
import argparse
import json
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_   	=	logging.get_logger(__name__)






def 		_UpperCAmelCase   (	UpperCamelCase:     Any  ):






	"""simple docstring"""


	if "resnet-50" in model_name:
		__lowerCAmelCase         =							ResNetConfig.from_pretrained("microsoft/resnet-50"  )
	elif "resnet-101" in model_name:
		__lowerCAmelCase         =							ResNetConfig.from_pretrained("microsoft/resnet-101"  )
	else:
		raise ValueError("Model name should include either resnet50 or resnet101"  )

	__lowerCAmelCase         =							DetrConfig(use_timm_backbone=SCREAMING_SNAKE_CASE__			,							backbone_config=SCREAMING_SNAKE_CASE__  )

	# set label attributes
	__lowerCAmelCase         =							"""panoptic""" in model_name
	if is_panoptic:
		__lowerCAmelCase         =							2_5_0
	else:
		__lowerCAmelCase         =							9_1
		__lowerCAmelCase         =							"""huggingface/label-files"""
		__lowerCAmelCase         =							"""coco-detection-id2label.json"""
		__lowerCAmelCase         =							json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__			,							repo_type="dataset"  )			,							"r"  )  )
		__lowerCAmelCase         =							{int(SCREAMING_SNAKE_CASE__  ): v for k, v in idalabel.items()}
		__lowerCAmelCase         =							idalabel
		__lowerCAmelCase         =							{v: k for k, v in idalabel.items()}

	return config, is_panoptic






def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							[]

	# stem
	# fmt: off
	rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight")  )
	rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight")  )
	rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias")  )
	rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean")  )
	rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var")  )
	# stages
	for stage_idx in range(len(config.backbone_config.depths  )  ):
		for layer_idx in range(config.backbone_config.depths[stage_idx]  ):
			# shortcut
			if layer_idx == 0:
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
				    )  )
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
				    )  )
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
				    )  )
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
				    )  )
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
				    )  )
			# 3 convs
			for i in range(3  ):
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
				    )  )
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
				    )  )
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
				    )  )
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
				    )  )
				rename_keys.append(
				    (
				        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
				        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
				    )  )
    # fmt: on

	for i in range(config.encoder_layers  ):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (
		        F"transformer.encoder.layers.{i}.self_attn.out_proj.weight",
		        F"encoder.layers.{i}.self_attn.out_proj.weight",
		    )  )
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")  )
		rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight")  )
		rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias")  )
		rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight")  )
		rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias")  )
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")  )
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias")  )
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight")  )
		rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias")  )
		# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
		rename_keys.append(
		    (
		        F"transformer.decoder.layers.{i}.self_attn.out_proj.weight",
		        F"decoder.layers.{i}.self_attn.out_proj.weight",
		    )  )
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")  )
		rename_keys.append(
		    (
		        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
		        F"decoder.layers.{i}.encoder_attn.out_proj.weight",
		    )  )
		rename_keys.append(
		    (
		        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
		        F"decoder.layers.{i}.encoder_attn.out_proj.bias",
		    )  )
		rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight")  )
		rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias")  )
		rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight")  )
		rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias")  )
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")  )
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias")  )
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")  )
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")  )
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight")  )
		rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias")  )

	# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
	rename_keys.extend(
	    [
	        ("input_proj.weight", "input_projection.weight"),
	        ("input_proj.bias", "input_projection.bias"),
	        ("query_embed.weight", "query_position_embeddings.weight"),
	        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
	        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
	        ("class_embed.weight", "class_labels_classifier.weight"),
	        ("class_embed.bias", "class_labels_classifier.bias"),
	        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
	        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
	        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
	        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
	        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
	        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
	    ]  )

	return rename_keys






def 		_UpperCAmelCase   (	UpperCamelCase:     Dict			,							UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     Dict  ):






	"""simple docstring"""


	__lowerCAmelCase         =							state_dict.pop(SCREAMING_SNAKE_CASE__  )
	__lowerCAmelCase         =							val






def 		_UpperCAmelCase   (	UpperCamelCase:     List[Any]			,							UpperCamelCase:     str=False  ):






	"""simple docstring"""


	__lowerCAmelCase         =							""""""
	if is_panoptic:
		__lowerCAmelCase         =							"""detr."""

	# first: transformer encoder
	for i in range(6  ):
		# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
		__lowerCAmelCase         =							state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"  )
		__lowerCAmelCase         =							state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"  )
		# next, add query, keys and values (in that order) to the state dict
		__lowerCAmelCase         =							in_proj_weight[:2_5_6, :]
		__lowerCAmelCase         =							in_proj_bias[:2_5_6]
		__lowerCAmelCase         =							in_proj_weight[2_5_6:5_1_2, :]
		__lowerCAmelCase         =							in_proj_bias[2_5_6:5_1_2]
		__lowerCAmelCase         =							in_proj_weight[-2_5_6:, :]
		__lowerCAmelCase         =							in_proj_bias[-2_5_6:]
	# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
	for i in range(6  ):
		# read in weights + bias of input projection layer of self-attention
		__lowerCAmelCase         =							state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"  )
		__lowerCAmelCase         =							state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"  )
		# next, add query, keys and values (in that order) to the state dict
		__lowerCAmelCase         =							in_proj_weight[:2_5_6, :]
		__lowerCAmelCase         =							in_proj_bias[:2_5_6]
		__lowerCAmelCase         =							in_proj_weight[2_5_6:5_1_2, :]
		__lowerCAmelCase         =							in_proj_bias[2_5_6:5_1_2]
		__lowerCAmelCase         =							in_proj_weight[-2_5_6:, :]
		__lowerCAmelCase         =							in_proj_bias[-2_5_6:]
		# read in weights + bias of input projection layer of cross-attention
		__lowerCAmelCase         =							state_dict.pop(
		    F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"  )
		__lowerCAmelCase         =							state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"  )
		# next, add query, keys and values (in that order) of cross-attention to the state dict
		__lowerCAmelCase         =							in_proj_weight_cross_attn[:2_5_6, :]
		__lowerCAmelCase         =							in_proj_bias_cross_attn[:2_5_6]
		__lowerCAmelCase         =							in_proj_weight_cross_attn[2_5_6:5_1_2, :]
		__lowerCAmelCase         =							in_proj_bias_cross_attn[2_5_6:5_1_2]
		__lowerCAmelCase         =							in_proj_weight_cross_attn[-2_5_6:, :]
		__lowerCAmelCase         =							in_proj_bias_cross_attn[-2_5_6:]






