






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 						UpperCAmelCase__ :







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

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







		def  A__      (					self		):
						_A	:				List[Any]   =      ids_tensor([self.batch_size, self.text_seq_length]       ,self.vocab_size		)

						_A	:				Tuple   =      ids_tensor([self.batch_size, self.text_seq_length, 4]       ,self.range_bbox		)
						_A	:				int   =      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]:
																		_A	:				Optional[int]   =      bbox[i, j, 3]
																		_A	:				Optional[Any]   =      bbox[i, j, 1]
																		_A	:				int   =      tmp_coordinate
														if bbox[i, j, 2] < bbox[i, j, 0]:
																		_A	:				Tuple   =      bbox[i, j, 2]
																		_A	:				Union[str, Any]   =      bbox[i, j, 0]
																		_A	:				Union[str, Any]   =      tmp_coordinate
						_A	:				Union[str, Any]   =      tf.constant(A__		)

						_A	:				int   =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

						_A	:				str   =      None
						if self.use_input_mask:
										_A	:				List[Any]   =      random_attention_mask([self.batch_size, self.text_seq_length]		)

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

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

						_A	:				Optional[Any]   =      LayoutLMvaConfig(
						    vocab_size=self.vocab_size       ,hidden_size=self.hidden_size       ,num_hidden_layers=self.num_hidden_layers       ,num_attention_heads=self.num_attention_heads       ,intermediate_size=self.intermediate_size       ,hidden_act=self.hidden_act       ,hidden_dropout_prob=self.hidden_dropout_prob       ,attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,max_position_embeddings=self.max_position_embeddings       ,type_vocab_size=self.type_vocab_size       ,initializer_range=self.initializer_range       ,coordinate_size=self.coordinate_size       ,shape_size=self.shape_size       ,input_size=self.image_size       ,patch_size=self.patch_size       ,)

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







		def  A__      (					self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__		):
						_A	:				List[str]   =      TFLayoutLMvaModel(config=A__		)

						# text + image
						_A	:				Dict   =      model(A__       ,pixel_values=A__       ,training=A__		)
						_A	:				Dict   =      model(
						    A__       ,bbox=A__       ,pixel_values=A__       ,attention_mask=A__       ,token_type_ids=A__       ,training=A__       ,)
						_A	:				List[Any]   =      model(A__       ,bbox=A__       ,pixel_values=A__       ,training=A__		)

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

						# text only
						_A	:				int   =      model(A__       ,training=A__		)
						self.parent.assertEqual(
						    result.last_hidden_state.shape       ,(self.batch_size, self.text_seq_length, self.hidden_size)		)

						# image only
						_A	:				str   =      model({'''pixel_values''': pixel_values}       ,training=A__		)
						self.parent.assertEqual(
						    result.last_hidden_state.shape       ,(self.batch_size, self.image_seq_length, self.hidden_size)		)







		def  A__      (					self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__		):
						_A	:				List[Any]   =      self.num_labels
						_A	:				Tuple   =      TFLayoutLMvaForSequenceClassification(config=A__		)
						_A	:				Optional[Any]   =      model(
						    A__       ,bbox=A__       ,pixel_values=A__       ,attention_mask=A__       ,token_type_ids=A__       ,labels=A__       ,training=A__       ,)
						self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_labels)		)







		def  A__      (					self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__		):
						_A	:				Dict   =      self.num_labels
						_A	:				List[Any]   =      TFLayoutLMvaForTokenClassification(config=A__		)
						_A	:				Optional[int]   =      model(
						    A__       ,bbox=A__       ,pixel_values=A__       ,attention_mask=A__       ,token_type_ids=A__       ,labels=A__       ,training=A__       ,)
						self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.text_seq_length, self.num_labels)		)







		def  A__      (					self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__		):
						_A	:				List[str]   =      2
						_A	:				List[Any]   =      TFLayoutLMvaForQuestionAnswering(config=A__		)
						_A	:				List[str]   =      model(
						    A__       ,bbox=A__       ,pixel_values=A__       ,attention_mask=A__       ,token_type_ids=A__       ,start_positions=A__       ,end_positions=A__       ,training=A__       ,)
						self.parent.assertEqual(result.start_logits.shape       ,(self.batch_size, self.seq_length)		)
						self.parent.assertEqual(result.end_logits.shape       ,(self.batch_size, self.seq_length)		)







