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()
_lowerCamelCase   :				Tuple     			=						logging.get_logger(__name__)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[int]					):
					SCREAMING_SNAKE_CASE           =    'huggingface/label-files'
					SCREAMING_SNAKE_CASE           =    'imagenet-1k-id2label.json'
					SCREAMING_SNAKE_CASE           =    json.load(open(hf_hub_download(_A ,	_A ,	repo_type="dataset"					) ,	"r"					)					)
					SCREAMING_SNAKE_CASE           =    {int(_A					): v for k, v in idalabel.items()}
					SCREAMING_SNAKE_CASE           =    {v: k for k, v in idalabel.items()}

					SCREAMING_SNAKE_CASE           =    '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"
					SCREAMING_SNAKE_CASE           =    BitConfig(
					    conv_layer=_A ,	num_labels=1_0_0_0 ,	idalabel=_A ,	labelaid=_A ,	)

					return config





def 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any]					):
					if "stem.conv" in name:
										SCREAMING_SNAKE_CASE           =    name.replace("stem.conv" ,	"bit.embedder.convolution"					)
					if "blocks" in name:
										SCREAMING_SNAKE_CASE           =    name.replace("blocks" ,	"layers"					)
					if "head.fc" in name:
										SCREAMING_SNAKE_CASE           =    name.replace("head.fc" ,	"classifier.1"					)
					if name.startswith("norm"					):
										SCREAMING_SNAKE_CASE           =    'bit.' + name
					if "bit" not in name and "classifier" not in name:
										SCREAMING_SNAKE_CASE           =    'bit.encoder.' + name

					return name





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    'http://images.cocodataset.org/val2017/000000039769.jpg'
					SCREAMING_SNAKE_CASE           =    Image.open(requests.get(_A ,	stream=_A					).raw					)
					return im





@torch.no_grad()
def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							Dict=False					):
					SCREAMING_SNAKE_CASE           =    get_config(_A					)

					# load original model from timm
					SCREAMING_SNAKE_CASE           =    create_model(_A ,	pretrained=_A					)
					timm_model.eval()

					# load state_dict of original model
					SCREAMING_SNAKE_CASE           =    timm_model.state_dict()
					for key in state_dict.copy().keys():
										SCREAMING_SNAKE_CASE           =    state_dict.pop(_A					)
										SCREAMING_SNAKE_CASE           =    val.squeeze() if 'head' in key else val

					# load HuggingFace model
					SCREAMING_SNAKE_CASE           =    BitForImageClassification(_A					)
					model.eval()
					model.load_state_dict(_A					)

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

					SCREAMING_SNAKE_CASE           =    {
					    'bilinear': PILImageResampling.BILINEAR,
					    'bicubic': PILImageResampling.BICUBIC,
					    'nearest': PILImageResampling.NEAREST,
					}

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

					SCREAMING_SNAKE_CASE           =    prepare_img()
					SCREAMING_SNAKE_CASE           =    transform(_A					).unsqueeze(0					)
					SCREAMING_SNAKE_CASE           =    processor(_A ,	return_tensors="pt"					).pixel_values

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

					# verify logits
					with torch.no_grad():
										SCREAMING_SNAKE_CASE           =    model(_A					)
										SCREAMING_SNAKE_CASE           =    outputs.logits

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

					if pytorch_dump_folder_path is not None:
										Path(_A					).mkdir(exist_ok=_A					)
										print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}"					)
										model.save_pretrained(_A					)
										processor.save_pretrained(_A					)

					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__":
							_lowerCamelCase   :				Union[str, Any]     			=						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.''',
							)

							_lowerCamelCase   :				Optional[Any]     			=						parser.parse_args()
							convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_lowerCamelCase   :				Optional[Any]     			=						logging.get_logger(__name__)





def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Dict					):
					try:
										with open(UpperCAmelCase__ ,	"rb"					) as flax_state_f:
															SCREAMING_SNAKE_CASE           =    from_bytes(UpperCAmelCase__ ,	flax_state_f.read()					)
					except UnpicklingError as e:
										try:
															with open(UpperCAmelCase__					) as f:
																				if f.read().startswith("version"					):
																									raise OSError(
																									    "You seem to have cloned a repository without having git-lfs installed. Please"
																									    " install git-lfs and run `git lfs install` followed by `git lfs pull` in the"
																									    " folder you cloned."					)
																				else:
																									raise ValueError from e
										except (UnicodeDecodeError, ValueError):
															raise EnvironmentError(F"Unable to convert {model_file} to Flax deserializable object. "					)

					return load_flax_weights_in_pytorch_model(UpperCAmelCase__ ,	UpperCAmelCase__					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[Any]					):

