



import argparse
import json
from pathlib import Path

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

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


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







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase=False							,					__UpperCAmelCase=False	)       ->       Optional[int]:



						'''simple docstring'''






						UpperCAmelCase_     							=     "backbone." if is_semantic else ""

						UpperCAmelCase_     							=     []
						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'''),
						    ]	)

						if has_lm_head:
												# mask token + shared relative position bias + layernorm
												rename_keys.extend(
												    [
												        ('''mask_token''', '''beit.embeddings.mask_token'''),
												        (
												            '''rel_pos_bias.relative_position_bias_table''',
												            '''beit.encoder.relative_position_bias.relative_position_bias_table''',
												        ),
												        (
												            '''rel_pos_bias.relative_position_index''',
												            '''beit.encoder.relative_position_bias.relative_position_index''',
												        ),
												        ('''norm.weight''', '''layernorm.weight'''),
												        ('''norm.bias''', '''layernorm.bias'''),
												    ]	)
						elif is_semantic:
												# semantic segmentation classification heads
												rename_keys.extend(
												    [
												        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
												        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
												        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
												        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.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 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase=False							,					__UpperCAmelCase=False	)       ->       Union[str, Any]:



						'''simple docstring'''






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

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

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

												UpperCAmelCase_     							=     gamma_a
												UpperCAmelCase_     							=     gamma_a

												# relative_position bias table + index
												if not has_lm_head:
																		# each layer has its own relative position bias
																		UpperCAmelCase_     							=     state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_bias_table"	)
																		UpperCAmelCase_     							=     state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_index"	)

																		UpperCAmelCase_     							=     table
																		UpperCAmelCase_     							=     index







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       List[str]:



						'''simple docstring'''






						UpperCAmelCase_     							=     dct.pop(_SCREAMING_SNAKE_CASE	)
						UpperCAmelCase_     							=     val







def 				A			(		)       ->       Tuple:



						'''simple docstring'''






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







@torch.no_grad()
def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase	)       ->       Any:



						'''simple docstring'''






						UpperCAmelCase_     							=     BeitConfig()
						UpperCAmelCase_     							=     False
						UpperCAmelCase_     							=     False
						UpperCAmelCase_     							=     "huggingface/label-files"
						# set config parameters based on URL
						if checkpoint_url[-9:-4] == "pt22k":
												# masked image modeling
												UpperCAmelCase_     							=     True
												UpperCAmelCase_     							=     True
												UpperCAmelCase_     							=     True
						elif checkpoint_url[-9:-4] == "ft22k":
												# intermediate fine-tuning on ImageNet-22k
												UpperCAmelCase_     							=     True
												UpperCAmelCase_     							=     2_1841
												UpperCAmelCase_     							=     "imagenet-22k-id2label.json"
												UpperCAmelCase_     							=     json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE							,					repo_type='''dataset'''	)							,					'''r'''	)	)
												UpperCAmelCase_     							=     {int(_SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
												# this dataset contains 21843 labels but the model only has 21841
												# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
												del idalabel[9205]
												del idalabel[1_5027]
												UpperCAmelCase_     							=     idalabel
												UpperCAmelCase_     							=     {v: k for k, v in idalabel.items()}
						elif checkpoint_url[-8:-4] == "to1k":
												# fine-tuning on ImageNet-1k
												UpperCAmelCase_     							=     True
												UpperCAmelCase_     							=     1000
												UpperCAmelCase_     							=     "imagenet-1k-id2label.json"
												UpperCAmelCase_     							=     json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE							,					repo_type='''dataset'''	)							,					'''r'''	)	)
												UpperCAmelCase_     							=     {int(_SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
												UpperCAmelCase_     							=     idalabel
												UpperCAmelCase_     							=     {v: k for k, v in idalabel.items()}
												if "384" in checkpoint_url:
																		UpperCAmelCase_     							=     384
												if "512" in checkpoint_url:
																		UpperCAmelCase_     							=     512
						elif "ade20k" in checkpoint_url:
												# fine-tuning
												UpperCAmelCase_     							=     True
												UpperCAmelCase_     							=     150
												UpperCAmelCase_     							=     "ade20k-id2label.json"
												UpperCAmelCase_     							=     json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE							,					repo_type='''dataset'''	)							,					'''r'''	)	)
												UpperCAmelCase_     							=     {int(_SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
												UpperCAmelCase_     							=     idalabel
												UpperCAmelCase_     							=     {v: k for k, v in idalabel.items()}
												UpperCAmelCase_     							=     640
												UpperCAmelCase_     							=     True
						else:
												raise ValueError('''Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''''	)

