
import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def 		_UpperCAmelCase   (	UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     Optional[int]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							old_name

	if "patch_embed" in old_name:
		__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							old_name.split("."  )

		if layer == "0":
			__lowerCAmelCase         =							old_name.replace("0"			,							"convolution1"  )
		elif layer == "1":
			__lowerCAmelCase         =							old_name.replace("1"			,							"batchnorm_before"  )
		elif layer == "3":
			__lowerCAmelCase         =							old_name.replace("3"			,							"convolution2"  )
		else:
			__lowerCAmelCase         =							old_name.replace("4"			,							"batchnorm_after"  )

	if "network" in old_name and re.search(R"\d\.\d"			,							UpperCamelCase  ):
		__lowerCAmelCase         =							R"\b\d{2}\b"
		if bool(re.search(UpperCamelCase			,							UpperCamelCase  )  ):
			__lowerCAmelCase         =							re.search(R"\d\.\d\d."			,							UpperCamelCase  ).group()
		else:
			__lowerCAmelCase         =							re.search(R"\d\.\d."			,							UpperCamelCase  ).group()
		if int(match[0]  ) < 6:
			__lowerCAmelCase         =							old_name.replace(UpperCamelCase			,							""  )
			__lowerCAmelCase         =							trimmed_name.replace("network"			,							match[0] + ".meta4D_layers.blocks." + match[2:-1]  )
			__lowerCAmelCase         =							"intermediate_stages." + trimmed_name
		else:
			__lowerCAmelCase         =							old_name.replace(UpperCamelCase			,							""  )
			if int(match[2]  ) < num_meta4D_last_stage:
				__lowerCAmelCase         =							trimmed_name.replace("network"			,							"meta4D_layers.blocks." + match[2]  )
			else:
				__lowerCAmelCase         =							str(int(match[2]  ) - num_meta4D_last_stage  )
				__lowerCAmelCase         =							trimmed_name.replace("network"			,							"meta3D_layers.blocks." + layer_index  )
				if "norm1" in old_name:
					__lowerCAmelCase         =							trimmed_name.replace("norm1"			,							"layernorm1"  )
				elif "norm2" in old_name:
					__lowerCAmelCase         =							trimmed_name.replace("norm2"			,							"layernorm2"  )
				elif "fc1" in old_name:
					__lowerCAmelCase         =							trimmed_name.replace("fc1"			,							"linear_in"  )
				elif "fc2" in old_name:
					__lowerCAmelCase         =							trimmed_name.replace("fc2"			,							"linear_out"  )

			__lowerCAmelCase         =							"last_stage." + trimmed_name

	elif "network" in old_name and re.search(R".\d."			,							UpperCamelCase  ):
		__lowerCAmelCase         =							old_name.replace("network"			,							"intermediate_stages"  )

	if "fc" in new_name:
		__lowerCAmelCase         =							new_name.replace("fc"			,							"convolution"  )
	elif ("norm1" in new_name) and ("layernorm1" not in new_name):
		__lowerCAmelCase         =							new_name.replace("norm1"			,							"batchnorm_before"  )
	elif ("norm2" in new_name) and ("layernorm2" not in new_name):
		__lowerCAmelCase         =							new_name.replace("norm2"			,							"batchnorm_after"  )
	if "proj" in new_name:
		__lowerCAmelCase         =							new_name.replace("proj"			,							"projection"  )
	if "dist_head" in new_name:
		__lowerCAmelCase         =							new_name.replace("dist_head"			,							"distillation_classifier"  )
	elif "head" in new_name:
		__lowerCAmelCase         =							new_name.replace("head"			,							"classifier"  )
	elif "patch_embed" in new_name:
		__lowerCAmelCase         =							"efficientformer." + new_name
	elif new_name == "norm.weight" or new_name == "norm.bias":
		__lowerCAmelCase         =							new_name.replace("norm"			,							"layernorm"  )
		__lowerCAmelCase         =							"efficientformer." + new_name
	else:
		__lowerCAmelCase         =							"efficientformer.encoder." + new_name

	return new_name






def 		_UpperCAmelCase   (	UpperCamelCase:     Tuple			,							UpperCamelCase:     Dict  ):