def 		_UpperCAmelCase   (	):






	"""simple docstring"""


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

	return im






@torch.no_grad()
def 		_UpperCAmelCase   (	UpperCamelCase:     str			,							UpperCamelCase:     Optional[Any]=None			,							UpperCamelCase:     List[Any]=False  ):






	"""simple docstring"""


	__lowerCAmelCase         =							get_detr_config(SCREAMING_SNAKE_CASE__  )

	# load original model from torch hub
	__lowerCAmelCase         =							{
	    """detr-resnet-50""": """detr_resnet50""",
	    """detr-resnet-101""": """detr_resnet101""",
	}
	logger.info(F"Converting model {model_name}..."  )
	__lowerCAmelCase         =							torch.hub.load("facebookresearch/detr"			,							model_name_to_original_name[model_name]			,							pretrained=SCREAMING_SNAKE_CASE__  ).eval()
	__lowerCAmelCase         =							detr.state_dict()
	# rename keys
	for src, dest in create_rename_keys(SCREAMING_SNAKE_CASE__  ):
		if is_panoptic:
			__lowerCAmelCase         =							"""detr.""" + src
		rename_key(SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__  )
	# query, key and value matrices need special treatment
	read_in_q_k_v(SCREAMING_SNAKE_CASE__			,							is_panoptic=SCREAMING_SNAKE_CASE__  )
	# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
	__lowerCAmelCase         =							"""detr.model.""" if is_panoptic else """model."""
	for key in state_dict.copy().keys():
		if is_panoptic:
			if (
			    key.startswith("detr"  )
			    and not key.startswith("class_labels_classifier"  )
			    and not key.startswith("bbox_predictor"  )
			):
				__lowerCAmelCase         =							state_dict.pop(SCREAMING_SNAKE_CASE__  )
				__lowerCAmelCase         =							val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				__lowerCAmelCase         =							state_dict.pop(SCREAMING_SNAKE_CASE__  )
				__lowerCAmelCase         =							val
			elif key.startswith("bbox_attention"  ) or key.startswith("mask_head"  ):
				continue
			else:
				__lowerCAmelCase         =							state_dict.pop(SCREAMING_SNAKE_CASE__  )
				__lowerCAmelCase         =							val
		else:
			if not key.startswith("class_labels_classifier"  ) and not key.startswith("bbox_predictor"  ):
				__lowerCAmelCase         =							state_dict.pop(SCREAMING_SNAKE_CASE__  )
				__lowerCAmelCase         =							val

    # finally, create HuggingFace model and load state dict
	__lowerCAmelCase         =							DetrForSegmentation(SCREAMING_SNAKE_CASE__  ) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE__  )
	model.load_state_dict(SCREAMING_SNAKE_CASE__  )
	model.eval()

	# verify our conversion on an image
	__lowerCAmelCase         =							"""coco_panoptic""" if is_panoptic else """coco_detection"""
	__lowerCAmelCase         =							DetrImageProcessor(format=SCREAMING_SNAKE_CASE__  )

	__lowerCAmelCase         =							processor(images=prepare_img()			,							return_tensors="pt"  )
	__lowerCAmelCase         =							encoding["""pixel_values"""]

	__lowerCAmelCase         =							detr(SCREAMING_SNAKE_CASE__  )
	__lowerCAmelCase         =							model(SCREAMING_SNAKE_CASE__  )

	assert torch.allclose(outputs.logits			,							original_outputs["pred_logits"]			,							atol=1e-3  )
	assert torch.allclose(outputs.pred_boxes			,							original_outputs["pred_boxes"]			,							atol=1e-3  )
	if is_panoptic:
		assert torch.allclose(outputs.pred_masks			,							original_outputs["pred_masks"]			,							atol=1e-4  )
	print("Looks ok!"  )

	if pytorch_dump_folder_path is not None:
		# Save model and image processor
		logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."  )
		Path(SCREAMING_SNAKE_CASE__  ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__  )
		model.save_pretrained(SCREAMING_SNAKE_CASE__  )
		processor.save_pretrained(SCREAMING_SNAKE_CASE__  )

	if push_to_hub:
		# Upload model and image processor to the hub
		logger.info("Uploading PyTorch model and image processor to the hub..."  )
		model.push_to_hub(F"nielsr/{model_name}"  )
		processor.push_to_hub(F"nielsr/{model_name}"  )


if __name__ == "__main__":
							UpperCamelCase_   	=	argparse.ArgumentParser()

							parser.add_argument(
							    "--model_name",
							    default="detr-resnet-50",
							    type=str,
							    choices=["detr-resnet-50", "detr-resnet-101"],
							    help="Name of the DETR model you\'d like to convert.",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
							)
							parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
							UpperCamelCase_   	=	parser.parse_args()
							convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, 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 ) )






