





'''simple docstring'''


import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
       from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor

class  lowerCAmelCase_		:




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=13      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=[1, 2, 1]      ,  _lowerCAmelCase=[2, 2, 4]      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=2.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=10      ,  _lowerCAmelCase=8      ,  )     ->				Dict:
     _lowerCAmelCase						      =   parent
     _lowerCAmelCase						      =   batch_size
     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   embed_dim
     _lowerCAmelCase						      =   depths
     _lowerCAmelCase						      =   num_heads
     _lowerCAmelCase						      =   window_size
     _lowerCAmelCase						      =   mlp_ratio
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   drop_path_rate
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   use_absolute_embeddings
     _lowerCAmelCase						      =   patch_norm
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   is_training
     _lowerCAmelCase						      =   scope
     _lowerCAmelCase						      =   use_labels
     _lowerCAmelCase						      =   type_sequence_label_size
     _lowerCAmelCase						      =   encoder_stride




 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

     _lowerCAmelCase						      =   None
     if self.use_labels:
         _lowerCAmelCase						      =   ids_tensor([self.batch_size]      ,  self.type_sequence_label_size		)

     _lowerCAmelCase						      =   self.get_config()

     return config, pixel_values, labels




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     return SwinvaConfig(
         image_size=self.image_size      ,  patch_size=self.patch_size      ,  num_channels=self.num_channels      ,  embed_dim=self.embed_dim      ,  depths=self.depths      ,  num_heads=self.num_heads      ,  window_size=self.window_size      ,  mlp_ratio=self.mlp_ratio      ,  qkv_bias=self.qkv_bias      ,  hidden_dropout_prob=self.hidden_dropout_prob      ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,  drop_path_rate=self.drop_path_rate      ,  hidden_act=self.hidden_act      ,  use_absolute_embeddings=self.use_absolute_embeddings      ,  path_norm=self.patch_norm      ,  layer_norm_eps=self.layer_norm_eps      ,  initializer_range=self.initializer_range      ,  encoder_stride=self.encoder_stride      ,  )




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   SwinvaModel(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)

     _lowerCAmelCase						      =   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
     _lowerCAmelCase						      =   int(config.embed_dim * 2 ** (len(config.depths		) - 1)		)

     self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, expected_seq_len, expected_dim)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Tuple:
     _lowerCAmelCase						      =   SwinvaForMaskedImageModeling(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)
     self.parent.assertEqual(
         result.logits.shape      ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)		)

     # test greyscale images
     _lowerCAmelCase						      =   1
     _lowerCAmelCase						      =   SwinvaForMaskedImageModeling(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()

     _lowerCAmelCase						      =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, 1, self.image_size, self.image_size)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[int]:
     _lowerCAmelCase						      =   self.type_sequence_label_size
     _lowerCAmelCase						      =   SwinvaForImageClassification(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  labels=_lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.type_sequence_label_size)		)





 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.prepare_config_and_inputs()
     _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase						      =   config_and_inputs
     _lowerCAmelCase						      =   {"pixel_values": pixel_values}
     return config, inputs_dict







@require_torch
class  lowerCAmelCase_		(		__magic_name__    ,__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				Optional[int]								=       (
     (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
 )
 __lowerCamelCase    :				Any								=       (
     {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
     if is_torch_available()
     else {}
 )

 __lowerCamelCase    :				Optional[int]								=       False
 __lowerCamelCase    :				List[Any]								=       False
 __lowerCamelCase    :				Dict								=       False
 __lowerCamelCase    :				str								=       False




 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   SwinvaModelTester(self		)
     _lowerCAmelCase						      =   ConfigTester(self      ,  config_class=_lowerCAmelCase      ,  embed_dim=37		)




 def 				_snake_case (						self		)     ->				int:
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()




 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCAmelCase		)




 @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."		)
 def 				_snake_case (						self		)     ->				Optional[int]:
     pass




 @unittest.skip(reason="Swinv2 does not use inputs_embeds"		)
 def 				_snake_case (						self		)     ->				Tuple:
     pass




 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         self.assertIsInstance(model.get_input_embeddings()      ,  (nn.Module)		)
         _lowerCAmelCase						      =   model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(_lowerCAmelCase      ,  nn.Linear		)		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         _lowerCAmelCase						      =   inspect.signature(model.forward		)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _lowerCAmelCase						      =   [*signature.parameters.keys()]

