





'''simple docstring'''






import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput




class  _A (					__lowercase		):

		def __init__(   self					:   str  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Union[str, Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Any							      =     dataset
					__snake_case					:      str							      =     process
					__snake_case					:      Union[str, Any]							      =     params

		def __len__(   self					:   int			)					->    Any:


					"""simple docstring"""

					return len(self.dataset			)

		def __getitem__(   self					:   Any  ,      __magic_name__					:   List[str]			)					->    Any:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     self.dataset[i]
					__snake_case					:      Dict							      =     self.process(__magic_name__  ,      **self.params			)
					return processed





class  _A (					__lowercase		):

		def __init__(   self					:   List[str]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   int  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   Optional[int]=None			)					->    List[Any]:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     loader
					__snake_case					:      List[Any]							      =     infer
					__snake_case					:      Tuple							      =     params
					if loader_batch_size == 1:
								# Let's spare some time by deactivating altogether
								__snake_case					:      Any							      =     None
					__snake_case					:      Tuple							      =     loader_batch_size

					# Internal bookkeeping
					__snake_case					:      Union[str, Any]							      =     None
					__snake_case					:      Optional[Any]							      =     None

		def __len__(   self					:   Tuple			)					->    Optional[int]:


					"""simple docstring"""

					return len(self.loader			)

		def __iter__(   self					:   Optional[int]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     iter(self.loader			)
					return self

		def 	lowercase__     (   self					:   Optional[int]			)					->    Dict:


					"""simple docstring"""

					if isinstance(self._loader_batch_data  ,      torch.Tensor			):
								# Batch data is simple tensor, just fetch the slice
								__snake_case					:      List[Any]							      =     self._loader_batch_data[self._loader_batch_index]
					else:
								# Batch data is assumed to be BaseModelOutput (or dict)
								__snake_case					:      Optional[Any]							      =     {}
								for k, element in self._loader_batch_data.items():
											if isinstance(__magic_name__  ,      __magic_name__			):
														# Convert ModelOutput to tuple first
														__snake_case					:      int							      =     element.to_tuple()
														if isinstance(element[0]  ,      torch.Tensor			):
																	__snake_case					:      List[str]							      =     tuple(el[self._loader_batch_index].unsqueeze(0			) for el in element			)
														elif isinstance(element[0]  ,      np.ndarray			):
																	__snake_case					:      Optional[Any]							      =     tuple(np.expand_dims(el[self._loader_batch_index]  ,      0			) for el in element			)
														continue
											if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(__magic_name__  ,      __magic_name__			):
														# Those are stored as lists of tensors so need specific unbatching.
														if isinstance(element[0]  ,      torch.Tensor			):
																	__snake_case					:      Any							      =     tuple(el[self._loader_batch_index].unsqueeze(0			) for el in element			)
														elif isinstance(element[0]  ,      np.ndarray			):
																	__snake_case					:      Optional[Any]							      =     tuple(np.expand_dims(el[self._loader_batch_index]  ,      0			) for el in element			)
														continue
											if element is None:
														# This can happen for optional data that get passed around
														__snake_case					:      Tuple							      =     None
											elif isinstance(element[self._loader_batch_index]  ,      torch.Tensor			):
														# Take correct batch data, but make it looked like batch_size=1
														# For compatibility with other methods within transformers

														__snake_case					:      List[str]							      =     element[self._loader_batch_index].unsqueeze(0			)
											elif isinstance(element[self._loader_batch_index]  ,      np.ndarray			):
														# Take correct batch data, but make it looked like batch_size=1
														# For compatibility with other methods within transformers
														__snake_case					:      Union[str, Any]							      =     np.expand_dims(element[self._loader_batch_index]  ,      0			)
											else:
														# This is typically a list, so no need to `unsqueeze`.
														__snake_case					:      int							      =     element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
								__snake_case					:      Union[str, Any]							      =     self._loader_batch_data.__class__(__magic_name__			)
					self._loader_batch_index += 1
					return result

		def 	lowercase__     (   self					:   Optional[int]			)					->    Optional[Any]:


