



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 				snake_case__:

					"""simple docstring"""






					def __init__(							self :     str				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     int=2				,       SCREAMING_SNAKE_CASE :     List[str]=3				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=4				,       SCREAMING_SNAKE_CASE :     str=2				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=7				,       SCREAMING_SNAKE_CASE :     int=True				,       SCREAMING_SNAKE_CASE :     str=True				,       SCREAMING_SNAKE_CASE :     List[str]=True				,       SCREAMING_SNAKE_CASE :     Dict=True				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=99				,       SCREAMING_SNAKE_CASE :     int=36				,       SCREAMING_SNAKE_CASE :     Tuple=2				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=4				,       SCREAMING_SNAKE_CASE :     int=37				,       SCREAMING_SNAKE_CASE :     Dict="gelu"				,       SCREAMING_SNAKE_CASE :     Any=0.1				,       SCREAMING_SNAKE_CASE :     str=0.1				,       SCREAMING_SNAKE_CASE :     Dict=512				,       SCREAMING_SNAKE_CASE :     int=16				,       SCREAMING_SNAKE_CASE :     Any=2				,       SCREAMING_SNAKE_CASE :     Optional[int]=0.02				,       SCREAMING_SNAKE_CASE :     Dict=6				,       SCREAMING_SNAKE_CASE :     Optional[Any]=6				,       SCREAMING_SNAKE_CASE :     Any=3				,       SCREAMING_SNAKE_CASE :     List[str]=4				,       SCREAMING_SNAKE_CASE :     Optional[int]=None				,       SCREAMING_SNAKE_CASE :     Optional[Any]=1_000				,       ):
									lowercase__					:   str													=					parent
									lowercase__					:   int													=					batch_size
									lowercase__					:   int													=					num_channels
									lowercase__					:   Dict													=					image_size
									lowercase__					:   List[str]													=					patch_size
									lowercase__					:   List[str]													=					is_training
									lowercase__					:   Dict													=					use_input_mask
									lowercase__					:   Any													=					use_token_type_ids
									lowercase__					:   Any													=					use_labels
									lowercase__					:   List[Any]													=					vocab_size
									lowercase__					:   Union[str, Any]													=					hidden_size
									lowercase__					:   Optional[int]													=					num_hidden_layers
									lowercase__					:   Any													=					num_attention_heads
									lowercase__					:   Tuple													=					intermediate_size
									lowercase__					:   Any													=					hidden_act
									lowercase__					:   Tuple													=					hidden_dropout_prob
									lowercase__					:   Optional[int]													=					attention_probs_dropout_prob
									lowercase__					:   int													=					max_position_embeddings
									lowercase__					:   Optional[Any]													=					type_vocab_size
									lowercase__					:   List[str]													=					type_sequence_label_size
									lowercase__					:   Dict													=					initializer_range
									lowercase__					:   Optional[Any]													=					coordinate_size
									lowercase__					:   Optional[Any]													=					shape_size
									lowercase__					:   Union[str, Any]													=					num_labels
									lowercase__					:   Union[str, Any]													=					num_choices
									lowercase__					:   List[Any]													=					scope
									lowercase__					:   str													=					range_bbox

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







					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   Dict													=					ids_tensor([self.batch_size, self.text_seq_length]				,       self.vocab_size					)

									lowercase__					:   str													=					ids_tensor([self.batch_size, self.text_seq_length, 4]				,       self.range_bbox					)
									lowercase__					:   List[str]													=					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]:
																					lowercase__					:   Union[str, Any]													=					bbox[i, j, 3]
																					lowercase__					:   List[Any]													=					bbox[i, j, 1]
																					lowercase__					:   Optional[Any]													=					tmp_coordinate
																	if bbox[i, j, 2] < bbox[i, j, 0]:
																					lowercase__					:   Dict													=					bbox[i, j, 2]
																					lowercase__					:   str													=					bbox[i, j, 0]
																					lowercase__					:   List[str]													=					tmp_coordinate
									lowercase__					:   Optional[Any]													=					tf.constant(SCREAMING_SNAKE_CASE					)

									lowercase__					:   Optional[Any]													=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

									lowercase__					:   Tuple													=					None
									if self.use_input_mask:
													lowercase__					:   Dict													=					random_attention_mask([self.batch_size, self.text_seq_length]					)

									lowercase__					:   List[Any]													=					None
									if self.use_token_type_ids:
													lowercase__					:   Tuple													=					ids_tensor([self.batch_size, self.text_seq_length]				,       self.type_vocab_size					)

									lowercase__					:   List[Any]													=					None
									lowercase__					:   Tuple													=					None
									if self.use_labels:
													lowercase__					:   List[Any]													=					ids_tensor([self.batch_size]				,       self.type_sequence_label_size					)
													lowercase__					:   List[str]													=					ids_tensor([self.batch_size, self.text_seq_length]				,       self.num_labels					)

									lowercase__					:   str													=					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 			snake_case					(							self :     int				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     List[str]				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     Tuple					):
									lowercase__					:   Union[str, Any]													=					TFLayoutLMvaModel(config=SCREAMING_SNAKE_CASE					)

