


"""simple docstring"""


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      :
    def __init__( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Any=2							,					SCREAMING_SNAKE_CASE__							:					int=3							,					SCREAMING_SNAKE_CASE__							:					Dict=4							,					SCREAMING_SNAKE_CASE__							:					Tuple=2							,					SCREAMING_SNAKE_CASE__							:					str=7							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=True							,					SCREAMING_SNAKE_CASE__							:					Any=True							,					SCREAMING_SNAKE_CASE__							:					Tuple=True							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=True							,					SCREAMING_SNAKE_CASE__							:					str=99							,					SCREAMING_SNAKE_CASE__							:					Any=36							,					SCREAMING_SNAKE_CASE__							:					Tuple=2							,					SCREAMING_SNAKE_CASE__							:					List[str]=4							,					SCREAMING_SNAKE_CASE__							:					str=37							,					SCREAMING_SNAKE_CASE__							:					Any="gelu"							,					SCREAMING_SNAKE_CASE__							:					Dict=0.1							,					SCREAMING_SNAKE_CASE__							:					Dict=0.1							,					SCREAMING_SNAKE_CASE__							:					int=5_12							,					SCREAMING_SNAKE_CASE__							:					List[Any]=16							,					SCREAMING_SNAKE_CASE__							:					int=2							,					SCREAMING_SNAKE_CASE__							:					Any=0.02							,					SCREAMING_SNAKE_CASE__							:					List[Any]=6							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=6							,					SCREAMING_SNAKE_CASE__							:					List[str]=3							,					SCREAMING_SNAKE_CASE__							:					Any=4							,					SCREAMING_SNAKE_CASE__							:					List[Any]=None							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=10_00							,					):
          lowerCamelCase__           =		parent
          lowerCamelCase__           =		batch_size
          lowerCamelCase__           =		num_channels
          lowerCamelCase__           =		image_size
          lowerCamelCase__           =		patch_size
          lowerCamelCase__           =		is_training
          lowerCamelCase__           =		use_input_mask
          lowerCamelCase__           =		use_token_type_ids
          lowerCamelCase__           =		use_labels
          lowerCamelCase__           =		vocab_size
          lowerCamelCase__           =		hidden_size
          lowerCamelCase__           =		num_hidden_layers
          lowerCamelCase__           =		num_attention_heads
          lowerCamelCase__           =		intermediate_size
          lowerCamelCase__           =		hidden_act
          lowerCamelCase__           =		hidden_dropout_prob
          lowerCamelCase__           =		attention_probs_dropout_prob
          lowerCamelCase__           =		max_position_embeddings
          lowerCamelCase__           =		type_vocab_size
          lowerCamelCase__           =		type_sequence_label_size
          lowerCamelCase__           =		initializer_range
          lowerCamelCase__           =		coordinate_size
          lowerCamelCase__           =		shape_size
          lowerCamelCase__           =		num_labels
          lowerCamelCase__           =		num_choices
          lowerCamelCase__           =		scope
          lowerCamelCase__           =		range_bbox

          # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
          lowerCamelCase__           =		text_seq_length
          lowerCamelCase__           =		(image_size // patch_size) ** 2 + 1
          lowerCamelCase__           =		self.text_seq_length + self.image_seq_length
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		ids_tensor([self.batch_size, self.text_seq_length]							,					self.vocab_size				)

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

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

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

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

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

          lowerCamelCase__           =		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      _UpperCamelCase       ( self							:					Any							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					int				):
          lowerCamelCase__           =		TFLayoutLMvaModel(config=SCREAMING_SNAKE_CASE__				)

          # text + image
          lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__							,					pixel_values=SCREAMING_SNAKE_CASE__							,					training=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		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__							,					)
          lowerCamelCase__           =		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
          lowerCamelCase__           =		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
          lowerCamelCase__           =		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      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					str				):
          lowerCamelCase__           =		self.num_labels
          lowerCamelCase__           =		TFLayoutLMvaForSequenceClassification(config=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		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      _UpperCamelCase       ( self							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]				):
          lowerCamelCase__           =		self.num_labels
          lowerCamelCase__           =		TFLayoutLMvaForTokenClassification(config=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		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      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					Any							,					SCREAMING_SNAKE_CASE__							:					Any							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					List[str]				):
          lowerCamelCase__           =		2
          lowerCamelCase__           =		TFLayoutLMvaForQuestionAnswering(config=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		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      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		self.prepare_config_and_inputs()
          ((lowerCamelCase__)    ,  (lowerCamelCase__)    ,  (lowerCamelCase__)    ,  (lowerCamelCase__)    ,  (lowerCamelCase__)    ,  (lowerCamelCase__)    ,  (lowerCamelCase__)    ,  (lowerCamelCase__))           =		config_and_inputs
          lowerCamelCase__           =		{
              '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      (      SCREAMING_SNAKE_CASE_		,		SCREAMING_SNAKE_CASE_		,		unittest.TestCase ):
    a_    :				Optional[int]             =    (
        (
            TFLayoutLMvaModel,
            TFLayoutLMvaForQuestionAnswering,
            TFLayoutLMvaForSequenceClassification,
            TFLayoutLMvaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    a_    :				Union[str, Any]             =    (
        {'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
        if is_tf_available()
        else {}
    )