					"""simple docstring"""

					if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
								# We are currently unrolling a batch so we just need to return
								# the current item within a batch
								return self.loader_batch_item()

					# We're out of items within a batch
					__snake_case					:      List[Any]							      =     next(self.iterator			)
					__snake_case					:      Union[str, Any]							      =     self.infer(__magic_name__  ,      **self.params			)
					# We now have a batch of "inferred things".
					if self.loader_batch_size is not None:
								# Try to infer the size of the batch
								if isinstance(__magic_name__  ,      torch.Tensor			):
											__snake_case					:      List[Any]							      =     processed
								else:
											__snake_case					:      Optional[Any]							      =     list(processed.keys()			)[0]
											__snake_case					:      Dict							      =     processed[key]
								if isinstance(__magic_name__  ,      __magic_name__			):
											__snake_case					:      List[str]							      =     len(__magic_name__			)
								else:
											__snake_case					:      int							      =     first_tensor.shape[0]
								if 0 < observed_batch_size < self.loader_batch_size:
											# could be last batch so we can't unroll as many
											# elements.
											__snake_case					:      Optional[int]							      =     observed_batch_size
								# Setting internal index to unwrap the batch
								__snake_case					:      Union[str, Any]							      =     processed
								__snake_case					:      Any							      =     0
								return self.loader_batch_item()
					else:
								# We're not unrolling batches
								return processed





class  _A (					__lowercase		):

		def __init__(   self					:   Optional[int]  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   int=None			)					->    Tuple:


					"""simple docstring"""

					super().__init__(__magic_name__  ,      __magic_name__  ,      __magic_name__			)

		def __iter__(   self					:   Tuple			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     iter(self.loader			)
					__snake_case					:      List[str]							      =     None
					return self

		def 	lowercase__     (   self					:   Tuple			)					->    Tuple:


					"""simple docstring"""

					if self.subiterator is None:
								__snake_case					:      Any							      =     self.infer(next(self.iterator			)  ,      **self.params			)
					try:
								# Try to return next item
								__snake_case					:      List[Any]							      =     next(self.subiterator			)
					except StopIteration:
								# When a preprocess iterator ends, we can start lookig at the next item
								# ChunkIterator will keep feeding until ALL elements of iterator
								# all have created their subiterator and have been iterating against.
								#
								# Another way to look at it, is we're basically flattening lists of lists
								# into a single list, but with generators
								__snake_case					:      List[Any]							      =     self.infer(next(self.iterator			)  ,      **self.params			)
								__snake_case					:      List[str]							      =     next(self.subiterator			)
					return processed





class  _A (					__lowercase		):

		def __iter__(   self					:   Optional[Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     iter(self.loader			)
					return self

		def 	lowercase__     (   self					:   List[str]			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      Dict							      =     False
					__snake_case					:      Union[str, Any]							      =     []
					if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
								while self._loader_batch_index < self.loader_batch_size:
											__snake_case					:      Union[str, Any]							      =     self.loader_batch_item()
											__snake_case					:      List[str]							      =     item.pop("""is_last"""			)
											accumulator.append(__magic_name__			)
											if is_last:
														return accumulator

					while not is_last:
								__snake_case					:      Union[str, Any]							      =     self.infer(next(self.iterator			)  ,      **self.params			)
								if self.loader_batch_size is not None:
											if isinstance(__magic_name__  ,      torch.Tensor			):
														__snake_case					:      str							      =     processed
											else:
														__snake_case					:      Optional[Any]							      =     list(processed.keys()			)[0]
														__snake_case					:      List[str]							      =     processed[key]
											if isinstance(__magic_name__  ,      __magic_name__			):
														__snake_case					:      Tuple							      =     len(__magic_name__			)
											else:
														__snake_case					:      Dict							      =     first_tensor.shape[0]
											if 0 < observed_batch_size < self.loader_batch_size:
														# could be last batch so we can't unroll as many
														# elements.
														__snake_case					:      List[str]							      =     observed_batch_size
											__snake_case					:      List[Any]							      =     processed
											__snake_case					:      str							      =     0
											while self._loader_batch_index < self.loader_batch_size:
														__snake_case					:      List[Any]							      =     self.loader_batch_item()
														__snake_case					:      List[Any]							      =     item.pop("""is_last"""			)
														accumulator.append(__magic_name__			)
														if is_last:
																	return accumulator
								else:
											__snake_case					:      Tuple							      =     processed
											__snake_case					:      List[Any]							      =     item.pop("""is_last"""			)
											accumulator.append(__magic_name__			)
					return accumulator





