

"""simple docstring"""

import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
    import torch

    from transformers import (
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
    )






class  __a				:





       """simple docstring"""


       def __init__(      self ,				snake_case ,				snake_case=13 ,				snake_case=7 ,				snake_case=True ,				snake_case=True ,				snake_case=True ,				snake_case=True ,				snake_case=99 ,				snake_case=32 ,				snake_case=5 ,				snake_case=4 ,				snake_case=37 ,				snake_case="gelu" ,				snake_case=0.1 ,				snake_case=0.1 ,				snake_case=512 ,				snake_case=16 ,				snake_case=2 ,				snake_case=0.02 ,				snake_case=3 ,				snake_case=4 ,				snake_case=None ,				snake_case=1_000 ,				):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							parent
        lowerCAmelCase__      :							str    =							batch_size
        lowerCAmelCase__      :							List[Any]    =							seq_length
        lowerCAmelCase__      :							Tuple    =							is_training
        lowerCAmelCase__      :							int    =							use_input_mask
        lowerCAmelCase__      :							Tuple    =							use_token_type_ids
        lowerCAmelCase__      :							str    =							use_labels
        lowerCAmelCase__      :							Any    =							vocab_size
        lowerCAmelCase__      :							List[str]    =							hidden_size
        lowerCAmelCase__      :							str    =							num_hidden_layers
        lowerCAmelCase__      :							Tuple    =							num_attention_heads
        lowerCAmelCase__      :							List[str]    =							intermediate_size
        lowerCAmelCase__      :							Tuple    =							hidden_act
        lowerCAmelCase__      :							str    =							hidden_dropout_prob
        lowerCAmelCase__      :							List[str]    =							attention_probs_dropout_prob
        lowerCAmelCase__      :							Union[str, Any]    =							max_position_embeddings
        lowerCAmelCase__      :							str    =							type_vocab_size
        lowerCAmelCase__      :							Optional[int]    =							type_sequence_label_size
        lowerCAmelCase__      :							str    =							initializer_range
        lowerCAmelCase__      :							List[str]    =							num_labels
        lowerCAmelCase__      :							str    =							num_choices
        lowerCAmelCase__      :							Any    =							scope
        lowerCAmelCase__      :							int    =							range_bbox






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[int]    =							ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size      )

        lowerCAmelCase__      :							Optional[Any]    =							ids_tensor([self.batch_size, self.seq_length, 4] ,				self.range_bbox      )
        # Ensure that bbox is legal
        for i in range(bbox.shape[0]      ):
         for j in range(bbox.shape[1]      ):
          if bbox[i, j, 3] < bbox[i, j, 1]:
           lowerCAmelCase__      :							str    =							bbox[i, j, 3]
           lowerCAmelCase__      :							List[Any]    =							bbox[i, j, 1]
           lowerCAmelCase__      :							Optional[Any]    =							t
          if bbox[i, j, 2] < bbox[i, j, 0]:
           lowerCAmelCase__      :							str    =							bbox[i, j, 2]
           lowerCAmelCase__      :							Optional[int]    =							bbox[i, j, 0]
           lowerCAmelCase__      :							Union[str, Any]    =							t

        lowerCAmelCase__      :							List[Any]    =							None
        if self.use_input_mask:
         lowerCAmelCase__      :							List[Any]    =							ids_tensor([self.batch_size, self.seq_length] ,				vocab_size=2      )

        lowerCAmelCase__      :							int    =							None
        if self.use_token_type_ids:
         lowerCAmelCase__      :							int    =							ids_tensor([self.batch_size, self.seq_length] ,				self.type_vocab_size      )

        lowerCAmelCase__      :							Dict    =							None
        lowerCAmelCase__      :							int    =							None
        lowerCAmelCase__      :							List[Any]    =							None
        if self.use_labels:
         lowerCAmelCase__      :							int    =							ids_tensor([self.batch_size] ,				self.type_sequence_label_size      )
         lowerCAmelCase__      :							Any    =							ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels      )
         lowerCAmelCase__      :							Dict    =							ids_tensor([self.batch_size] ,				self.num_choices      )

        lowerCAmelCase__      :							Any    =							self.get_config()

        return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return LayoutLMConfig(
            vocab_size=self.vocab_size ,				hidden_size=self.hidden_size ,				num_hidden_layers=self.num_hidden_layers ,				num_attention_heads=self.num_attention_heads ,				intermediate_size=self.intermediate_size ,				hidden_act=self.hidden_act ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				max_position_embeddings=self.max_position_embeddings ,				type_vocab_size=self.type_vocab_size ,				initializer_range=self.initializer_range ,				)