									# text + image
									lowercase__					:   Dict													=					model(SCREAMING_SNAKE_CASE				,       pixel_values=SCREAMING_SNAKE_CASE				,       training=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Optional[int]													=					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				,       )
									lowercase__					:   Tuple													=					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
									lowercase__					:   str													=					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
									lowercase__					:   Any													=					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 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     List[str]				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     List[str]					):
									lowercase__					:   List[Any]													=					self.num_labels
									lowercase__					:   Union[str, Any]													=					TFLayoutLMvaForSequenceClassification(config=SCREAMING_SNAKE_CASE					)
									lowercase__					:   List[str]													=					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 			snake_case					(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Dict					):
									lowercase__					:   Tuple													=					self.num_labels
									lowercase__					:   Optional[Any]													=					TFLayoutLMvaForTokenClassification(config=SCREAMING_SNAKE_CASE					)
									lowercase__					:   int													=					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 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     List[str]					):
									lowercase__					:   str													=					2
									lowercase__					:   Dict													=					TFLayoutLMvaForQuestionAnswering(config=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Optional[int]													=					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 			snake_case					(							self :     List[str]					):
									lowercase__					:   List[str]													=					self.prepare_config_and_inputs()
									((lowercase__)							,							(lowercase__)							,							(lowercase__)							,							(lowercase__)							,							(lowercase__)							,							(lowercase__)							,							(lowercase__)							,							(lowercase__))					:   Optional[int]													=					config_and_inputs
									lowercase__					:   Dict													=					{
									    "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 				snake_case__(_UpperCamelCase    ,      _UpperCamelCase    ,      unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		(
					    (
					        TFLayoutLMvaModel,
					        TFLayoutLMvaForQuestionAnswering,
					        TFLayoutLMvaForSequenceClassification,
					        TFLayoutLMvaForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					lowercase_								=		(
					    {"""document-question-answering""": TFLayoutLMvaForQuestionAnswering, """feature-extraction""": TFLayoutLMvaModel}
					    if is_tf_available()
					    else {}
					)

					lowercase_								=		False
					lowercase_								=		False
					lowercase_								=		False







					def 			snake_case					(							self :     Dict				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Dict					):
									return True







					def 			snake_case					(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     str=False					):
									lowercase__					:   Any													=					copy.deepcopy(SCREAMING_SNAKE_CASE					)

									if model_class in get_values(SCREAMING_SNAKE_CASE					):
													lowercase__					:   List[str]													=					{
													    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					):
																	lowercase__					:   Optional[Any]													=					tf.ones(self.model_tester.batch_size				,       dtype=tf.intaa					)
													elif model_class in get_values(SCREAMING_SNAKE_CASE					):
																	lowercase__					:   List[str]													=					tf.zeros(self.model_tester.batch_size				,       dtype=tf.intaa					)
																	lowercase__					:   Tuple													=					tf.zeros(self.model_tester.batch_size				,       dtype=tf.intaa					)
													elif model_class in get_values(SCREAMING_SNAKE_CASE					):
																	lowercase__					:   Dict													=					tf.zeros(self.model_tester.batch_size				,       dtype=tf.intaa					)
													elif model_class in get_values(SCREAMING_SNAKE_CASE					):
																	lowercase__					:   str													=					tf.zeros(
																	    (self.model_tester.batch_size, self.model_tester.text_seq_length)				,       dtype=tf.intaa					)

									return inputs_dict







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Optional[Any]													=					TFLayoutLMvaModelTester(self					)
									lowercase__					:   Dict													=					ConfigTester(self				,       config_class=SCREAMING_SNAKE_CASE				,       hidden_size=37					)







					def 			snake_case					(							self :     Tuple					):
									self.config_tester.run_common_tests()







					def 			snake_case					(							self :     str					):
									lowercase__							,							lowercase__					:   Optional[Any]													=					self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
													lowercase__					:   List[Any]													=					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
																	lowercase__					:   List[str]													=					self._prepare_for_class(inputs_dict.copy()				,       SCREAMING_SNAKE_CASE				,       return_labels=SCREAMING_SNAKE_CASE					)
																	lowercase__					:   Optional[Any]													=					prepared_for_class[
																	    sorted(prepared_for_class.keys() - inputs_dict.keys()				,       reverse=SCREAMING_SNAKE_CASE					)[0]
																	]
																	lowercase__					:   List[str]													=					added_label.shape.as_list()[:1]