    a_    :				Optional[Any]             =    False
    a_    :				List[Any]             =    False
    a_    :				str             =    False
    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]				):
          return True
    def      _UpperCamelCase       ( self							:					Any							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Any=False				):
          lowerCamelCase__           =		copy.deepcopy(SCREAMING_SNAKE_CASE__				)

          if model_class in get_values(SCREAMING_SNAKE_CASE__				):
                lowerCamelCase__           =		{
                    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__				):
                      lowerCamelCase__           =		tf.ones(self.model_tester.batch_size							,					dtype=tf.intaa				)
                elif model_class in get_values(SCREAMING_SNAKE_CASE__				):
                      lowerCamelCase__           =		tf.zeros(self.model_tester.batch_size							,					dtype=tf.intaa				)
                      lowerCamelCase__           =		tf.zeros(self.model_tester.batch_size							,					dtype=tf.intaa				)
                elif model_class in get_values(SCREAMING_SNAKE_CASE__				):
                      lowerCamelCase__           =		tf.zeros(self.model_tester.batch_size							,					dtype=tf.intaa				)
                elif model_class in get_values(SCREAMING_SNAKE_CASE__				):
                      lowerCamelCase__           =		tf.zeros(
                          (self.model_tester.batch_size, self.model_tester.text_seq_length)							,					dtype=tf.intaa				)

          return inputs_dict
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          lowerCamelCase__           =		TFLayoutLMvaModelTester(self				)
          lowerCamelCase__           =		ConfigTester(self							,					config_class=SCREAMING_SNAKE_CASE__							,					hidden_size=37				)
    def      _UpperCamelCase       ( self							:					Tuple				):
          self.config_tester.run_common_tests()
    def      _UpperCamelCase       ( self							:					Any				):
          lowerCamelCase__    ,  lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
                lowerCamelCase__           =		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
                      lowerCamelCase__           =		self._prepare_for_class(inputs_dict.copy()							,					SCREAMING_SNAKE_CASE__							,					return_labels=SCREAMING_SNAKE_CASE__				)
                      lowerCamelCase__           =		prepared_for_class[
                          sorted(prepared_for_class.keys() - inputs_dict.keys()							,					reverse=SCREAMING_SNAKE_CASE__				)[0]
                      ]
                      lowerCamelCase__           =		added_label.shape.as_list()[:1]

                      # Test that model correctly compute the loss with kwargs
                      lowerCamelCase__           =		self._prepare_for_class(inputs_dict.copy()							,					SCREAMING_SNAKE_CASE__							,					return_labels=SCREAMING_SNAKE_CASE__				)
                      lowerCamelCase__           =		prepared_for_class.pop('input_ids'				)

                      lowerCamelCase__           =		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
                      lowerCamelCase__           =		self._prepare_for_class(inputs_dict.copy()							,					SCREAMING_SNAKE_CASE__							,					return_labels=SCREAMING_SNAKE_CASE__				)
                      lowerCamelCase__           =		prepared_for_class.pop('input_ids'				)
                      if "labels" in prepared_for_class:
                            lowerCamelCase__           =		prepared_for_class['labels'].numpy()
                            if len(labels.shape				) > 1 and labels.shape[1] != 1:
                                  lowerCamelCase__           =		-1_00
                                  lowerCamelCase__           =		tf.convert_to_tensor(SCREAMING_SNAKE_CASE__				)
                                  lowerCamelCase__           =		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
                      lowerCamelCase__           =		self._prepare_for_class(inputs_dict.copy()							,					SCREAMING_SNAKE_CASE__							,					return_labels=SCREAMING_SNAKE_CASE__				)
                      lowerCamelCase__           =		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
                      lowerCamelCase__           =		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
                      lowerCamelCase__           =		prepared_for_class.keys() - inputs_dict.keys()
                      lowerCamelCase__           =		inspect.signature(model.call				).parameters
                      lowerCamelCase__           =		list(signature.keys()				)

