





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    :       List[Any]		       =    logging.get_logger(__name__)

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

   __lowerCAmelCase				      =      '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"
   __lowerCAmelCase				      =      BitConfig(
       conv_layer=lowerCAmelCase_,      num_labels=1000,      idalabel=lowerCAmelCase_,      labelaid=lowerCAmelCase_,      )

   return config

def     a_       (					lowerCAmelCase_ :     List[str]      ):
   if "stem.conv" in name:
      __lowerCAmelCase				      =      name.replace('stem.conv',      'bit.embedder.convolution'      )
   if "blocks" in name:
      __lowerCAmelCase				      =      name.replace('blocks',      'layers'      )
   if "head.fc" in name:
      __lowerCAmelCase				      =      name.replace('head.fc',      'classifier.1'      )
   if name.startswith('norm'      ):
      __lowerCAmelCase				      =      'bit.' + name
   if "bit" not in name and "classifier" not in name:
      __lowerCAmelCase				      =      'bit.encoder.' + name

   return name

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

@torch.no_grad()
def     a_       (					lowerCAmelCase_ :     str,      lowerCAmelCase_ :     Any,      lowerCAmelCase_ :     Optional[int]=False      ):
   __lowerCAmelCase				      =      get_config(lowerCAmelCase_      )

   # load original model from timm
   __lowerCAmelCase				      =      create_model(lowerCAmelCase_,      pretrained=lowerCAmelCase_      )
   timm_model.eval()

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

   # load HuggingFace model
   __lowerCAmelCase				      =      BitForImageClassification(lowerCAmelCase_      )
   model.eval()
   model.load_state_dict(lowerCAmelCase_      )

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

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

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

   __lowerCAmelCase				      =      prepare_img()
   __lowerCAmelCase				      =      transform(lowerCAmelCase_      ).unsqueeze(0      )
   __lowerCAmelCase				      =      processor(lowerCAmelCase_,      return_tensors='pt'      ).pixel_values

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

   # verify logits
   with torch.no_grad():
      __lowerCAmelCase				      =      model(lowerCAmelCase_      )
      __lowerCAmelCase				      =      outputs.logits

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

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

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







'''simple docstring'''



import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.roformer.modeling_flax_roformer import (
         FlaxRoFormerForMaskedLM,
         FlaxRoFormerForMultipleChoice,
         FlaxRoFormerForQuestionAnswering,
         FlaxRoFormerForSequenceClassification,
         FlaxRoFormerForTokenClassification,
         FlaxRoFormerModel,
     )





class      lowerCAmelCase (			unittest.TestCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=13     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=99     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=5     ,	__SCREAMING_SNAKE_CASE=4     ,	__SCREAMING_SNAKE_CASE=37     ,	__SCREAMING_SNAKE_CASE="gelu"     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=512     ,	__SCREAMING_SNAKE_CASE=16     ,	__SCREAMING_SNAKE_CASE=2     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=4     ,	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	parent
            __snake_case            =	batch_size
            __snake_case            =	seq_length
            __snake_case            =	is_training
            __snake_case            =	use_attention_mask
            __snake_case            =	use_token_type_ids
            __snake_case            =	use_labels
            __snake_case            =	vocab_size
            __snake_case            =	hidden_size
            __snake_case            =	num_hidden_layers
            __snake_case            =	num_attention_heads
            __snake_case            =	intermediate_size
            __snake_case            =	hidden_act
            __snake_case            =	hidden_dropout_prob
            __snake_case            =	attention_probs_dropout_prob
            __snake_case            =	max_position_embeddings
            __snake_case            =	type_vocab_size
            __snake_case            =	type_sequence_label_size
            __snake_case            =	initializer_range
            __snake_case            =	num_choices


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



            '''simple docstring'''




            __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size	)

            __snake_case            =	None
            if self.use_attention_mask:
                 __snake_case            =	random_attention_mask([self.batch_size, self.seq_length]	)

            __snake_case            =	None
            if self.use_token_type_ids:
                 __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.type_vocab_size	)

            __snake_case            =	RoFormerConfig(
                vocab_size=self.vocab_size     ,	hidden_size=self.hidden_size     ,	num_hidden_layers=self.num_hidden_layers     ,	num_attention_heads=self.num_attention_heads     ,	intermediate_size=self.intermediate_size     ,	hidden_act=self.hidden_act     ,	hidden_dropout_prob=self.hidden_dropout_prob     ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,	max_position_embeddings=self.max_position_embeddings     ,	type_vocab_size=self.type_vocab_size     ,	is_decoder=__SCREAMING_SNAKE_CASE     ,	initializer_range=self.initializer_range     ,	)

            return config, input_ids, token_type_ids, attention_mask




       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self.prepare_config_and_inputs()
            __snake_case       ,     __snake_case       ,     __snake_case       ,     __snake_case            =	config_and_inputs
            __snake_case            =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
            return config, inputs_dict





@require_flax
class      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   Tuple										=      True

       __lowercase	:   Optional[int]										=      (
           (
               FlaxRoFormerModel,
               FlaxRoFormerForMaskedLM,
               FlaxRoFormerForSequenceClassification,
               FlaxRoFormerForTokenClassification,
               FlaxRoFormerForMultipleChoice,
               FlaxRoFormerForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	FlaxRoFormerModelTester(self	)




       @slow
       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 __snake_case            =	model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''     ,	from_pt=__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	model(np.ones((1, 1)	)	)
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)





@require_flax
class      lowerCAmelCase (			unittest.TestCase):


       @slow
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''	)
            __snake_case            =	jnp.array([[0, 1, 2, 3, 4, 5]]	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)[0]

            __snake_case            =	5_0000

            __snake_case            =	(1, 6, vocab_size)
            self.assertEqual(output.shape     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	jnp.array(
                [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]	)

            self.assertTrue(jnp.allclose(output[:, :3, :3]     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-4	)	)






