


'''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__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_=2 ,   lowerCamelCase_=3 ,   lowerCamelCase_=4 ,   lowerCamelCase_=2 ,   lowerCamelCase_=7 ,   lowerCamelCase_=True ,   lowerCamelCase_=True ,   lowerCamelCase_=True ,   lowerCamelCase_=True ,   lowerCamelCase_=99 ,   lowerCamelCase_=36 ,   lowerCamelCase_=2 ,   lowerCamelCase_=4 ,   lowerCamelCase_=37 ,   lowerCamelCase_="gelu" ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=5_12 ,   lowerCamelCase_=16 ,   lowerCamelCase_=2 ,   lowerCamelCase_=0.02 ,   lowerCamelCase_=6 ,   lowerCamelCase_=6 ,   lowerCamelCase_=3 ,   lowerCamelCase_=4 ,   lowerCamelCase_=None ,   lowerCamelCase_=10_00 ,   )	->							List[Any]:
														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   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														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   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							List[Any]:
														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   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Any:
														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   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							int:
														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   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Any:
														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   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     self.prepare_config_and_inputs()
														(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__    (							a__    ,    a__    ,    unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       Optional[Any]     						=				(
							    (
							        TFLayoutLMvaModel,
							        TFLayoutLMvaForQuestionAnswering,
							        TFLayoutLMvaForSequenceClassification,
							        TFLayoutLMvaForTokenClassification,
							    )
							    if is_tf_available()
							    else ()
							)
							lowercase__      :       Union[str, Any]     						=				(
							    {"document-question-answering": TFLayoutLMvaForQuestionAnswering, "feature-extraction": TFLayoutLMvaModel}
							    if is_tf_available()
							    else {}
							)

							lowercase__      :       Optional[Any]     						=				False
							lowercase__      :       Any     						=				False
							lowercase__      :       List[str]     						=				False







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							List[str]:
														return True







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_=False       )	->							dict:
														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   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														lowerCAmelCase__	       =     TFLayoutLMvaModelTester(self       )
														lowerCAmelCase__	       =     ConfigTester(self ,   config_class=_SCREAMING_SNAKE_CASE ,   hidden_size=37       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														self.config_tester.run_common_tests()







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														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   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														(
														    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   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														(
														    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   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														(
														    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   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														(
														    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   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														(
														    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   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:





														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       (					)						->   Tuple:
							lowerCAmelCase__	       =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
							return image






@require_tf
class 		a__    (							unittest.TestCase  ):





							'''simple docstring'''






							@cached_property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														return LayoutLMvaImageProcessor(apply_ocr=_SCREAMING_SNAKE_CASE       ) if is_vision_available() else None







							@slow
							def   __SCREAMING_SNAKE_CASE							(		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(
														    [[-0.0_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 math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


__UpperCAmelCase       =							logging.get_logger(__name__)




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       int     						=				"linear"
							lowercase__      :       Any     						=				"cosine"
							lowercase__      :       Optional[int]     						=				"cosine_with_restarts"
							lowercase__      :       Optional[Any]     						=				"polynomial"
							lowercase__      :       Tuple     						=				"constant"
							lowercase__      :       Optional[int]     						=				"constant_with_warmup"
							lowercase__      :       Optional[int]     						=				"piecewise_constant"


def  _snake_case       (					A			,   A = -1	)						->   Any:
							return LambdaLR(A			,   lambda A	: 1			,   last_epoch=A	)


def  _snake_case       (					A			,   A			,   A = -1	)						->   Optional[Any]:

							def lr_lambda(A	):
														if current_step < num_warmup_steps:
																					return float(A	) / float(max(1.0			,   A	)	)
														return 1.0

							return LambdaLR(A			,   A			,   last_epoch=A	)


def  _snake_case       (					A			,   A			,   A = -1	)						->   Union[str, Any]:
							lowerCAmelCase__	       =     {}
							lowerCAmelCase__	       =     step_rules.split(''','''	)
							for rule_str in rule_list[:-1]:
														lowerCAmelCase__       , lowerCAmelCase__	       =     rule_str.split(''':'''	)
														lowerCAmelCase__	       =     int(A	)
														lowerCAmelCase__	       =     float(A	)
														lowerCAmelCase__	       =     value
							lowerCAmelCase__	       =     float(rule_list[-1]	)

							def create_rules_function(A			,   A	):
														def rule_func(A	) -> float:
																					lowerCAmelCase__	       =     sorted(rules_dict.keys()	)
																					for i, sorted_step in enumerate(A	):
																												if steps < sorted_step:
																																			return rules_dict[sorted_steps[i]]
																					return last_lr_multiple

