





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()
__lowerCAmelCase 	=      logging.get_logger(__name__)

def      a    (   _lowercase :  Tuple			,    _lowercase :  Optional[Any]=False			,    _lowercase :  Dict=False							)  ->List[str]:
		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    (   _lowercase :  int			,    _lowercase :  Union[str, Any]			,    _lowercase :  str=False			,    _lowercase :  Union[str, Any]=False							)  ->str:
		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    (   _lowercase :  int			,    _lowercase :  Optional[Any]			,    _lowercase :  int							)  ->Union[str, Any]:
		UpperCamelCase__								=						dct.pop(_lowercase							)
		UpperCamelCase__								=						val

def      a    (   )  ->Any:
		UpperCamelCase__								=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		UpperCamelCase__								=						Image.open(requests.get(_lowercase			,    stream=_lowercase							).raw							)
		return im

@torch.no_grad()
def      a    (   _lowercase :  str			,    _lowercase :  str							)  ->List[str]:
		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_1_8_4_1
				UpperCamelCase__								=						"""imagenet-22k-id2label.json"""
				UpperCamelCase__								=						json.load(open(hf_hub_download(_lowercase			,    _lowercase			,    repo_type="""dataset"""							)			,    """r"""							)							)
				UpperCamelCase__								=						{int(_lowercase							): 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[9_2_0_5]
				del idalabel[1_5_0_2_7]
				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__								=						1_0_0_0
				UpperCamelCase__								=						"""imagenet-1k-id2label.json"""
				UpperCamelCase__								=						json.load(open(hf_hub_download(_lowercase			,    _lowercase			,    repo_type="""dataset"""							)			,    """r"""							)							)
				UpperCamelCase__								=						{int(_lowercase							): v for k, v in idalabel.items()}
				UpperCamelCase__								=						idalabel
				UpperCamelCase__								=						{v: k for k, v in idalabel.items()}
				if "384" in checkpoint_url:
						UpperCamelCase__								=						3_8_4
				if "512" in checkpoint_url:
						UpperCamelCase__								=						5_1_2
		elif "ade20k" in checkpoint_url:
				# fine-tuning
				UpperCamelCase__								=						True
				UpperCamelCase__								=						1_5_0
				UpperCamelCase__								=						"""ade20k-id2label.json"""
				UpperCamelCase__								=						json.load(open(hf_hub_download(_lowercase			,    _lowercase			,    repo_type="""dataset"""							)			,    """r"""							)							)
				UpperCamelCase__								=						{int(_lowercase							): v for k, v in idalabel.items()}
				UpperCamelCase__								=						idalabel
				UpperCamelCase__								=						{v: k for k, v in idalabel.items()}
				UpperCamelCase__								=						6_4_0
				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__								=						1_0_2_4
				UpperCamelCase__								=						4_0_9_6
				UpperCamelCase__								=						2_4
				UpperCamelCase__								=						1_6
				if "ade20k" in checkpoint_url:
						UpperCamelCase__								=						6_4_0
						UpperCamelCase__								=						[7, 1_1, 1_5, 2_3]
		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(_lowercase			,    map_location="""cpu"""			,    check_hash=_lowercase							)
		UpperCamelCase__								=						state_dict["""model"""] if """ade20k""" not in checkpoint_url else state_dict["""state_dict"""]

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

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

		# Check outputs on an image
		if is_semantic:
				UpperCamelCase__								=						BeitImageProcessor(size=config.image_size			,    do_center_crop=_lowercase							)
				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=_lowercase							)
				UpperCamelCase__								=						prepare_img()

		UpperCamelCase__								=						image_processor(images=_lowercase			,    return_tensors="""pt"""							)
		UpperCamelCase__								=						encoding["""pixel_values"""]

		UpperCamelCase__								=						model(_lowercase							)
		UpperCamelCase__								=						outputs.logits

