






"""simple docstring"""







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__a       :	Tuple             = logging.get_logger(__name__)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_=False):
					a__          =  []
					for i in range(config.num_hidden_layers):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append((f'blocks.{i}.norm1.weight', f'vit.encoder.layer.{i}.layernorm_before.weight'))
										rename_keys.append((f'blocks.{i}.norm1.bias', f'vit.encoder.layer.{i}.layernorm_before.bias'))
										rename_keys.append((f'blocks.{i}.attn.proj.weight', f'vit.encoder.layer.{i}.attention.output.dense.weight'))
										rename_keys.append((f'blocks.{i}.attn.proj.bias', f'vit.encoder.layer.{i}.attention.output.dense.bias'))
										rename_keys.append((f'blocks.{i}.norm2.weight', f'vit.encoder.layer.{i}.layernorm_after.weight'))
										rename_keys.append((f'blocks.{i}.norm2.bias', f'vit.encoder.layer.{i}.layernorm_after.bias'))
										rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'vit.encoder.layer.{i}.intermediate.dense.weight'))
										rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'vit.encoder.layer.{i}.intermediate.dense.bias'))
										rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'vit.encoder.layer.{i}.output.dense.weight'))
										rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'vit.encoder.layer.{i}.output.dense.bias'))

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        ('''cls_token''', '''vit.embeddings.cls_token'''),
					        ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
					        ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
					        ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
					    ])

					if base_model:
										# layernorm + pooler
										rename_keys.extend(
										    [
										        ('''norm.weight''', '''layernorm.weight'''),
										        ('''norm.bias''', '''layernorm.bias'''),
										        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
										        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
										    ])

										# if just the base model, we should remove "vit" from all keys that start with "vit"
										a__          =  [(pair[0], pair[1][4:]) if pair[1].startswith('''vit''') else pair for pair in rename_keys]
					else:
										# layernorm + classification head
										rename_keys.extend(
										    [
										        ('''norm.weight''', '''vit.layernorm.weight'''),
										        ('''norm.bias''', '''vit.layernorm.bias'''),
										        ('''head.weight''', '''classifier.weight'''),
										        ('''head.bias''', '''classifier.bias'''),
										    ])

					return rename_keys





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False):
					for i in range(config.num_hidden_layers):
										if base_model:
															a__          =  ''''''
										else:
															a__          =  '''vit.'''
										# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
										a__          =  state_dict.pop(f'blocks.{i}.attn.qkv.weight')
										a__          =  state_dict.pop(f'blocks.{i}.attn.qkv.bias')
										# next, add query, keys and values (in that order) to the state dict
										a__          =  in_proj_weight[
										    : config.hidden_size, :
										]
										a__          =  in_proj_bias[: config.hidden_size]
										a__          =  in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										a__          =  in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										a__          =  in_proj_weight[
										    -config.hidden_size :, :
										]
										a__          =  in_proj_bias[-config.hidden_size :]





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					a__          =  ['''head.weight''', '''head.bias''']
					for k in ignore_keys:
										state_dict.pop(lowerCamelCase_ ,     lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  dct.pop(lowerCamelCase_)
					a__          =  val





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					a__          =  Image.open(requests.get(lowerCamelCase_ ,     stream=lowerCamelCase_).raw)
					return im





@torch.no_grad()
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  ViTConfig()
					a__          =  False
					# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
					if vit_name[-5:] == "in21k":
										a__          =  True
										a__          =  int(vit_name[-12:-10])
										a__          =  int(vit_name[-9:-6])
					else:
										a__          =  1000
										a__          =  '''huggingface/label-files'''
										a__          =  '''imagenet-1k-id2label.json'''
										a__          =  json.load(open(hf_hub_download(lowerCamelCase_ ,     lowerCamelCase_ ,     repo_type='''dataset''') ,     '''r'''))
										a__          =  {int(lowerCamelCase_): v for k, v in idalabel.items()}
										a__          =  idalabel
										a__          =  {v: k for k, v in idalabel.items()}
										a__          =  int(vit_name[-6:-4])
										a__          =  int(vit_name[-3:])
					# size of the architecture
					if "deit" in vit_name:
										if vit_name[9:].startswith('''tiny'''):
															a__          =  192
															a__          =  768
															a__          =  12
															a__          =  3
										elif vit_name[9:].startswith('''small'''):
															a__          =  384
															a__          =  1536
															a__          =  12
															a__          =  6
										else:
															pass
					else:
										if vit_name[4:].startswith('''small'''):
															a__          =  768
															a__          =  2304
															a__          =  8
															a__          =  8
										elif vit_name[4:].startswith('''base'''):
															pass
										elif vit_name[4:].startswith('''large'''):
															a__          =  1024
															a__          =  4096
															a__          =  24
															a__          =  16
										elif vit_name[4:].startswith('''huge'''):
															a__          =  1280
															a__          =  5120
															a__          =  32
															a__          =  16

