






from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
       TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
       TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
       TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
       LayoutLMvaConfig,
       TFLayoutLMvaForQuestionAnswering,
       TFLayoutLMvaForSequenceClassification,
       TFLayoutLMvaForTokenClassification,
       TFLayoutLMvaModel,
   )

if is_vision_available():
   from PIL import Image

   from transformers import LayoutLMvaImageProcessor



class 				_A			:
    """simple docstring"""

    def __init__(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     int=2 ,     __SCREAMING_SNAKE_CASE      :     Any=3 ,     __SCREAMING_SNAKE_CASE      :     Tuple=4 ,     __SCREAMING_SNAKE_CASE      :     List[Any]=2 ,     __SCREAMING_SNAKE_CASE      :     Any=7 ,     __SCREAMING_SNAKE_CASE      :     str=True ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=True ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=True ,     __SCREAMING_SNAKE_CASE      :     List[Any]=True ,     __SCREAMING_SNAKE_CASE      :     str=99 ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=36 ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=2 ,     __SCREAMING_SNAKE_CASE      :     List[Any]=4 ,     __SCREAMING_SNAKE_CASE      :     List[str]=37 ,     __SCREAMING_SNAKE_CASE      :     Optional[int]="gelu" ,     __SCREAMING_SNAKE_CASE      :     List[str]=0.1 ,     __SCREAMING_SNAKE_CASE      :     List[Any]=0.1 ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=512 ,     __SCREAMING_SNAKE_CASE      :     List[str]=16 ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=2 ,     __SCREAMING_SNAKE_CASE      :     List[str]=0.02 ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=6 ,     __SCREAMING_SNAKE_CASE      :     str=6 ,     __SCREAMING_SNAKE_CASE      :     Tuple=3 ,     __SCREAMING_SNAKE_CASE      :     List[str]=4 ,     __SCREAMING_SNAKE_CASE      :     Tuple=None ,     __SCREAMING_SNAKE_CASE      :     List[Any]=1000 ,     )      ->							Any:
           __UpperCAmelCase     				=parent
           __UpperCAmelCase     				=batch_size
           __UpperCAmelCase     				=num_channels
           __UpperCAmelCase     				=image_size
           __UpperCAmelCase     				=patch_size
           __UpperCAmelCase     				=is_training
           __UpperCAmelCase     				=use_input_mask
           __UpperCAmelCase     				=use_token_type_ids
           __UpperCAmelCase     				=use_labels
           __UpperCAmelCase     				=vocab_size
           __UpperCAmelCase     				=hidden_size
           __UpperCAmelCase     				=num_hidden_layers
           __UpperCAmelCase     				=num_attention_heads
           __UpperCAmelCase     				=intermediate_size
           __UpperCAmelCase     				=hidden_act
           __UpperCAmelCase     				=hidden_dropout_prob
           __UpperCAmelCase     				=attention_probs_dropout_prob
           __UpperCAmelCase     				=max_position_embeddings
           __UpperCAmelCase     				=type_vocab_size
           __UpperCAmelCase     				=type_sequence_label_size
           __UpperCAmelCase     				=initializer_range
           __UpperCAmelCase     				=coordinate_size
           __UpperCAmelCase     				=shape_size
           __UpperCAmelCase     				=num_labels
           __UpperCAmelCase     				=num_choices
           __UpperCAmelCase     				=scope
           __UpperCAmelCase     				=range_bbox

           # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
           __UpperCAmelCase     				=text_seq_length
           __UpperCAmelCase     				=(image_size // patch_size) ** 2 + 1
           __UpperCAmelCase     				=self.text_seq_length + self.image_seq_length



    def _a							(				self      :     int      )      ->							Optional[Any]:
           __UpperCAmelCase     				=ids_tensor([self.batch_size, self.text_seq_length] ,     self.vocab_size      )