         _lowerCAmelCase						      =   ["pixel_values"]
         self.assertListEqual(arg_names[:1]      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()
     _lowerCAmelCase						      =   True

     for model_class in self.all_model_classes:
         _lowerCAmelCase						      =   True
         _lowerCAmelCase						      =   False
         _lowerCAmelCase						      =   True
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         model.to(_lowerCAmelCase		)
         model.eval()
         with torch.no_grad():
             _lowerCAmelCase						      =   model(**self._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase		)		)
         _lowerCAmelCase						      =   outputs.attentions
         _lowerCAmelCase						      =   len(self.model_tester.depths		)
         self.assertEqual(len(_lowerCAmelCase		)      ,  _lowerCAmelCase		)

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         _lowerCAmelCase						      =   True
         _lowerCAmelCase						      =   config.window_size**2
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         model.to(_lowerCAmelCase		)
         model.eval()
         with torch.no_grad():
             _lowerCAmelCase						      =   model(**self._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase		)		)
         _lowerCAmelCase						      =   outputs.attentions
         self.assertEqual(len(_lowerCAmelCase		)      ,  _lowerCAmelCase		)

         self.assertListEqual(
             list(attentions[0].shape[-3:]		)      ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,  )
         _lowerCAmelCase						      =   len(_lowerCAmelCase		)

         # Check attention is always last and order is fine
         _lowerCAmelCase						      =   True
         _lowerCAmelCase						      =   True
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         model.to(_lowerCAmelCase		)
         model.eval()
         with torch.no_grad():
             _lowerCAmelCase						      =   model(**self._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase		)		)

         if hasattr(self.model_tester      ,  "num_hidden_states_types"		):
             _lowerCAmelCase						      =   self.model_tester.num_hidden_states_types
         else:
             # also another +1 for reshaped_hidden_states
             _lowerCAmelCase						      =   2
         self.assertEqual(out_len + added_hidden_states      ,  len(_lowerCAmelCase		)		)

         _lowerCAmelCase						      =   outputs.attentions

         self.assertEqual(len(_lowerCAmelCase		)      ,  _lowerCAmelCase		)

         self.assertListEqual(
             list(self_attentions[0].shape[-3:]		)      ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,  )




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				List[Any]:
     _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()

     with torch.no_grad():
         _lowerCAmelCase						      =   model(**self._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase		)		)

     _lowerCAmelCase						      =   outputs.hidden_states

     _lowerCAmelCase						      =   getattr(
         self.model_tester      ,  "expected_num_hidden_layers"      ,  len(self.model_tester.depths		) + 1		)
     self.assertEqual(len(_lowerCAmelCase		)      ,  _lowerCAmelCase		)

     # Swinv2 has a different seq_length
     _lowerCAmelCase						      =   (
         config.patch_size
         if isinstance(config.patch_size      ,  collections.abc.Iterable		)
         else (config.patch_size, config.patch_size)
     )

     _lowerCAmelCase						      =   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

     self.assertListEqual(
         list(hidden_states[0].shape[-2:]		)      ,  [num_patches, self.model_tester.embed_dim]      ,  )

     _lowerCAmelCase						      =   outputs.reshaped_hidden_states
     self.assertEqual(len(_lowerCAmelCase		)      ,  _lowerCAmelCase		)

     _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase						      =   reshaped_hidden_states[0].shape
     _lowerCAmelCase						      =   (
         reshaped_hidden_states[0].view(_lowerCAmelCase      ,  _lowerCAmelCase      ,  height * width		).permute(0      ,  2      ,  1		)
     )
     self.assertListEqual(
         list(reshaped_hidden_states.shape[-2:]		)      ,  [num_patches, self.model_tester.embed_dim]      ,  )




 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()

     _lowerCAmelCase						      =   (
         self.model_tester.image_size
         if isinstance(self.model_tester.image_size      ,  collections.abc.Iterable		)
         else (self.model_tester.image_size, self.model_tester.image_size)
     )

     for model_class in self.all_model_classes:
         _lowerCAmelCase						      =   True
         self.check_hidden_states_output(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _lowerCAmelCase						      =   True

         self.check_hidden_states_output(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()
     _lowerCAmelCase						      =   3

