

import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
       import torch


lowerCamelCase           =					random.Random()




def 			UpperCAmelCase__				(			_A      : Any	,      _A      : List[str]=1.0	,      _A      : Any=None	,      _A      : Optional[int]=None							):





  '''simple docstring'''







  if rng is None:
    a__  =global_rng

  a__  =[]
  for batch_idx in range(shape[0]							):
    values.append([]							)
    for _ in range(shape[1]							):
      values[-1].append(rng.random() * scale							)

  return values







@require_torch
class 						__magic_name__			(							unittest.TestCase							):

   '''simple docstring'''




   def __init__(				self,    lowercase_,    lowercase_=7,    lowercase_=400,    lowercase_=2000,    lowercase_=1,    lowercase_=0.0,    lowercase_=16000,    lowercase_=True,    lowercase_=80,    lowercase_=16,    lowercase_=64,    lowercase_="hann_window",    lowercase_=80,    lowercase_=7600,    lowercase_=1E-10,    lowercase_=True,    )							->    Optional[int]:






     """simple docstring"""






     a__  =parent
     a__  =batch_size
     a__  =min_seq_length
     a__  =max_seq_length
     a__  =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     a__  =feature_size
     a__  =padding_value
     a__  =sampling_rate
     a__  =do_normalize
     a__  =num_mel_bins
     a__  =hop_length
     a__  =win_length
     a__  =win_function
     a__  =fmin
     a__  =fmax
     a__  =mel_floor
     a__  =return_attention_mask



   def  _UpperCAmelCase (				self       )							->    Tuple:






     """simple docstring"""






     return {
         "feature_size": self.feature_size,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "do_normalize": self.do_normalize,
         "num_mel_bins": self.num_mel_bins,
         "hop_length": self.hop_length,
         "win_length": self.win_length,
         "win_function": self.win_function,
         "fmin": self.fmin,
         "fmax": self.fmax,
         "mel_floor": self.mel_floor,
         "return_attention_mask": self.return_attention_mask,
     }



   def  _UpperCAmelCase (				self,    lowercase_=False,    lowercase_=False       )							->    Dict:






     """simple docstring"""






     def _flatten(lowercase_       ):
       return list(itertools.chain(*__a       )       )

     if equal_length:
       a__  =floats_list((self.batch_size, self.max_seq_length)       )
     else:
       # make sure that inputs increase in size
       a__  =[
           _flatten(floats_list((x, self.feature_size)       )       )
           for x in range(self.min_seq_length,    self.max_seq_length,    self.seq_length_diff       )
       ]

     if numpify:
       a__  =[np.asarray(__a       ) for x in speech_inputs]

     return speech_inputs



   def  _UpperCAmelCase (				self,    lowercase_=False,    lowercase_=False       )							->    Dict:






     """simple docstring"""






     if equal_length:
       a__  =[floats_list((self.max_seq_length, self.num_mel_bins)       ) for _ in range(self.batch_size       )]
     else:
       # make sure that inputs increase in size
       a__  =[
           floats_list((x, self.num_mel_bins)       )
           for x in range(self.min_seq_length,    self.max_seq_length,    self.seq_length_diff       )
       ]

     if numpify:
       a__  =[np.asarray(__a       ) for x in speech_inputs]

     return speech_inputs







@require_torch
class 						__magic_name__			(							lowerCAmelCase_		,       unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__ :	Optional[Any]    =   SpeechTaFeatureExtractor



   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     a__  =SpeechTaFeatureExtractionTester(self       )



   def  _UpperCAmelCase (				self,    lowercase_       )							->    Union[str, Any]:






     """simple docstring"""






     self.assertTrue(np.all(np.mean(__a,    axis=0       ) < 1E-3       )       )
     self.assertTrue(np.all(np.abs(np.var(__a,    axis=0       ) - 1       ) < 1E-3       )       )



   def  _UpperCAmelCase (				self       )							->    List[str]:






