


'''simple docstring'''


import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel





class     lowercase__  :





							'''simple docstring'''


							def __init__(    self    ,					__snake_case    ,					__snake_case=99    ,					__snake_case=13    ,					__snake_case=7    ,					__snake_case=9    ,					__snake_case=True    ,					__snake_case=True    ,					__snake_case=False    ,					__snake_case=32    ,					__snake_case=5    ,					__snake_case=4    ,					__snake_case=37    ,					__snake_case=8    ,					__snake_case=0.1    ,					__snake_case=0.002    ,					__snake_case=1    ,					__snake_case=0    ,					__snake_case=0    ,					__snake_case=None    ,					__snake_case=None    ,					):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      parent
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      batch_size
											_SCREAMING_SNAKE_CASE :       Tuple     		=      encoder_seq_length
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      decoder_seq_length
											# For common tests
											_SCREAMING_SNAKE_CASE :       Dict     		=      self.decoder_seq_length
											_SCREAMING_SNAKE_CASE :       Tuple     		=      is_training
											_SCREAMING_SNAKE_CASE :       int     		=      use_attention_mask
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      use_labels
											_SCREAMING_SNAKE_CASE :       str     		=      vocab_size
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      hidden_size
											_SCREAMING_SNAKE_CASE :       int     		=      num_hidden_layers
											_SCREAMING_SNAKE_CASE :       int     		=      num_attention_heads
											_SCREAMING_SNAKE_CASE :       Any     		=      d_ff
											_SCREAMING_SNAKE_CASE :       str     		=      relative_attention_num_buckets
											_SCREAMING_SNAKE_CASE :       Dict     		=      dropout_rate
											_SCREAMING_SNAKE_CASE :       Any     		=      initializer_factor
											_SCREAMING_SNAKE_CASE :       str     		=      eos_token_id
											_SCREAMING_SNAKE_CASE :       List[str]     		=      pad_token_id
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      decoder_start_token_id
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      None
											_SCREAMING_SNAKE_CASE :       List[str]     		=      decoder_layers






							def UpperCAmelCase_	(    self  ):
											return TaConfig.from_pretrained("""google/umt5-base"""  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case=None    ,					__snake_case=None    ,					__snake_case=None    ,					__snake_case=None    ,					__snake_case=None    ,					):
											if attention_mask is None:
															_SCREAMING_SNAKE_CASE :       Tuple     		=      input_ids.ne(config.pad_token_id  )
											if decoder_attention_mask is None:
															_SCREAMING_SNAKE_CASE :       List[str]     		=      decoder_input_ids.ne(config.pad_token_id  )
											if head_mask is None:
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      torch.ones(config.num_hidden_layers    ,					config.num_attention_heads    ,					device=__snake_case  )
											if decoder_head_mask is None:
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      torch.ones(config.num_decoder_layers    ,					config.num_attention_heads    ,					device=__snake_case  )
											if cross_attn_head_mask is None:
															_SCREAMING_SNAKE_CASE :       Any     		=      torch.ones(
															    config.num_decoder_layers    ,					config.num_attention_heads    ,					device=__snake_case  )
											return {
											    "input_ids": input_ids,
											    "decoder_input_ids": decoder_input_ids,
											    "attention_mask": attention_mask,
											    "decoder_attention_mask": decoder_attention_mask,
											    "head_mask": head_mask,
											    "decoder_head_mask": decoder_head_mask,
											    "cross_attn_head_mask": cross_attn_head_mask,
											}






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      ids_tensor([self.batch_size, self.encoder_seq_length]    ,					self.vocab_size  )
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      ids_tensor([self.batch_size, self.decoder_seq_length]    ,					self.vocab_size  )

											# we need to clamp the input ids here to avoid having pad token in between
											# this is because for NllbMoe the position_ids are prepared such that
											# all pad tokens have pos id = 2 and rest are between 2..seq_length
											# and the seq_length here is seq_length - num_pad_tokens
											# but when using past, there is no way of knowing if the past input ids had
											# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
											# position_ids being off by num_pad_tokens in past input
											_SCREAMING_SNAKE_CASE :       int     		=      input_ids.clamp(self.pad_token_id + 1  )
											_SCREAMING_SNAKE_CASE :       str     		=      decoder_input_ids.clamp(self.pad_token_id + 1  )

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.get_config()
											_SCREAMING_SNAKE_CASE :       int     		=      config.num_attention_heads
											_SCREAMING_SNAKE_CASE :       int     		=      self.prepare_inputs_dict(__snake_case    ,					__snake_case    ,					__snake_case  )
											return config, input_dict