    # load original model from timm
					a__          =  timm.create_model(lowerCamelCase_ ,     pretrained=lowerCamelCase_)
					timm_model.eval()

					# load state_dict of original model, remove and rename some keys
					a__          =  timm_model.state_dict()
					if base_model:
										remove_classification_head_(lowerCamelCase_)
					a__          =  create_rename_keys(lowerCamelCase_ ,     lowerCamelCase_)
					for src, dest in rename_keys:
										rename_key(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					read_in_q_k_v(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)

					# load HuggingFace model
					if vit_name[-5:] == "in21k":
										a__          =  ViTModel(lowerCamelCase_).eval()
					else:
										a__          =  ViTForImageClassification(lowerCamelCase_).eval()
					model.load_state_dict(lowerCamelCase_)

					# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
					if "deit" in vit_name:
										a__          =  DeiTImageProcessor(size=config.image_size)
					else:
										a__          =  ViTImageProcessor(size=config.image_size)
					a__          =  image_processor(images=prepare_img() ,     return_tensors='''pt''')
					a__          =  encoding['''pixel_values''']
					a__          =  model(lowerCamelCase_)

					if base_model:
										a__          =  timm_model.forward_features(lowerCamelCase_)
										assert timm_pooled_output.shape == outputs.pooler_output.shape
										assert torch.allclose(lowerCamelCase_ ,     outputs.pooler_output ,     atol=1E-3)
					else:
										a__          =  timm_model(lowerCamelCase_)
										assert timm_logits.shape == outputs.logits.shape
										assert torch.allclose(lowerCamelCase_ ,     outputs.logits ,     atol=1E-3)

					Path(lowerCamelCase_).mkdir(exist_ok=lowerCamelCase_)
					print(f'Saving model {vit_name} to {pytorch_dump_folder_path}')
					model.save_pretrained(lowerCamelCase_)
					print(f'Saving image processor to {pytorch_dump_folder_path}')
					image_processor.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
	__a       :	List[Any]             = argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--vit_name',
	    default='vit_base_patch16_224',
	    type=str,
	    help='Name of the ViT timm model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)

	__a       :	Tuple             = parser.parse_args()
	convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)









"""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        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     List[str]				,		__A:     str				,		__A:     Any=2				,		__A:     str=3				,		__A:     Tuple=4				,		__A:     Dict=2				,		__A:     List[Any]=7				,		__A:     Any=True				,		__A:     Any=True				,		__A:     List[str]=True				,		__A:     Optional[int]=True				,		__A:     Optional[int]=99				,		__A:     Tuple=36				,		__A:     List[str]=2				,		__A:     Dict=4				,		__A:     List[str]=37				,		__A:     Optional[int]="gelu"				,		__A:     Optional[int]=0.1				,		__A:     Tuple=0.1				,		__A:     List[Any]=512				,		__A:     List[str]=16				,		__A:     Any=2				,		__A:     Union[str, Any]=0.0_2				,		__A:     Optional[int]=6				,		__A:     Union[str, Any]=6				,		__A:     Union[str, Any]=3				,		__A:     Tuple=4				,		__A:     Optional[int]=None				,		__A:     Optional[Any]=1000				,		):

												'''simple docstring'''

												a__          =  parent
												a__          =  batch_size
												a__          =  num_channels
												a__          =  image_size
												a__          =  patch_size
												a__          =  is_training
												a__          =  use_input_mask
												a__          =  use_token_type_ids
												a__          =  use_labels
												a__          =  vocab_size
												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  intermediate_size
												a__          =  hidden_act
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  max_position_embeddings
												a__          =  type_vocab_size
												a__          =  type_sequence_label_size
												a__          =  initializer_range
												a__          =  coordinate_size
												a__          =  shape_size
												a__          =  num_labels
												a__          =  num_choices
												a__          =  scope
												a__          =  range_bbox

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




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  ids_tensor([self.batch_size, self.text_seq_length]				,		self.vocab_size    )

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

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

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

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

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

												a__          =  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  lowercase		(   self:     List[str]				,		__A:     List[str]				,		__A:     List[str]				,		__A:     List[str]				,		__A:     int				,		__A:     Any				,		__A:     Any    ):

												'''simple docstring'''

												a__          =  TFLayoutLMvaModel(config=__A    )

												# text + image
												a__          =  model(__A				,		pixel_values=__A				,		training=__A    )
												a__          =  model(
												    __A				,		bbox=__A				,		pixel_values=__A				,		attention_mask=__A				,		token_type_ids=__A				,		training=__A				,		)
												a__          =  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__          =  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__          =  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  lowercase		(   self:     Optional[int]				,		__A:     Any				,		__A:     str				,		__A:     List[str]				,		__A:     List[str]				,		__A:     List[str]				,		__A:     Optional[Any]				,		__A:     Any    ):