	"""simple docstring"""


	for key in checkpoint.copy().keys():
		__lowerCAmelCase         =							checkpoint.pop(UpperCamelCase  )
		__lowerCAmelCase         =							val

	return checkpoint






def 		_UpperCAmelCase   (	):






	"""simple docstring"""


	__lowerCAmelCase         =							"http://images.cocodataset.org/val2017/000000039769.jpg"
	__lowerCAmelCase         =							Image.open(requests.get(UpperCamelCase			,							stream=UpperCamelCase  ).raw  )

	return image






def 		_UpperCAmelCase   (	UpperCamelCase:     Path			,							UpperCamelCase:     Path			,							UpperCamelCase:     Path			,							UpperCamelCase:     bool  ):






	"""simple docstring"""


	__lowerCAmelCase         =							torch.load(UpperCamelCase			,							map_location="cpu"  )["model"]
	__lowerCAmelCase         =							EfficientFormerConfig.from_json_file(UpperCamelCase  )
	__lowerCAmelCase         =							EfficientFormerForImageClassificationWithTeacher(UpperCamelCase  )
	__lowerCAmelCase         =							"_".join(checkpoint_path.split("/"  )[-1].split("."  )[0].split("_"  )[:-1]  )

	__lowerCAmelCase         =							config.depths[-1] - config.num_metaad_blocks + 1
	__lowerCAmelCase         =							convert_torch_checkpoint(UpperCamelCase			,							UpperCamelCase  )

	model.load_state_dict(UpperCamelCase  )
	model.eval()

	__lowerCAmelCase         =							{
	    "bilinear": PILImageResampling.BILINEAR,
	    "bicubic": PILImageResampling.BICUBIC,
	    "nearest": PILImageResampling.NEAREST,
	}

	# prepare image
	__lowerCAmelCase         =							prepare_img()
	__lowerCAmelCase         =							2_5_6
	__lowerCAmelCase         =							2_2_4
	__lowerCAmelCase         =							EfficientFormerImageProcessor(
	    size={"shortest_edge": image_size}			,							crop_size={"height": crop_size, "width": crop_size}			,							resample=pillow_resamplings["bicubic"]			,							)
	__lowerCAmelCase         =							processor(images=UpperCamelCase			,							return_tensors="pt"  ).pixel_values

	# original processing pipeline
	__lowerCAmelCase         =							Compose(
	    [
	        Resize(UpperCamelCase			,							interpolation=pillow_resamplings["bicubic"]  ),
	        CenterCrop(UpperCamelCase  ),
	        ToTensor(),
	        Normalize(UpperCamelCase			,							UpperCamelCase  ),
	    ]  )
	__lowerCAmelCase         =							image_transforms(UpperCamelCase  ).unsqueeze(0  )

	assert torch.allclose(UpperCamelCase			,							UpperCamelCase  )

	__lowerCAmelCase         =							model(UpperCamelCase  )
	__lowerCAmelCase         =							outputs.logits

	__lowerCAmelCase         =							(1, 1_0_0_0)

	if "l1" in model_name:
		__lowerCAmelCase         =							torch.Tensor(
		    [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]  )
		assert torch.allclose(logits[0, :1_0]			,							UpperCamelCase			,							atol=1e-3  )
		assert logits.shape == expected_shape
	elif "l3" in model_name:
		__lowerCAmelCase         =							torch.Tensor(
		    [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]  )
		assert torch.allclose(logits[0, :1_0]			,							UpperCamelCase			,							atol=1e-3  )
		assert logits.shape == expected_shape
	elif "l7" in model_name:
		__lowerCAmelCase         =							torch.Tensor(
		    [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]  )
		assert logits.shape == expected_shape
	else:
		raise ValueError(
		    F"Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"  )

