





'''simple docstring'''






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      :   Tuple								=     logging.get_logger(__name__)





def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						str=False   , lowerCAmelCase :						List[Any]=False):



				"""simple docstring"""


				_A :				Tuple				=  "backbone." if is_semantic else ""

				_A :				Tuple				=  []
				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      lowercase  (  lowerCAmelCase :						Any   , lowerCAmelCase :						List[Any]   , lowerCAmelCase :						str=False   , lowerCAmelCase :						Any=False):



				"""simple docstring"""


				for i in range(config.num_hidden_layers):
								_A :				Tuple				=  "backbone." if is_semantic else ""
								# queries, keys and values
								_A :				Optional[Any]				=  state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight""")
								_A :				Optional[int]				=  state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias""")
								_A :				List[str]				=  state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias""")

								_A :				str				=  in_proj_weight[
								    : config.hidden_size, :
								]
								_A :				List[Any]				=  q_bias
								_A :				List[str]				=  in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								_A :				Tuple				=  in_proj_weight[
								    -config.hidden_size :, :
								]
								_A :				List[str]				=  v_bias

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

								_A :				str				=  gamma_a
								_A :				Optional[Any]				=  gamma_a

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

												_A :				Tuple				=  table
												_A :				Optional[Any]				=  index





def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						List[Any]   , lowerCAmelCase :						List[Any]):



				"""simple docstring"""


				_A :				Tuple				=  dct.pop(_UpperCAmelCase)
				_A :				str				=  val





def      lowercase  (  ):



				"""simple docstring"""


				_A :				int				=  "http://images.cocodataset.org/val2017/000000039769.jpg"
				_A :				Optional[int]				=  Image.open(requests.get(_UpperCAmelCase   , stream=_UpperCAmelCase).raw)
				return im





@torch.no_grad()
def      lowercase  (  lowerCAmelCase :						Dict   , lowerCAmelCase :						List[str]):



				"""simple docstring"""


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

				_A :				Union[str, Any]				=  create_rename_keys(_UpperCAmelCase   , has_lm_head=_UpperCAmelCase   , is_semantic=_UpperCAmelCase)
				for src, dest in rename_keys:
								rename_key(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase)
				read_in_q_k_v(_UpperCAmelCase   , _UpperCAmelCase   , has_lm_head=_UpperCAmelCase   , is_semantic=_UpperCAmelCase)
				if is_semantic:
								# add prefix to decoder keys
								for key, val in state_dict.copy().items():
												_A :				List[Any]				=  state_dict.pop(_UpperCAmelCase)
												if key.startswith('''backbone.fpn'''):
																_A :				Dict				=  key.replace('''backbone.fpn'''   , '''fpn''')
												_A :				Tuple				=  val

    # load HuggingFace model
				if checkpoint_url[-9:-4] == "pt22k":
								_A :				Optional[int]				=  BeitForMaskedImageModeling(_UpperCAmelCase)
				elif "ade20k" in checkpoint_url:
								_A :				str				=  BeitForSemanticSegmentation(_UpperCAmelCase)
				else:
								_A :				str				=  BeitForImageClassification(_UpperCAmelCase)
				model.eval()
				model.load_state_dict(_UpperCAmelCase)

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

				_A :				Tuple				=  image_processor(images=_UpperCAmelCase   , return_tensors='''pt''')
				_A :				str				=  encoding["pixel_values"]

				_A :				Union[str, Any]				=  model(_UpperCAmelCase)
				_A :				Dict				=  outputs.logits

				# verify logits
				_A :				Optional[Any]				=  torch.Size([1, 1000])
				if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''):
								_A :				int				=  torch.Size([1, 196, 8192])
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''):
								_A :				List[str]				=  torch.Size([1, 196, 8192])
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''):
								_A :				Union[str, Any]				=  torch.Size([1, 2_1841])
								_A :				Tuple				=  torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5])
								_A :				str				=  2397
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''):
								_A :				List[str]				=  torch.Size([1, 2_1841])
								_A :				Any				=  torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1])
								_A :				Union[str, Any]				=  2396
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''):
								_A :				Any				=  torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9])
								_A :				Tuple				=  285
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''):
								_A :				Dict				=  torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8])
								_A :				Dict				=  281
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''):
								_A :				List[Any]				=  torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7])
								_A :				List[Any]				=  761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''):
								_A :				Any				=  torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6])
								_A :				Optional[Any]				=  761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''):
								_A :				Tuple				=  torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7])
								_A :				Tuple				=  761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''):
								_A :				int				=  torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]])
								_A :				Any				=  761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''):
								_A :				Any				=  torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2])
								_A :				Dict				=  761
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''):
								_A :				str				=  (1, 150, 160, 160)
								_A :				str				=  torch.tensor(
								    [
								        [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
								        [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
								        [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
								    ])
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''):
								_A :				str				=  (1, 150, 160, 160)
								_A :				List[Any]				=  torch.tensor(
								    [
								        [[-4.3_3_0_5, -2.3_0_4_9, -3.0_1_6_1], [-2.9_5_9_1, -1.5_3_0_5, -2.2_2_5_1], [-3.4_1_9_8, -1.8_0_0_4, -2.9_0_6_2]],
								        [[-5.8_9_2_2, -3.7_4_3_5, -4.3_9_7_8], [-4.2_0_6_3, -2.7_8_7_2, -3.4_7_5_5], [-4.2_7_9_1, -3.1_8_7_4, -4.1_6_8_1]],
								        [[0.9_8_9_5, 4.3_4_6_7, 4.7_6_6_3], [4.2_4_7_6, 5.6_8_3_0, 6.1_5_1_8], [4.5_5_5_0, 6.2_4_9_5, 6.5_1_5_4]],
								    ])
				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]   , _UpperCAmelCase   , 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]   , _UpperCAmelCase   , 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(_UpperCAmelCase).mkdir(exist_ok=_UpperCAmelCase)
				print(f"""Saving model to {pytorch_dump_folder_path}""")
				model.save_pretrained(_UpperCAmelCase)
				print(f"""Saving image processor to {pytorch_dump_folder_path}""")
				image_processor.save_pretrained(_UpperCAmelCase)


if __name__ == "__main__":
	__UpperCamelCase      :   List[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.'''
	)
	__UpperCamelCase      :   int								=     parser.parse_args()
	convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__      :   Any      	=			{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   Optional[int]      	=			["LlamaTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   int      	=			["LlamaTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   int      	=			[
			    "OpenLlamaForCausalLM",
			    "OpenLlamaModel",
			    "OpenLlamaPreTrainedModel",
			    "OpenLlamaForSequenceClassification",
			]


if TYPE_CHECKING:
			from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_open_llama import (
						    OpenLlamaForCausalLM,
						    OpenLlamaForSequenceClassification,
						    OpenLlamaModel,
						    OpenLlamaPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase__      :   Dict      	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