           __UpperCAmelCase     				=ids_tensor([self.batch_size, self.text_seq_length, 4] ,     self.range_bbox      )
           __UpperCAmelCase     				=bbox.numpy()
           # 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]:
                                __UpperCAmelCase     				=bbox[i, j, 3]
                                __UpperCAmelCase     				=bbox[i, j, 1]
                                __UpperCAmelCase     				=tmp_coordinate
                         if bbox[i, j, 2] < bbox[i, j, 0]:
                                __UpperCAmelCase     				=bbox[i, j, 2]
                                __UpperCAmelCase     				=bbox[i, j, 0]
                                __UpperCAmelCase     				=tmp_coordinate
           __UpperCAmelCase     				=tf.constant(__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

           __UpperCAmelCase     				=None
           if self.use_input_mask:
                  __UpperCAmelCase     				=random_attention_mask([self.batch_size, self.text_seq_length]      )

           __UpperCAmelCase     				=None
           if self.use_token_type_ids:
                  __UpperCAmelCase     				=ids_tensor([self.batch_size, self.text_seq_length] ,     self.type_vocab_size      )

           __UpperCAmelCase     				=None
           __UpperCAmelCase     				=None
           if self.use_labels:
                  __UpperCAmelCase     				=ids_tensor([self.batch_size] ,     self.type_sequence_label_size      )
                  __UpperCAmelCase     				=ids_tensor([self.batch_size, self.text_seq_length] ,     self.num_labels      )

           __UpperCAmelCase     				=LayoutLMvaConfig(
               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 ,     coordinate_size=self.coordinate_size ,     shape_size=self.shape_size ,     input_size=self.image_size ,     patch_size=self.patch_size ,     )

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



    def _a							(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     Any ,     __SCREAMING_SNAKE_CASE      :     str ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     List[str] ,     __SCREAMING_SNAKE_CASE      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     int      )      ->							Tuple:
           __UpperCAmelCase     				=TFLayoutLMvaModel(config=__SCREAMING_SNAKE_CASE      )

           # text + image
           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE ,     pixel_values=__SCREAMING_SNAKE_CASE ,     training=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=model(
               __SCREAMING_SNAKE_CASE ,     bbox=__SCREAMING_SNAKE_CASE ,     pixel_values=__SCREAMING_SNAKE_CASE ,     attention_mask=__SCREAMING_SNAKE_CASE ,     token_type_ids=__SCREAMING_SNAKE_CASE ,     training=__SCREAMING_SNAKE_CASE ,     )
           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE ,     bbox=__SCREAMING_SNAKE_CASE ,     pixel_values=__SCREAMING_SNAKE_CASE ,     training=__SCREAMING_SNAKE_CASE      )

           self.parent.assertEqual(result.last_hidden_state.shape ,     (self.batch_size, self.seq_length, self.hidden_size)      )

           # text only
           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE ,     training=__SCREAMING_SNAKE_CASE      )
           self.parent.assertEqual(
               result.last_hidden_state.shape ,     (self.batch_size, self.text_seq_length, self.hidden_size)      )

           # image only
           __UpperCAmelCase     				=model({"""pixel_values""": pixel_values} ,     training=__SCREAMING_SNAKE_CASE      )
           self.parent.assertEqual(
               result.last_hidden_state.shape ,     (self.batch_size, self.image_seq_length, self.hidden_size)      )



    def _a							(				self      :     str ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Any ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Dict      )      ->							List[str]:
           __UpperCAmelCase     				=self.num_labels
           __UpperCAmelCase     				=TFLayoutLMvaForSequenceClassification(config=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=model(
               __SCREAMING_SNAKE_CASE ,     bbox=__SCREAMING_SNAKE_CASE ,     pixel_values=__SCREAMING_SNAKE_CASE ,     attention_mask=__SCREAMING_SNAKE_CASE ,     token_type_ids=__SCREAMING_SNAKE_CASE ,     labels=__SCREAMING_SNAKE_CASE ,     training=__SCREAMING_SNAKE_CASE ,     )
           self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.num_labels)      )



    def _a							(				self      :     List[str] ,     __SCREAMING_SNAKE_CASE      :     Any ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     str ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Any ,     __SCREAMING_SNAKE_CASE      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Optional[int]      )      ->							List[Any]:
           __UpperCAmelCase     				=self.num_labels
           __UpperCAmelCase     				=TFLayoutLMvaForTokenClassification(config=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=model(
               __SCREAMING_SNAKE_CASE ,     bbox=__SCREAMING_SNAKE_CASE ,     pixel_values=__SCREAMING_SNAKE_CASE ,     attention_mask=__SCREAMING_SNAKE_CASE ,     token_type_ids=__SCREAMING_SNAKE_CASE ,     labels=__SCREAMING_SNAKE_CASE ,     training=__SCREAMING_SNAKE_CASE ,     )
           self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.text_seq_length, self.num_labels)      )



