




"""simple docstring"""





import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


A				=							logging.get_logger(__name__)

A				=							list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
A				=							tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class   a__					:
			lowercase_		=						field(
			    default=__magic_name__				,     metadata={"help": "Model type selected in the list: " + ", ".join(__magic_name__ )} )
			lowercase_		=						field(
			    default=__magic_name__				,     metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."} )
			lowercase_		=						field(
			    default=1_2_8				,     metadata={
			        "help": (
			            "The maximum total input sequence length after tokenization. Sequences longer "
			            "than this will be truncated, sequences shorter will be padded."
			        )
			    }				,     )
			lowercase_		=						field(
			    default=1_2_8				,     metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."}				,     )
			lowercase_		=						field(
			    default=6_4				,     metadata={
			        "help": (
			            "The maximum number of tokens for the question. Questions longer than this will "
			            "be truncated to this length."
			        )
			    }				,     )
			lowercase_		=						field(
			    default=3_0				,     metadata={
			        "help": (
			            "The maximum length of an answer that can be generated. This is needed because the start "
			            "and end predictions are not conditioned on one another."
			        )
			    }				,     )
			lowercase_		=						field(
			    default=__magic_name__				,     metadata={"help": "Overwrite the cached training and evaluation sets"} )
			lowercase_		=						field(
			    default=__magic_name__				,     metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."} )
			lowercase_		=						field(
			    default=0.0				,     metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."} )
			lowercase_		=						field(
			    default=2_0				,     metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."} )
			lowercase_		=						field(
			    default=0				,     metadata={
			        "help": (
			            "language id of input for language-specific xlm models (see"
			            " tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
			        )
			    }				,     )
			lowercase_		=						field(default=1				,     metadata={"help": "multiple threads for converting example to features"} )






class   a__					(						__magic_name__ ):
			lowercase_		=						"train"
			lowercase_		=						"dev"






class   a__					(						__magic_name__ ):
			lowercase_		=						42
			lowercase_		=						42
			lowercase_		=						42
			lowercase_		=						42


			def __init__(   self   :     Tuple   ,	UpperCamelCase_   :     SquadDataTrainingArguments   ,	UpperCamelCase_   :     PreTrainedTokenizer   ,	UpperCamelCase_   :     Optional[int] = None   ,	UpperCamelCase_   :     Union[str, Split] = Split.train   ,	UpperCamelCase_   :     Optional[bool] = False   ,	UpperCamelCase_   :     Optional[str] = None   ,	UpperCamelCase_   :     Optional[str] = "pt"   ,	):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[int]												=       args
						__UpperCAmelCase						:			Any												=       is_language_sensitive
						__UpperCAmelCase						:			Optional[Any]												=       SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
						if isinstance(UpperCamelCase_   ,	UpperCamelCase_):
									try:
												__UpperCAmelCase						:			Any												=       Split[mode]
									except KeyError:
												raise KeyError("mode is not a valid split name")
						__UpperCAmelCase						:			Optional[int]												=       mode
						# Load data features from cache or dataset file
						__UpperCAmelCase						:			int												=       "v2" if args.version_2_with_negative else "v1"
						__UpperCAmelCase						:			Optional[Any]												=       os.path.join(
						    cache_dir if cache_dir is not None else args.data_dir   ,	F"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}"   ,	)

						# Make sure only the first process in distributed training processes the dataset,
						# and the others will use the cache.
						__UpperCAmelCase						:			Optional[int]												=       cached_features_file + ".lock"
						with FileLock(UpperCamelCase_):
									if os.path.exists(UpperCamelCase_) and not args.overwrite_cache:
												__UpperCAmelCase						:			Union[str, Any]												=       time.time()
												__UpperCAmelCase						:			Union[str, Any]												=       torch.load(UpperCamelCase_)

