



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 			_snake_case     (   a__):
				UpperCamelCase__			:   Union[str, Any]       						=["""image_processor""", """tokenizer"""]
				UpperCamelCase__			:   Tuple       						="""LayoutLMv2ImageProcessor"""
				UpperCamelCase__			:   Tuple       						=("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")




				def __init__(  self   :  int,   __lowercase   :  int=None,   __lowercase   :  List[Any]=None,   **__lowercase   :  Optional[int]  ):
							if "feature_extractor" in kwargs:
										warnings.warn(
										    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
										    " instead.",   __lowercase,   )
										lowercase__   					=			kwargs.pop("feature_extractor"  )

							lowercase__   					=			image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
										raise ValueError("You need to specify an `image_processor`."  )
							if tokenizer is None:
										raise ValueError("You need to specify a `tokenizer`."  )

							super().__init__(__lowercase,   __lowercase  )




				def __call__(  self   :  str,   __lowercase   :  str,   __lowercase   :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,   __lowercase   :  Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,   __lowercase   :  Union[List[List[int]], List[List[List[int]]]] = None,   __lowercase   :  Optional[Union[List[int], List[List[int]]]] = None,   __lowercase   :  bool = True,   __lowercase   :  Union[bool, str, PaddingStrategy] = False,   __lowercase   :  Union[bool, str, TruncationStrategy] = None,   __lowercase   :  Optional[int] = None,   __lowercase   :  int = 0,   __lowercase   :  Optional[int] = None,   __lowercase   :  Optional[bool] = None,   __lowercase   :  Optional[bool] = None,   __lowercase   :  bool = False,   __lowercase   :  bool = False,   __lowercase   :  bool = False,   __lowercase   :  bool = False,   __lowercase   :  bool = True,   __lowercase   :  Optional[Union[str, TensorType]] = None,   **__lowercase   :  List[str],   ):
							# verify input
							if self.image_processor.apply_ocr and (boxes is not None):
										raise ValueError(
										    "You cannot provide bounding boxes "
										    "if you initialized the image processor with apply_ocr set to True."  )

							if self.image_processor.apply_ocr and (word_labels is not None):
										raise ValueError(
										    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."  )

							if return_overflowing_tokens is True and return_offsets_mapping is False:
										raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."  )

							# first, apply the image processor
							lowercase__   					=			self.image_processor(images=__lowercase,   return_tensors=__lowercase  )

							# second, apply the tokenizer
							if text is not None and self.image_processor.apply_ocr and text_pair is None:
										if isinstance(__lowercase,   __lowercase  ):
													lowercase__   					=			[text]  # add batch dimension (as the image processor always adds a batch dimension)
										lowercase__   					=			features["words"]

							lowercase__   					=			self.tokenizer(
							    text=text if text is not None else features["words"],   text_pair=text_pair if text_pair is not None else None,   boxes=boxes if boxes is not None else features["boxes"],   word_labels=__lowercase,   add_special_tokens=__lowercase,   padding=__lowercase,   truncation=__lowercase,   max_length=__lowercase,   stride=__lowercase,   pad_to_multiple_of=__lowercase,   return_token_type_ids=__lowercase,   return_attention_mask=__lowercase,   return_overflowing_tokens=__lowercase,   return_special_tokens_mask=__lowercase,   return_offsets_mapping=__lowercase,   return_length=__lowercase,   verbose=__lowercase,   return_tensors=__lowercase,   **__lowercase,   )

							# add pixel values
							lowercase__   					=			features.pop("pixel_values"  )
							if return_overflowing_tokens is True:
										lowercase__   					=			self.get_overflowing_images(__lowercase,   encoded_inputs["overflow_to_sample_mapping"]  )
							lowercase__   					=			images

							return encoded_inputs




				def 							A__ (  self   :  Optional[Any],   __lowercase   :  Optional[Any],   __lowercase   :  Any  ):
							# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
							lowercase__   					=			[]
							for sample_idx in overflow_to_sample_mapping:
										images_with_overflow.append(images[sample_idx]  )

							if len(__lowercase  ) != len(__lowercase  ):
										raise ValueError(
										    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
										    F''' {len(__lowercase  )} and {len(__lowercase  )}'''  )

							return images_with_overflow




				def 							A__ (  self   :  int,   *__lowercase   :  int,   **__lowercase   :  Optional[Any]  ):
							return self.tokenizer.batch_decode(*__lowercase,   **__lowercase  )




				def 							A__ (  self   :  Tuple,   *__lowercase   :  int,   **__lowercase   :  int  ):
							return self.tokenizer.decode(*__lowercase,   **__lowercase  )




				@property
				def 							A__ (  self   :  Optional[int]  ):
							return ["input_ids", "bbox", "attention_mask", "image"]




				@property
				def 							A__ (  self   :  int  ):
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",   __lowercase,   )
							return self.image_processor_class




