






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


A__      :     List[str]       =					['text', 'image', 'audio']






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     []

				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     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     []

				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  _UpperCAmelCase   :

				"""simple docstring"""




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

								'''simple docstring'''
								self.assertTrue(hasattr(self.tool,    '''inputs'''    )    )
								self.assertTrue(hasattr(self.tool,    '''outputs'''    )    )

								lowercase__  					=     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    )

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




				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     create_inputs(self.tool.inputs    )
								lowercase__  					=     self.tool(*lowerCamelCase    )

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

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




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								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		:       str    ):

								'''simple docstring'''
								lowercase__  					=     create_inputs(self.tool.inputs    )
								lowercase__  					=     self.tool(*lowerCamelCase    )

								if not isinstance(lowerCamelCase,    lowerCamelCase    ):
												lowercase__  					=     [outputs]

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

								for output, output_type in zip(lowerCamelCase,    self.tool.outputs    ):
												lowercase__  					=     AGENT_TYPE_MAPPING[output_type]
												self.assertTrue(isinstance(lowerCamelCase,    lowerCamelCase    )    )




				def 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								lowercase__  					=     create_inputs(self.tool.inputs    )

								lowercase__  					=     []

								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
								lowercase__  					=     self.tool(*lowerCamelCase    )

								if not isinstance(lowerCamelCase,    lowerCamelCase    ):
												lowercase__  					=     [outputs]

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












from jiwer import compute_measures

import datasets


A__      :     Tuple       =					'\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'

A__      :     Optional[int]       =					'\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

A__      :     Dict       =					'\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> wer = datasets.load_metric("wer")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,_KWARGS_DESCRIPTION      )
class  _UpperCAmelCase   (			datasets.Metric      ):

				"""simple docstring"""




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

								'''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/jitsi/jiwer/'''],    reference_urls=[
								        '''https://en.wikipedia.org/wiki/Word_error_rate''',
								    ],    )




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       str=None,    lowerCamelCase		:       Union[str, Any]=None,    lowerCamelCase		:       List[str]=False    ):

								'''simple docstring'''
								if concatenate_texts:
												return compute_measures(lowerCamelCase,    lowerCamelCase    )["wer"]
								else:
												lowercase__  					=     0
												lowercase__  					=     0
												for prediction, reference in zip(lowerCamelCase,    lowerCamelCase    ):
																lowercase__  					=     compute_measures(lowerCamelCase,    lowerCamelCase    )
																incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																total += measures["substitutions"] + measures["deletions"] + measures["hits"]
												return incorrect / total