    def _a							(				self      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Dict ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Dict ,     __SCREAMING_SNAKE_CASE      :     int ,     __SCREAMING_SNAKE_CASE      :     Any      )      ->							str:
           __UpperCAmelCase     				=2
           __UpperCAmelCase     				=TFLayoutLMvaForQuestionAnswering(config=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=model(
               __SCREAMING_SNAKE_CASE ,     bbox=__SCREAMING_SNAKE_CASE ,     pixel_values=__SCREAMING_SNAKE_CASE ,     attention_mask=__SCREAMING_SNAKE_CASE ,     token_type_ids=__SCREAMING_SNAKE_CASE ,     start_positions=__SCREAMING_SNAKE_CASE ,     end_positions=__SCREAMING_SNAKE_CASE ,     training=__SCREAMING_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 _a							(				self      :     str      )      ->							Optional[int]:
           __UpperCAmelCase     				=self.prepare_config_and_inputs()
           ((__UpperCAmelCase)				, (__UpperCAmelCase)				, (__UpperCAmelCase)				, (__UpperCAmelCase)				, (__UpperCAmelCase)				, (__UpperCAmelCase)				, (__UpperCAmelCase)				, (__UpperCAmelCase))     				=config_and_inputs
           __UpperCAmelCase     				={
               """input_ids""": input_ids,
               """bbox""": bbox,
               """pixel_values""": pixel_values,
               """token_type_ids""": token_type_ids,
               """attention_mask""": input_mask,
           }
           return config, inputs_dict




@require_tf
class 				_A			(     UpperCamelCase					,					UpperCamelCase					,					unittest.TestCase				):
    """simple docstring"""

    lowerCamelCase	:							List[str]				= (
        (
            TFLayoutLMvaModel,
            TFLayoutLMvaForQuestionAnswering,
            TFLayoutLMvaForSequenceClassification,
            TFLayoutLMvaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    lowerCamelCase	:							Tuple				= (
        {'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
        if is_tf_available()
        else {}
    )

    lowerCamelCase	:							Any				= False
    lowerCamelCase	:							Optional[Any]				= False
    lowerCamelCase	:							Any				= False



    def _a							(				self      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Any ,     __SCREAMING_SNAKE_CASE      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     int ,     __SCREAMING_SNAKE_CASE      :     Dict      )      ->							str:
           return True



    def _a							(				self      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Tuple=False      )      ->							dict:
           __UpperCAmelCase     				=copy.deepcopy(__SCREAMING_SNAKE_CASE      )

           if model_class in get_values(__SCREAMING_SNAKE_CASE      ):
                  __UpperCAmelCase     				={
                      k: tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE ,     1      ) ,     (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)      )
                      if isinstance(__SCREAMING_SNAKE_CASE ,     tf.Tensor      ) and v.ndim > 0
                      else v
                      for k, v in inputs_dict.items()
                  }

           if return_labels:
                  if model_class in get_values(__SCREAMING_SNAKE_CASE      ):
                         __UpperCAmelCase     				=tf.ones(self.model_tester.batch_size ,     dtype=tf.intaa      )
                  elif model_class in get_values(__SCREAMING_SNAKE_CASE      ):
                         __UpperCAmelCase     				=tf.zeros(self.model_tester.batch_size ,     dtype=tf.intaa      )
                         __UpperCAmelCase     				=tf.zeros(self.model_tester.batch_size ,     dtype=tf.intaa      )
                  elif model_class in get_values(__SCREAMING_SNAKE_CASE      ):
                         __UpperCAmelCase     				=tf.zeros(self.model_tester.batch_size ,     dtype=tf.intaa      )
                  elif model_class in get_values(__SCREAMING_SNAKE_CASE      ):
                         __UpperCAmelCase     				=tf.zeros(
                             (self.model_tester.batch_size, self.model_tester.text_seq_length) ,     dtype=tf.intaa      )