     """simple docstring"""






     a__  =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
     # create three inputs of length 800, 1000, and 1200
     a__  =[floats_list((1, x)       )[0] for x in range(800,    1400,    200       )]
     a__  =[np.asarray(__a       ) for speech_input in speech_inputs]

     # Test not batched input
     a__  =feat_extract(speech_inputs[0],    return_tensors='''np'''       ).input_values
     a__  =feat_extract(np_speech_inputs[0],    return_tensors='''np'''       ).input_values
     self.assertTrue(np.allclose(__a,    __a,    atol=1E-3       )       )

     # Test batched
     a__  =feat_extract(__a,    return_tensors='''np'''       ).input_values
     a__  =feat_extract(__a,    return_tensors='''np'''       ).input_values
     for enc_seq_a, enc_seq_a in zip(__a,    __a       ):
       self.assertTrue(np.allclose(__a,    __a,    atol=1E-3       )       )



   def  _UpperCAmelCase (				self       )							->    List[str]:






     """simple docstring"""






     a__  =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
     a__  =[floats_list((1, x)       )[0] for x in range(800,    1400,    200       )]

     a__  =['longest', 'max_length', 'do_not_pad']
     a__  =[None, 1600, None]
     for max_length, padding in zip(__a,    __a       ):
       a__  =feat_extract(__a,    padding=__a,    max_length=__a,    return_tensors='''np'''       )
       a__  =processed.input_values

       self._check_zero_mean_unit_variance(input_values[0][:800]       )
       self.assertTrue(input_values[0][800:].sum() < 1E-6       )
       self._check_zero_mean_unit_variance(input_values[1][:1000]       )
       self.assertTrue(input_values[0][1000:].sum() < 1E-6       )
       self._check_zero_mean_unit_variance(input_values[2][:1200]       )



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     a__  =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
     a__  =range(800,    1400,    200       )
     a__  =[floats_list((1, x)       )[0] for x in lengths]

     a__  =['longest', 'max_length', 'do_not_pad']
     a__  =[None, 1600, None]

     for max_length, padding in zip(__a,    __a       ):
       a__  =feat_extract(__a,    max_length=__a,    padding=__a       )
       a__  =processed.input_values

       self._check_zero_mean_unit_variance(input_values[0][:800]       )
       self._check_zero_mean_unit_variance(input_values[1][:1000]       )
       self._check_zero_mean_unit_variance(input_values[2][:1200]       )



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






     a__  =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
     a__  =[floats_list((1, x)       )[0] for x in range(800,    1400,    200       )]
     a__  =feat_extract(
         __a,    truncation=__a,    max_length=1000,    padding='''max_length''',    return_tensors='''np'''       )
     a__  =processed.input_values

     self._check_zero_mean_unit_variance(input_values[0, :800]       )
     self._check_zero_mean_unit_variance(input_values[1]       )
     self._check_zero_mean_unit_variance(input_values[2]       )



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






     a__  =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
     a__  =[floats_list((1, x)       )[0] for x in range(800,    1400,    200       )]
     a__  =feat_extract(
         __a,    truncation=__a,    max_length=1000,    padding='''longest''',    return_tensors='''np'''       )
     a__  =processed.input_values

     self._check_zero_mean_unit_variance(input_values[0, :800]       )
     self._check_zero_mean_unit_variance(input_values[1, :1000]       )
     self._check_zero_mean_unit_variance(input_values[2]       )

     # make sure that if max_length < longest -> then pad to max_length
     self.assertTrue(input_values.shape == (3, 1000)       )

     a__  =[floats_list((1, x)       )[0] for x in range(800,    1400,    200       )]
     a__  =feat_extract(
         __a,    truncation=__a,    max_length=2000,    padding='''longest''',    return_tensors='''np'''       )
     a__  =processed.input_values

     self._check_zero_mean_unit_variance(input_values[0, :800]       )
     self._check_zero_mean_unit_variance(input_values[1, :1000]       )
     self._check_zero_mean_unit_variance(input_values[2]       )