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[int]    =							LayoutLMModel(config=snake_case      )
        model.to(snake_case      )
        model.eval()
        lowerCAmelCase__      :							Any    =							model(snake_case ,				snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case      )
        lowerCAmelCase__      :							str    =							model(snake_case ,				snake_case ,				token_type_ids=snake_case      )
        lowerCAmelCase__      :							Dict    =							model(snake_case ,				snake_case      )
        self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)      )
        self.parent.assertEqual(result.pooler_output.shape ,				(self.batch_size, self.hidden_size)      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Tuple    =							LayoutLMForMaskedLM(config=snake_case      )
        model.to(snake_case      )
        model.eval()
        lowerCAmelCase__      :							str    =							model(snake_case ,				snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case ,				labels=snake_case      )
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							self.num_labels
        lowerCAmelCase__      :							List[str]    =							LayoutLMForSequenceClassification(snake_case      )
        model.to(snake_case      )
        model.eval()
        lowerCAmelCase__      :							Tuple    =							model(
            snake_case ,				snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case ,				labels=snake_case      )
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							int    =							self.num_labels
        lowerCAmelCase__      :							int    =							LayoutLMForTokenClassification(config=snake_case      )
        model.to(snake_case      )
        model.eval()
        lowerCAmelCase__      :							Union[str, Any]    =							model(snake_case ,				snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case ,				labels=snake_case      )
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[int]    =							LayoutLMForQuestionAnswering(config=snake_case      )
        model.to(snake_case      )
        model.eval()
        lowerCAmelCase__      :							Optional[int]    =							model(
            snake_case ,				bbox=snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case ,				start_positions=snake_case ,				end_positions=snake_case ,				)
        self.parent.assertEqual(result.start_logits.shape ,				(self.batch_size, self.seq_length)      )
        self.parent.assertEqual(result.end_logits.shape ,				(self.batch_size, self.seq_length)      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							self.prepare_config_and_inputs()
        (
            (
            lowerCAmelCase__
        ) ,	(
            lowerCAmelCase__
        ) ,	(
            lowerCAmelCase__
        ) ,	(
            lowerCAmelCase__
        ) ,	(
            lowerCAmelCase__
        ) ,	(
            lowerCAmelCase__
        ) ,	(
            lowerCAmelCase__
        ) ,	(
            lowerCAmelCase__
        ) ,	
        )      :							Optional[int]    =							config_and_inputs
        lowerCAmelCase__      :							Any    =							{
            "input_ids": input_ids,
            "bbox": bbox,
            "token_type_ids": token_type_ids,
            "attention_mask": input_mask,
        }
        return config, inputs_dict






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





       """simple docstring"""


       __UpperCamelCase				:			List[Any]          =	(
           (
               LayoutLMModel,
               LayoutLMForMaskedLM,
               LayoutLMForSequenceClassification,
               LayoutLMForTokenClassification,
               LayoutLMForQuestionAnswering,
           )
           if is_torch_available()
           else None
       )
       __UpperCamelCase				:			str          =	(
           {
               'document-question-answering': LayoutLMForQuestionAnswering,
               'feature-extraction': LayoutLMModel,
               'fill-mask': LayoutLMForMaskedLM,
               'text-classification': LayoutLMForSequenceClassification,
               'token-classification': LayoutLMForTokenClassification,
               'zero-shot': LayoutLMForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       __UpperCamelCase				:			List[Any]          =	True






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[Any]    =							LayoutLMModelTester(self      )
        lowerCAmelCase__      :							Optional[Any]    =							ConfigTester(self ,				config_class=snake_case ,				hidden_size=37      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        self.config_tester.run_common_tests()






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Dict    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[int]    =							self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
         lowerCAmelCase__      :							List[Any]    =							type
         self.model_tester.create_and_check_model(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							int    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*snake_case      )






       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass




def 			SCREAMING_SNAKE_CASE       (   ) ->	List[str]:
 # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
 # fmt: off
 lowerCAmelCase__      :							Dict    =							torch.tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]				,				device=lowercase__      )  # noqa: E231
 lowerCAmelCase__      :							Dict    =							torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]				,				device=lowercase__      )  # noqa: E231
 lowerCAmelCase__      :							Optional[Any]    =							torch.tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]				,				device=lowercase__      )  # noqa: E231
 lowerCAmelCase__      :							str    =							torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]				,				device=lowercase__      )  # noqa: E231
 # these are sequence labels (i.e. at the token level)
 lowerCAmelCase__      :							Dict    =							torch.tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]				,				device=lowercase__      )  # noqa: E231
 # fmt: on

 return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class  __a				(	unittest.TestCase					):