		# verify logits
		UpperCamelCase__								=						torch.Size([1, 1_0_0_0]							)
		if checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k"""							):
				UpperCamelCase__								=						torch.Size([1, 1_9_6, 8_1_9_2]							)
		elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k"""							):
				UpperCamelCase__								=						torch.Size([1, 1_9_6, 8_1_9_2]							)
		elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22k"""							):
				UpperCamelCase__								=						torch.Size([1, 2_1_8_4_1]							)
				UpperCamelCase__								=						torch.tensor([2.2_288, 2.4_671, 0.7_395]							)
				UpperCamelCase__								=						2_3_9_7
		elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22k"""							):
				UpperCamelCase__								=						torch.Size([1, 2_1_8_4_1]							)
				UpperCamelCase__								=						torch.tensor([1.6_881, -0.2_787, 0.5_901]							)
				UpperCamelCase__								=						2_3_9_6
		elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft1k"""							):
				UpperCamelCase__								=						torch.tensor([0.1_241, 0.0_798, -0.6_569]							)
				UpperCamelCase__								=						2_8_5
		elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22kto1k"""							):
				UpperCamelCase__								=						torch.tensor([-1.2_385, -1.0_987, -1.0_108]							)
				UpperCamelCase__								=						2_8_1
		elif checkpoint_url[:-4].endswith("""beit_base_patch16_384_pt22k_ft22kto1k"""							):
				UpperCamelCase__								=						torch.tensor([-1.5_303, -0.9_484, -0.3_147]							)
				UpperCamelCase__								=						7_6_1
		elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft1k"""							):
				UpperCamelCase__								=						torch.tensor([0.4_610, -0.0_928, 0.2_086]							)
				UpperCamelCase__								=						7_6_1
		elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22kto1k"""							):
				UpperCamelCase__								=						torch.tensor([-0.4_804, 0.6_257, -0.1_837]							)
				UpperCamelCase__								=						7_6_1
		elif checkpoint_url[:-4].endswith("""beit_large_patch16_384_pt22k_ft22kto1k"""							):
				UpperCamelCase__								=						torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]							)
				UpperCamelCase__								=						7_6_1
		elif checkpoint_url[:-4].endswith("""beit_large_patch16_512_pt22k_ft22kto1k"""							):
				UpperCamelCase__								=						torch.tensor([-0.3_062, 0.7_261, 0.4_852]							)
				UpperCamelCase__								=						7_6_1
		elif checkpoint_url[:-4].endswith("""beit_base_patch16_640_pt22k_ft22ktoade20k"""							):
				UpperCamelCase__								=						(1, 1_5_0, 1_6_0, 1_6_0)
				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, 1_5_0, 1_6_0, 1_6_0)
				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]			,    _lowercase			,    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]			,    _lowercase			,    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(_lowercase							).mkdir(exist_ok=_lowercase							)
		print(F"""Saving model to {pytorch_dump_folder_path}"""							)
		model.save_pretrained(_lowercase							)
		print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
		image_processor.save_pretrained(_lowercase							)


if __name__ == "__main__":
				__lowerCAmelCase 	=      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."
				)
				__lowerCAmelCase 	=      parser.parse_args()
				convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)












from __future__ import annotations

def      a    (   _lowercase :  float | int			,    _lowercase :  float | int			,    _lowercase :  float | int			,    )  ->list[float | int]:
		if not all((nth_term, start_term_a, common_ratio_r)							):
				return []
		UpperCamelCase__								=						[]
		UpperCamelCase__								=						1
		UpperCamelCase__								=						common_ratio_r
		for _ in range(int(_lowercase							)							):
				if not series:
						series.append(_lowercase							)
				else:
						power += 1
						series.append(float(start_term_a * multiple							)							)
						UpperCamelCase__								=						pow(float(_lowercase							)			,    _lowercase							)
		return series


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__lowerCAmelCase 	=      float(input("Enter the last number (n term) of the Geometric Series"))
				__lowerCAmelCase 	=      float(input("Enter the starting term (a) of the Geometric Series"))
				__lowerCAmelCase 	=      float(
				    input("Enter the common ratio between two terms (r) of the Geometric Series")
				)
				print("Formula of Geometric Series => a + ar + ar^2 ... +ar^n")
				print(geometric_series(nth_term, start_term_a, common_ratio_r))





