

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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Any		,				SCREAMING_SNAKE_CASE_:							List[Any]=False		,				SCREAMING_SNAKE_CASE_:							Union[str, Any]=False					)	->					int:




				'''simple docstring'''





				A__						=	"backbone." if is_semantic else ""

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

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        (F'{prefix}cls_token', "beit.embeddings.cls_token"),
				        (F'{prefix}patch_embed.proj.weight', "beit.embeddings.patch_embeddings.projection.weight"),
				        (F'{prefix}patch_embed.proj.bias', "beit.embeddings.patch_embeddings.projection.bias"),
				        (F'{prefix}pos_embed', "beit.embeddings.position_embeddings"),
				    ]					)

				if has_lm_head:
								# mask token + layernorm
								rename_keys.extend(
								    [
								        ("mask_token", "beit.embeddings.mask_token"),
								        ("norm.weight", "layernorm.weight"),
								        ("norm.bias", "layernorm.bias"),
								    ]					)
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ("fc_norm.weight", "beit.pooler.layernorm.weight"),
								        ("fc_norm.bias", "beit.pooler.layernorm.bias"),
								        ("head.weight", "classifier.weight"),
								        ("head.bias", "classifier.bias"),
								    ]					)

				return rename_keys



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple		,				SCREAMING_SNAKE_CASE_:							List[Any]		,				SCREAMING_SNAKE_CASE_:							List[str]=False		,				SCREAMING_SNAKE_CASE_:							int=False					)	->					int:




				'''simple docstring'''





				for i in range(config.num_hidden_layers					):
								A__						=	"backbone." if is_semantic else ""
								# queries, keys and values
								A__						=	state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight'					)
								A__						=	state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias'					)
								A__						=	state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias'					)

								A__						=	in_proj_weight[
								    : config.hidden_size, :
								]
								A__						=	q_bias
								A__						=	in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								A__						=	in_proj_weight[
								    -config.hidden_size :, :
								]
								A__						=	v_bias

								# gamma_1 and gamma_2
								# we call them lambda because otherwise they are renamed when using .from_pretrained
								A__						=	state_dict.pop(F'{prefix}blocks.{i}.gamma_1'					)
								A__						=	state_dict.pop(F'{prefix}blocks.{i}.gamma_2'					)

								A__						=	gamma_a
								A__						=	gamma_a



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple		,				SCREAMING_SNAKE_CASE_:							Any		,				SCREAMING_SNAKE_CASE_:							Optional[Any]					)	->					int:




				'''simple docstring'''





				A__						=	dct.pop(SCREAMING_SNAKE_CASE_					)
				A__						=	val



def     lowerCAmelCase__							(		)	->					Any:




				'''simple docstring'''





				A__						=	"http://images.cocodataset.org/val2017/000000039769.jpg"
				A__						=	Image.open(requests.get(SCREAMING_SNAKE_CASE_		,				stream=SCREAMING_SNAKE_CASE_					).raw					)
				return im



@torch.no_grad()
def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[int]		,				SCREAMING_SNAKE_CASE_:							Union[str, Any]		,				SCREAMING_SNAKE_CASE_:							str=False					)	->					List[Any]:




				'''simple docstring'''





				A__						=	False if "rvlcdip" in checkpoint_url else True
				A__						=	BeitConfig(use_absolute_position_embeddings=SCREAMING_SNAKE_CASE_		,				use_mask_token=SCREAMING_SNAKE_CASE_					)

				# size of the architecture
				if "large" in checkpoint_url or "dit-l" in checkpoint_url:
								A__						=	1_0_2_4
								A__						=	4_0_9_6
								A__						=	2_4
								A__						=	1_6

				# labels
				if "rvlcdip" in checkpoint_url:
								A__						=	1_6
								A__						=	"huggingface/label-files"
								A__						=	"rvlcdip-id2label.json"
								A__						=	json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				repo_type="dataset"					)		,				"r"					)					)
								A__						=	{int(SCREAMING_SNAKE_CASE_					): v for k, v in idalabel.items()}
								A__						=	idalabel
								A__						=	{v: k for k, v in idalabel.items()}

