



from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
							from ..models.auto import AutoTokenizer
class 			_snake_case     :




				def 							A__ (  self   :  List[str],   __lowercase   :  Optional[int]  ):
							raise NotImplementedError()




				def 							A__ (  self   :  List[Any]  ):
							raise NotImplementedError()
class 			_snake_case     (   lowercase__):




				def __init__(  self   :  List[str],   __lowercase   :  "AutoTokenizer",   __lowercase   :  bool = False,   **__lowercase   :  List[Any]  ):
							lowercase__   					=			tokenizer
							lowercase__   					=			skip_prompt
							lowercase__   					=			decode_kwargs

							# variables used in the streaming process
							lowercase__   					=			[]
							lowercase__   					=			0
							lowercase__   					=			True




				def 							A__ (  self   :  Union[str, Any],   __lowercase   :  Any  ):
							if len(value.shape  ) > 1 and value.shape[0] > 1:
										raise ValueError("TextStreamer only supports batch size 1"  )
							elif len(value.shape  ) > 1:
										lowercase__   					=			value[0]

							if self.skip_prompt and self.next_tokens_are_prompt:
										lowercase__   					=			False
										return

							# Add the new token to the cache and decodes the entire thing.
							self.token_cache.extend(value.tolist()  )
							lowercase__   					=			self.tokenizer.decode(self.token_cache,   **self.decode_kwargs  )

							# After the symbol for a new line, we flush the cache.
							if text.endswith("\n"  ):
										lowercase__   					=			text[self.print_len :]
										lowercase__   					=			[]
										lowercase__   					=			0
							# If the last token is a CJK character, we print the characters.
							elif len(__lowercase  ) > 0 and self._is_chinese_char(ord(text[-1]  )  ):
										lowercase__   					=			text[self.print_len :]
										self.print_len += len(__lowercase  )
							# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
							# which may change with the subsequent token -- there are probably smarter ways to do this!)
							else:
										lowercase__   					=			text[self.print_len : text.rfind(" "  ) + 1]
										self.print_len += len(__lowercase  )

							self.on_finalized_text(__lowercase  )




				def 							A__ (  self   :  Optional[Any]  ):
							# Flush the cache, if it exists
							if len(self.token_cache  ) > 0:
										lowercase__   					=			self.tokenizer.decode(self.token_cache,   **self.decode_kwargs  )
										lowercase__   					=			text[self.print_len :]
										lowercase__   					=			[]
										lowercase__   					=			0
							else:
										lowercase__   					=			""

							lowercase__   					=			True
							self.on_finalized_text(__lowercase,   stream_end=__lowercase  )




				def 							A__ (  self   :  Optional[Any],   __lowercase   :  str,   __lowercase   :  bool = False  ):
							print(__lowercase,   flush=__lowercase,   end="" if not stream_end else None  )




				def 							A__ (  self   :  Optional[int],   __lowercase   :  Union[str, Any]  ):
							# This defines a "chinese character" as anything in the CJK Unicode block:
							#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
							#
							# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
							# despite its name. The modern Korean Hangul alphabet is a different block,
							# as is Japanese Hiragana and Katakana. Those alphabets are used to write
							# space-separated words, so they are not treated specially and handled
							# like the all of the other languages.
							if (
							    (cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
							    or (cp >= 0x3_4_0_0 and cp <= 0x4_d_b_f)  #
							    or (cp >= 0x2_0_0_0_0 and cp <= 0x2_a_6_d_f)  #
							    or (cp >= 0x2_a_7_0_0 and cp <= 0x2_b_7_3_f)  #
							    or (cp >= 0x2_b_7_4_0 and cp <= 0x2_b_8_1_f)  #
							    or (cp >= 0x2_b_8_2_0 and cp <= 0x2_c_e_a_f)  #
							    or (cp >= 0xf_9_0_0 and cp <= 0xf_a_f_f)
							    or (cp >= 0x2_f_8_0_0 and cp <= 0x2_f_a_1_f)  #
							):  #
										return True

							return False
class 			_snake_case     (   lowercase__):




				def __init__(  self   :  Dict,   __lowercase   :  "AutoTokenizer",   __lowercase   :  bool = False,   __lowercase   :  Optional[float] = None,   **__lowercase   :  str  ):
							super().__init__(__lowercase,   __lowercase,   **__lowercase  )
							lowercase__   					=			Queue()
							lowercase__   					=			None
							lowercase__   					=			timeout




				def 							A__ (  self   :  List[str],   __lowercase   :  str,   __lowercase   :  bool = False  ):
							self.text_queue.put(__lowercase,   timeout=self.timeout  )
							if stream_end:
										self.text_queue.put(self.stop_signal,   timeout=self.timeout  )




				def __iter__(  self   :  str  ):
							return self




				def 							A__ (  self   :  Dict  ):
							lowercase__   					=			self.text_queue.get(timeout=self.timeout  )
							if value == self.stop_signal:
										raise StopIteration()
							else:
										return value









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)