           return inputs_dict



    def _a							(				self      :     Optional[int]      )      ->							Optional[int]:
           __UpperCAmelCase     				=TFLayoutLMvaModelTester(self      )
           __UpperCAmelCase     				=ConfigTester(self ,     config_class=__SCREAMING_SNAKE_CASE ,     hidden_size=37      )



    def _a							(				self      :     Tuple      )      ->							List[str]:
           self.config_tester.run_common_tests()



    def _a							(				self      :     Any      )      ->							Any:
           __UpperCAmelCase				, __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                  __UpperCAmelCase     				=model_class(__SCREAMING_SNAKE_CASE      )
                  if getattr(__SCREAMING_SNAKE_CASE ,     """hf_compute_loss""" ,     __SCREAMING_SNAKE_CASE      ):
                         # The number of elements in the loss should be the same as the number of elements in the label
                         __UpperCAmelCase     				=self._prepare_for_class(inputs_dict.copy() ,     __SCREAMING_SNAKE_CASE ,     return_labels=__SCREAMING_SNAKE_CASE      )
                         __UpperCAmelCase     				=prepared_for_class[
                             sorted(prepared_for_class.keys() - inputs_dict.keys() ,     reverse=__SCREAMING_SNAKE_CASE      )[0]
                         ]
                         __UpperCAmelCase     				=added_label.shape.as_list()[:1]

                         # Test that model correctly compute the loss with kwargs
                         __UpperCAmelCase     				=self._prepare_for_class(inputs_dict.copy() ,     __SCREAMING_SNAKE_CASE ,     return_labels=__SCREAMING_SNAKE_CASE      )
                         __UpperCAmelCase     				=prepared_for_class.pop("""input_ids"""      )

                         __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )[0]
                         self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

                         # Test that model correctly compute the loss when we mask some positions
                         __UpperCAmelCase     				=self._prepare_for_class(inputs_dict.copy() ,     __SCREAMING_SNAKE_CASE ,     return_labels=__SCREAMING_SNAKE_CASE      )
                         __UpperCAmelCase     				=prepared_for_class.pop("""input_ids"""      )
                         if "labels" in prepared_for_class:
                                __UpperCAmelCase     				=prepared_for_class["""labels"""].numpy()
                                if len(labels.shape      ) > 1 and labels.shape[1] != 1:
                                       __UpperCAmelCase     				=-100
                                       __UpperCAmelCase     				=tf.convert_to_tensor(__SCREAMING_SNAKE_CASE      )
                                       __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )[0]
                                       self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )
                                       self.assertTrue(not np.any(np.isnan(loss.numpy()      )      )      )

                # Test that model correctly compute the loss with a dict
                         __UpperCAmelCase     				=self._prepare_for_class(inputs_dict.copy() ,     __SCREAMING_SNAKE_CASE ,     return_labels=__SCREAMING_SNAKE_CASE      )
                         __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE      )[0]
                         self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

                         # Test that model correctly compute the loss with a tuple
                         __UpperCAmelCase     				=self._prepare_for_class(inputs_dict.copy() ,     __SCREAMING_SNAKE_CASE ,     return_labels=__SCREAMING_SNAKE_CASE      )

                         # Get keys that were added with the _prepare_for_class function
                         __UpperCAmelCase     				=prepared_for_class.keys() - inputs_dict.keys()
                         __UpperCAmelCase     				=inspect.signature(model.call      ).parameters
                         __UpperCAmelCase     				=list(signature.keys()      )

                         # Create a dictionary holding the location of the tensors in the tuple
                         __UpperCAmelCase     				={0: """input_ids"""}
                         for label_key in label_keys:
                                __UpperCAmelCase     				=signature_names.index(__SCREAMING_SNAKE_CASE      )
                                __UpperCAmelCase     				=label_key
                         __UpperCAmelCase     				=sorted(tuple_index_mapping.items()      )
                         # Initialize a list with their default values, update the values and convert to a tuple
                         __UpperCAmelCase     				=[]

