



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
_UpperCAmelCase     =			pytest.mark.integration


_UpperCAmelCase     =			{"""comet"""}
_UpperCAmelCase     =			importlib.util.find_spec("""fairseq""") is not None

_UpperCAmelCase     =			{"""code_eval"""}
_UpperCAmelCase     =			os.name == """nt"""

_UpperCAmelCase     =			{"""bertscore""", """frugalscore""", """perplexity"""}
_UpperCAmelCase     =			importlib.util.find_spec("""transformers""") is not None





def      __magic_name__				(							lowercase ):





   @wraps(SCREAMING_SNAKE_CASE__ )
   def wrapper(self							,			lowercase ):
      if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
         self.skipTest("""\"test requires Fairseq\"""" )
      else:
         test_case(self							,			SCREAMING_SNAKE_CASE__ )

   return wrapper





def      __magic_name__				(							lowercase ):





   @wraps(SCREAMING_SNAKE_CASE__ )
   def wrapper(self							,			lowercase ):
      if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
         self.skipTest("""\"test requires transformers\"""" )
      else:
         test_case(self							,			SCREAMING_SNAKE_CASE__ )

   return wrapper





def      __magic_name__				(							lowercase ):





   @wraps(SCREAMING_SNAKE_CASE__ )
   def wrapper(self							,			lowercase ):
      if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
         self.skipTest("""\"test not supported on Windows\"""" )
      else:
         test_case(self							,			SCREAMING_SNAKE_CASE__ )

   return wrapper





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=[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(
    _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase       )
@local
class 					a	(  parameterized.TestCase       ):
  UpperCamelCase	:				Optional[Any]     						=					{}
  UpperCamelCase	:				Any     						=					None



  @pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""		)
  @pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""		)
  def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					Tuple		)     ->					Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			="""[...]"""
     SCREAMING_SNAKE_CASE_: Optional[int]     			=importlib.import_module(
         datasets.load.metric_module_factory(os.path.join("""metrics"""       ,		lowercase__		)		).module_path		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=datasets.load.import_main_class(metric_module.__name__       ,		dataset=lowercase__		)
     # check parameters
     SCREAMING_SNAKE_CASE_: Dict     			=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(lowercase__       ,		metric_module.__name__		):
        with self.use_local_metrics():
           try:
              SCREAMING_SNAKE_CASE_: Optional[int]     			=doctest.testmod(lowercase__       ,		verbose=lowercase__       ,		raise_on_error=lowercase__		)
           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 	lowerCamelCase__       (						self :					Optional[int]       ,		lowerCAmelCase :					Any		)     ->					str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			="""[...]"""
     SCREAMING_SNAKE_CASE_: Any     			=importlib.import_module(
         datasets.load.metric_module_factory(os.path.join("""metrics"""       ,		lowercase__		)		).module_path		)
     # run doctest
     with self.use_local_metrics():
        SCREAMING_SNAKE_CASE_: Optional[int]     			=doctest.testmod(lowercase__       ,		verbose=lowercase__       ,		raise_on_error=lowercase__		)
     self.assertEqual(results.failed       ,		0		)
     self.assertGreater(results.attempted       ,		1		)



  @contextmanager
  def 	lowerCamelCase__       (						self :					List[Any]       ,		lowerCAmelCase :					List[str]       ,		lowerCAmelCase :					List[Any]		)     ->					Dict:


     '''simple docstring'''




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



  @contextmanager
  def 	lowerCamelCase__       (						self :					str		)     ->					Union[str, Any]:


     '''simple docstring'''




     def load_local_metric(lowerCAmelCase :					Any       ,		*lowerCAmelCase :					Optional[Any]       ,		**lowerCAmelCase :					Optional[Any]		):
        return load_metric(os.path.join("""metrics"""       ,		lowercase__		)       ,		*lowercase__       ,		**lowercase__		)

     with patch("""datasets.load_metric"""		) as mock_load_metric:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=load_local_metric
        yield