												# Legacy cache files have only features, while new cache files
												# will have dataset and examples also.
												__UpperCAmelCase						:			Optional[Any]												=       self.old_features["features"]
												__UpperCAmelCase						:			int												=       self.old_features.get("dataset"   ,	UpperCamelCase_)
												__UpperCAmelCase						:			Tuple												=       self.old_features.get("examples"   ,	UpperCamelCase_)
												logger.info(
												    F"Loading features from cached file {cached_features_file} [took %.3f s]"   ,	time.time() - start)

												if self.dataset is None or self.examples is None:
															logger.warning(
															    F"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"
															    " future run")
									else:
												if mode == Split.dev:
															__UpperCAmelCase						:			Union[str, Any]												=       self.processor.get_dev_examples(args.data_dir)
												else:
															__UpperCAmelCase						:			List[str]												=       self.processor.get_train_examples(args.data_dir)

												__UpperCAmelCase						,  __UpperCAmelCase						:			int												=       squad_convert_examples_to_features(
												    examples=self.examples   ,	tokenizer=UpperCamelCase_   ,	max_seq_length=args.max_seq_length   ,	doc_stride=args.doc_stride   ,	max_query_length=args.max_query_length   ,	is_training=mode == Split.train   ,	threads=args.threads   ,	return_dataset=UpperCamelCase_   ,	)

												__UpperCAmelCase						:			Union[str, Any]												=       time.time()
												torch.save(
												    {"features": self.features, "dataset": self.dataset, "examples": self.examples}   ,	UpperCamelCase_   ,	)
												# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
												logger.info(
												    F"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]")


			def __len__(   self   :     Union[str, Any]):


						"""simple docstring"""




						return len(self.features)


			def __getitem__(   self   :     int   ,	UpperCamelCase_   :     Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			List[Any]												=       self.features[i]

						__UpperCAmelCase						:			List[Any]												=       torch.tensor(feature.input_ids   ,	dtype=torch.long)
						__UpperCAmelCase						:			Tuple												=       torch.tensor(feature.attention_mask   ,	dtype=torch.long)
						__UpperCAmelCase						:			int												=       torch.tensor(feature.token_type_ids   ,	dtype=torch.long)
						__UpperCAmelCase						:			str												=       torch.tensor(feature.cls_index   ,	dtype=torch.long)
						__UpperCAmelCase						:			Optional[int]												=       torch.tensor(feature.p_mask   ,	dtype=torch.float)
						__UpperCAmelCase						:			List[Any]												=       torch.tensor(feature.is_impossible   ,	dtype=torch.float)

						__UpperCAmelCase						:			str												=       {
						    "input_ids": input_ids,
						    "attention_mask": attention_mask,
						    "token_type_ids": token_type_ids,
						}

						if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
									del inputs["token_type_ids"]

						if self.args.model_type in ["xlnet", "xlm"]:
									inputs.update({"cls_index": cls_index, "p_mask": p_mask})
									if self.args.version_2_with_negative:
												inputs.update({"is_impossible": is_impossible})
									if self.is_language_sensitive:
												inputs.update({"langs": (torch.ones(input_ids.shape   ,	dtype=torch.intaa) * self.args.lang_id)})

						if self.mode == Split.train:
									__UpperCAmelCase						:			Tuple												=       torch.tensor(feature.start_position   ,	dtype=torch.long)
									__UpperCAmelCase						:			List[Any]												=       torch.tensor(feature.end_position   ,	dtype=torch.long)
									inputs.update({"start_positions": start_positions, "end_positions": end_positions})

						return inputs




"""simple docstring"""


import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
			lowerCamelCase_					=  True if '''large''' in model_name or '''huge''' in model_name else False
			lowerCamelCase_					=  True if '''large''' in model_name or '''huge''' in model_name else False
			lowerCamelCase_					=  True if '''large''' in model_name or '''huge''' in model_name else False

			if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
						if "fl3" in model_name:
									lowerCamelCase_					=  [3, 3, 3, 3]
									lowerCamelCase_					=  [5, 5, 5, 5]
						elif "fl4" in model_name:
									lowerCamelCase_					=  [4, 4, 4, 4]
									lowerCamelCase_					=  [3, 3, 3, 3]