												'''simple docstring'''

												a__          =  self.num_labels
												a__          =  TFLayoutLMvaForSequenceClassification(config=__A    )
												a__          =  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  lowercase		(   self:     str				,		__A:     List[str]				,		__A:     int				,		__A:     str				,		__A:     Any				,		__A:     str				,		__A:     str				,		__A:     str    ):

												'''simple docstring'''

												a__          =  self.num_labels
												a__          =  TFLayoutLMvaForTokenClassification(config=__A    )
												a__          =  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  lowercase		(   self:     Union[str, Any]				,		__A:     Any				,		__A:     List[Any]				,		__A:     Any				,		__A:     List[str]				,		__A:     Any				,		__A:     Optional[int]				,		__A:     Tuple    ):

												'''simple docstring'''

												a__          =  2
												a__          =  TFLayoutLMvaForQuestionAnswering(config=__A    )
												a__          =  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  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												((a__)       ,(a__)       ,(a__)       ,(a__)       ,(a__)       ,(a__)       ,(a__)       ,(a__))          =  config_and_inputs
												a__          =  {
												    '''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        _SCREAMING_SNAKE_CASE						(  __snake_case       , __snake_case       , unittest.TestCase							):





							"""simple docstring"""





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

							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False




							def  lowercase		(   self:     List[str]				,		__A:     Dict				,		__A:     Optional[Any]				,		__A:     str				,		__A:     Union[str, Any]				,		__A:     Optional[Any]    ):

												'''simple docstring'''

												return True




							def  lowercase		(   self:     Dict				,		__A:     Optional[Any]				,		__A:     Any				,		__A:     List[Any]=False    ):

												'''simple docstring'''

												a__          =  copy.deepcopy(__A    )

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

												return inputs_dict




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  TFLayoutLMvaModelTester(self    )
												a__          =  ConfigTester(self				,		config_class=__A				,		hidden_size=37    )




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												self.config_tester.run_common_tests()




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__       ,a__          =  self.model_tester.prepare_config_and_inputs_for_common()
												for model_class in self.all_model_classes:
																	a__          =  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__          =  self._prepare_for_class(inputs_dict.copy()				,		__A				,		return_labels=__A    )
																						a__          =  prepared_for_class[
																						    sorted(prepared_for_class.keys() - inputs_dict.keys()				,		reverse=__A    )[0]
																						]
																						a__          =  added_label.shape.as_list()[:1]

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

																						a__          =  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__          =  self._prepare_for_class(inputs_dict.copy()				,		__A				,		return_labels=__A    )
																						a__          =  prepared_for_class.pop('''input_ids'''    )
																						if "labels" in prepared_for_class:
																											a__          =  prepared_for_class['''labels'''].numpy()
																											if len(labels.shape    ) > 1 and labels.shape[1] != 1:
																																a__          =  -100
																																a__          =  tf.convert_to_tensor(__A    )
																																a__          =  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__          =  self._prepare_for_class(inputs_dict.copy()				,		__A				,		return_labels=__A    )
																						a__          =  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__          =  self._prepare_for_class(inputs_dict.copy()				,		__A				,		return_labels=__A    )

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

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

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

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

																						a__          =  tuple(__A    )

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

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




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												(
												    (
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,
												)          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(__A				,		__A				,		__A				,		__A				,		__A				,		__A    )




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												(
												    (
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,
												)          =  self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																	a__          =  type
																	self.model_tester.create_and_check_model(__A				,		__A				,		__A				,		__A				,		__A				,		__A    )




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												(
												    (
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,
												)          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(
												    __A				,		__A				,		__A				,		__A				,		__A				,		__A				,		__A    )




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												(
												    (
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,
												)          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(
												    __A				,		__A				,		__A				,		__A				,		__A				,		__A				,		__A    )




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												(
												    (
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,
												)          =  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  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	a__          =  TFLayoutLMvaModel.from_pretrained(__A    )
																	self.assertIsNotNone(__A    )





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
					return image







@require_tf
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@cached_property
							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												return LayoutLMvaImageProcessor(apply_ocr=__A    ) if is_vision_available() else None





							@slow
							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  TFLayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''    )

												a__          =  self.default_image_processor
												a__          =  prepare_img()
												a__          =  image_processor(images=__A				,		return_tensors='''tf'''    ).pixel_values

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

												# forward pass
												a__          =  model(input_ids=__A				,		bbox=__A				,		pixel_values=__A				,		training=__A    )

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

												a__          =  tf.constant(
												    [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]    )

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