		def  A__      (					self		):
						_A	:				Any   =      self.prepare_config_and_inputs()
						((_A)  ,				(_A)  ,				(_A)  ,				(_A)  ,				(_A)  ,				(_A)  ,				(_A)  ,				(_A))	:				Tuple   =      config_and_inputs
						_A	:				Any   =      {
						    '''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 						UpperCAmelCase__ (							__snake_case     ,				__snake_case     ,				unittest.TestCase							):
		__snake_case      :       List[str]     =   (
		    (
		        TFLayoutLMvaModel,
		        TFLayoutLMvaForQuestionAnswering,
		        TFLayoutLMvaForSequenceClassification,
		        TFLayoutLMvaForTokenClassification,
		    )
		    if is_tf_available()
		    else ()
		)
		__snake_case      :       List[Any]     =   (
		    {"document-question-answering": TFLayoutLMvaForQuestionAnswering, "feature-extraction": TFLayoutLMvaModel}
		    if is_tf_available()
		    else {}
		)

		__snake_case      :       List[Any]     =   False
		__snake_case      :       str     =   False
		__snake_case      :       Union[str, Any]     =   False







		def  A__      (					self       ,A__       ,A__       ,A__       ,A__       ,A__		):
						return True







		def  A__      (					self       ,A__       ,A__       ,A__=False		):
						_A	:				Union[str, Any]   =      copy.deepcopy(A__		)

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

						if return_labels:
										if model_class in get_values(A__		):
														_A	:				Union[str, Any]   =      tf.ones(self.model_tester.batch_size       ,dtype=tf.intaa		)
										elif model_class in get_values(A__		):
														_A	:				Union[str, Any]   =      tf.zeros(self.model_tester.batch_size       ,dtype=tf.intaa		)
														_A	:				int   =      tf.zeros(self.model_tester.batch_size       ,dtype=tf.intaa		)
										elif model_class in get_values(A__		):
														_A	:				List[Any]   =      tf.zeros(self.model_tester.batch_size       ,dtype=tf.intaa		)
										elif model_class in get_values(A__		):
														_A	:				Union[str, Any]   =      tf.zeros(
														    (self.model_tester.batch_size, self.model_tester.text_seq_length)       ,dtype=tf.intaa		)

						return inputs_dict







		def  A__      (					self		):
						_A	:				int   =      TFLayoutLMvaModelTester(self		)
						_A	:				List[Any]   =      ConfigTester(self       ,config_class=A__       ,hidden_size=37		)







		def  A__      (					self		):
						self.config_tester.run_common_tests()







		def  A__      (					self		):
						_A  ,				_A	:				int   =      self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
										_A	:				Tuple   =      model_class(A__		)
										if getattr(A__       ,'''hf_compute_loss'''       ,A__		):
														# The number of elements in the loss should be the same as the number of elements in the label
														_A	:				Tuple   =      self._prepare_for_class(inputs_dict.copy()       ,A__       ,return_labels=A__		)
														_A	:				Dict   =      prepared_for_class[
														    sorted(prepared_for_class.keys() - inputs_dict.keys()       ,reverse=A__		)[0]
														]
														_A	:				Optional[int]   =      added_label.shape.as_list()[:1]

														# Test that model correctly compute the loss with kwargs
														_A	:				Optional[Any]   =      self._prepare_for_class(inputs_dict.copy()       ,A__       ,return_labels=A__		)
														_A	:				int   =      prepared_for_class.pop('''input_ids'''		)

														_A	:				Optional[int]   =      model(A__       ,**A__		)[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
														_A	:				List[str]   =      self._prepare_for_class(inputs_dict.copy()       ,A__       ,return_labels=A__		)
														_A	:				Optional[int]   =      prepared_for_class.pop('''input_ids'''		)
														if "labels" in prepared_for_class:
																		_A	:				Any   =      prepared_for_class['''labels'''].numpy()
																		if len(labels.shape		) > 1 and labels.shape[1] != 1:
																						_A	:				List[Any]   =      -100
																						_A	:				List[str]   =      tf.convert_to_tensor(A__		)
																						_A	:				Union[str, Any]   =      model(A__       ,**A__		)[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
														_A	:				Optional[Any]   =      self._prepare_for_class(inputs_dict.copy()       ,A__       ,return_labels=A__		)
														_A	:				int   =      model(A__		)[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
														_A	:				str   =      self._prepare_for_class(inputs_dict.copy()       ,A__       ,return_labels=A__		)

