
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()
A  :			Any     				=				logging.get_logger(__name__)


def 					__lowerCAmelCase    (    a__		,					a__=False		,					a__=False						) ->						Union[str, Any]:
				__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'''),
				    ]						)

				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 					__lowerCAmelCase    (    a__		,					a__		,					a__=False		,					a__=False						) ->						Dict:
				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

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

												__a    							=					table
												__a    							=					index


def 					__lowerCAmelCase    (    a__		,					a__		,					a__						) ->						str:
				__a    							=					dct.pop(a__						)
				__a    							=					val


def 					__lowerCAmelCase    (    ) ->						List[str]:
				__a    							=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__a    							=					Image.open(requests.get(a__		,					stream=a__						).raw						)
				return im


@torch.no_grad()
def 					__lowerCAmelCase    (    a__		,					a__						) ->						Optional[Any]:
				__a    							=					BeitConfig()
				__a    							=					False
				__a    							=					False
				__a    							=					'''huggingface/label-files'''
				# set config parameters based on URL
				if checkpoint_url[-9:-4] == "pt22k":
								# masked image modeling
								__a    							=					True
								__a    							=					True
								__a    							=					True
				elif checkpoint_url[-9:-4] == "ft22k":
								# intermediate fine-tuning on ImageNet-22k
								__a    							=					True
								__a    							=					2_1841
								__a    							=					'''imagenet-22k-id2label.json'''
								__a    							=					json.load(open(hf_hub_download(a__		,					a__		,					repo_type='''dataset'''						)		,					'''r'''						)						)
								__a    							=					{int(a__						): 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]
								__a    							=					idalabel
								__a    							=					{v: k for k, v in idalabel.items()}
				elif checkpoint_url[-8:-4] == "to1k":
								# fine-tuning on ImageNet-1k
								__a    							=					True
								__a    							=					1000
								__a    							=					'''imagenet-1k-id2label.json'''
								__a    							=					json.load(open(hf_hub_download(a__		,					a__		,					repo_type='''dataset'''						)		,					'''r'''						)						)
								__a    							=					{int(a__						): v for k, v in idalabel.items()}
								__a    							=					idalabel
								__a    							=					{v: k for k, v in idalabel.items()}
								if "384" in checkpoint_url:
												__a    							=					384
								if "512" in checkpoint_url:
												__a    							=					512
				elif "ade20k" in checkpoint_url:
								# fine-tuning
								__a    							=					True
								__a    							=					150
								__a    							=					'''ade20k-id2label.json'''
								__a    							=					json.load(open(hf_hub_download(a__		,					a__		,					repo_type='''dataset'''						)		,					'''r'''						)						)
								__a    							=					{int(a__						): v for k, v in idalabel.items()}
								__a    							=					idalabel
								__a    							=					{v: k for k, v in idalabel.items()}
								__a    							=					640
								__a    							=					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:
								__a    							=					1024
								__a    							=					4096
								__a    							=					24
								__a    							=					16
								if "ade20k" in checkpoint_url:
												__a    							=					640
												__a    							=					[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
				__a    							=					torch.hub.load_state_dict_from_url(a__		,					map_location='''cpu'''		,					check_hash=a__						)
				__a    							=					state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

				__a    							=					create_rename_keys(a__		,					has_lm_head=a__		,					is_semantic=a__						)
				for src, dest in rename_keys:
								rename_key(a__		,					a__		,					a__						)
				read_in_q_k_v(a__		,					a__		,					has_lm_head=a__		,					is_semantic=a__						)
				if is_semantic:
								# add prefix to decoder keys
								for key, val in state_dict.copy().items():
												__a    							=					state_dict.pop(a__						)
												if key.startswith('''backbone.fpn'''						):
																__a    							=					key.replace('''backbone.fpn'''		,					'''fpn'''						)
												__a    							=					val

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

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

				__a    							=					image_processor(images=a__		,					return_tensors='''pt'''						)
				__a    							=					encoding['''pixel_values''']

				__a    							=					model(a__						)
				__a    							=					outputs.logits

				# verify logits
				__a    							=					torch.Size([1, 1000]						)
				if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''						):
								__a    							=					torch.Size([1, 196, 8192]						)
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''						):
								__a    							=					torch.Size([1, 196, 8192]						)
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''						):
								__a    							=					torch.Size([1, 2_1841]						)
								__a    							=					torch.tensor([2.2_288, 2.4_671, 0.7_395]						)
								__a    							=					2397
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''						):
								__a    							=					torch.Size([1, 2_1841]						)
								__a    							=					torch.tensor([1.6_881, -0.2_787, 0.5_901]						)
								__a    							=					2396
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''						):
								__a    							=					torch.tensor([0.1_241, 0.0_798, -0.6_569]						)
								__a    							=					285
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''						):
								__a    							=					torch.tensor([-1.2_385, -1.0_987, -1.0_108]						)
								__a    							=					281
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''						):
								__a    							=					torch.tensor([-1.5_303, -0.9_484, -0.3_147]						)
								__a    							=					761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''						):
								__a    							=					torch.tensor([0.4_610, -0.0_928, 0.2_086]						)
								__a    							=					761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''						):
								__a    							=					torch.tensor([-0.4_804, 0.6_257, -0.1_837]						)
								__a    							=					761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''						):
								__a    							=					torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]						)
								__a    							=					761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''						):
								__a    							=					torch.tensor([-0.3_062, 0.7_261, 0.4_852]						)
								__a    							=					761
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''						):
								__a    							=					(1, 150, 160, 160)
								__a    							=					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'''						):
								__a    							=					(1, 150, 160, 160)
								__a    							=					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]		,					a__		,					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]		,					a__		,					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(a__						).mkdir(exist_ok=a__						)
				print(F"""Saving model to {pytorch_dump_folder_path}"""						)
				model.save_pretrained(a__						)
				print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
				image_processor.save_pretrained(a__						)


if __name__ == "__main__":
					A  :			str     				=				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.'
					)
					A  :			Tuple     				=				parser.parse_args()
					convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