                         for name in signature_names:
                                if name != "kwargs":
                                       list_input.append(signature[name].default      )

                         for index, value in sorted_tuple_index_mapping:
                                __UpperCAmelCase     				=prepared_for_class[value]

                         __UpperCAmelCase     				=tuple(__SCREAMING_SNAKE_CASE      )

                         # Send to model
                         __UpperCAmelCase     				=model(tuple_input[:-1]      )[0]

                         self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )



    def _a							(				self      :     Tuple      )      ->							Optional[int]:
           (
               (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, 
           )     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Optional[Any]      )      ->							Tuple:
           (
               (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, 
           )     				=self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                  __UpperCAmelCase     				=type
                  self.model_tester.create_and_check_model(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     List[Any]      )      ->							Tuple:
           (
               (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, 
           )     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(
               __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     str      )      ->							Any:
           (
               (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, 
           )     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(
               __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     List[Any]      )      ->							int:
           (
               (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, (
               __UpperCAmelCase
           )				, 
           )     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(
               __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )



    @slow
    def _a							(				self      :     List[str]      )      ->							Any:
           for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  __UpperCAmelCase     				=TFLayoutLMvaModel.from_pretrained(__SCREAMING_SNAKE_CASE      )
                  self.assertIsNotNone(__SCREAMING_SNAKE_CASE      )







def       lowercase__	(					)	->				Optional[Any]:







       """simple docstring"""






       __UpperCAmelCase     				=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
       return image





@require_tf
class 				_A			(     unittest.TestCase				):
    """simple docstring"""

    @cached_property
    def _a							(				self      :     Tuple      )      ->							str:
           return LayoutLMvaImageProcessor(apply_ocr=__SCREAMING_SNAKE_CASE      ) if is_vision_available() else None



    @slow
    def _a							(				self      :     str      )      ->							Optional[Any]:
           __UpperCAmelCase     				=TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""      )

           __UpperCAmelCase     				=self.default_image_processor
           __UpperCAmelCase     				=prepare_img()
           __UpperCAmelCase     				=image_processor(images=__SCREAMING_SNAKE_CASE ,     return_tensors="""tf"""      ).pixel_values

           __UpperCAmelCase     				=tf.constant([[1, 2]]      )
           __UpperCAmelCase     				=tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]      ) ,     axis=0      )

           # forward pass
           __UpperCAmelCase     				=model(input_ids=__SCREAMING_SNAKE_CASE ,     bbox=__SCREAMING_SNAKE_CASE ,     pixel_values=__SCREAMING_SNAKE_CASE ,     training=__SCREAMING_SNAKE_CASE      )

           # verify the logits
           __UpperCAmelCase     				=(1, 199, 768)
           self.assertEqual(outputs.last_hidden_state.shape ,     __SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=tf.constant(
               [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]      )

           self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] ,     __SCREAMING_SNAKE_CASE ,     atol=1e-4      )      )









"""simple docstring"""

import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union

from .. import config
from .filelock import FileLock
from .logging import get_logger


A_						:    Optional[int]				=      get_logger(__name__)
class     lowerCAmelCase__						:


	'''simple docstring'''







	def __init__(      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Optional[str] = None    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     (
					    os.path.join(_SCREAMING_SNAKE_CASE     ,       config.EXTRACTED_DATASETS_DIR    ) if cache_dir else config.EXTRACTED_DATASETS_PATH
					)
					SCREAMING_SNAKE_CASE						:							Dict							=     Extractor





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	str:





					"""simple docstring"""


					from .file_utils import hash_url_to_filename

					# Path where we extract compressed archives
					# We extract in the cache dir, and get the extracted path name by hashing the original path"
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     os.path.abspath(_SCREAMING_SNAKE_CASE    )
					return os.path.join(self.extract_dir     ,       hash_url_to_filename(_SCREAMING_SNAKE_CASE    )    )





	def 			_lowerCAmelCase     (      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       bool    )						->	bool:





					"""simple docstring"""


					return force_extract or (
					    not os.path.isfile(_SCREAMING_SNAKE_CASE    ) and not (os.path.isdir(_SCREAMING_SNAKE_CASE    ) and os.listdir(_SCREAMING_SNAKE_CASE    ))
					)







	def 			_lowerCAmelCase     (      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       bool = False    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     self.extractor.infer_extractor_format(_SCREAMING_SNAKE_CASE    )
					if not extractor_format:
									return input_path
					SCREAMING_SNAKE_CASE						:							List[str]							=     self._get_output_path(_SCREAMING_SNAKE_CASE    )
					if self._do_extract(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
									self.extractor.extract(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					return output_path




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	@classmethod
	@abstractmethod
	def 			_lowerCAmelCase     (      cls				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       **_SCREAMING_SNAKE_CASE				:       Tuple    )						->	bool:





					"""simple docstring"""


					...







	@staticmethod
	@abstractmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					...




class     lowerCAmelCase__						(	_lowerCamelCase							,							_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		List[bytes]  						= []





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       int    )						->	Any:





					"""simple docstring"""


					with open(_SCREAMING_SNAKE_CASE     ,       'rb'    ) as f:
									return f.read(_SCREAMING_SNAKE_CASE    )







	@classmethod
	def 			_lowerCAmelCase     (      cls				:       str     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       bytes = b""    )						->	bool:





					"""simple docstring"""


					if not magic_number:
									SCREAMING_SNAKE_CASE						:							Any							=     max(len(_SCREAMING_SNAKE_CASE    ) for cls_magic_number in cls.magic_numbers    )
									try:
													SCREAMING_SNAKE_CASE						:							int							=     cls.read_magic_number(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
									except OSError:
													return False
					return any(magic_number.startswith(_SCREAMING_SNAKE_CASE    ) for cls_magic_number in cls.magic_numbers    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	@classmethod
	def 			_lowerCAmelCase     (      cls				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       **_SCREAMING_SNAKE_CASE				:       List[Any]    )						->	bool:





					"""simple docstring"""


					return tarfile.is_tarfile(_SCREAMING_SNAKE_CASE    )





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Dict    )						->	Dict:





					"""simple docstring"""


					def resolved(_SCREAMING_SNAKE_CASE				:       str    ) -> str:
									return os.path.realpath(os.path.abspath(_SCREAMING_SNAKE_CASE    )    )

					def badpath(_SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       str    ) -> bool:
									# joinpath will ignore base if path is absolute
									return not resolved(os.path.join(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )    ).startswith(_SCREAMING_SNAKE_CASE    )

					def badlink(_SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       str    ) -> bool:
									# Links are interpreted relative to the directory containing the link
									SCREAMING_SNAKE_CASE						:							List[str]							=     resolved(os.path.join(_SCREAMING_SNAKE_CASE     ,       os.path.dirname(info.name    )    )    )
									return badpath(info.linkname     ,       base=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							str							=     resolved(_SCREAMING_SNAKE_CASE    )

					for finfo in members:
									if badpath(finfo.name     ,       _SCREAMING_SNAKE_CASE    ):
													logger.error(f"""Extraction of {finfo.name} is blocked (illegal path)"""    )
									elif finfo.issym() and badlink(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
													logger.error(f"""Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}"""    )
									elif finfo.islnk() and badlink(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
													logger.error(f"""Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}"""    )
									else:
													yield finfo







	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					os.makedirs(_SCREAMING_SNAKE_CASE     ,       exist_ok=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     tarfile.open(_SCREAMING_SNAKE_CASE    )
					tar_file.extractall(_SCREAMING_SNAKE_CASE     ,       members=TarExtractor.safemembers(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )    )
					tar_file.close()




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= [B'''\x1F\x8B''']





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					with gzip.open(_SCREAMING_SNAKE_CASE     ,       'rb'    ) as gzip_file:
									with open(_SCREAMING_SNAKE_CASE     ,       'wb'    ) as extracted_file:
													shutil.copyfileobj(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[Any]  						= [
	    B'''PK\x03\x04''',
	    B'''PK\x05\x06''',  # empty archive
	    B'''PK\x07\x08''',  # spanned archive
	]





	@classmethod
	def 			_lowerCAmelCase     (      cls				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       bytes = b""    )						->	bool:





					"""simple docstring"""


					if super().is_extractable(_SCREAMING_SNAKE_CASE     ,       magic_number=_SCREAMING_SNAKE_CASE    ):
									return True
					try:
									# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
									# From: https://github.com/python/cpython/pull/5053
									from zipfile import (
									    _CD_SIGNATURE,
									    _ECD_DISK_NUMBER,
									    _ECD_DISK_START,
									    _ECD_ENTRIES_TOTAL,
									    _ECD_OFFSET,
									    _ECD_SIZE,
									    _EndRecData,
									    sizeCentralDir,
									    stringCentralDir,
									    structCentralDir,
									)

									with open(_SCREAMING_SNAKE_CASE     ,       'rb'    ) as fp:
													SCREAMING_SNAKE_CASE						:							List[str]							=     _EndRecData(_SCREAMING_SNAKE_CASE    )
													if endrec:
																	if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
																					return True  # Empty zipfiles are still zipfiles
																	elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
																					fp.seek(endrec[_ECD_OFFSET]    )  # Central directory is on the same disk
																					if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
																									SCREAMING_SNAKE_CASE						:							str							=     fp.read(_SCREAMING_SNAKE_CASE    )  # CD is where we expect it to be
																									if len(_SCREAMING_SNAKE_CASE    ) == sizeCentralDir:
																													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     struct.unpack(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )  # CD is the right size
																													if centdir[_CD_SIGNATURE] == stringCentralDir:
																																	return True  # First central directory entry  has correct magic number
									return False
					except Exception:  # catch all errors in case future python versions change the zipfile internals
									return False







	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					os.makedirs(_SCREAMING_SNAKE_CASE     ,       exist_ok=_SCREAMING_SNAKE_CASE    )
					with zipfile.ZipFile(_SCREAMING_SNAKE_CASE     ,       'r'    ) as zip_file:
									zip_file.extractall(_SCREAMING_SNAKE_CASE    )
									zip_file.close()




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= [B'''\xFD\x37\x7A\x58\x5A\x00''']





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					with lzma.open(_SCREAMING_SNAKE_CASE    ) as compressed_file:
									with open(_SCREAMING_SNAKE_CASE     ,       'wb'    ) as extracted_file:
													shutil.copyfileobj(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Dict  						= [B'''Rar!\x1a\x07\x00''', B'''Rar!\x1a\x07\x01\x00''']  # RAR_ID  # RAR5_ID





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					if not config.RARFILE_AVAILABLE:
									raise ImportError('Please pip install rarfile'    )
					import rarfile

					os.makedirs(_SCREAMING_SNAKE_CASE     ,       exist_ok=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							str							=     rarfile.RarFile(_SCREAMING_SNAKE_CASE    )
					rf.extractall(_SCREAMING_SNAKE_CASE    )
					rf.close()




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= [B'''\x28\xb5\x2F\xFD''']





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					if not config.ZSTANDARD_AVAILABLE:
									raise ImportError('Please pip install zstandard'    )
					import zstandard as zstd

					SCREAMING_SNAKE_CASE						:							Any							=     zstd.ZstdDecompressor()
					with open(_SCREAMING_SNAKE_CASE     ,       'rb'    ) as ifh, open(_SCREAMING_SNAKE_CASE     ,       'wb'    ) as ofh:
									dctx.copy_stream(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= [B'''\x42\x5A\x68''']





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					with bza.open(_SCREAMING_SNAKE_CASE     ,       'rb'    ) as compressed_file:
									with open(_SCREAMING_SNAKE_CASE     ,       'wb'    ) as extracted_file:
													shutil.copyfileobj(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		str  						= [B'''\x37\x7A\xBC\xAF\x27\x1C''']





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					if not config.PY7ZR_AVAILABLE:
									raise ImportError('Please pip install py7zr'    )
					import pyazr

					os.makedirs(_SCREAMING_SNAKE_CASE     ,       exist_ok=_SCREAMING_SNAKE_CASE    )
					with pyazr.SevenZipFile(_SCREAMING_SNAKE_CASE     ,       'r'    ) as archive:
									archive.extractall(_SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		List[str]  						= [B'''\x04\x22\x4D\x18''']