class  _A (					__lowercase		):

		def __init__(   self					:   str  ,      __magic_name__					:   Dataset  ,      __magic_name__					:   str			)					->    str:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     dataset
					__snake_case					:      Optional[int]							      =     key

		def __len__(   self					:   str			)					->    Tuple:


					"""simple docstring"""

					return len(self.dataset			)

		def __getitem__(   self					:   Optional[Any]  ,      __magic_name__					:   Dict			)					->    Optional[Any]:


					"""simple docstring"""

					return self.dataset[i][self.key]





class  _A (					__lowercase		):

		def __init__(   self					:   Optional[Any]  ,      __magic_name__					:   Dataset  ,      __magic_name__					:   str  ,      __magic_name__					:   str			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     dataset
					__snake_case					:      Any							      =     keya
					__snake_case					:      int							      =     keya

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


					"""simple docstring"""

					return len(self.dataset			)

		def __getitem__(   self					:   Union[str, Any]  ,      __magic_name__					:   Tuple			)					->    Dict:


					"""simple docstring"""

					return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}







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__     (       UpperCamelCase_					:							str						)->	int:
    A__					=       '''huggingface/label-files'''
    A__					=       '''imagenet-1k-id2label.json'''
    A__					=       json.load(open(hf_hub_download(UpperCamelCase_			,							UpperCamelCase_			,							repo_type='''dataset'''						)			,							'''r'''						)						)
    A__					=       {int(UpperCamelCase_						): v for k, v in idalabel.items()}
    A__					=       {v: k for k, v in idalabel.items()}

    A__					=       '''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"
    A__					=       BitConfig(
        conv_layer=UpperCamelCase_			,							num_labels=1_0_0_0			,							idalabel=UpperCamelCase_			,							labelaid=UpperCamelCase_			,							)

    return config



def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[Any]						)->	Optional[int]:
    if "stem.conv" in name:
        A__					=       name.replace('''stem.conv'''			,							'''bit.embedder.convolution'''						)
    if "blocks" in name:
        A__					=       name.replace('''blocks'''			,							'''layers'''						)
    if "head.fc" in name:
        A__					=       name.replace('''head.fc'''			,							'''classifier.1'''						)
    if name.startswith('''norm'''						):
        A__					=       '''bit.''' + name
    if "bit" not in name and "classifier" not in name:
        A__					=       '''bit.encoder.''' + name

    return name



def 		lowerCAmelCase__     (       )->	str:
    A__					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A__					=       Image.open(requests.get(UpperCamelCase_			,							stream=UpperCamelCase_						).raw						)
    return im



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							Tuple			,							UpperCamelCase_					:							List[Any]=False						)->	Optional[int]:
    A__					=       get_config(UpperCamelCase_						)

    # load original model from timm
    A__					=       create_model(UpperCamelCase_			,							pretrained=UpperCamelCase_						)
    timm_model.eval()

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

    # load HuggingFace model
    A__					=       BitForImageClassification(UpperCamelCase_						)
    model.eval()
    model.load_state_dict(UpperCamelCase_						)

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

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

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

    A__					=       prepare_img()
    A__					=       transform(UpperCamelCase_						).unsqueeze(0						)
    A__					=       processor(UpperCamelCase_			,							return_tensors='''pt'''						).pixel_values

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

    # verify logits
    with torch.no_grad():
        A__					=       model(UpperCamelCase_						)
        A__					=       outputs.logits

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

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

    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)