						# size of the architecture
						if "base" in checkpoint_url:
												pass
						elif "large" in checkpoint_url:
												UpperCAmelCase_     							=     1024
												UpperCAmelCase_     							=     4096
												UpperCAmelCase_     							=     24
												UpperCAmelCase_     							=     16
												if "ade20k" in checkpoint_url:
																		UpperCAmelCase_     							=     640
																		UpperCAmelCase_     							=     [7, 11, 15, 23]
						else:
												raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''	)

						# load state_dict of original model, remove and rename some keys
						UpperCAmelCase_     							=     torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE							,					map_location='''cpu'''							,					check_hash=_SCREAMING_SNAKE_CASE	)
						UpperCAmelCase_     							=     state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]

						UpperCAmelCase_     							=     create_rename_keys(_SCREAMING_SNAKE_CASE							,					has_lm_head=_SCREAMING_SNAKE_CASE							,					is_semantic=_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							,					is_semantic=_SCREAMING_SNAKE_CASE	)
						if is_semantic:
												# add prefix to decoder keys
												for key, val in state_dict.copy().items():
																		UpperCAmelCase_     							=     state_dict.pop(_SCREAMING_SNAKE_CASE	)
																		if key.startswith('''backbone.fpn'''	):
																								UpperCAmelCase_     							=     key.replace('''backbone.fpn'''							,					'''fpn'''	)
																		UpperCAmelCase_     							=     val

    # load HuggingFace model
						if checkpoint_url[-9:-4] == "pt22k":
												UpperCAmelCase_     							=     BeitForMaskedImageModeling(_SCREAMING_SNAKE_CASE	)
						elif "ade20k" in checkpoint_url:
												UpperCAmelCase_     							=     BeitForSemanticSegmentation(_SCREAMING_SNAKE_CASE	)
						else:
												UpperCAmelCase_     							=     BeitForImageClassification(_SCREAMING_SNAKE_CASE	)
						model.eval()
						model.load_state_dict(_SCREAMING_SNAKE_CASE	)

						# Check outputs on an image
						if is_semantic:
												UpperCAmelCase_     							=     BeitImageProcessor(size=config.image_size							,					do_center_crop=_SCREAMING_SNAKE_CASE	)
												UpperCAmelCase_     							=     load_dataset('''hf-internal-testing/fixtures_ade20k'''							,					split='''test'''	)
												UpperCAmelCase_     							=     Image.open(ds[0]['''file''']	)
						else:
												UpperCAmelCase_     							=     BeitImageProcessor(
												    size=config.image_size							,					resample=PILImageResampling.BILINEAR							,					do_center_crop=_SCREAMING_SNAKE_CASE	)
												UpperCAmelCase_     							=     prepare_img()

						UpperCAmelCase_     							=     image_processor(images=_SCREAMING_SNAKE_CASE							,					return_tensors='''pt'''	)
						UpperCAmelCase_     							=     encoding["pixel_values"]

						UpperCAmelCase_     							=     model(_SCREAMING_SNAKE_CASE	)
						UpperCAmelCase_     							=     outputs.logits

