
'''simple docstring'''

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






def 	_snake_case						(      A_       :    Union[str, Any]   ):
  """simple docstring"""







  a_      :	List[Any]      =							"""huggingface/label-files"""
  a_      :	Optional[Any]      =							"""imagenet-1k-id2label.json"""
  a_      :	List[str]      =							json.load(open(hf_hub_download(A_     ,		A_     ,		repo_type="""dataset"""   )     ,		"""r"""   )   )
  a_      :	Optional[Any]      =							{int(A_   ): v for k, v in idalabel.items()}
  a_      :	Tuple      =							{v: k for k, v in idalabel.items()}

  a_      :	Union[str, Any]      =							"""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_      :	Union[str, Any]      =							BitConfig(
      conv_layer=A_     ,		num_labels=1000     ,		idalabel=A_     ,		labelaid=A_     ,		)

  return config






def 	_snake_case						(      A_       :    str   ):
  """simple docstring"""







  if "stem.conv" in name:
    a_      :	Optional[Any]      =							name.replace("""stem.conv"""     ,		"""bit.embedder.convolution"""   )
  if "blocks" in name:
    a_      :	Union[str, Any]      =							name.replace("""blocks"""     ,		"""layers"""   )
  if "head.fc" in name:
    a_      :	Tuple      =							name.replace("""head.fc"""     ,		"""classifier.1"""   )
  if name.startswith("""norm"""   ):
    a_      :	int      =							"""bit.""" + name
  if "bit" not in name and "classifier" not in name:
    a_      :	Optional[Any]      =							"""bit.encoder.""" + name

  return name






def 	_snake_case						(      ):
  """simple docstring"""







  a_      :	Union[str, Any]      =							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  a_      :	Dict      =							Image.open(requests.get(A_     ,		stream=A_   ).raw   )
  return im






@torch.no_grad()
def 	_snake_case						(      A_       :    List[str]     ,		A_       :    List[str]     ,		A_       :    Dict=False   ):
  """simple docstring"""







  a_      :	Tuple      =							get_config(A_   )

  # load original model from timm
  a_      :	Any      =							create_model(A_     ,		pretrained=A_   )
  timm_model.eval()

  # load state_dict of original model
  a_      :	List[str]      =							timm_model.state_dict()
  for key in state_dict.copy().keys():
    a_      :	Any      =							state_dict.pop(A_   )
    a_      :	Union[str, Any]      =							val.squeeze() if """head""" in key else val

  # load HuggingFace model
  a_      :	List[str]      =							BitForImageClassification(A_   )
  model.eval()
  model.load_state_dict(A_   )

  # create image processor
  a_      :	Dict      =							create_transform(**resolve_data_config({}     ,		model=A_   )   )
  a_      :	Optional[int]      =							transform.transforms

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

  a_      :	str      =							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()     ,		)

  a_      :	Any      =							prepare_img()
  a_      :	Union[str, Any]      =							transform(A_   ).unsqueeze(0   )
  a_      :	Any      =							processor(A_     ,		return_tensors="""pt"""   ).pixel_values

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

  # verify logits
  with torch.no_grad():
    a_      :	Tuple      =							model(A_   )
    a_      :	Optional[Any]      =							outputs.logits

  print("""Logits:"""     ,		logits[0, :3]   )
  print("""Predicted class:"""     ,		model.config.idalabel[logits.argmax(-1   ).item()]   )
  a_      :	Dict      =							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__":
    __snake_case:  Tuple								=			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.",
    )

    __snake_case:  List[str]								=			parser.parse_args()
    convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''

import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()


class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					PriorTransformer
     a_    	=					"hidden_states"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							4
       a_      :	List[str]      =							8
       a_      :	List[str]      =							7

       a_      :	Tuple      =							floats_tensor((batch_size, embedding_dim) ).to(lowerCAmelCase_ )

       a_      :	List[Any]      =							floats_tensor((batch_size, embedding_dim) ).to(lowerCAmelCase_ )
       a_      :	List[Any]      =							floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(lowerCAmelCase_ )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=0 ):