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

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

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

                      lowerCamelCase__           =		tuple(SCREAMING_SNAKE_CASE__				)

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

                      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]				)
    def      _UpperCamelCase       ( self							:					Dict				):
          (
              (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  
          )           =		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      _UpperCamelCase       ( self							:					Any				):
          (
              (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  
          )           =		self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
                lowerCamelCase__           =		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      _UpperCamelCase       ( self							:					Union[str, Any]				):
          (
              (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  
          )           =		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      _UpperCamelCase       ( self							:					Tuple				):
          (
              (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  
          )           =		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      _UpperCamelCase       ( self							:					Optional[int]				):
          (
              (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  (
              lowerCamelCase__
          )    ,  
          )           =		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      _UpperCamelCase       ( self							:					Optional[int]				):
          for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                lowerCamelCase__           =		TFLayoutLMvaModel.from_pretrained(SCREAMING_SNAKE_CASE__				)
                self.assertIsNotNone(SCREAMING_SNAKE_CASE__				)





def   snake_case						(				)->		Dict:
      '''simple docstring'''


      lowerCamelCase__           =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
      return image






@require_tf
class   _a      (      unittest.TestCase ):
    @cached_property
    def      _UpperCamelCase       ( self							:					str				):
          return LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE__				) if is_vision_available() else None


    @slow
    def      _UpperCamelCase       ( self							:					Dict				):
          lowerCamelCase__           =		TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'				)

          lowerCamelCase__           =		self.default_image_processor
          lowerCamelCase__           =		prepare_img()
          lowerCamelCase__           =		image_processor(images=SCREAMING_SNAKE_CASE__							,					return_tensors='tf'				).pixel_values

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

          # forward pass
          lowerCamelCase__           =		model(input_ids=SCREAMING_SNAKE_CASE__							,					bbox=SCREAMING_SNAKE_CASE__							,					pixel_values=SCREAMING_SNAKE_CASE__							,					training=SCREAMING_SNAKE_CASE__				)

          # verify the logits
          lowerCamelCase__           =		(1, 1_99, 7_68)
          self.assertEqual(outputs.last_hidden_state.shape							,					SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		tf.constant(
              [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]				)

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







"""simple docstring"""


import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


_snake_case          =							re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_snake_case          =							10
_snake_case          =							256



def   snake_case						(				_a:     List[str]							)->		Optional[MinHash]:
      '''simple docstring'''


      if len(_a							) < MIN_NUM_TOKENS:
            return None
      lowerCamelCase__           =		MinHash(num_perm=_a							)
      for token in set(_a							):
            min_hash.update(token.encode()							)
      return min_hash



def   snake_case						(				_a:     str							)->		Set[str]:
      '''simple docstring'''


      return {t for t in NON_ALPHA.split(_a							) if len(t.strip()							) > 0}



class   _a      :
    def __init__( self							:					List[Any]							,					*,
        SCREAMING_SNAKE_CASE__							:					float = 0.85							,					):
          lowerCamelCase__           =		duplication_jaccard_threshold
          lowerCamelCase__           =		NUM_PERM
          lowerCamelCase__           =		MinHashLSH(threshold=self._duplication_jaccard_threshold							,					num_perm=self._num_perm				)

          lowerCamelCase__           =		defaultdict(SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					MinHash				):
          lowerCamelCase__           =		self._index.query(SCREAMING_SNAKE_CASE__				)
          if code_key in self._index.keys:
                print(F'Duplicate key {code_key}'				)
                return

          self._index.insert(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
          if len(SCREAMING_SNAKE_CASE__				) > 0:
                for base_duplicate in close_duplicates:
                      if base_duplicate in self._duplicate_clusters:
                            self._duplicate_clusters[base_duplicate].add(SCREAMING_SNAKE_CASE__				)
                            break
                else:
                      self._duplicate_clusters[close_duplicates[0]].add(SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		[]
          for base, duplicates in self._duplicate_clusters.items():
                lowerCamelCase__           =		[base] + list(SCREAMING_SNAKE_CASE__				)
                # reformat the cluster to be a list of dict
                lowerCamelCase__           =		[{'base_index': el[0], 'repo_name': el[1], 'path': el[2]} for el in cluster]
                duplicate_clusters.append(SCREAMING_SNAKE_CASE__				)
          return duplicate_clusters