			if "tiny" in model_name or "small" in model_name or "base" in model_name:
						lowerCamelCase_					=  [3, 3, 3, 3]
						if "lrf" in model_name:
									lowerCamelCase_					=  [3, 3, 3, 3]
						else:
									lowerCamelCase_					=  [2, 2, 2, 2]

			if "tiny" in model_name:
						lowerCamelCase_					=  96
			elif "small" in model_name:
						lowerCamelCase_					=  96
			elif "base" in model_name:
						lowerCamelCase_					=  128
			elif "large" in model_name:
						lowerCamelCase_					=  192
			elif "xlarge" in model_name:
						lowerCamelCase_					=  256
			elif "huge" in model_name:
						lowerCamelCase_					=  352

			# set label information
			lowerCamelCase_					=  '''huggingface/label-files'''
			if "large" in model_name or "huge" in model_name:
						lowerCamelCase_					=  '''imagenet-22k-id2label.json'''
			else:
						lowerCamelCase_					=  '''imagenet-1k-id2label.json'''

			lowerCamelCase_					=  json.load(open(hf_hub_download(lowerCAmelCase__	,lowerCAmelCase__	,repo_type='''dataset'''						)	,'''r'''						)						)
			lowerCamelCase_					=  {int(lowerCAmelCase__						): v for k, v in idalabel.items()}
			lowerCamelCase_					=  {v: k for k, v in idalabel.items()}

			lowerCamelCase_					=  FocalNetConfig(
			    embed_dim=lowerCAmelCase__	,depths=lowerCAmelCase__	,focal_levels=lowerCAmelCase__	,focal_windows=lowerCAmelCase__	,use_conv_embed=lowerCAmelCase__	,idalabel=lowerCAmelCase__	,labelaid=lowerCAmelCase__	,use_post_layernorm=lowerCAmelCase__	,use_layerscale=lowerCAmelCase__	,)

			return config






def  lowercase						(	lowerCAmelCase__						):
			if "patch_embed.proj" in name:
						lowerCamelCase_					=  name.replace('''patch_embed.proj'''	,'''embeddings.patch_embeddings.projection'''						)
			if "patch_embed.norm" in name:
						lowerCamelCase_					=  name.replace('''patch_embed.norm'''	,'''embeddings.norm'''						)
			if "layers" in name:
						lowerCamelCase_					=  '''encoder.''' + name
			if "encoder.layers" in name:
						lowerCamelCase_					=  name.replace('''encoder.layers'''	,'''encoder.stages'''						)
			if "downsample.proj" in name:
						lowerCamelCase_					=  name.replace('''downsample.proj'''	,'''downsample.projection'''						)
			if "blocks" in name:
						lowerCamelCase_					=  name.replace('''blocks'''	,'''layers'''						)
			if "modulation.f.weight" in name or "modulation.f.bias" in name:
						lowerCamelCase_					=  name.replace('''modulation.f'''	,'''modulation.projection_in'''						)
			if "modulation.h.weight" in name or "modulation.h.bias" in name:
						lowerCamelCase_					=  name.replace('''modulation.h'''	,'''modulation.projection_context'''						)
			if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
						lowerCamelCase_					=  name.replace('''modulation.proj'''	,'''modulation.projection_out'''						)

			if name == "norm.weight":
						lowerCamelCase_					=  '''layernorm.weight'''
			if name == "norm.bias":
						lowerCamelCase_					=  '''layernorm.bias'''

			if "head" in name:
						lowerCamelCase_					=  name.replace('''head'''	,'''classifier'''						)
			else:
						lowerCamelCase_					=  '''focalnet.''' + name

			return name






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=False						):
			# fmt: off
			lowerCamelCase_					=  {
			    '''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
			    '''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
			    '''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
			    '''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
			    '''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
			    '''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
			    '''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
			    '''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
			    '''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
			    '''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
			}
			# fmt: on

			lowerCamelCase_					=  model_name_to_url[model_name]
			print('''Checkpoint URL: '''	,lowerCAmelCase__						)
			lowerCamelCase_					=  torch.hub.load_state_dict_from_url(lowerCAmelCase__	,map_location='''cpu'''						)['''model''']

