



'''simple docstring'''






from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


lowerCAmelCase:					List[Any]						       =						['text', 'image', 'audio']
def       lowerCamelCase__       (						_A				):
  a						:					Optional[Any]        =     []

  for input_type in input_types:
    if input_type == "text":
      inputs.append('Text input'				)
    elif input_type == "image":
      inputs.append(
          Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'				)				) / '000000039769.png'				).resize((512, 512)				)				)
    elif input_type == "audio":
      inputs.append(torch.ones(3000				)				)
    elif isinstance(__lowerCAmelCase ,			__lowerCAmelCase				):
      inputs.append(create_inputs(__lowerCAmelCase				)				)
    else:
      raise ValueError(f"""Invalid type requested: {input_type}"""				)

  return inputs
def       lowerCamelCase__       (						_A				):
  a						:					Optional[Any]        =     []

  for output in outputs:
    if isinstance(__lowerCAmelCase ,			(str, AgentText)				):
      output_types.append('text'				)
    elif isinstance(__lowerCAmelCase ,			(Image.Image, AgentImage)				):
      output_types.append('image'				)
    elif isinstance(__lowerCAmelCase ,			(torch.Tensor, AgentAudio)				):
      output_types.append('audio'				)
    else:
      raise ValueError(f"""Invalid output: {output}"""				)

  return output_types

@is_tool_test
class 							a__:







    def 			lowercase_       (      self :  Any	):
      self.assertTrue(hasattr(self.tool						,			'inputs'	)	)
      self.assertTrue(hasattr(self.tool						,			'outputs'	)	)

      a						:					int        =     self.tool.inputs
      for _input in inputs:
        if isinstance(_input						,			lowerCamelCase__	):
          for __input in _input:
            self.assertTrue(__input in authorized_types	)
        else:
          self.assertTrue(_input in authorized_types	)

      a						:					Dict        =     self.tool.outputs
      for _output in outputs:
        self.assertTrue(_output in authorized_types	)







    def 			lowercase_       (      self :  Any	):
      a						:					Any        =     create_inputs(self.tool.inputs	)
      a						:					Optional[int]        =     self.tool(*lowerCamelCase__	)

      # There is a single output
      if len(self.tool.outputs	) == 1:
        a						:					str        =     [outputs]

      self.assertListEqual(output_types(lowerCamelCase__	)						,			self.tool.outputs	)







    def 			lowercase_       (      self :  Dict	):
      self.assertTrue(hasattr(self.tool						,			'description'	)	)
      self.assertTrue(hasattr(self.tool						,			'default_checkpoint'	)	)
      self.assertTrue(self.tool.description.startswith('This is a tool that'	)	)







    def 			lowercase_       (      self :  Union[str, Any]	):
      a						:					Union[str, Any]        =     create_inputs(self.tool.inputs	)
      a						:					Dict        =     self.tool(*lowerCamelCase__	)

      if not isinstance(lowerCamelCase__						,			lowerCamelCase__	):
        a						:					List[str]        =     [outputs]

      self.assertEqual(len(lowerCamelCase__	)						,			len(self.tool.outputs	)	)

      for output, output_type in zip(lowerCamelCase__						,			self.tool.outputs	):
        a						:					Optional[Any]        =     AGENT_TYPE_MAPPING[output_type]
        self.assertTrue(isinstance(lowerCamelCase__						,			lowerCamelCase__	)	)







    def 			lowercase_       (      self :  Optional[int]	):
      a						:					Optional[int]        =     create_inputs(self.tool.inputs	)

      a						:					List[Any]        =     []

      for _input, input_type in zip(lowerCamelCase__						,			self.tool.inputs	):
        if isinstance(lowerCamelCase__						,			lowerCamelCase__	):
          _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input	) for _input_type in input_type]	)
        else:
          _inputs.append(AGENT_TYPE_MAPPING[input_type](_input	)	)

        # Should not raise an error
      a						:					str        =     self.tool(*lowerCamelCase__	)

      if not isinstance(lowerCamelCase__						,			lowerCamelCase__	):
        a						:					Dict        =     [outputs]

      self.assertEqual(len(lowerCamelCase__	)						,			len(self.tool.outputs	)	)



'''simple docstring'''


from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


lowerCamelCase__					=   '\\n@misc{wu2016googles,\n      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n'

lowerCamelCase__					=   '\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n'

lowerCamelCase__					=   '\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    \'google_bleu\': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results["google_bleu"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results["google_bleu"], 2))\n        0.4\n'






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




			def 							lowerCAmelCase__			(						self     :   int       )      ->MetricInfo:



					'''simple docstring'''







					return datasets.MetricInfo(
					    description=_DESCRIPTION		,     citation=_CITATION		,     inputs_description=_KWARGS_DESCRIPTION		,     features=datasets.Features(
					        {
					            "predictions": datasets.Sequence(datasets.Value("string"		,     id="token"       )		,     id="sequence"       ),
					            "references": datasets.Sequence(
					                datasets.Sequence(datasets.Value("string"		,     id="token"       )		,     id="sequence"       )		,     id="references"       ),
					        }       )		,     )






			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   List[List[List[str]]]		,     lowerCamelCase__     :   List[List[str]]		,     lowerCamelCase__     :   int = 1		,     lowerCamelCase__     :   int = 4		,     )      ->Dict[str, float]:



					'''simple docstring'''







					return {
					    "google_bleu": gleu_score.corpus_gleu(
					        list_of_references=lowerCamelCase__		,     hypotheses=lowerCamelCase__		,     min_len=lowerCamelCase__		,     max_len=lowerCamelCase__       )
					}