     # make sure that if max_length > longest -> then pad to longest
     self.assertTrue(input_values.shape == (3, 1200)       )



   def  _UpperCAmelCase (				self       )							->    List[str]:






     """simple docstring"""






     a__  =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
     a__  =np.random.rand(100       ).astype(np.floataa       )
     a__  =np_speech_inputs.tolist()

     for inputs in [py_speech_inputs, np_speech_inputs]:
       a__  =feature_extractor.pad([{'''input_values''': inputs}],    return_tensors='''np'''       )
       self.assertTrue(np_processed.input_values.dtype == np.floataa       )
       a__  =feature_extractor.pad([{'''input_values''': inputs}],    return_tensors='''pt'''       )
       self.assertTrue(pt_processed.input_values.dtype == torch.floataa       )



   def  _UpperCAmelCase (				self       )							->    Union[str, Any]:






     """simple docstring"""






     a__  =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
     # create three inputs of length 800, 1000, and 1200
     a__  =[floats_list((1, x)       )[0] for x in range(800,    1400,    200       )]
     a__  =[np.asarray(__a       ) for speech_input in speech_inputs]

     # Test feature size
     a__  =feature_extractor(audio_target=__a,    padding=__a,    return_tensors='''np'''       ).input_values
     self.assertTrue(input_values.ndim == 3       )
     self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins       )

     # Test not batched input
     a__  =feature_extractor(speech_inputs[0],    return_tensors='''np'''       ).input_values
     a__  =feature_extractor(np_speech_inputs[0],    return_tensors='''np'''       ).input_values
     self.assertTrue(np.allclose(__a,    __a,    atol=1E-3       )       )

     # Test batched
     a__  =feature_extractor(__a,    return_tensors='''np'''       ).input_values
     a__  =feature_extractor(__a,    return_tensors='''np'''       ).input_values
     for enc_seq_a, enc_seq_a in zip(__a,    __a       ):
       self.assertTrue(np.allclose(__a,    __a,    atol=1E-3       )       )

     # Test 2-D numpy arrays are batched.
     a__  =[floats_list((1, x)       )[0] for x in (800, 800, 800)]
     a__  =np.asarray(__a       )
     a__  =feature_extractor(__a,    return_tensors='''np'''       ).input_values
     a__  =feature_extractor(__a,    return_tensors='''np'''       ).input_values
     for enc_seq_a, enc_seq_a in zip(__a,    __a       ):
       self.assertTrue(np.allclose(__a,    __a,    atol=1E-3       )       )



   def  _UpperCAmelCase (				self       )							->    Tuple:






     """simple docstring"""






     a__  =self.feat_extract_tester.prepare_inputs_for_target()
     a__  =self.feature_extraction_class(**self.feat_extract_dict       )
     a__  =feat_extract.model_input_names[0]

     a__  =BatchFeature({input_name: speech_inputs}       )

     self.assertTrue(all(len(__a       ) == len(__a       ) for x, y in zip(__a,    processed_features[input_name]       )       )       )

     a__  =self.feat_extract_tester.prepare_inputs_for_target(equal_length=__a       )
     a__  =BatchFeature({input_name: speech_inputs},    tensor_type='''np'''       )

     a__  =processed_features[input_name]

     if len(batch_features_input.shape       ) < 3:
       a__  =batch_features_input[:, :, None]

     self.assertTrue(
         batch_features_input.shape
         == (self.feat_extract_tester.batch_size, len(speech_inputs[0]       ), self.feat_extract_tester.num_mel_bins)       )



   @require_torch
   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     a__  =self.feat_extract_tester.prepare_inputs_for_target(equal_length=__a       )
     a__  =self.feature_extraction_class(**self.feat_extract_dict       )
     a__  =feat_extract.model_input_names[0]

     a__  =BatchFeature({input_name: speech_inputs},    tensor_type='''pt'''       )

     a__  =processed_features[input_name]

     if len(batch_features_input.shape       ) < 3:
       a__  =batch_features_input[:, :, None]

     self.assertTrue(
         batch_features_input.shape
         == (self.feat_extract_tester.batch_size, len(speech_inputs[0]       ), self.feat_extract_tester.num_mel_bins)       )



