

import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
a_    :						Optional[int]    		=      pytest.mark.integration


a_    :						Optional[int]    		=      {"comet"}
a_    :						List[str]    		=      importlib.util.find_spec("fairseq") is not None

a_    :						Tuple    		=      {"code_eval"}
a_    :						List[Any]    		=      os.name == "nt"

a_    :						Dict    		=      {"bertscore", "frugalscore", "perplexity"}
a_    :						Union[str, Any]    		=      importlib.util.find_spec("transformers") is not None


def        __lowerCAmelCase    (       _UpperCamelCase		:  Any	)		->							Optional[Any]:
 '''simple docstring'''


 @wraps(_UpperCamelCase	)
 def wrapper(self		:  List[str]		,							_UpperCamelCase		:  int	):
  if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
   self.skipTest('"test requires Fairseq"'	)
  else:
   test_case(self		,							_UpperCamelCase	)

 return wrapper


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[str]	)		->							List[str]:
 '''simple docstring'''


 @wraps(_UpperCamelCase	)
 def wrapper(self		:  str		,							_UpperCamelCase		:  List[str]	):
  if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
   self.skipTest('"test requires transformers"'	)
  else:
   test_case(self		,							_UpperCamelCase	)

 return wrapper


def        __lowerCAmelCase    (       _UpperCamelCase		:  int	)		->							str:
 '''simple docstring'''


 @wraps(_UpperCamelCase	)
 def wrapper(self		:  Union[str, Any]		,							_UpperCamelCase		:  Any	):
  if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
   self.skipTest('"test not supported on Windows"'	)
  else:
   test_case(self		,							_UpperCamelCase	)

 return wrapper


def        __lowerCAmelCase    (       )		->							Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			[metric_dir.split(os.sep	)[-2] for metric_dir in glob.glob('./metrics/*/'	)]
 return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished




@parameterized.named_parameters(get_local_metric_names()   )
@for_all_test_methods(
    SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE   )
@local
class      UpperCamelCase (     parameterized.TestCase   ):
    __UpperCamelCase         ={}
    __UpperCamelCase         =None

    @pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'    )
    @pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'    )
    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'[...]'
     SCREAMING_SNAKE_CASE								=			importlib.import_module(
         datasets.load.metric_module_factory(os.path.join('metrics'							,    snake_case__    )    ).module_path    )
     SCREAMING_SNAKE_CASE								=			datasets.load.import_main_class(metric_module.__name__							,    dataset=snake_case__    )
     # check parameters
     SCREAMING_SNAKE_CASE								=			inspect.signature(metric._compute    ).parameters
     self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()    )    )  # no **kwargs
     # run doctest
     with self.patch_intensive_calls(snake_case__							,    metric_module.__name__    ):
      with self.use_local_metrics():
       try:
        SCREAMING_SNAKE_CASE								=			doctest.testmod(snake_case__							,    verbose=snake_case__							,    raise_on_error=snake_case__    )
       except doctest.UnexpectedException as e:
        raise e.exc_info[1]  # raise the exception that doctest caught
     self.assertEqual(results.failed							,    0    )
     self.assertGreater(results.attempted							,    1    )

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

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'[...]'
     SCREAMING_SNAKE_CASE								=			importlib.import_module(
         datasets.load.metric_module_factory(os.path.join('metrics'							,    snake_case__    )    ).module_path    )
     # run doctest
     with self.use_local_metrics():
      SCREAMING_SNAKE_CASE								=			doctest.testmod(snake_case__							,    verbose=snake_case__							,    raise_on_error=snake_case__    )
     self.assertEqual(results.failed							,    0    )
     self.assertGreater(results.attempted							,    1    )

    @contextmanager
    def UpperCamelCase			(			self :      int							,    snake_case__ :      Optional[Any]							,    snake_case__ :      str    ):

     """simple docstring"""






     if metric_name in self.INTENSIVE_CALLS_PATCHER:
      with self.INTENSIVE_CALLS_PATCHER[metric_name](snake_case__    ):
       yield
     else:
      yield

    @contextmanager
    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     def load_local_metric(snake_case__ :      Tuple							,    *snake_case__ :      int							,    **snake_case__ :      List[str]    ):
      return load_metric(os.path.join('metrics'							,    snake_case__    )							,    *snake_case__							,    **snake_case__    )

     with patch('datasets.load_metric'    ) as mock_load_metric:
      SCREAMING_SNAKE_CASE								=			load_local_metric
      yield