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[str]     		=      self.prepare_config_and_inputs()
											return config, inputs_dict






							def UpperCAmelCase_	(    self  ):
											return TaConfig(
											    vocab_size=166    ,					d_model=self.hidden_size    ,					d_ff=self.d_ff    ,					d_kv=self.hidden_size // self.num_attention_heads    ,					num_layers=self.num_hidden_layers    ,					num_decoder_layers=self.decoder_layers    ,					num_heads=self.num_attention_heads    ,					relative_attention_num_buckets=self.relative_attention_num_buckets    ,					dropout_rate=self.dropout_rate    ,					initializer_factor=self.initializer_factor    ,					eos_token_id=self.eos_token_id    ,					bos_token_id=self.pad_token_id    ,					pad_token_id=self.pad_token_id    ,					decoder_start_token_id=self.decoder_start_token_id    ,					)






							def UpperCAmelCase_	(    self  ):
											return TaConfig(
											    vocab_size=self.vocab_size    ,					d_model=self.hidden_size    ,					d_ff=self.d_ff    ,					d_kv=self.hidden_size // self.num_attention_heads    ,					num_layers=self.num_hidden_layers    ,					num_decoder_layers=self.decoder_layers    ,					num_heads=self.num_attention_heads    ,					relative_attention_num_buckets=self.relative_attention_num_buckets    ,					dropout_rate=self.dropout_rate    ,					initializer_factor=self.initializer_factor    ,					eos_token_id=self.eos_token_id    ,					bos_token_id=self.pad_token_id    ,					pad_token_id=self.pad_token_id    ,					decoder_start_token_id=self.decoder_start_token_id    ,					)






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      UMTaModel(config=__snake_case  )
											model.to(__snake_case  )
											model.eval()
											_SCREAMING_SNAKE_CASE :       str     		=      model(
											    input_ids=__snake_case    ,					decoder_input_ids=__snake_case    ,					attention_mask=__snake_case    ,					decoder_attention_mask=__snake_case    ,					)
											_SCREAMING_SNAKE_CASE :       str     		=      model(input_ids=__snake_case    ,					decoder_input_ids=__snake_case  )
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      result.last_hidden_state
											_SCREAMING_SNAKE_CASE :       Dict     		=      result.past_key_values
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      result.encoder_last_hidden_state

											self.parent.assertEqual(encoder_output.size()    ,					(self.batch_size, self.encoder_seq_length, self.hidden_size)  )
											self.parent.assertEqual(decoder_output.size()    ,					(self.batch_size, self.decoder_seq_length, self.hidden_size)  )
											# There should be `num_layers` key value embeddings stored in decoder_past
											self.parent.assertEqual(len(__snake_case  )    ,					config.num_layers  )
											# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
											self.parent.assertEqual(len(decoder_past[0]  )    ,					4  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       str     		=      UMTaModel(config=__snake_case  ).get_decoder().to(__snake_case  ).eval()
											# first forward pass
											_SCREAMING_SNAKE_CASE :       str     		=      model(__snake_case    ,					use_cache=__snake_case  )
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      model(__snake_case  )
											_SCREAMING_SNAKE_CASE :       str     		=      model(__snake_case    ,					use_cache=__snake_case  )

											self.parent.assertTrue(len(__snake_case  ) == len(__snake_case  )  )
											self.parent.assertTrue(len(__snake_case  ) == len(__snake_case  ) + 1  )

											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       int     		=      outputs.to_tuple()

											# create hypothetical next token and extent to next_input_ids
											_SCREAMING_SNAKE_CASE :       Tuple     		=      ids_tensor((self.batch_size, 1)    ,					config.vocab_size  )

											# append to next input_ids and
											_SCREAMING_SNAKE_CASE :       int     		=      torch.cat([input_ids, next_tokens]    ,					dim=-1  )

											_SCREAMING_SNAKE_CASE :       Dict     		=      model(__snake_case  )["""last_hidden_state"""]
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      model(__snake_case    ,					past_key_values=__snake_case  )["""last_hidden_state"""]

											# select random slice
											_SCREAMING_SNAKE_CASE :       Tuple     		=      ids_tensor((1,)    ,					output_from_past.shape[-1]  ).item()
											_SCREAMING_SNAKE_CASE :       Tuple     		=      output_from_no_past[:, -1, random_slice_idx].detach()
											_SCREAMING_SNAKE_CASE :       Dict     		=      output_from_past[:, 0, random_slice_idx].detach()