   @require_torch
   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     a__  =self.feature_extraction_class(**self.feat_extract_dict       )
     a__  =self.feat_extract_tester.prepare_inputs_for_target()
     a__  =feat_extract.model_input_names[0]

     a__  =BatchFeature({input_name: speech_inputs}       )

     a__  =feat_extract.num_mel_bins  # hack!

     a__  =feat_extract.pad(__a,    padding='''longest''',    return_tensors='''np'''       )[input_name]
     a__  =feat_extract.pad(__a,    padding='''longest''',    return_tensors='''pt'''       )[input_name]

     self.assertTrue(abs(input_np.astype(np.floataa       ).sum() - input_pt.numpy().astype(np.floataa       ).sum()       ) < 1E-2       )



   def  _UpperCAmelCase (				self       )							->    Union[str, Any]:






     """simple docstring"""






     a__  =self.feat_extract_dict
     a__  =True
     a__  =self.feature_extraction_class(**__a       )
     a__  =self.feat_extract_tester.prepare_inputs_for_target()
     a__  =[len(__a       ) for x in speech_inputs]
     a__  =feat_extract.model_input_names[0]

     a__  =BatchFeature({input_name: speech_inputs}       )

     a__  =feat_extract.num_mel_bins  # hack!

     a__  =feat_extract.pad(__a,    padding='''longest''',    return_tensors='''np'''       )
     self.assertIn('''attention_mask''',    __a       )
     self.assertListEqual(list(processed.attention_mask.shape       ),    list(processed[input_name].shape[:2]       )       )
     self.assertListEqual(processed.attention_mask.sum(-1       ).tolist(),    __a       )



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






     a__  =self.feat_extract_dict
     a__  =True
     a__  =self.feature_extraction_class(**__a       )
     a__  =self.feat_extract_tester.prepare_inputs_for_target()
     a__  =[len(__a       ) for x in speech_inputs]
     a__  =feat_extract.model_input_names[0]

     a__  =BatchFeature({input_name: speech_inputs}       )
     a__  =min(__a       )

     a__  =feat_extract.num_mel_bins  # hack!

     a__  =feat_extract.pad(
         __a,    padding='''max_length''',    max_length=__a,    truncation=__a,    return_tensors='''np'''       )
     self.assertIn('''attention_mask''',    __a       )
     self.assertListEqual(
         list(processed_pad.attention_mask.shape       ),    [processed_pad[input_name].shape[0], max_length]       )
     self.assertListEqual(
         processed_pad.attention_mask[:, :max_length].sum(-1       ).tolist(),    [max_length for x in speech_inputs]       )



   def  _UpperCAmelCase (				self,    lowercase_       )							->    Union[str, Any]:






     """simple docstring"""






     from datasets import load_dataset

     a__  =load_dataset('''hf-internal-testing/librispeech_asr_dummy''',    '''clean''',    split='''validation'''       )
     # automatic decoding with librispeech
     a__  =ds.sort('''id'''       ).select(range(__a       )       )[:num_samples]['audio']

     return [x["array"] for x in speech_samples]



   def  _UpperCAmelCase (				self       )							->    Union[str, Any]:






     """simple docstring"""






     a__  =torch.tensor(
         [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
          3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
          2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
          4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
          7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
          4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]       )
     # fmt: on

     a__  =self._load_datasamples(1       )
     a__  =SpeechTaFeatureExtractor()
     a__  =feature_extractor(__a,    return_tensors='''pt'''       ).input_values
     self.assertEquals(input_values.shape,    (1, 93680)       )
     self.assertTrue(torch.allclose(input_values[0, :30],    __a,    atol=1E-6       )       )



   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     a__  =torch.tensor(
         [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
          -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
          -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
          -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]       )
     # fmt: on

     a__  =self._load_datasamples(1       )
     a__  =SpeechTaFeatureExtractor()
     a__  =feature_extractor(audio_target=__a,    return_tensors='''pt'''       ).input_values
     self.assertEquals(input_values.shape,    (1, 366, 80)       )
     self.assertTrue(torch.allclose(input_values[0, 0, :30],    __a,    atol=1E-4       )       )