																	# Test that model correctly compute the loss with kwargs
																	lowercase__					:   str													=					self._prepare_for_class(inputs_dict.copy()				,       SCREAMING_SNAKE_CASE				,       return_labels=SCREAMING_SNAKE_CASE					)
																	lowercase__					:   List[str]													=					prepared_for_class.pop("input_ids"					)

																	lowercase__					:   Optional[int]													=					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
																	lowercase__					:   List[str]													=					self._prepare_for_class(inputs_dict.copy()				,       SCREAMING_SNAKE_CASE				,       return_labels=SCREAMING_SNAKE_CASE					)
																	lowercase__					:   str													=					prepared_for_class.pop("input_ids"					)
																	if "labels" in prepared_for_class:
																					lowercase__					:   Union[str, Any]													=					prepared_for_class["labels"].numpy()
																					if len(labels.shape					) > 1 and labels.shape[1] != 1:
																									lowercase__					:   List[Any]													=					-100
																									lowercase__					:   List[Any]													=					tf.convert_to_tensor(SCREAMING_SNAKE_CASE					)
																									lowercase__					:   List[Any]													=					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
																	lowercase__					:   Tuple													=					self._prepare_for_class(inputs_dict.copy()				,       SCREAMING_SNAKE_CASE				,       return_labels=SCREAMING_SNAKE_CASE					)
																	lowercase__					:   Any													=					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
																	lowercase__					:   List[str]													=					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
																	lowercase__					:   Tuple													=					prepared_for_class.keys() - inputs_dict.keys()
																	lowercase__					:   List[Any]													=					inspect.signature(model.call					).parameters
																	lowercase__					:   int													=					list(signature.keys()					)

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

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

																	for index, value in sorted_tuple_index_mapping:
																					lowercase__					:   List[Any]													=					prepared_for_class[value]

																	lowercase__					:   List[Any]													=					tuple(SCREAMING_SNAKE_CASE					)

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

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







					def 			snake_case					(							self :     Optional[int]					):
									(
									    (
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							
									)					:   Any													=					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 			snake_case					(							self :     Tuple					):
									(
									    (
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							
									)					:   Tuple													=					self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
													lowercase__					:   List[Any]													=					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 			snake_case					(							self :     str					):
									(
									    (
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							
									)					:   Union[str, Any]													=					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 			snake_case					(							self :     Optional[int]					):
									(
									    (
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							
									)					:   Union[str, Any]													=					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 			snake_case					(							self :     List[str]					):
									(
									    (
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							
									)					:   str													=					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 			snake_case					(							self :     List[str]					):
									for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowercase__					:   str													=					TFLayoutLMvaModel.from_pretrained(SCREAMING_SNAKE_CASE					)
													self.assertIsNotNone(SCREAMING_SNAKE_CASE					)






def  __lowerCamelCase (       ):

				"""simple docstring"""


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

@require_tf
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					@cached_property
					def 			snake_case					(							self :     Union[str, Any]					):
									return LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE					) if is_vision_available() else None







					@slow
					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Dict													=					TFLayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"					)

									lowercase__					:   Any													=					self.default_image_processor
									lowercase__					:   Optional[Any]													=					prepare_img()
									lowercase__					:   str													=					image_processor(images=SCREAMING_SNAKE_CASE				,       return_tensors="tf"					).pixel_values

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

									# forward pass
									lowercase__					:   Union[str, Any]													=					model(input_ids=SCREAMING_SNAKE_CASE				,       bbox=SCREAMING_SNAKE_CASE				,       pixel_values=SCREAMING_SNAKE_CASE				,       training=SCREAMING_SNAKE_CASE					)

									# verify the logits
									lowercase__					:   Any													=					(1, 199, 768)
									self.assertEqual(outputs.last_hidden_state.shape				,       SCREAMING_SNAKE_CASE					)

									lowercase__					:   int													=					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					)					)









import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Optional[Any]													=					{}
				lowercase__					:   Tuple													=					tokenizer(example["content"]					,			truncation=lowerCamelCase__						)["input_ids"]
				lowercase__					:   Optional[int]													=					len(example["content"]						) / len(output["input_ids"]						)
				return output


lowerCAmelCase__					  =  HfArgumentParser(PretokenizationArguments)
lowerCAmelCase__					  =  parser.parse_args()
if args.num_workers is None:
				lowerCAmelCase__					  =  multiprocessing.cpu_count()
lowerCAmelCase__					  =  AutoTokenizer.from_pretrained(args.tokenizer_dir)

lowerCAmelCase__					  =  time.time()
lowerCAmelCase__					  =  load_dataset(args.dataset_name, split='''train''')
print(f'''Dataset loaded in {time.time()-t_start:.2f}s''')

lowerCAmelCase__					  =  time.time()
lowerCAmelCase__					  =  ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        '''repo_name''',
        '''path''',
        '''copies''',
        '''size''',
        '''content''',
        '''license''',
        '''hash''',
        '''line_mean''',
        '''line_max''',
        '''alpha_frac''',
        '''autogenerated''',
    ],
)
print(f'''Dataset tokenized in {time.time()-t_start:.2f}s''')

lowerCAmelCase__					  =  time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')