														return rule_func

							lowerCAmelCase__	       =     create_rules_function(A			,   A	)

							return LambdaLR(A			,   A			,   last_epoch=A	)


def  _snake_case       (					A			,   A			,   A			,   A=-1	)						->   Optional[int]:

							def lr_lambda(A	):
														if current_step < num_warmup_steps:
																					return float(A	) / float(max(1			,   A	)	)
														return max(
														    0.0			,   float(num_training_steps - current_step	) / float(max(1			,   num_training_steps - num_warmup_steps	)	)	)

							return LambdaLR(A			,   A			,   A	)


def  _snake_case       (					A			,   A			,   A			,   A = 0.5			,   A = -1	)						->   List[str]:

							def lr_lambda(A	):
														if current_step < num_warmup_steps:
																					return float(A	) / float(max(1			,   A	)	)
														lowerCAmelCase__	       =     float(current_step - num_warmup_steps	) / float(max(1			,   num_training_steps - num_warmup_steps	)	)
														return max(0.0			,   0.5 * (1.0 + math.cos(math.pi * float(A	) * 2.0 * progress	))	)

							return LambdaLR(A			,   A			,   A	)


def  _snake_case       (					A			,   A			,   A			,   A = 1			,   A = -1	)						->   Union[str, Any]:

							def lr_lambda(A	):
														if current_step < num_warmup_steps:
																					return float(A	) / float(max(1			,   A	)	)
														lowerCAmelCase__	       =     float(current_step - num_warmup_steps	) / float(max(1			,   num_training_steps - num_warmup_steps	)	)
														if progress >= 1.0:
																					return 0.0
														return max(0.0			,   0.5 * (1.0 + math.cos(math.pi * ((float(A	) * progress) % 1.0)	))	)

							return LambdaLR(A			,   A			,   A	)


def  _snake_case       (					A			,   A			,   A			,   A=1E-7			,   A=1.0			,   A=-1	)						->   Union[str, Any]:
							lowerCAmelCase__	       =     optimizer.defaults['''lr''']
							if not (lr_init > lr_end):
														raise ValueError(F"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""	)

							def lr_lambda(A	):
														if current_step < num_warmup_steps:
																					return float(A	) / float(max(1			,   A	)	)
														elif current_step > num_training_steps:
																					return lr_end / lr_init  # as LambdaLR multiplies by lr_init
														else:
																					lowerCAmelCase__	       =     lr_init - lr_end
																					lowerCAmelCase__	       =     num_training_steps - num_warmup_steps
																					lowerCAmelCase__	       =     1 - (current_step - num_warmup_steps) / decay_steps
																					lowerCAmelCase__	       =     lr_range * pct_remaining**power + lr_end
																					return decay / lr_init  # as LambdaLR multiplies by lr_init

							return LambdaLR(A			,   A			,   A	)


__UpperCAmelCase       =							{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}






def  _snake_case       (					A			,   A			,   A = None			,   A = None			,   A = None			,   A = 1			,   A = 1.0			,   A = -1			,   )						->   int:
							lowerCAmelCase__	       =     SchedulerType(A	)
							lowerCAmelCase__	       =     TYPE_TO_SCHEDULER_FUNCTION[name]
							if name == SchedulerType.CONSTANT:
														return schedule_func(A			,   last_epoch=A	)

							if name == SchedulerType.PIECEWISE_CONSTANT:
														return schedule_func(A			,   step_rules=A			,   last_epoch=A	)

							# All other schedulers require `num_warmup_steps`
							if num_warmup_steps is None:
														raise ValueError(F"""{name} requires `num_warmup_steps`, please provide that argument."""	)

							if name == SchedulerType.CONSTANT_WITH_WARMUP:
														return schedule_func(A			,   num_warmup_steps=A			,   last_epoch=A	)

							# All other schedulers require `num_training_steps`
							if num_training_steps is None:
														raise ValueError(F"""{name} requires `num_training_steps`, please provide that argument."""	)

							if name == SchedulerType.COSINE_WITH_RESTARTS:
														return schedule_func(
														    A			,   num_warmup_steps=A			,   num_training_steps=A			,   num_cycles=A			,   last_epoch=A			,   )

							if name == SchedulerType.POLYNOMIAL:
														return schedule_func(
														    A			,   num_warmup_steps=A			,   num_training_steps=A			,   power=A			,   last_epoch=A			,   )

							return schedule_func(
							    A			,   num_warmup_steps=A			,   num_training_steps=A			,   last_epoch=A	)