					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    "Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"
										    " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
										    " instructions."					)
										raise

					# check if we have bf16 weights
					SCREAMING_SNAKE_CASE           =    flatten_dict(jax.tree_util.tree_map(lambda UpperCAmelCase__					: x.dtype == jnp.bfloataa ,	UpperCAmelCase__					)					).values()
					if any(UpperCAmelCase__					):
										# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

										# and bf16 is not fully supported in PT yet.
										logger.warning(
										    "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
										    "before loading those in PyTorch model."					)
										SCREAMING_SNAKE_CASE           =    jax.tree_util.tree_map(
										    lambda UpperCAmelCase__					: params.astype(np.floataa					) if params.dtype == jnp.bfloataa else params ,	UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    ""

					SCREAMING_SNAKE_CASE           =    flatten_dict(UpperCAmelCase__ ,	sep="."					)
					SCREAMING_SNAKE_CASE           =    pt_model.state_dict()

					# keep track of unexpected & missing keys
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    set(pt_model_dict.keys()					)

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										SCREAMING_SNAKE_CASE           =    flax_key_tuple.split("."					)

										if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
															SCREAMING_SNAKE_CASE           =    flax_key_tuple_array[:-1] + ["weight"]
															SCREAMING_SNAKE_CASE           =    jnp.transpose(UpperCAmelCase__ ,	(3, 2, 0, 1)					)
										elif flax_key_tuple_array[-1] == "kernel":
															SCREAMING_SNAKE_CASE           =    flax_key_tuple_array[:-1] + ["weight"]
															SCREAMING_SNAKE_CASE           =    flax_tensor.T
										elif flax_key_tuple_array[-1] == "scale":
															SCREAMING_SNAKE_CASE           =    flax_key_tuple_array[:-1] + ["weight"]

										if "time_embedding" not in flax_key_tuple_array:
															for i, flax_key_tuple_string in enumerate(UpperCAmelCase__					):
																				SCREAMING_SNAKE_CASE           =    (
																				    flax_key_tuple_string.replace("_0" ,	".0"					)
																				    .replace("_1" ,	".1"					)
																				    .replace("_2" ,	".2"					)
																				    .replace("_3" ,	".3"					)
																				    .replace("_4" ,	".4"					)
																				    .replace("_5" ,	".5"					)
																				    .replace("_6" ,	".6"					)
																				    .replace("_7" ,	".7"					)
																				    .replace("_8" ,	".8"					)
																				    .replace("_9" ,	".9"					)
																				)

										SCREAMING_SNAKE_CASE           =    ".".join(UpperCAmelCase__					)

										if flax_key in pt_model_dict:
															if flax_tensor.shape != pt_model_dict[flax_key].shape:
																				raise ValueError(
																				    F"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
																				    F"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."					)
															else:
																				# add weight to pytorch dict
																				SCREAMING_SNAKE_CASE           =    np.asarray(UpperCAmelCase__					) if not isinstance(UpperCAmelCase__ ,	np.ndarray					) else flax_tensor
																				SCREAMING_SNAKE_CASE           =    torch.from_numpy(UpperCAmelCase__					)
																				# remove from missing keys
																				missing_keys.remove(UpperCAmelCase__					)
										else:
															# weight is not expected by PyTorch model
															unexpected_keys.append(UpperCAmelCase__					)

					pt_model.load_state_dict(UpperCAmelCase__					)

					# re-transform missing_keys to list
					SCREAMING_SNAKE_CASE           =    list(UpperCAmelCase__					)

					if len(UpperCAmelCase__					) > 0:
										logger.warning(
										    "Some weights of the Flax model were not used when initializing the PyTorch model"
										    F" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
										    F" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
										    " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
										    F" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
										    " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
										    " FlaxBertForSequenceClassification model)."					)
					if len(UpperCAmelCase__					) > 0:
										logger.warning(
										    F"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
										    F" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
										    " use it for predictions and inference."					)

					return pt_model