       """simple docstring"""


       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[Any]    =							LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"      ).to(snake_case      )

        lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Dict    =							prepare_layoutlm_batch_inputs()

        # forward pass
        lowerCAmelCase__      :							str    =							model(input_ids=snake_case ,				bbox=snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case      )

        # test the sequence output on [0, :3, :3]
        lowerCAmelCase__      :							Optional[Any]    =							torch.tensor(
            [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]] ,				device=snake_case ,				)

        self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] ,				snake_case ,				atol=1e-3      )      )

        # test the pooled output on [1, :3]
        lowerCAmelCase__      :							Optional[Any]    =							torch.tensor([-0.6_580, -0.0_214, 0.8_552] ,				device=snake_case      )

        self.assertTrue(torch.allclose(outputs.pooler_output[1, :3] ,				snake_case ,				atol=1e-3      )      )






       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased" ,				num_labels=2      ).to(
            snake_case      )

        lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Union[str, Any]    =							prepare_layoutlm_batch_inputs()

        # forward pass
        lowerCAmelCase__      :							List[str]    =							model(
            input_ids=snake_case ,				bbox=snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case ,				labels=torch.tensor([1, 1] ,				device=snake_case      ) ,				)

        # test whether we get a loss as a scalar
        lowerCAmelCase__      :							Tuple    =							outputs.loss
        lowerCAmelCase__      :							List[str]    =							torch.Size([]      )
        self.assertEqual(loss.shape ,				snake_case      )

        # test the shape of the logits
        lowerCAmelCase__      :							List[str]    =							outputs.logits
        lowerCAmelCase__      :							List[str]    =							torch.Size((2, 2)      )
        self.assertEqual(logits.shape ,				snake_case      )






       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased" ,				num_labels=13      ).to(
            snake_case      )

        lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							List[Any]    =							prepare_layoutlm_batch_inputs()

        # forward pass
        lowerCAmelCase__      :							List[Any]    =							model(
            input_ids=snake_case ,				bbox=snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case ,				labels=snake_case      )

        # test the loss calculation to be around 2.65
        # expected_loss = torch.tensor(2.65, device=torch_device)

        # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
        # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

        # test the shape of the logits
        lowerCAmelCase__      :							Dict    =							outputs.logits
        lowerCAmelCase__      :							Optional[Any]    =							torch.Size((2, 25, 13)      )
        self.assertEqual(logits.shape ,				snake_case      )






       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[Any]    =							LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"      ).to(snake_case      )

        lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Dict    =							prepare_layoutlm_batch_inputs()

        # forward pass
        lowerCAmelCase__      :							Any    =							model(input_ids=snake_case ,				bbox=snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case      )

        # test the shape of the logits
        lowerCAmelCase__      :							Any    =							torch.Size((2, 25)      )
        self.assertEqual(outputs.start_logits.shape ,				snake_case      )
        self.assertEqual(outputs.end_logits.shape ,				snake_case      )









"""simple docstring"""

import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets



@contextmanager
def 			SCREAMING_SNAKE_CASE       (   ) ->	Any:
 def filter_log(lowercase__      ):
  return False if "This IS expected if you are initializing" in record.msg else True

 lowerCAmelCase__      :							Tuple    =							datasets.utils.logging.get_logger("transformers.modeling_utils"      )
 logger.addFilter(lowercase__      )
 try:
  yield
 finally:
  logger.removeFilter(lowercase__      )


_UpperCamelCase		    =						"""\
@inproceedings{bert-score,
  title={BERTScore: Evaluating Text Generation with BERT},
  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=SkeHuCVFDr}
}
"""

_UpperCamelCase		    =						"""\
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
sentences by cosine similarity.
It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
generation tasks.

See the project's README at https://github.com/Tiiiger/bert_score#readme for more information.
"""

_UpperCamelCase		    =						"""
BERTScore Metrics with the hashcode from a source against one or more references.

Args:
    predictions (list of str): Prediction/candidate sentences.
    references (list of str or list of list of str): Reference sentences.
    lang (str): Language of the sentences; required (e.g. 'en').
    model_type (str): Bert specification, default using the suggested
        model for the target language; has to specify at least one of
        `model_type` or `lang`.
    num_layers (int): The layer of representation to use,
        default using the number of layers tuned on WMT16 correlation data.
    verbose (bool): Turn on intermediate status update.
    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
    device (str): On which the contextual embedding model will be allocated on.
        If this argument is None, the model lives on cuda:0 if cuda is available.
    nthreads (int): Number of threads.
    batch_size (int): Bert score processing batch size,
        at least one of `model_type` or `lang`. `lang` needs to be
        specified when `rescale_with_baseline` is True.
    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
    baseline_path (str): Customized baseline file.
    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.