			# rename keys
			for key in state_dict.copy().keys():
						lowerCamelCase_					=  state_dict.pop(lowerCAmelCase__						)
						lowerCamelCase_					=  val

			lowerCamelCase_					=  get_focalnet_config(lowerCAmelCase__						)
			lowerCamelCase_					=  FocalNetForImageClassification(lowerCAmelCase__						)
			model.eval()

			# load state dict
			model.load_state_dict(lowerCAmelCase__						)

			# verify conversion
			lowerCamelCase_					=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''

			lowerCamelCase_					=  BitImageProcessor(
			    do_resize=lowerCAmelCase__	,size={'''shortest_edge''': 256}	,resample=PILImageResampling.BILINEAR	,do_center_crop=lowerCAmelCase__	,crop_size=224	,do_normalize=lowerCAmelCase__	,image_mean=lowerCAmelCase__	,image_std=lowerCAmelCase__	,)
			lowerCamelCase_					=  Image.open(requests.get(lowerCAmelCase__	,stream=lowerCAmelCase__						).raw						)
			lowerCamelCase_					=  processor(images=lowerCAmelCase__	,return_tensors='''pt'''						)

			lowerCamelCase_					=  transforms.Compose(
			    [
			        transforms.Resize(256						),
			        transforms.CenterCrop(224						),
			        transforms.ToTensor(),
			        transforms.Normalize(mean=[0.485, 0.456, 0.406]	,std=[0.229, 0.224, 0.225]						),
			    ]						)

			lowerCamelCase_					=  image_transforms(lowerCAmelCase__						).unsqueeze(0						)

			# verify pixel_values
			assert torch.allclose(inputs.pixel_values	,lowerCAmelCase__	,atol=1E-4						)

			lowerCamelCase_					=  model(**lowerCAmelCase__						)

			lowerCamelCase_					=  outputs.logits.argmax(-1						).item()
			print('''Predicted class:'''	,model.config.idalabel[predicted_class_idx]						)

			print('''First values of logits:'''	,outputs.logits[0, :3]						)

			if model_name == "focalnet-tiny":
						lowerCamelCase_					=  torch.tensor([0.2_166, -0.4_368, 0.2_191]						)
			elif model_name == "focalnet-tiny-lrf":
						lowerCamelCase_					=  torch.tensor([1.1_669, 0.0_125, -0.1_695]						)
			elif model_name == "focalnet-small":
						lowerCamelCase_					=  torch.tensor([0.4_917, -0.0_430, 0.1_341]						)
			elif model_name == "focalnet-small-lrf":
						lowerCamelCase_					=  torch.tensor([-0.2_588, -0.5_342, -0.2_331]						)
			elif model_name == "focalnet-base":
						lowerCamelCase_					=  torch.tensor([-0.1_655, -0.4_090, -0.1_730]						)
			elif model_name == "focalnet-base-lrf":
						lowerCamelCase_					=  torch.tensor([0.5_306, -0.0_483, -0.3_928]						)
			assert torch.allclose(outputs.logits[0, :3]	,lowerCAmelCase__	,atol=1E-4						)
			print('''Looks ok!'''						)

			if pytorch_dump_folder_path is not None:
						print(f"Saving model and processor of {model_name} to {pytorch_dump_folder_path}"						)
						model.save_pretrained(lowerCAmelCase__						)
						processor.save_pretrained(lowerCAmelCase__						)

			if push_to_hub:
						print(f"Pushing model and processor of {model_name} to the hub..."						)
						model.push_to_hub(f"{model_name}"						)
						processor.push_to_hub(f"{model_name}"						)


if __name__ == "__main__":
						A_    							=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--model_name""",
						    default="""focalnet-tiny""",
						    type=str,
						    help="""Name of the FocalNet model you'd like to convert.""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
						)
						parser.add_argument(
						    """--push_to_hub""",
						    action="""store_true""",
						    help="""Whether to push the model and processor to the hub.""",
						)

						A_    							=					parser.parse_args()
						convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



