objects['torch_and_scipy']    )
					self.assertIn('OnnxStableDiffusionPipeline'							,    objects['torch_and_transformers_and_onnx']    )

				def UpperCamelCase			(			self :      Union[str, Any]    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			create_dummy_object('CONSTANT'							,    '\'torch\''    )
					self.assertEqual(snake_case__							,    '\nCONSTANT = None\n'    )

					SCREAMING_SNAKE_CASE								=			create_dummy_object('function'							,    '\'torch\''    )
					self.assertEqual(
					    snake_case__							,    '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'    )

					SCREAMING_SNAKE_CASE								=			'\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n'
					SCREAMING_SNAKE_CASE								=			create_dummy_object('FakeClass'							,    '\'torch\''    )
					self.assertEqual(snake_case__							,    snake_case__    )




				def UpperCamelCase			(			self :      Tuple    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			'# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n'
					SCREAMING_SNAKE_CASE								=			create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}    )
					self.assertEqual(dummy_files['torch']							,    snake_case__    )




import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


a_    :						List[str]    		=      version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
      from nltk import word_tokenize


a_    :						Dict    		=      "\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

a_    :						str    		=      "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

a_    :						int    		=      "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"




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

    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     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'    ),
             }    )							,    codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py']							,    reference_urls=[
             'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
             'https://en.wikipedia.org/wiki/METEOR',
         ]							,    )

    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      int    ):

     """simple docstring"""






     import nltk

     nltk.download('wordnet'    )
     if NLTK_VERSION >= version.Version('3.6.5'    ):
      nltk.download('punkt'    )
     if NLTK_VERSION >= version.Version('3.6.6'    ):
      nltk.download('omw-1.4'    )




    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      str							,    snake_case__ :      List[Any]							,    snake_case__ :      List[Any]=0.9							,    snake_case__ :      Optional[Any]=3							,    snake_case__ :      Any=0.5    ):

     """simple docstring"""






     if NLTK_VERSION >= version.Version('3.6.5'    ):
      SCREAMING_SNAKE_CASE								=			[
          meteor_score.single_meteor_score(
              word_tokenize(snake_case__    )							,    word_tokenize(snake_case__    )							,    alpha=snake_case__							,    beta=snake_case__							,    gamma=snake_case__    )
          for ref, pred in zip(snake_case__							,    snake_case__    )
      ]
     else:
      SCREAMING_SNAKE_CASE								=			[
          meteor_score.single_meteor_score(snake_case__							,    snake_case__							,    alpha=snake_case__							,    beta=snake_case__							,    gamma=snake_case__    )
          for ref, pred in zip(snake_case__							,    snake_case__    )
      ]

     return {"meteor": np.mean(snake_case__    )}