	# Save Checkpoints
	Path(UpperCamelCase  ).mkdir(exist_ok=UpperCamelCase  )
	model.save_pretrained(UpperCamelCase  )
	print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"  )
	processor.save_pretrained(UpperCamelCase  )
	print(F"Processor successfuly saved at {pytorch_dump_path}"  )

	if push_to_hub:
		print("Pushing model to the hub..."  )

		model.push_to_hub(
		    repo_id=F"Bearnardd/{pytorch_dump_path}"			,							commit_message="Add model"			,							use_temp_dir=UpperCamelCase			,							)
		processor.push_to_hub(
		    repo_id=F"Bearnardd/{pytorch_dump_path}"			,							commit_message="Add image processor"			,							use_temp_dir=UpperCamelCase			,							)


if __name__ == "__main__":
					UpperCamelCase_   	=	argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--pytorch_model_path",
					    default=None,
					    type=str,
					    required=True,
					    help="Path to EfficientFormer pytorch checkpoint.",
					)
					parser.add_argument(
					    "--config_file",
					    default=None,
					    type=str,
					    required=True,
					    help="The json file for EfficientFormer model config.",
					)
					parser.add_argument(
					    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
					)

					parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
					parser.add_argument(
					    "--no-push_to_hub",
					    dest="push_to_hub",
					    action="store_false",
					    help="Do not push model and image processor to the hub",
					)
					parser.set_defaults(push_to_hub=True)

					UpperCamelCase_   	=	parser.parse_args()
					convert_efficientformer_checkpoint(
					    checkpoint_path=args.pytorch_model_path,
					    efficientformer_config_file=args.config_file,
					    pytorch_dump_path=args.pytorch_dump_path,
					    push_to_hub=args.push_to_hub,
					)








from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
					    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
					    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
					    LayoutLMvaConfig,
					    TFLayoutLMvaForQuestionAnswering,
					    TFLayoutLMvaForSequenceClassification,
					    TFLayoutLMvaForTokenClassification,
					    TFLayoutLMvaModel,
					)

if is_vision_available():
					from PIL import Image

					from transformers import LayoutLMvaImageProcessor

class       a		:







							def __init__(						self  :  Optional[int]      ,				snake_case__  :  Optional[Any]      ,				snake_case__  :  Optional[Any]=2      ,				snake_case__  :  Any=3      ,				snake_case__  :  str=4      ,				snake_case__  :  Tuple=2      ,				snake_case__  :  Optional[Any]=7      ,				snake_case__  :  int=True      ,				snake_case__  :  Dict=True      ,				snake_case__  :  int=True      ,				snake_case__  :  List[Any]=True      ,				snake_case__  :  Union[str, Any]=99      ,				snake_case__  :  str=36      ,				snake_case__  :  Tuple=2      ,				snake_case__  :  Optional[int]=4      ,				snake_case__  :  Optional[Any]=37      ,				snake_case__  :  int="gelu"      ,				snake_case__  :  List[Any]=0.1      ,				snake_case__  :  int=0.1      ,				snake_case__  :  Optional[Any]=512      ,				snake_case__  :  Union[str, Any]=16      ,				snake_case__  :  Optional[int]=2      ,				snake_case__  :  Any=0.0_2      ,				snake_case__  :  List[str]=6      ,				snake_case__  :  Optional[int]=6      ,				snake_case__  :  Any=3      ,				snake_case__  :  str=4      ,				snake_case__  :  int=None      ,				snake_case__  :  Any=1_000      ,				):
								"""simple docstring"""







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

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







							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								__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(snake_case__ )

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

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

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

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

								__lowerCAmelCase         =							LayoutLMvaConfig(
								    vocab_size=self.vocab_size      ,				hidden_size=self.hidden_size      ,				num_hidden_layers=self.num_hidden_layers      ,				num_attention_heads=self.num_attention_heads      ,				intermediate_size=self.intermediate_size      ,				hidden_act=self.hidden_act      ,				hidden_dropout_prob=self.hidden_dropout_prob      ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,				max_position_embeddings=self.max_position_embeddings      ,				type_vocab_size=self.type_vocab_size      ,				initializer_range=self.initializer_range      ,				coordinate_size=self.coordinate_size      ,				shape_size=self.shape_size      ,				input_size=self.image_size      ,				patch_size=self.patch_size      ,				)