'''simple docstring'''



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
__lowercase	:						Tuple       =	pytest.mark.integration


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

__lowercase	:						str       =	{'code_eval'}
__lowercase	:						List[Any]       =	os.name == 'nt'

__lowercase	:						Optional[Any]       =	{'bertscore', 'frugalscore', 'perplexity'}
__lowercase	:						Optional[Any]       =	importlib.util.find_spec('transformers') is not None






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					@wraps(_SCREAMING_SNAKE_CASE   )
					def wrapper(self      :   int						,      _SCREAMING_SNAKE_CASE      :   List[Any]   ):
										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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					@wraps(_SCREAMING_SNAKE_CASE   )
					def wrapper(self      :   Dict						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
										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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					@wraps(_SCREAMING_SNAKE_CASE   )
					def wrapper(self      :   Optional[int]						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
										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   lowerCamelCase							():
					__a			:       List[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(
    lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_					)
@local
class 							__UpperCamelCase				(		parameterized.TestCase					):
	A_				=    {}
	A_				=    None




	@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'							)
	@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'							)
	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						__a			:       int					    =  '[...]'
						__a			:       Tuple					    =  importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join('metrics'						,				__a							)							).module_path							)
						__a			:       Optional[Any]					    =  datasets.load.import_main_class(metric_module.__name__						,				dataset=__a							)
						# check parameters
						__a			:       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(__a						,				metric_module.__name__							):
											with self.use_local_metrics():
																try:
																					__a			:       str					    =  doctest.testmod(__a						,				verbose=__a						,				raise_on_error=__a							)
																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						,				__a							):

						'''simple docstring'''





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




	@contextmanager
	def       __UpperCAmelCase							(   self						,				__a						,				__a							):

						'''simple docstring'''





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




	@contextmanager
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						def load_local_metric(__a						,				*__a						,				**__a							):
											return load_metric(os.path.join('metrics'						,				__a							)						,				*__a						,				**__a							)

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





	@classmethod
	def       __UpperCAmelCase							(   cls						,				__a							):

						'''simple docstring'''





						def wrapper(__a							):
											__a			:       Optional[Any]					    =  contextmanager(__a							)
											__a			:       str					    =  patcher
											return patcher

						return wrapper






@LocalMetricTest.register_intensive_calls_patcher('bleurt'   )
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					import tensorflow.compat.va as tf
					from bleurt.score import Predictor

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





					class 							__UpperCamelCase				(		lowerCAmelCase_					):




						def       __UpperCAmelCase							(   self						,				__a							):

											'''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:
										__a			:       Dict					    =  MockedPredictor()
										yield






@LocalMetricTest.register_intensive_calls_patcher('bertscore'   )
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					import torch

					def bert_cos_score_idf(_SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   str						,      *_SCREAMING_SNAKE_CASE      :   int						,      **_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
										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:
										__a			:       str					    =  bert_cos_score_idf
										yield






@LocalMetricTest.register_intensive_calls_patcher('comet'   )
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					def load_from_checkpoint(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):





										class 							__UpperCamelCase				:




											def       __UpperCAmelCase							(   self						,				__a						,				*__a						,				**__a							):

																'''simple docstring'''





																assert len(__a							) == 2
																__a			:       Dict					    =  [0.19, 0.92]
																return scores, sum(__a							) / len(__a							)



										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:
										__a			:       str					    =  None
										with patch('comet.load_from_checkpoint'   ) as mock_load_from_checkpoint:
															__a			:       int					    =  load_from_checkpoint
															yield






def   lowerCamelCase							():
					__a			:       Optional[Any]					    =  load_metric(os.path.join('metrics'						,      'seqeval'   )   )
					__a			:       List[str]					    =  'ERROR'
					__a			:       List[str]					    =  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   )