       '''simple docstring'''

       torch.manual_seed(lowerCAmelCase_ )
       a_      :	List[Any]      =							4
       a_      :	Union[str, Any]      =							8
       a_      :	Optional[int]      =							7

       a_      :	List[Any]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )

       a_      :	Optional[Any]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )
       a_      :	Optional[Any]      =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(lowerCAmelCase_ )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return (4, 8)


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return (4, 8)


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							{
           """num_attention_heads""": 2,
           """attention_head_dim""": 4,
           """num_layers""": 2,
           """embedding_dim""": 8,
           """num_embeddings""": 7,
           """additional_embeddings""": 4,
       }
       a_      :	Optional[int]      =							self.dummy_input
       return init_dict, inputs_dict


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      , a_      :	Optional[Any]      =							PriorTransformer.from_pretrained(
           """hf-internal-testing/prior-dummy"""							,		output_loading_info=lowerCAmelCase_ )
       self.assertIsNotNone(lowerCAmelCase_ )
       self.assertEqual(len(loading_info["""missing_keys"""] )							,		0 )

       model.to(lowerCAmelCase_ )
       a_      :	List[Any]      =							model(**self.dummy_input )[0]

       assert hidden_states is not None, "Make sure output is not None"


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      , a_      :	Optional[int]      =							self.prepare_init_args_and_inputs_for_common()

       a_      :	str      =							self.model_class(**lowerCAmelCase_ )
       a_      :	Optional[Any]      =							inspect.signature(model.forward )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       a_      :	str      =							[*signature.parameters.keys()]

       a_      :	str      =							["""hidden_states""", """timestep"""]
       self.assertListEqual(arg_names[:2]							,		lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy""" )
       a_      :	Optional[Any]      =							model.to(lowerCAmelCase_ )

       if hasattr(lowerCAmelCase_							,		"""set_default_attn_processor""" ):
         model.set_default_attn_processor()

       a_      :	Tuple      =							self.get_dummy_seed_input()

       with torch.no_grad():
         a_      :	List[str]      =							model(**lowerCAmelCase_ )[0]

       a_      :	Optional[Any]      =							output[0, :5].flatten().cpu()
       print(lowerCAmelCase_ )

       # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
       # the expected output slices are not the same for CPU and GPU.
       a_      :	List[str]      =							torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239] )
       self.assertTrue(torch_all_close(lowerCAmelCase_							,		lowerCAmelCase_							,		rtol=1E-2 ) )







@slow
class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=1							,		lowerCAmelCase_=7_68							,		lowerCAmelCase_=77							,		lowerCAmelCase_=0 ):

       '''simple docstring'''

       torch.manual_seed(lowerCAmelCase_ )
       a_      :	str      =							batch_size
       a_      :	Dict      =							embedding_dim
       a_      :	Tuple      =							num_embeddings

       a_      :	Optional[Any]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )

       a_      :	List[str]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )
       a_      :	List[Any]      =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(lowerCAmelCase_ )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     @parameterized.expand(
         [
             # fmt: off
             [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
             [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
             # fmt: on
         ] )
     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Any      =							PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""							,		subfolder="""prior""" )
       model.to(lowerCAmelCase_ )
       a_      :	Optional[int]      =							self.get_dummy_seed_input(seed=lowerCAmelCase_ )

       with torch.no_grad():
         a_      :	str      =							model(**lowerCAmelCase_ )[0]

       assert list(sample.shape ) == [1, 7_68]

       a_      :	int      =							sample[0, :8].flatten().cpu()
       print(lowerCAmelCase_ )
       a_      :	str      =							torch.tensor(lowerCAmelCase_ )

       assert torch_all_close(lowerCAmelCase_							,		lowerCAmelCase_							,		atol=1E-3 )