				@property
				def 							A__ (  self   :  Union[str, Any]  ):
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",   __lowercase,   )
							return self.image_processor









import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			"huggingface/label-files"
			lowercase__   					=			"imagenet-1k-id2label.json"
			lowercase__   					=			json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					repo_type="dataset"					)  ,					"r"					)					)
			lowercase__   					=			{int(SCREAMING_SNAKE_CASE_					): v for k, v in idalabel.items()}
			lowercase__   					=			{v: k for k, v in idalabel.items()}

			lowercase__   					=			"std_conv" if "bit" in model_name else False

			# note that when using BiT as backbone for ViT-hybrid checkpoints,
			# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
			# config.conv_layer = "std_conv_same"
			lowercase__   					=			BitConfig(
			    conv_layer=SCREAMING_SNAKE_CASE_  ,					num_labels=1000  ,					idalabel=SCREAMING_SNAKE_CASE_  ,					labelaid=SCREAMING_SNAKE_CASE_  ,					)

			return config



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if "stem.conv" in name:
						lowercase__   					=			name.replace("stem.conv"  ,					"bit.embedder.convolution"					)
			if "blocks" in name:
						lowercase__   					=			name.replace("blocks"  ,					"layers"					)
			if "head.fc" in name:
						lowercase__   					=			name.replace("head.fc"  ,					"classifier.1"					)
			if name.startswith("norm"					):
						lowercase__   					=			"bit." + name
			if "bit" not in name and "classifier" not in name:
						lowercase__   					=			"bit.encoder." + name

			return name



def __lowerCAmelCase	(       ):
			lowercase__   					=			"http://images.cocodataset.org/val2017/000000039769.jpg"
			lowercase__   					=			Image.open(requests.get(SCREAMING_SNAKE_CASE_  ,					stream=SCREAMING_SNAKE_CASE_					).raw					)
			return im



@torch.no_grad()
def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=False					):
			lowercase__   					=			get_config(SCREAMING_SNAKE_CASE_					)

			# load original model from timm
			lowercase__   					=			create_model(SCREAMING_SNAKE_CASE_  ,					pretrained=SCREAMING_SNAKE_CASE_					)
			timm_model.eval()

			# load state_dict of original model
			lowercase__   					=			timm_model.state_dict()
			for key in state_dict.copy().keys():
						lowercase__   					=			state_dict.pop(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			val.squeeze() if "head" in key else val

			# load HuggingFace model
			lowercase__   					=			BitForImageClassification(SCREAMING_SNAKE_CASE_					)
			model.eval()
			model.load_state_dict(SCREAMING_SNAKE_CASE_					)

			# create image processor
			lowercase__   					=			create_transform(**resolve_data_config({}  ,					model=SCREAMING_SNAKE_CASE_					)					)
			lowercase__   					=			transform.transforms

			lowercase__   					=			{
			    "bilinear": PILImageResampling.BILINEAR,
			    "bicubic": PILImageResampling.BICUBIC,
			    "nearest": PILImageResampling.NEAREST,
			}

			lowercase__   					=			BitImageProcessor(
			    do_resize=SCREAMING_SNAKE_CASE_  ,					size={"shortest_edge": timm_transforms[0].size}  ,					resample=pillow_resamplings[timm_transforms[0].interpolation.value]  ,					do_center_crop=SCREAMING_SNAKE_CASE_  ,					crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}  ,					do_normalize=SCREAMING_SNAKE_CASE_  ,					image_mean=timm_transforms[-1].mean.tolist()  ,					image_std=timm_transforms[-1].std.tolist()  ,					)

			lowercase__   					=			prepare_img()
			lowercase__   					=			transform(SCREAMING_SNAKE_CASE_					).unsqueeze(0					)
			lowercase__   					=			processor(SCREAMING_SNAKE_CASE_  ,					return_tensors="pt"					).pixel_values

			# verify pixel values
			assert torch.allclose(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

			# verify logits
			with torch.no_grad():
						lowercase__   					=			model(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			outputs.logits

			print("Logits:"  ,					logits[0, :3]					)
			print("Predicted class:"  ,					model.config.idalabel[logits.argmax(-1					).item()]					)
			lowercase__   					=			timm_model(SCREAMING_SNAKE_CASE_					)
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(SCREAMING_SNAKE_CASE_  ,					outputs.logits  ,					atol=1e-3					)
			print("Looks ok!"					)

			if pytorch_dump_folder_path is not None:
						Path(SCREAMING_SNAKE_CASE_					).mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)
						print(f'''Saving model {model_name} and processor to {pytorch_dump_folder_path}'''					)
						model.save_pretrained(SCREAMING_SNAKE_CASE_					)
						processor.save_pretrained(SCREAMING_SNAKE_CASE_					)

			if push_to_hub:
						print(f'''Pushing model {model_name} and processor to the hub'''					)
						model.push_to_hub(f'''ybelkada/{model_name}'''					)
						processor.push_to_hub(f'''ybelkada/{model_name}'''					)


if __name__ == "__main__":
							lowercase_						=       argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--model_name""",
							    default="""resnetv2_50x1_bitm""",
							    type=str,
							    help="""Name of the BiT timm 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 to the hub.""",
							)

							lowercase_						=       parser.parse_args()
							convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