    def      _UpperCamelCase       ( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]				):
          lowerCamelCase__           =		self.get_duplicate_clusters()
          with open(SCREAMING_SNAKE_CASE__							,					'w'				) as f:
                json.dump(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)





def   snake_case						(				_a:     Union[str, Any]							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__    ,  lowerCamelCase__           =		element
      lowerCamelCase__           =		get_min_hash([t for t in NON_ALPHA.split(data['content']							) if len(t.strip()							) > 0]							)
      if min_hash is not None:
            return (index, data["repo_name"], data["path"]), min_hash



def   snake_case						(				_a:     Type[Dataset]							)->		Tuple:
      '''simple docstring'''


      with mp.Pool() as pool:
            for data in pool.imap_unordered(
                _compute_min_hash		,   ThreadedIterator(_a		,   max_queue_size=10000							)		,   chunksize=100		,   ):
                  if data is not None:
                        yield data



def   snake_case						(				_a:     Type[Dataset]		,   _a:     float							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		DuplicationIndex(duplication_jaccard_threshold=_a							)

      for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(_a							)							)		,   max_queue_size=100							)							):
            di.add(_a		,   _a							)

      # Returns a List[Cluster] where Cluster is List[str] with the filenames.
      return di.get_duplicate_clusters()



def   snake_case						(				_a:     str		,   _a:     str							)->		float:
      '''simple docstring'''


      lowerCamelCase__           =		get_tokens(_a							)
      lowerCamelCase__           =		get_tokens(_a							)
      return len(tokensa & tokensa							) / len(tokensa | tokensa							)


_snake_case          =							None



def   snake_case						(				_a:     Dict		,   _a:     Union[str, Any]							)->		List[str]:
      '''simple docstring'''


      lowerCamelCase__           =		[]
      for elementa in cluster:
            lowerCamelCase__           =		_shared_dataset[elementa['base_index']]['content']
            for elementa in extremes:
                  lowerCamelCase__           =		_shared_dataset[elementa['base_index']]['content']
                  if jaccard_similarity(_a		,   _a							) >= jaccard_threshold:
                        elementa["copies"] += 1
                        break
            else:
                  lowerCamelCase__           =		1
                  extremes.append(_a							)
      return extremes



def   snake_case						(				_a:     Any		,   _a:     Tuple		,   _a:     Dict							)->		Union[str, Any]:
      '''simple docstring'''


      global _shared_dataset
      lowerCamelCase__           =		dataset
      lowerCamelCase__           =		[]
      lowerCamelCase__           =		partial(_find_cluster_extremes_shared		,   jaccard_threshold=_a							)
      with mp.Pool() as pool:
            for extremes in tqdm(
                pool.imap_unordered(
                    _a		,   _a		,   )		,   total=len(_a							)		,   ):
                  extremes_list.append(_a							)
      return extremes_list






def   snake_case						(				_a:     Type[Dataset]		,   _a:     float = 0.85							)->		Tuple[Type[Dataset], List[List[Dict]]]:
      '''simple docstring'''


      lowerCamelCase__           =		make_duplicate_clusters(_a		,   _a							)
      lowerCamelCase__           =		{x['base_index'] for cluster in duplicate_clusters for x in cluster}
      lowerCamelCase__           =		{}
      lowerCamelCase__           =		find_extremes(_a		,   _a		,   _a							)
      for extremes in extremes_clusters:
            for element in extremes:
                  lowerCamelCase__           =		element
      lowerCamelCase__           =		duplicate_indices - set(extreme_dict.keys()							)
      lowerCamelCase__           =		dataset.filter(lambda _a		,   _a							: idx not in remove_indices		,   with_indices=_a							)

      # update duplicate_clusters
      for cluster in duplicate_clusters:
            for element in cluster:
                  lowerCamelCase__           =		element['base_index'] in extreme_dict
                  if element["is_extreme"]:
                        lowerCamelCase__           =		extreme_dict[element['base_index']]['copies']

      print(F'Original dataset size: {len(_a							)}'							)
      print(F'Number of duplicate clusters: {len(_a							)}'							)
      print(F'Files in duplicate cluster: {len(_a							)}'							)
      print(F'Unique files in duplicate cluster: {len(_a							)}'							)
      print(F'Filtered dataset size: {len(_a							)}'							)

      return ds_filter, duplicate_clusters