    @classmethod
    def UpperCamelCase			(			cls :      Any							,    snake_case__ :      Any    ):

     """simple docstring"""






     def wrapper(snake_case__ :      Any    ):
      SCREAMING_SNAKE_CASE								=			contextmanager(snake_case__    )
      SCREAMING_SNAKE_CASE								=			patcher
      return patcher

     return wrapper


@LocalMetricTest.register_intensive_calls_patcher('bleurt'	)
def        __lowerCAmelCase    (       _UpperCamelCase		:  Any	)		->							Dict:
 '''simple docstring'''


 import tensorflow.compat.va as tf
 from bleurt.score import Predictor

 tf.flags.DEFINE_string('sv'		,							''		,							''	)  # handle pytest cli flags




 class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):

     def UpperCamelCase			(			self :      str							,    snake_case__ :      Optional[Any]    ):

      """simple docstring"""






      assert len(input_dict['input_ids']    ) == 2
      return np.array([1.03, 1.04]    )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
 with patch('bleurt.score._create_predictor'	) as mock_create_predictor:
  SCREAMING_SNAKE_CASE								=			MockedPredictor()
  yield


@LocalMetricTest.register_intensive_calls_patcher('bertscore'	)
def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[Any]	)		->							str:
 '''simple docstring'''


 import torch

 def bert_cos_score_idf(_UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  Any		,							*_UpperCamelCase		:  Dict		,							**_UpperCamelCase		:  str	):
  return torch.tensor([[1.0, 1.0, 1.0]] * len(_UpperCamelCase	)	)

 # mock get_model which is supposed to do download a bert model
 # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
 with patch('bert_score.scorer.get_model'	), patch(
     'bert_score.scorer.bert_cos_score_idf'	) as mock_bert_cos_score_idf:
  SCREAMING_SNAKE_CASE								=			bert_cos_score_idf
  yield


@LocalMetricTest.register_intensive_calls_patcher('comet'	)
def        __lowerCAmelCase    (       _UpperCamelCase		:  Dict	)		->							List[Any]:
 '''simple docstring'''


 def load_from_checkpoint(_UpperCamelCase		:  Any	):




  class      UpperCamelCase :

      def UpperCamelCase			(			self :      str							,    snake_case__ :      Any							,    *snake_case__ :      List[Any]							,    **snake_case__ :      Dict    ):

       """simple docstring"""






       assert len(snake_case__    ) == 2
       SCREAMING_SNAKE_CASE								=			[0.19, 0.92]
       return scores, sum(snake_case__    ) / len(snake_case__    )


  return Model()

 # mock load_from_checkpoint which is supposed to do download a bert model
 # mock load_from_checkpoint which is supposed to do download a bert model
 with patch('comet.download_model'	) as mock_download_model:
  SCREAMING_SNAKE_CASE								=			None
  with patch('comet.load_from_checkpoint'	) as mock_load_from_checkpoint:
   SCREAMING_SNAKE_CASE								=			load_from_checkpoint
   yield




def        __lowerCAmelCase    (       )		->							List[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			load_metric(os.path.join('metrics'		,							'seqeval'	)	)
 SCREAMING_SNAKE_CASE								=			'ERROR'
 SCREAMING_SNAKE_CASE								=			f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
 with pytest.raises(_UpperCamelCase		,							match=re.escape(_UpperCamelCase	)	):
  metric.compute(predictions=[]		,							references=[]		,							scheme=_UpperCamelCase	)




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         ="openai/whisper-base"
    __UpperCamelCase         =(
        "This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
        "transcribed text."
    )
    __UpperCamelCase         ="transcriber"
    __UpperCamelCase         =WhisperProcessor
    __UpperCamelCase         =WhisperForConditionalGeneration

    __UpperCamelCase         =["audio"]
    __UpperCamelCase         =["text"]

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

     """simple docstring"""






     return self.pre_processor(snake_case__							,    return_tensors='pt'    ).input_features

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      Tuple    ):

     """simple docstring"""






     return self.model.generate(inputs=snake_case__    )




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

     """simple docstring"""






     return self.pre_processor.batch_decode(snake_case__							,    skip_special_tokens=snake_case__    )[0]