											# test that outputs are equal for slice
											self.parent.assertTrue(torch.allclose(__snake_case    ,					__snake_case    ,					atol=1e-3  )  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       str     		=      UMTaModel(config=__snake_case  ).to(__snake_case  ).half().eval()
											_SCREAMING_SNAKE_CASE :       str     		=      model(**__snake_case  )["""last_hidden_state"""]
											self.parent.assertFalse(torch.isnan(__snake_case  ).any().item()  )





@require_torch
class     lowercase__  (						_snake_case     ,     _snake_case     ,     _snake_case     ,     unittest.TestCase					):





							'''simple docstring'''


							A_							:      int      	=  (
							    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
							)
							A_							:      Union[str, Any]      	=  (UMTaForConditionalGeneration,) if is_torch_available() else ()
							A_							:      Optional[Any]      	=  (
							    {
							        """conversational""": UMTaForConditionalGeneration,
							        """feature-extraction""": UMTaModel,
							        """summarization""": UMTaForConditionalGeneration,
							        """text2text-generation""": UMTaForConditionalGeneration,
							        """translation""": UMTaForConditionalGeneration,
							        """question-answering""": UMTaForQuestionAnswering,
							    }
							    if is_torch_available()
							    else {}
							)
							A_							:      Optional[int]      	=  True
							A_							:      List[Any]      	=  False
							A_							:      Any      	=  False
							A_							:      Optional[int]      	=  True
							A_							:      Any      	=  True
							# The small UMT5 model needs higher percentages for CPU/MP tests
							A_							:      List[str]      	=  [0.8, 0.9]






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      UMTaModelTester(self  )






							@unittest.skip("""Test has a segmentation fault on torch 1.8.0"""  )
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.model_tester.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      UMTaModel(config_and_inputs[0]  ).to(__snake_case  )
											with tempfile.TemporaryDirectory() as tmpdirname:
															torch.onnx.export(
															    __snake_case    ,					(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])    ,					f"""{tmpdirname}/t5_test.onnx"""    ,					export_params=__snake_case    ,					opset_version=9    ,					input_names=["""input_ids""", """decoder_input_ids"""]    ,					)






							@unittest.skipIf(torch_device == """cpu"""    ,					"""Cant do half precision"""  )
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model_fpaa_forward(*__snake_case  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.model_tester.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE :       str     		=      config_and_inputs[0]
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      UMTaForConditionalGeneration(__snake_case  ).eval()
											model.to(__snake_case  )

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      {
											    """head_mask""": torch.zeros(config.num_layers    ,					config.num_heads    ,					device=__snake_case  ),
											    """decoder_head_mask""": torch.zeros(config.num_decoder_layers    ,					config.num_heads    ,					device=__snake_case  ),
											    """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers    ,					config.num_heads    ,					device=__snake_case  ),
											}

											for attn_name, (name, mask) in zip(__snake_case    ,					head_masking.items()  ):
															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      {name: mask}
															# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
															if name == "head_mask":
																			_SCREAMING_SNAKE_CASE :       Optional[int]     		=      torch.ones(
																			    config.num_decoder_layers    ,					config.num_heads    ,					device=__snake_case  )

															_SCREAMING_SNAKE_CASE :       List[Any]     		=      model.generate(
															    config_and_inputs[1]["""input_ids"""]    ,					num_beams=1    ,					max_length=3    ,					output_attentions=__snake_case    ,					return_dict_in_generate=__snake_case    ,					**__snake_case    ,					)
															# We check the state of decoder_attentions and cross_attentions just from the last step
															_SCREAMING_SNAKE_CASE :       Any     		=      out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
															self.assertEqual(sum([w.sum().item() for w in attn_weights]  )    ,					0.0  )






							@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases."""  )
							def UpperCAmelCase_	(    self  ):
											pass





@require_torch
@require_sentencepiece
@require_tokenizers
class     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


							@slow
							@unittest.skip(
							    """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"""  )
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Any     		=      UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""    ,					return_dict=__snake_case  ).to(__snake_case  )
											_SCREAMING_SNAKE_CASE :       Dict     		=      AutoTokenizer.from_pretrained("""google/umt5-small"""    ,					use_fast=__snake_case    ,					legacy=__snake_case  )
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      [
											    """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
											    """No se como puedo <extra_id_0>.""",
											    """This is the reason why we <extra_id_0> them.""",
											    """The <extra_id_0> walks in <extra_id_1>, seats""",
											    """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
											]
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      tokenizer(__snake_case    ,					return_tensors="""pt"""    ,					padding=__snake_case  ).input_ids
											# fmt: off
											_SCREAMING_SNAKE_CASE :       Dict     		=      torch.tensor(
											    [
											        [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
											        [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
											    ]  )
											# fmt: on
											torch.testing.assert_allclose(__snake_case    ,					__snake_case  )