A  :			List[str]     				=				logging.get_logger(__name__)







class     __A(				nn.Module    ):




				def __init__(  self       )					->   Union[str, Any]:


								'''simple docstring'''




								super().__init__()
								if version.parse(torch.__version__       ) < version.parse('''1.12.0'''       ):
												raise ImportError(
												    F"""You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use """
												    '''PytorchGELUTanh. Please upgrade torch.'''       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return nn.functional.gelu(_snake_case	,  approximate='''tanh'''       )







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi       ) * (input + 0.04_4715 * torch.pow(_snake_case	,  3.0       ))       ))







class     __A(				nn.Module    ):




				def __init__(  self	,  _snake_case = False       )					->   Tuple:


								'''simple docstring'''




								super().__init__()
								if use_gelu_python:
												__a    							=					self._gelu_python
								else:
												__a    							=					nn.functional.gelu




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0       )       ))




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return self.act(_snake_case       )







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return 0.5 * input * (1.0 + torch.tanh(input * 0.79_7884_5608 * (1.0 + 0.04_4715 * input * input)       ))







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return input * torch.sigmoid(1.702 * input       )







class     __A(				nn.Module    ):




				def __init__(  self	,  _snake_case	,  _snake_case       )					->   Optional[int]:


								'''simple docstring'''




								if min > max:
												raise ValueError(F"""min should be < max (got min: {min}, max: {max})"""       )

								super().__init__()
								__a    							=					min
								__a    							=					max




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return torch.clip(gelu(_snake_case       )	,  self.min	,  self.max       )







class     __A(				nn.Module    ):




				def __init__(  self       )					->   str:


								'''simple docstring'''




								super().__init__()
								__a    							=					math.sqrt(2 / math.pi       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_4715 * torch.pow(_snake_case	,  3       ))       ))







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return nn.functional.silu(_snake_case       )







class     __A(				nn.Module    ):




				def __init__(  self       )					->   List[Any]:


								'''simple docstring'''




								super().__init__()
								if version.parse(torch.__version__       ) < version.parse('''1.9.0'''       ):
												__a    							=					self._mish_python
								else:
												__a    							=					nn.functional.mish




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return input * torch.tanh(nn.functional.softplus(_snake_case       )       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return self.act(_snake_case       )







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return input







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case=0.70_7107	,  _snake_case=0.28_2095       )					->   int:


								'''simple docstring'''




								__a    							=					(input - mu).div(sigma * math.sqrt(2.0       )       )
								return 0.5 * (1.0 + torch.erf(_snake_case       ))







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Any:


								'''simple docstring'''




								__a    							=					nn.functional.relu(_snake_case       )
								__a    							=					torch.square(_snake_case       )
								return squared







class     __A(				a    ):




				def __getitem__(  self	,  _snake_case       )					->   Dict:


								'''simple docstring'''




								__a    							=					super().__getitem__(_snake_case       )
								__a							,  __a    							=					content if isinstance(_snake_case	,  _snake_case       ) else (content, {})
								return cls(**_snake_case       )



A  :			str     				=				{
    'gelu': GELUActivation,
    'gelu_10': (ClippedGELUActivation, {'min': -1_0, 'max': 1_0}),
    'gelu_fast': FastGELUActivation,
    'gelu_new': NewGELUActivation,
    'gelu_python': (GELUActivation, {'use_gelu_python': True}),
    'gelu_pytorch_tanh': PytorchGELUTanh,
    'gelu_accurate': AccurateGELUActivation,
    'laplace': LaplaceActivation,
    'linear': LinearActivation,
    'mish': MishActivation,
    'quick_gelu': QuickGELUActivation,
    'relu': nn.ReLU,
    'relu2': ReLUSquaredActivation,
    'relu6': nn.ReLUa,
    'sigmoid': nn.Sigmoid,
    'silu': SiLUActivation,
    'swish': SiLUActivation,
    'tanh': nn.Tanh,
}
A  :			Any     				=				ClassInstantier(ACTaCLS)


def 					__lowerCAmelCase    (    a__						) ->						Optional[int]:
				if activation_string in ACTaFN:
								return ACTaFN[activation_string]
				else:
								raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()						)}"""						)


# For backwards compatibility with: from activations import gelu_python
A  :			Optional[Any]     				=				get_activation('gelu_python')
A  :			str     				=				get_activation('gelu_new')
A  :			Any     				=				get_activation('gelu')
A  :			int     				=				get_activation('gelu_fast')
A  :			Any     				=				get_activation('quick_gelu')
A  :			int     				=				get_activation('silu')
A  :			List[str]     				=				get_activation('mish')






A  :			Dict     				=				get_activation('linear')