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







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  Union[str, Any]      ,				snake_case__  :  Tuple      ,				snake_case__  :  Tuple      ,				snake_case__  :  Optional[int]      ,				snake_case__  :  Any      ,				snake_case__  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							TFLayoutLMvaModel(config=snake_case__ )

								# text + image
								__lowerCAmelCase         =							model(snake_case__      ,				pixel_values=snake_case__      ,				training=snake_case__ )
								__lowerCAmelCase         =							model(
								    snake_case__      ,				bbox=snake_case__      ,				pixel_values=snake_case__      ,				attention_mask=snake_case__      ,				token_type_ids=snake_case__      ,				training=snake_case__      ,				)
								__lowerCAmelCase         =							model(snake_case__      ,				bbox=snake_case__      ,				pixel_values=snake_case__      ,				training=snake_case__ )

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

								# text only
								__lowerCAmelCase         =							model(snake_case__      ,				training=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=snake_case__ )
								self.parent.assertEqual(
								    result.last_hidden_state.shape      ,				(self.batch_size, self.image_seq_length, self.hidden_size) )







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  Optional[int]      ,				snake_case__  :  str      ,				snake_case__  :  List[Any]      ,				snake_case__  :  str      ,				snake_case__  :  Any      ,				snake_case__  :  Tuple      ,				snake_case__  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.num_labels
								__lowerCAmelCase         =							TFLayoutLMvaForSequenceClassification(config=snake_case__ )
								__lowerCAmelCase         =							model(
								    snake_case__      ,				bbox=snake_case__      ,				pixel_values=snake_case__      ,				attention_mask=snake_case__      ,				token_type_ids=snake_case__      ,				labels=snake_case__      ,				training=snake_case__      ,				)
								self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_labels) )







							def       UpperCAmelCase__					(						self  :  Optional[int]      ,				snake_case__  :  Dict      ,				snake_case__  :  Any      ,				snake_case__  :  Optional[Any]      ,				snake_case__  :  int      ,				snake_case__  :  str      ,				snake_case__  :  Any      ,				snake_case__  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.num_labels
								__lowerCAmelCase         =							TFLayoutLMvaForTokenClassification(config=snake_case__ )
								__lowerCAmelCase         =							model(
								    snake_case__      ,				bbox=snake_case__      ,				pixel_values=snake_case__      ,				attention_mask=snake_case__      ,				token_type_ids=snake_case__      ,				labels=snake_case__      ,				training=snake_case__      ,				)
								self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.text_seq_length, self.num_labels) )







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  str      ,				snake_case__  :  Optional[int]      ,				snake_case__  :  Any      ,				snake_case__  :  Any      ,				snake_case__  :  Dict      ,				snake_case__  :  Dict      ,				snake_case__  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							2
								__lowerCAmelCase         =							TFLayoutLMvaForQuestionAnswering(config=snake_case__ )
								__lowerCAmelCase         =							model(
								    snake_case__      ,				bbox=snake_case__      ,				pixel_values=snake_case__      ,				attention_mask=snake_case__      ,				token_type_ids=snake_case__      ,				start_positions=snake_case__      ,				end_positions=snake_case__      ,				training=snake_case__      ,				)
								self.parent.assertEqual(result.start_logits.shape      ,				(self.batch_size, self.seq_length) )
								self.parent.assertEqual(result.end_logits.shape      ,				(self.batch_size, self.seq_length) )







							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.prepare_config_and_inputs()
								((__lowerCAmelCase)   ,						(__lowerCAmelCase)   ,						(__lowerCAmelCase)   ,						(__lowerCAmelCase)   ,						(__lowerCAmelCase)   ,						(__lowerCAmelCase)   ,						(__lowerCAmelCase)   ,						(__lowerCAmelCase))         =							config_and_inputs
								__lowerCAmelCase         =							{
								    "input_ids": input_ids,
								    "bbox": bbox,
								    "pixel_values": pixel_values,
								    "token_type_ids": token_type_ids,
								    "attention_mask": input_mask,
								}
								return config, inputs_dict