     _lowerCAmelCase						      =   (
         self.model_tester.image_size
         if isinstance(self.model_tester.image_size      ,  collections.abc.Iterable		)
         else (self.model_tester.image_size, self.model_tester.image_size)
     )
     _lowerCAmelCase						      =   (
         config.patch_size
         if isinstance(config.patch_size      ,  collections.abc.Iterable		)
         else (config.patch_size, config.patch_size)
     )

     _lowerCAmelCase						      =   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
     _lowerCAmelCase						      =   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

     for model_class in self.all_model_classes:
         _lowerCAmelCase						      =   True
         self.check_hidden_states_output(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  (padded_height, padded_width)		)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _lowerCAmelCase						      =   True
         self.check_hidden_states_output(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  (padded_height, padded_width)		)




 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase		)




 @slow
 def 				_snake_case (						self		)     ->				Optional[int]:
     for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						      =   SwinvaModel.from_pretrained(_lowerCAmelCase		)
         self.assertIsNotNone(_lowerCAmelCase		)





 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()

     _lowerCAmelCase						      =   _config_zero_init(_lowerCAmelCase		)
     for model_class in self.all_model_classes:
         _lowerCAmelCase						      =   model_class(config=_lowerCAmelCase		)
         for name, param in model.named_parameters():
             if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
                 self.assertIn(
                     ((param.data.mean() * 1E9).round() / 1E9).item()      ,  [0.0, 1.0]      ,  msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''      ,  )







@require_vision
@require_torch
class  lowerCAmelCase_		(		unittest.TestCase		):




 @cached_property
 def 				_snake_case (						self		)     ->				Optional[Any]:
     return (
         AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"		)
         if is_vision_available()
         else None
     )





 @slow
 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"		).to(
         _lowerCAmelCase		)
     _lowerCAmelCase						      =   self.default_image_processor

     _lowerCAmelCase						      =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
     _lowerCAmelCase						      =   image_processor(images=_lowerCAmelCase      ,  return_tensors="pt"		).to(_lowerCAmelCase		)

     # forward pass
     with torch.no_grad():
         _lowerCAmelCase						      =   model(**_lowerCAmelCase		)

     # verify the logits
     _lowerCAmelCase						      =   torch.Size((1, 1000)		)
     self.assertEqual(outputs.logits.shape      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.tensor([-0.3947, -0.4306, 0.0026]		).to(_lowerCAmelCase		)
     self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,  _lowerCAmelCase      ,  atol=1E-4		)		)









'''simple docstring'''


import re
import string

import numpy as np

import datasets


_SCREAMING_SNAKE_CASE									=				"\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"

_SCREAMING_SNAKE_CASE									=				"\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    33.3\n\n"

_SCREAMING_SNAKE_CASE									=				"\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,_KWARGS_DESCRIPTION		)
class  lowerCAmelCase_		(		datasets.Metric		):




 def 				_snake_case (						self		)     ->				List[str]:
     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"		),
             }		)      ,  reference_urls=[]      ,  )





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=False      ,  )     ->				str:
     if regexes_to_ignore is not None:
         for s in regexes_to_ignore:
             _lowerCAmelCase						      =   np.array([re.sub(_lowerCAmelCase      ,  ""      ,  _lowerCAmelCase		) for x in predictions]		)
             _lowerCAmelCase						      =   np.array([re.sub(_lowerCAmelCase      ,  ""      ,  _lowerCAmelCase		) for x in references]		)
     else:
         _lowerCAmelCase						      =   np.asarray(_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.asarray(_lowerCAmelCase		)

     if ignore_case:
         _lowerCAmelCase						      =   np.char.lower(_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.char.lower(_lowerCAmelCase		)

     if ignore_punctuation:
         _lowerCAmelCase						      =   string.punctuation.maketrans(""      ,  ""      ,  string.punctuation		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)

     if ignore_numbers:
         _lowerCAmelCase						      =   string.digits.maketrans(""      ,  ""      ,  string.digits		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)

     _lowerCAmelCase						      =   predictions == references

     return {"exact_match": np.mean(_lowerCAmelCase		) * 100}