						# verify logits
						UpperCAmelCase_     							=     torch.Size([1, 1000]	)
						if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''	):
												UpperCAmelCase_     							=     torch.Size([1, 196, 8192]	)
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''	):
												UpperCAmelCase_     							=     torch.Size([1, 196, 8192]	)
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''	):
												UpperCAmelCase_     							=     torch.Size([1, 2_1841]	)
												UpperCAmelCase_     							=     torch.tensor([2.2_288, 2.4_671, 0.7_395]	)
												UpperCAmelCase_     							=     2397
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''	):
												UpperCAmelCase_     							=     torch.Size([1, 2_1841]	)
												UpperCAmelCase_     							=     torch.tensor([1.6_881, -0.2_787, 0.5_901]	)
												UpperCAmelCase_     							=     2396
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''	):
												UpperCAmelCase_     							=     torch.tensor([0.1_241, 0.0_798, -0.6_569]	)
												UpperCAmelCase_     							=     285
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''	):
												UpperCAmelCase_     							=     torch.tensor([-1.2_385, -1.0_987, -1.0_108]	)
												UpperCAmelCase_     							=     281
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''	):
												UpperCAmelCase_     							=     torch.tensor([-1.5_303, -0.9_484, -0.3_147]	)
												UpperCAmelCase_     							=     761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''	):
												UpperCAmelCase_     							=     torch.tensor([0.4_610, -0.0_928, 0.2_086]	)
												UpperCAmelCase_     							=     761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''	):
												UpperCAmelCase_     							=     torch.tensor([-0.4_804, 0.6_257, -0.1_837]	)
												UpperCAmelCase_     							=     761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''	):
												UpperCAmelCase_     							=     torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]	)
												UpperCAmelCase_     							=     761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''	):
												UpperCAmelCase_     							=     torch.tensor([-0.3_062, 0.7_261, 0.4_852]	)
												UpperCAmelCase_     							=     761
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''	):
												UpperCAmelCase_     							=     (1, 150, 160, 160)
												UpperCAmelCase_     							=     torch.tensor(
												    [
												        [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
												        [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
												        [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
												    ]	)
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''	):
												UpperCAmelCase_     							=     (1, 150, 160, 160)
												UpperCAmelCase_     							=     torch.tensor(
												    [
												        [[-4.3_305, -2.3_049, -3.0_161], [-2.9_591, -1.5_305, -2.2_251], [-3.4_198, -1.8_004, -2.9_062]],
												        [[-5.8_922, -3.7_435, -4.3_978], [-4.2_063, -2.7_872, -3.4_755], [-4.2_791, -3.1_874, -4.1_681]],
												        [[0.9_895, 4.3_467, 4.7_663], [4.2_476, 5.6_830, 6.1_518], [4.5_550, 6.2_495, 6.5_154]],
												    ]	)
						else:
												raise ValueError('''Can\'t verify logits as model is not supported'''	)

						assert logits.shape == expected_shape, "Shape of logits not as expected"
						if not has_lm_head:
												if is_semantic:
																		assert torch.allclose(
																		    logits[0, :3, :3, :3]							,					_SCREAMING_SNAKE_CASE							,					atol=1e-3	), "First elements of logits not as expected"
												else:
																		print('''Predicted class idx:'''							,					logits.argmax(-1	).item()	)
																		assert torch.allclose(
																		    logits[0, :3]							,					_SCREAMING_SNAKE_CASE							,					atol=1e-3	), "First elements of logits not as expected"
																		assert logits.argmax(-1	).item() == expected_class_idx, "Predicted class index 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 __name__ == "__main__":
							UpperCamelCase_	       =   argparse.ArgumentParser()

							parser.add_argument(
							    "--checkpoint_url",
							    default="https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.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."
							)
							UpperCamelCase_	       =   parser.parse_args()
							convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








'''simple docstring'''
import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

_lowerCamelCase	 =			[(1,)]


def       a__    (  )			-> str:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[int]      =   bool(os.getenv("RUN_SLOW"     ,							0							)							)
	if run_slow:
		assert True
	else:
		assert False, "should have been skipped"


def       a__    (  )			-> str:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[Any]      =   bool(os.getenv("RUN_SLOW"     ,							0							)							)
	if run_slow and torch_device == "cuda":
		assert True
	else:
		assert False, "should have been skipped"
@require_torch
class  _snake_case						(unittest.TestCase):






			@slow
			@require_torch_gpu
			def 						UpperCamelCase__				(   self			):
				check_slow_torch_cuda()






			@require_torch_gpu
			@slow
			def 						UpperCamelCase__				(   self			):
				check_slow_torch_cuda()






			@parameterized.expand(_snake_case			)
			@slow
			def 						UpperCamelCase__				(   self       ,_snake_case=None			):
				check_slow()




@slow
@require_torch_gpu
def       a__    (  )			-> List[Any]:


	"""simple docstring"""





	check_slow_torch_cuda()


@require_torch_gpu
@slow
def       a__    (  )			-> List[str]:


	"""simple docstring"""





	check_slow_torch_cuda()


@slow
@pytest.mark.parametrize("param"     ,							[1]							)
def       a__    (  _SCREAMING_SNAKE_CASE					:    int							)			-> List[Any]:


	"""simple docstring"""





	check_slow()





@pytest.mark.parametrize("param"     ,							[1]							)
@slow
def       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]							)			-> Any:


	"""simple docstring"""





	check_slow()