@require_tf
class       a		(   __UpperCAmelCase      ,	__UpperCAmelCase      ,	unittest.TestCase					):
							lowercase_		:						List[Any]   							=  (
							    (
							        TFLayoutLMvaModel,
							        TFLayoutLMvaForQuestionAnswering,
							        TFLayoutLMvaForSequenceClassification,
							        TFLayoutLMvaForTokenClassification,
							    )
							    if is_tf_available()
							    else ()
							)
							lowercase_		:						Optional[int]   							=  (
							    {'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
							    if is_tf_available()
							    else {}
							)

							lowercase_		:						Dict   							=  False
							lowercase_		:						str   							=  False
							lowercase_		:						Any   							=  False







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				snake_case__  :  Tuple      ,				snake_case__  :  Optional[int]      ,				snake_case__  :  Optional[int]      ,				snake_case__  :  Any      ,				snake_case__  :  int ):
								"""simple docstring"""







								return True







							def       UpperCAmelCase__					(						self  :  Optional[int]      ,				snake_case__  :  Tuple      ,				snake_case__  :  Union[str, Any]      ,				snake_case__  :  Tuple=False ):
								"""simple docstring"""







								__lowerCAmelCase         =							copy.deepcopy(snake_case__ )

								if model_class in get_values(snake_case__ ):
									__lowerCAmelCase         =							{
									    k: tf.tile(tf.expand_dims(snake_case__      ,				1 )      ,				(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
									    if isinstance(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(snake_case__ ):
										__lowerCAmelCase         =							tf.ones(self.model_tester.batch_size      ,				dtype=tf.intaa )
									elif model_class in get_values(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(snake_case__ ):
										__lowerCAmelCase         =							tf.zeros(self.model_tester.batch_size      ,				dtype=tf.intaa )
									elif model_class in get_values(snake_case__ ):
										__lowerCAmelCase         =							tf.zeros(
										    (self.model_tester.batch_size, self.model_tester.text_seq_length)      ,				dtype=tf.intaa )

								return inputs_dict







							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							TFLayoutLMvaModelTester(self )
								__lowerCAmelCase         =							ConfigTester(self      ,				config_class=snake_case__      ,				hidden_size=37 )







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								self.config_tester.run_common_tests()







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase   ,						__lowerCAmelCase         =							self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
									__lowerCAmelCase         =							model_class(snake_case__ )
									if getattr(snake_case__      ,				"hf_compute_loss"      ,				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()      ,				snake_case__      ,				return_labels=snake_case__ )
										__lowerCAmelCase         =							prepared_for_class[
										    sorted(prepared_for_class.keys() - inputs_dict.keys()      ,				reverse=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()      ,				snake_case__      ,				return_labels=snake_case__ )
										__lowerCAmelCase         =							prepared_for_class.pop("input_ids" )

										__lowerCAmelCase         =							model(snake_case__      ,				**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()      ,				snake_case__      ,				return_labels=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         =							-100
												__lowerCAmelCase         =							tf.convert_to_tensor(snake_case__ )
												__lowerCAmelCase         =							model(snake_case__      ,				**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()      ,				snake_case__      ,				return_labels=snake_case__ )
										__lowerCAmelCase         =							model(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()      ,				snake_case__      ,				return_labels=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(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(snake_case__ )

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

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







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								(
								    (
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						
								)         =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__ )







							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								(
								    (
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						
								)         =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(
								    snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__ )







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								(
								    (
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						
								)         =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(
								    snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__ )







							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								(
								    (
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						(
								    __lowerCAmelCase
								)   ,						
								)         =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(
								    snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__ )







							@slow
							def       UpperCAmelCase__					(						self  :  int ):
								"""simple docstring"""







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






def 		_UpperCAmelCase   (	):






	"""simple docstring"""


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







@require_tf
class       a		(   unittest.TestCase					):







							@cached_property
							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







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







							@slow
							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							TFLayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base" )

								__lowerCAmelCase         =							self.default_image_processor
								__lowerCAmelCase         =							prepare_img()
								__lowerCAmelCase         =							image_processor(images=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=snake_case__      ,				bbox=snake_case__      ,				pixel_values=snake_case__      ,				training=snake_case__ )

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

								__lowerCAmelCase         =							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]      ,				snake_case__      ,				atol=1E-4 ) )