				# load state_dict of original model, remove and rename some keys
				A__						=	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_		,				map_location="cpu"					)["model"]

				A__						=	create_rename_keys(SCREAMING_SNAKE_CASE_		,				has_lm_head=SCREAMING_SNAKE_CASE_					)
				for src, dest in rename_keys:
								rename_key(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					)
				read_in_q_k_v(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				has_lm_head=SCREAMING_SNAKE_CASE_					)

				# load HuggingFace model
				A__						=	BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE_					) if has_lm_head else BeitForImageClassification(SCREAMING_SNAKE_CASE_					)
				model.eval()
				model.load_state_dict(SCREAMING_SNAKE_CASE_					)

				# Check outputs on an image
				A__						=	BeitImageProcessor(
				    size=config.image_size		,				resample=PILImageResampling.BILINEAR		,				do_center_crop=SCREAMING_SNAKE_CASE_					)
				A__						=	prepare_img()

				A__						=	image_processor(images=SCREAMING_SNAKE_CASE_		,				return_tensors="pt"					)
				A__						=	encoding["pixel_values"]

				A__						=	model(SCREAMING_SNAKE_CASE_					)
				A__						=	outputs.logits

				# verify logits
				A__						=	[1, 1_6] if "rvlcdip" in checkpoint_url else [1, 1_9_6, 8_1_9_2]
				assert logits.shape == torch.Size(SCREAMING_SNAKE_CASE_					), "Shape of logits not as expected"

				Path(SCREAMING_SNAKE_CASE_					).mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)
				print(F'Saving model to {pytorch_dump_folder_path}'					)
				model.save_pretrained(SCREAMING_SNAKE_CASE_					)
				print(F'Saving image processor to {pytorch_dump_folder_path}'					)
				image_processor.save_pretrained(SCREAMING_SNAKE_CASE_					)

				if push_to_hub:
								if has_lm_head:
												A__						=	"dit-base" if "base" in checkpoint_url else "dit-large"
								else:
												A__						=	"dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
								image_processor.push_to_hub(
								    repo_path_or_name=Path(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					)		,				organization="nielsr"		,				commit_message="Add image processor"		,				use_temp_dir=SCREAMING_SNAKE_CASE_		,				)
								model.push_to_hub(
								    repo_path_or_name=Path(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					)		,				organization="nielsr"		,				commit_message="Add model"		,				use_temp_dir=SCREAMING_SNAKE_CASE_		,				)


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

	parser.add_argument(
	    """--checkpoint_url""",
	    default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth""",
	    type=str,
	    help="""URL to the original PyTorch checkpoint (.pth file).""",
	)
	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""",
	)
	lowerCAmelCase__		      =       parser.parse_args()
	convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


"""simple docstring"""







import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
       from PIL import Image
else:







       class        UpperCamelCase		:
          @staticmethod
          def 			_UpperCAmelCase   (			*__UpperCamelCase     ,**__UpperCamelCase			)	->      Union[str, Any]:


               '''simple docstring'''


               pass







@is_pipeline_test
@require_vision
@require_torch
class        UpperCamelCase		(			unittest.TestCase  ):
   lowercase							=  MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				int													=	pipeline(
            'zero-shot-object-detection'     ,model='hf-internal-testing/tiny-random-owlvit-object-detection'			)

        lowercase_	:				Optional[int]													=	[
            {
                'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
                'candidate_labels': ['cat', 'remote', 'couch'],
            }
        ]
        return object_detector, examples
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase			)	->      str:


        '''simple docstring'''


        lowercase_	:				Tuple													=	object_detector(examples[0]     ,threshold=0.0			)

        lowercase_	:				Any													=	len(__UpperCamelCase			)
        self.assertGreater(__UpperCamelCase     ,0			)
        self.assertEqual(
            __UpperCamelCase     ,[
                {
                    'score': ANY(__UpperCamelCase			),
                    'label': ANY(__UpperCamelCase			),
                    'box': {'xmin': ANY(__UpperCamelCase			), 'ymin': ANY(__UpperCamelCase			), 'xmax': ANY(__UpperCamelCase			), 'ymax': ANY(__UpperCamelCase			)},
                }
                for i in range(__UpperCamelCase			)
            ]     ,)
   @require_tf
   @unittest.skip('Zero Shot Object Detection not implemented in TF'			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        pass
   @require_torch
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	pipeline(
            'zero-shot-object-detection'     ,model='hf-internal-testing/tiny-random-owlvit-object-detection'			)

        lowercase_	:				Optional[int]													=	object_detector(
            './tests/fixtures/tests_samples/COCO/000000039769.png'     ,candidate_labels=['cat', 'remote', 'couch']     ,threshold=0.64     ,)

        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,decimals=4			)     ,[
                {'score': 0.7235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
                {'score': 0.7218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
                {'score': 0.7184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
                {'score': 0.6748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
                {'score': 0.6656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
                {'score': 0.6614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
                {'score': 0.6456, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
                {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
                {'score': 0.6419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
            ]     ,)

        lowercase_	:				Union[str, Any]													=	object_detector(
            [
                {
                    'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
                    'candidate_labels': ['cat', 'remote', 'couch'],
                }
            ]     ,threshold=0.64     ,)

        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,decimals=4			)     ,[
                [
                    {'score': 0.7235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
                    {'score': 0.7218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
                    {'score': 0.7184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
                    {'score': 0.6748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
                    {'score': 0.6656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
                    {'score': 0.6614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
                    {'score': 0.6456, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
                    {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
                    {'score': 0.6419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
                ]
            ]     ,)
   @require_torch
   @slow
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Any													=	pipeline('zero-shot-object-detection'			)

        lowercase_	:				List[str]													=	object_detector(
            'http://images.cocodataset.org/val2017/000000039769.jpg'     ,candidate_labels=['cat', 'remote', 'couch']     ,)
        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,decimals=4			)     ,[
                {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
                {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
                {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
                {'score': 0.1474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
                {'score': 0.1208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
            ]     ,)

        lowercase_	:				Union[str, Any]													=	object_detector(
            [
                {
                    'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
                    'candidate_labels': ['cat', 'remote', 'couch'],
                },
                {
                    'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
                    'candidate_labels': ['cat', 'remote', 'couch'],
                },
            ]     ,)
        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,decimals=4			)     ,[
                [
                    {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
                    {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
                    {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
                    {'score': 0.1474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
                    {'score': 0.1208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
                ],
                [
                    {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
                    {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
                    {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
                    {'score': 0.1474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
                    {'score': 0.1208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
                ],
            ]     ,)
   @require_tf
   @unittest.skip('Zero Shot Object Detection not implemented in TF'			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        pass
   @require_torch
   @slow
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	0.2
        lowercase_	:				Any													=	pipeline('zero-shot-object-detection'			)

        lowercase_	:				str													=	object_detector(
            'http://images.cocodataset.org/val2017/000000039769.jpg'     ,candidate_labels=['cat', 'remote', 'couch']     ,threshold=__UpperCamelCase     ,)
        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,decimals=4			)     ,[
                {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
                {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
                {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
            ]     ,)







   @require_torch
   @slow
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Tuple													=	2
        lowercase_	:				Optional[Any]													=	pipeline('zero-shot-object-detection'			)

        lowercase_	:				List[Any]													=	object_detector(
            'http://images.cocodataset.org/val2017/000000039769.jpg'     ,candidate_labels=['cat', 'remote', 'couch']     ,top_k=__UpperCamelCase     ,)
        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,decimals=4			)     ,[
                {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
                {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
            ]     ,)