  @classmethod
  def 	lowerCamelCase__       (						cls :					int       ,		lowerCAmelCase :					Optional[Any]		)     ->					Any:


     '''simple docstring'''




     def wrapper(lowerCAmelCase :					Any		):
        SCREAMING_SNAKE_CASE_: List[str]     			=contextmanager(lowercase__		)
        SCREAMING_SNAKE_CASE_: int     			=patcher
        return patcher

     return wrapper







@LocalMetricTest.register_intensive_calls_patcher("""bleurt""" )
def      __magic_name__				(							lowercase ):
   import tensorflow.compat.va as tf
   from bleurt.score import Predictor

   tf.flags.DEFINE_string("""sv"""							,			""""""							,			"""""" )  # handle pytest cli flags
   class 					a	(  _UpperCAmelCase       ):



     def 	lowerCamelCase__       (						self :					Dict       ,		lowerCAmelCase :					Dict		)     ->					str:


        '''simple docstring'''




        assert len(input_dict["""input_ids"""]		) == 2
        return np.array([1.0_3, 1.0_4]		)

    # 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_: Optional[int]     			=MockedPredictor()
      yield





@LocalMetricTest.register_intensive_calls_patcher("""bertscore""" )
def      __magic_name__				(							lowercase ):
   import torch

   def bert_cos_score_idf(lowercase							,			lowercase							,			*lowercase							,			**lowercase ):
      return torch.tensor([[1.0, 1.0, 1.0]] * len(SCREAMING_SNAKE_CASE__ ) )

   # 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_: int     			=bert_cos_score_idf
      yield





@LocalMetricTest.register_intensive_calls_patcher("""comet""" )
def      __magic_name__				(							lowercase ):





   def load_from_checkpoint(lowercase ):
      class 					a	:



        def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					Any       ,		*lowerCAmelCase :					int       ,		**lowerCAmelCase :					int		)     ->					Any:


           '''simple docstring'''




           assert len(lowercase__		) == 2
           SCREAMING_SNAKE_CASE_: List[str]     			=[0.1_9, 0.9_2]
           return scores, sum(lowercase__		) / len(lowercase__		)







      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_: List[Any]     			=None
      with patch("""comet.load_from_checkpoint""" ) as mock_load_from_checkpoint:
         SCREAMING_SNAKE_CASE_: List[Any]     			=load_from_checkpoint
         yield





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








"""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


_UpperCAmelCase     =			["""text""", """image""", """audio"""]





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: str     			=[]

   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(lowercase							,			lowercase ):
         inputs.append(create_inputs(lowercase ) )
      else:
         raise ValueError(f'''Invalid type requested: {input_type}''' )

   return inputs





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: int     			=[]

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

   return output_types
@is_tool_test
class 					a	:



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					List[Any]:


     '''simple docstring'''




     self.assertTrue(hasattr(self.tool       ,		"""inputs"""		)		)
     self.assertTrue(hasattr(self.tool       ,		"""outputs"""		)		)

     SCREAMING_SNAKE_CASE_: Optional[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		)

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



  def 	lowerCamelCase__       (						self :					str		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=create_inputs(self.tool.inputs		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=self.tool(*lowerCAmelCase		)

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

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



  def 	lowerCamelCase__       (						self :					str		)     ->					Optional[Any]:


     '''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 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=create_inputs(self.tool.inputs		)
     SCREAMING_SNAKE_CASE_: Tuple     			=self.tool(*lowerCAmelCase		)

     if not isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: int     			=[outputs]

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

     for output, output_type in zip(lowerCAmelCase       ,		self.tool.outputs		):
        SCREAMING_SNAKE_CASE_: int     			=AGENT_TYPE_MAPPING[output_type]
        self.assertTrue(isinstance(lowerCAmelCase       ,		lowerCAmelCase		)		)





  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=create_inputs(self.tool.inputs		)

     SCREAMING_SNAKE_CASE_: Union[str, 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
     SCREAMING_SNAKE_CASE_: Dict     			=self.tool(*lowerCAmelCase		)

     if not isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: List[str]     			=[outputs]

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