														# Get keys that were added with the _prepare_for_class function
														_A	:				Any   =      prepared_for_class.keys() - inputs_dict.keys()
														_A	:				Union[str, Any]   =      inspect.signature(model.call		).parameters
														_A	:				int   =      list(signature.keys()		)

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

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

														for index, value in sorted_tuple_index_mapping:
																		_A	:				Dict   =      prepared_for_class[value]

														_A	:				List[Any]   =      tuple(A__		)

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

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







		def  A__      (					self		):
						(
						    (
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				
						)	:				Optional[int]   =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(A__       ,A__       ,A__       ,A__       ,A__       ,A__		)







		def  A__      (					self		):
						(
						    (
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				
						)	:				List[str]   =      self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
										_A	:				List[Any]   =      type
										self.model_tester.create_and_check_model(A__       ,A__       ,A__       ,A__       ,A__       ,A__		)







		def  A__      (					self		):
						(
						    (
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				
						)	:				Optional[int]   =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(
						    A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__		)







		def  A__      (					self		):
						(
						    (
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				
						)	:				Tuple   =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(
						    A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__		)







		def  A__      (					self		):
						(
						    (
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				
						)	:				List[str]   =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(
						    A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__		)







		@slow
		def  A__      (					self		):
						for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										_A	:				int   =      TFLayoutLMvaModel.from_pretrained(A__		)
										self.assertIsNotNone(A__		)



def  a__ ()		-> Union[str, Any]:
				_A	:				str   =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
				return image






@require_tf
class 						UpperCAmelCase__ (							unittest.TestCase							):







		@cached_property
		def  A__      (					self		):
						return LayoutLMvaImageProcessor(apply_ocr=A__		) if is_vision_available() else None







		@slow
		def  A__      (					self		):
						_A	:				Tuple   =      TFLayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''		)

						_A	:				str   =      self.default_image_processor
						_A	:				Optional[Any]   =      prepare_img()
						_A	:				Tuple   =      image_processor(images=A__       ,return_tensors='''tf'''		).pixel_values

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

						# forward pass
						_A	:				Optional[Any]   =      model(input_ids=A__       ,bbox=A__       ,pixel_values=A__       ,training=A__		)

						# verify the logits
						_A	:				Tuple   =      (1, 199, 768)
						self.assertEqual(outputs.last_hidden_state.shape       ,A__		)

						_A	:				int   =      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]       ,A__       ,atol=1E-4		)		)











def  a__ (__lowercase							:str					,  __lowercase							:str   )		-> float:

				def get_matched_characters(__lowercase							:str					,  __lowercase							:str   ) -> str:
								_A	:				Union[str, Any]   =      []
								_A	:				Dict   =      min(len(_stra   )					,  len(_stra   )   ) // 2
								for i, l in enumerate(_stra   ):
												_A	:				Tuple   =      int(max(0					,  i - limit   )   )
												_A	:				Union[str, Any]   =      int(min(i + limit + 1					,  len(_stra   )   )   )
												if l in _stra[left:right]:
																matched.append(__lowercase   )
																_A	:				List[Any]   =      f"""{_stra[0:_stra.index(__lowercase   )]} {_stra[_stra.index(__lowercase   ) + 1:]}"""

								return "".join(__lowercase   )

				# matching characters
				_A	:				str   =      get_matched_characters(__lowercase					,  __lowercase   )
				_A	:				Optional[Any]   =      get_matched_characters(__lowercase					,  __lowercase   )
				_A	:				Any   =      len(__lowercase   )

				# transposition
				_A	:				str   =      (
				    len([(ca, ca) for ca, ca in zip(__lowercase					,  __lowercase   ) if ca != ca]   ) // 2
				)

				if not match_count:
								_A	:				Optional[int]   =      0.0
				else:
								_A	:				str   =      (
								    1
								    / 3
								    * (
								        match_count / len(__lowercase   )
								        + match_count / len(__lowercase   )
								        + (match_count - transpositions) / match_count
								    )
								)

				# common prefix up to 4 characters
				_A	:				Any   =      0
				for ca, ca in zip(stra[:4]					,  stra[:4]   ):
								if ca == ca:
												prefix_len += 1
								else:
												break

				return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(jaro_winkler('hello', 'world'))