Returns:
    precision: Precision.
    recall: Recall.
    f1: F1 score.
    hashcode: Hashcode of the library.

Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bertscore = datasets.load_metric(\"bertscore\")
    >>> results = bertscore.compute(predictions=predictions, references=references, lang=\"en\")
    >>> print([round(v, 2) for v in results[\"f1\"]])
    [1.0, 1.0]
"""






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





       """simple docstring"""


       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return datasets.MetricInfo(
            description=_DESCRIPTION ,				citation=_CITATION ,				homepage="https://github.com/Tiiiger/bert_score" ,				inputs_description=_KWARGS_DESCRIPTION ,				features=datasets.Features(
                {
                    "predictions": datasets.Value("string" ,				id="sequence"      ),
                    "references": datasets.Sequence(datasets.Value("string" ,				id="sequence"      ) ,				id="references"      ),
                }      ) ,				codebase_urls=["https://github.com/Tiiiger/bert_score"] ,				reference_urls=[
                "https://github.com/Tiiiger/bert_score",
                "https://arxiv.org/abs/1904.09675",
            ] ,				)






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case=None ,				snake_case=None ,				snake_case=None ,				snake_case=False ,				snake_case=False ,				snake_case=None ,				snake_case=64 ,				snake_case=4 ,				snake_case=False ,				snake_case=False ,				snake_case=None ,				snake_case=False ,				):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							bert_score.utils.get_hash
        lowerCAmelCase__      :							List[Any]    =							bert_score.BERTScorer

        if version.parse(bert_score.__version__      ) >= version.parse("0.3.10"      ):
         lowerCAmelCase__      :							str    =							functools.partial(snake_case ,				use_fast_tokenizer=snake_case      )
         lowerCAmelCase__      :							Tuple    =							functools.partial(snake_case ,				use_fast_tokenizer=snake_case      )
        elif use_fast_tokenizer:
         raise ImportWarning(
             "To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"
             "You can install it with `pip install \"bert-score>=0.3.10\"`."      )

        if model_type is None:
         assert lang is not None, "either lang or model_type should be specified"
         lowerCAmelCase__      :							Dict    =							bert_score.utils.langamodel[lang.lower()]

        if num_layers is None:
         lowerCAmelCase__      :							int    =							bert_score.utils.modelalayers[model_type]

        lowerCAmelCase__      :							Optional[int]    =							get_hash(
            model=snake_case ,				num_layers=snake_case ,				idf=snake_case ,				rescale_with_baseline=snake_case ,				use_custom_baseline=baseline_path is not None ,				)

        with filter_logging_context():
         if not hasattr(self ,				"cached_bertscorer"      ) or self.cached_bertscorer.hash != hashcode:
          lowerCAmelCase__      :							Dict    =							scorer(
              model_type=snake_case ,				num_layers=snake_case ,				batch_size=snake_case ,				nthreads=snake_case ,				all_layers=snake_case ,				idf=snake_case ,				device=snake_case ,				lang=snake_case ,				rescale_with_baseline=snake_case ,				baseline_path=snake_case ,				)

        ((lowerCAmelCase__) ,	(lowerCAmelCase__) ,	(lowerCAmelCase__))      :							int    =							self.cached_bertscorer.score(
            cands=snake_case ,				refs=snake_case ,				verbose=snake_case ,				batch_size=snake_case ,				)
        lowerCAmelCase__      :							List[str]    =							{
            "precision": P.tolist(),
            "recall": R.tolist(),
            "f1": F.tolist(),
            "hashcode": hashcode,
        }
        return output_dict






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case=None ,				snake_case=None ,				**snake_case      ):





        """simple docstring"""

        if references is not None:
         lowerCAmelCase__      :							Union[str, Any]    =							[[ref] if isinstance(snake_case ,				snake_case      ) else ref for ref in references]
        super().add_batch(predictions=snake_case ,				references=snake_case ,				**snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case=None ,				snake_case=None ,				**snake_case      ):





        """simple docstring"""

        if isinstance(snake_case ,				snake_case      ):
         lowerCAmelCase__      :							str    =							[reference]
        super().add(prediction=snake_case ,				reference=snake_case ,				**snake_case      )