											_SCREAMING_SNAKE_CASE :       Tuple     		=      model.generate(input_ids.to(__snake_case  )  )
											_SCREAMING_SNAKE_CASE :       int     		=      [
											    """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
											    """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
											    """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
											    """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
											    """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
											]
											_SCREAMING_SNAKE_CASE :       str     		=      tokenizer.batch_decode(__snake_case  )
											self.assertEqual(__snake_case    ,					__snake_case  )








'''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
UpperCAmelCase_		:   Union[str, Any]							   =		pytest.mark.integration


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

UpperCAmelCase_		:   Optional[Any]							   =		{'code_eval'}
UpperCAmelCase_		:   Optional[int]							   =		os.name == 'nt'

UpperCAmelCase_		:   Dict							   =		{'bertscore', 'frugalscore', 'perplexity'}
UpperCAmelCase_		:   Dict							   =		importlib.util.find_spec('transformers') is not None







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				@wraps(SCREAMING_SNAKE_CASE__     )
				def wrapper(self							,      SCREAMING_SNAKE_CASE__     ):
								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 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				@wraps(SCREAMING_SNAKE_CASE__     )
				def wrapper(self							,      SCREAMING_SNAKE_CASE__     ):
								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 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				@wraps(SCREAMING_SNAKE_CASE__     )
				def wrapper(self							,      SCREAMING_SNAKE_CASE__     ):
								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 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Dict     		=      [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(
    _snake_case     ,     _snake_case     ,     _snake_case					)
@local
class     lowercase__  (						parameterized.TestCase					):





							'''simple docstring'''


							A_							:      Optional[int]      	=  {}
							A_							:      Union[str, Any]      	=  None






							@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""  )
							@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""  )
							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       str     		=      """[...]"""
											_SCREAMING_SNAKE_CASE :       Any     		=      importlib.import_module(
											    datasets.load.metric_module_factory(os.path.join("""metrics"""    ,					__snake_case  )  ).module_path  )
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      datasets.load.import_main_class(metric_module.__name__    ,					dataset=__snake_case  )
											# check parameters
											_SCREAMING_SNAKE_CASE :       Tuple     		=      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 :       int     		=      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    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      """[...]"""
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      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 :       List[str]     		=      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    ,					__snake_case    ,					__snake_case  ):
											if metric_name in self.INTENSIVE_CALLS_PATCHER:
															with self.INTENSIVE_CALLS_PATCHER[metric_name](__snake_case  ):
																			yield
											else:
															yield






							@contextmanager
							def UpperCAmelCase_	(    self  ):
											def load_local_metric(__snake_case    ,					*__snake_case    ,					**__snake_case  ):
															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 :       Union[str, Any]     		=      load_local_metric
															yield






							@classmethod
							def UpperCAmelCase_	(    cls    ,					__snake_case  ):
											def wrapper(__snake_case  ):
															_SCREAMING_SNAKE_CASE :       Any     		=      contextmanager(__snake_case  )
															_SCREAMING_SNAKE_CASE :       int     		=      patcher
															return patcher

											return wrapper







@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""     )
def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





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

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





				class     lowercase__  (						_snake_case					):





											'''simple docstring'''


											def UpperCAmelCase_	(    self    ,					__snake_case  ):
															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 :       Any     		=      MockedPredictor()
								yield







@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""     )
def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				import torch

				def bert_cos_score_idf(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      *SCREAMING_SNAKE_CASE__							,      **SCREAMING_SNAKE_CASE__     ):
								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 :       Any     		=      bert_cos_score_idf
								yield







@LocalMetricTest.register_intensive_calls_patcher("""comet"""     )
def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				def load_from_checkpoint(SCREAMING_SNAKE_CASE__     ):





								class     lowercase__  :





															'''simple docstring'''


															def UpperCAmelCase_	(    self    ,					__snake_case    ,					*__snake_case    ,					**__snake_case  ):
																			assert len(__snake_case  ) == 2
																			_SCREAMING_SNAKE_CASE :       Dict     		=      [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 :       Any     		=      None
								with patch("""comet.load_from_checkpoint"""     ) as mock_load_from_checkpoint:
												_SCREAMING_SNAKE_CASE :       List[str]     		=      load_from_checkpoint
												yield







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      load_metric(os.path.join("""metrics"""							,      """seqeval"""     )     )
				_SCREAMING_SNAKE_CASE :       List[str]     		=      """ERROR"""
				_SCREAMING_SNAKE_CASE :       Tuple     		=      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__     )