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					if not config.LZ4_AVAILABLE:
									raise ImportError('Please pip install lz4'    )
					import lza.frame

					with lza.frame.open(_SCREAMING_SNAKE_CASE     ,       'rb'    ) as compressed_file:
									with open(_SCREAMING_SNAKE_CASE     ,       'wb'    ) as extracted_file:
													shutil.copyfileobj(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						:


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Dict[str, Type[BaseExtractor]]  						= {
	    "tar": TarExtractor,
	    "gzip": GzipExtractor,
	    "zip": ZipExtractor,
	    "xz": XzExtractor,
	    "rar": RarExtractor,
	    "zstd": ZstdExtractor,
	    "bz2": BzipaExtractor,
	    "7z": SevenZipExtractor,  # <Added version="2.4.0"/>
	    "lz4": LzaExtractor,  # <Added version="2.4.0"/>
	}





	@classmethod
	def 			_lowerCAmelCase     (      cls				:       List[str]    )						->	Tuple:





					"""simple docstring"""


					return max(
					    len(_SCREAMING_SNAKE_CASE    )
					    for extractor in cls.extractors.values()
					    if issubclass(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					    for extractor_magic_number in extractor.magic_numbers    )





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       int    )						->	List[str]:





					"""simple docstring"""


					try:
									return MagicNumberBaseExtractor.read_magic_number(_SCREAMING_SNAKE_CASE     ,       magic_number_length=_SCREAMING_SNAKE_CASE    )
					except OSError:
									return b""





	@classmethod
	def 			_lowerCAmelCase     (      cls				:       Any     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       bool = False    )						->	bool:





					"""simple docstring"""


					warnings.warn(
					    'Method \'is_extractable\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '
					    'Use \'infer_extractor_format\' instead.'     ,       category=_SCREAMING_SNAKE_CASE     ,       )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     cls.infer_extractor_format(_SCREAMING_SNAKE_CASE    )
					if extractor_format:
									return True if not return_extractor else (True, cls.extractors[extractor_format])
					return False if not return_extractor else (False, None)





	@classmethod
	def 			_lowerCAmelCase     (      cls				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	str:  # <Added version="2.4.0"/>





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     cls._get_magic_number_max_length()
					SCREAMING_SNAKE_CASE						:							str							=     cls._read_magic_number(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					for extractor_format, extractor in cls.extractors.items():
									if extractor.is_extractable(_SCREAMING_SNAKE_CASE     ,       magic_number=_SCREAMING_SNAKE_CASE    ):
													return extractor_format







	@classmethod
	def 			_lowerCAmelCase     (      cls				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Optional[str] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[BaseExtractor] = "deprecated"     ,       )						->	None:





					"""simple docstring"""


					os.makedirs(os.path.dirname(_SCREAMING_SNAKE_CASE    )     ,       exist_ok=_SCREAMING_SNAKE_CASE    )
					# Prevent parallel extractions
					SCREAMING_SNAKE_CASE						:							List[str]							=     str(Path(_SCREAMING_SNAKE_CASE    ).with_suffix('.lock'    )    )
					with FileLock(_SCREAMING_SNAKE_CASE    ):
									shutil.rmtree(_SCREAMING_SNAKE_CASE     ,       ignore_errors=_SCREAMING_SNAKE_CASE    )
									if extractor_format or extractor != "deprecated":
													if extractor != "deprecated" or not isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):  # passed as positional arg
																	warnings.warn(
																	    'Parameter \'extractor\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '
																	    'Use \'extractor_format\' instead.'     ,       category=_SCREAMING_SNAKE_CASE     ,       )
																	SCREAMING_SNAKE_CASE						:							int							=     extractor if extractor != 'deprecated' else extractor_format
													else:
																	SCREAMING_SNAKE_CASE						:							int							=     cls.extractors[extractor_format]
													return extractor.extract(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
									else:
													warnings.warn(
													    'Parameter \'extractor_format\' was made required in version 2.4.0 and not passing it will raise an '
													    'exception in 3.0.0.'     ,       category=_SCREAMING_SNAKE_CASE     ,       )
													for extractor in cls.extractors.values():
																	if extractor.is_extractable(_SCREAMING_SNAKE_CASE    ):
																					return extractor.extract(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





