





"""simple docstring"""



import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification







def 		__lowercase     (   snake_case_  :  Tuple						)   ->Any:



    '''simple docstring'''







    __A    :						List[str]						 =     SwinvaConfig()
    __A    :						int						 =     swinva_name.split('''_'''						)

    __A    :						Dict						 =     name_split[1]
    if "to" in name_split[3]:
        __A    :						List[Any]						 =     int(name_split[3][-3:]						)
    else:
        __A    :						List[str]						 =     int(name_split[3]						)
    if "to" in name_split[2]:
        __A    :						Dict						 =     int(name_split[2][-2:]						)
    else:
        __A    :						Union[str, Any]						 =     int(name_split[2][6:]						)

    if model_size == "tiny":
        __A    :						str						 =     96
        __A    :						Optional[Any]						 =     (2, 2, 6, 2)
        __A    :						Any						 =     (3, 6, 12, 24)
    elif model_size == "small":
        __A    :						List[Any]						 =     96
        __A    :						Tuple						 =     (2, 2, 18, 2)
        __A    :						Any						 =     (3, 6, 12, 24)
    elif model_size == "base":
        __A    :						Tuple						 =     128
        __A    :						Dict						 =     (2, 2, 18, 2)
        __A    :						List[Any]						 =     (4, 8, 16, 32)
    else:
        __A    :						Optional[int]						 =     192
        __A    :						Optional[Any]						 =     (2, 2, 18, 2)
        __A    :						Union[str, Any]						 =     (6, 12, 24, 48)

    if "to" in swinva_name:
        __A    :						int						 =     (12, 12, 12, 6)

    if ("22k" in swinva_name) and ("to" not in swinva_name):
        __A    :						Union[str, Any]						 =     21841
        __A    :						Union[str, Any]						 =     '''huggingface/label-files'''
        __A    :						Optional[int]						 =     '''imagenet-22k-id2label.json'''
        __A    :						str						 =     json.load(open(hf_hub_download(snake_case_				,snake_case_				,repo_type='''dataset'''						)				,'''r'''						)						)
        __A    :						Union[str, Any]						 =     {int(snake_case_						): v for k, v in idalabel.items()}
        __A    :						List[Any]						 =     idalabel
        __A    :						Union[str, Any]						 =     {v: k for k, v in idalabel.items()}

    else:
        __A    :						int						 =     1000
        __A    :						Union[str, Any]						 =     '''huggingface/label-files'''
        __A    :						Union[str, Any]						 =     '''imagenet-1k-id2label.json'''
        __A    :						int						 =     json.load(open(hf_hub_download(snake_case_				,snake_case_				,repo_type='''dataset'''						)				,'''r'''						)						)
        __A    :						Tuple						 =     {int(snake_case_						): v for k, v in idalabel.items()}
        __A    :						Union[str, Any]						 =     idalabel
        __A    :						str						 =     {v: k for k, v in idalabel.items()}

    __A    :						List[Any]						 =     img_size
    __A    :						Union[str, Any]						 =     num_classes
    __A    :						List[Any]						 =     embed_dim
    __A    :						Dict						 =     depths
    __A    :						int						 =     num_heads
    __A    :						Any						 =     window_size

    return config







def 		__lowercase     (   snake_case_  :  List[str]						)   ->Any:



    '''simple docstring'''







    if "patch_embed.proj" in name:
        __A    :						Optional[int]						 =     name.replace('''patch_embed.proj'''				,'''embeddings.patch_embeddings.projection'''						)
    if "patch_embed.norm" in name:
        __A    :						Optional[Any]						 =     name.replace('''patch_embed.norm'''				,'''embeddings.norm'''						)
    if "layers" in name:
        __A    :						Tuple						 =     '''encoder.''' + name
    if "attn.proj" in name:
        __A    :						Union[str, Any]						 =     name.replace('''attn.proj'''				,'''attention.output.dense'''						)
    if "attn" in name:
        __A    :						str						 =     name.replace('''attn'''				,'''attention.self'''						)
    if "norm1" in name:
        __A    :						Tuple						 =     name.replace('''norm1'''				,'''layernorm_before'''						)
    if "norm2" in name:
        __A    :						Tuple						 =     name.replace('''norm2'''				,'''layernorm_after'''						)
    if "mlp.fc1" in name:
        __A    :						Dict						 =     name.replace('''mlp.fc1'''				,'''intermediate.dense'''						)
    if "mlp.fc2" in name:
        __A    :						Any						 =     name.replace('''mlp.fc2'''				,'''output.dense'''						)
    if "q_bias" in name:
        __A    :						Optional[Any]						 =     name.replace('''q_bias'''				,'''query.bias'''						)
    if "k_bias" in name:
        __A    :						Tuple						 =     name.replace('''k_bias'''				,'''key.bias'''						)
    if "v_bias" in name:
        __A    :						Dict						 =     name.replace('''v_bias'''				,'''value.bias'''						)
    if "cpb_mlp" in name:
        __A    :						str						 =     name.replace('''cpb_mlp'''				,'''continuous_position_bias_mlp'''						)
    if name == "norm.weight":
        __A    :						Optional[int]						 =     '''layernorm.weight'''
    if name == "norm.bias":
        __A    :						str						 =     '''layernorm.bias'''

    if "head" in name:
        __A    :						int						 =     name.replace('''head'''				,'''classifier'''						)
    else:
        __A    :						Union[str, Any]						 =     '''swinv2.''' + name

    return name







def 		__lowercase     (   snake_case_  :  Optional[Any]				,snake_case_  :  Optional[Any]						)   ->Dict:



    '''simple docstring'''







    for key in orig_state_dict.copy().keys():
        __A    :						int						 =     orig_state_dict.pop(snake_case_						)

        if "mask" in key:
            continue
        elif "qkv" in key:
            __A    :						str						 =     key.split('''.'''						)
            __A    :						int						 =     int(key_split[1]						)
            __A    :						Optional[int]						 =     int(key_split[3]						)
            __A    :						Dict						 =     model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                __A    :						Dict						 =     val[:dim, :]
                __A    :						Optional[Any]						 =     val[dim : dim * 2, :]
                __A    :						Optional[Any]						 =     val[-dim:, :]
            else:
                __A    :						Any						 =     val[:dim]
                __A    :						List[str]						 =     val[
                    dim : dim * 2
                ]
                __A    :						Tuple						 =     val[-dim:]
        else:
            __A    :						Optional[Any]						 =     val

    return orig_state_dict







def 		__lowercase     (   snake_case_  :  List[Any]				,snake_case_  :  Dict						)   ->int:



    '''simple docstring'''







    __A    :						Optional[int]						 =     timm.create_model(snake_case_				,pretrained=snake_case_						)
    timm_model.eval()

    __A    :						Any						 =     get_swinva_config(snake_case_						)
    __A    :						str						 =     SwinvaForImageClassification(snake_case_						)
    model.eval()

    __A    :						int						 =     convert_state_dict(timm_model.state_dict()				,snake_case_						)
    model.load_state_dict(snake_case_						)

    __A    :						Union[str, Any]						 =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''

    __A    :						Optional[Any]						 =     AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''				,'''-'''						)						)						)
    __A    :						Union[str, Any]						 =     Image.open(requests.get(snake_case_				,stream=snake_case_						).raw						)
    __A    :						List[Any]						 =     image_processor(images=snake_case_				,return_tensors='''pt'''						)

    __A    :						Optional[int]						 =     timm_model(inputs['''pixel_values''']						)
    __A    :						List[Any]						 =     model(**snake_case_						).logits

    assert torch.allclose(snake_case_				,snake_case_				,atol=1e-3						)

    print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""						)
    model.save_pretrained(snake_case_						)

    print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
    image_processor.save_pretrained(snake_case_						)

    model.push_to_hub(
        repo_path_or_name=Path(snake_case_				,snake_case_						)				,organization='''nandwalritik'''				,commit_message='''Add model'''				,)


if __name__ == "__main__":
       a_ =			argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--swinv2_name""",
           default="""swinv2_tiny_patch4_window8_256""",
           type=str,
           help="""Name of the Swinv2 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."""
       )

       a_ =			parser.parse_args()
       convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)











"""simple docstring"""



import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
       import os

       import jax.numpy as jnp
       from jax import jit

       from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
       from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

       a_ =			"""0.12"""  # assumed parallelism: 8


if is_torch_available():
       import torch







def 		__lowercase     (   snake_case_  :  Any				,snake_case_  :  int				,snake_case_  :  List[str]=None						)   ->Union[str, Any]:



    '''simple docstring'''







    if rng is None:
        __A    :						Union[str, Any]						 =     random.Random()

    __A    :						List[str]						 =     1
    for dim in shape:
        total_dims *= dim

    __A    :						List[str]						 =     []
    for _ in range(snake_case_						):
        values.append(rng.randint(0				,vocab_size - 1						)						)

    __A    :						Optional[Any]						 =     np.array(snake_case_				,dtype=jnp.intaa						).reshape(snake_case_						)

    return output







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  Union[str, Any]=None						)   ->int:



    '''simple docstring'''







    __A    :						int						 =     ids_tensor(snake_case_				,vocab_size=2				,rng=snake_case_						)
    # make sure that at least one token is attended to for each batch
    __A    :						str						 =     1
    return attn_mask






@require_flax
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     None
 _lowerCamelCase        =     ()

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A    :						Optional[int]						 =     self.model_tester.prepare_config_and_inputs_for_common()

     # cut to half length & take max batch_size 3
     __A    :						Optional[int]						 =     2
     __A    :						Tuple						 =     inputs['''input_ids'''].shape[-1] // 2
     __A    :						List[Any]						 =     inputs['''input_ids'''][:max_batch_size, :sequence_length]

     __A    :						Optional[Any]						 =     jnp.ones_like(__lowerCamelCase  )
     __A    :						List[Any]						 =     attention_mask[:max_batch_size, :sequence_length]

     # generate max 5 tokens
     __A    :						Optional[int]						 =     input_ids.shape[-1] + 5
     if config.eos_token_id is not None and config.pad_token_id is None:
         # hack to allow generate for models such as GPT2 as is done in `generate()`
         __A    :						Optional[int]						 =     config.eos_token_id
     return config, input_ids, attention_mask, max_length

 @is_pt_flax_cross_test
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						List[Any]						 =     self._get_input_ids_and_config()
     __A    :						List[Any]						 =     False
     __A    :						str						 =     max_length
     __A    :						Tuple						 =     0

     for model_class in self.all_generative_model_classes:
         __A    :						Union[str, Any]						 =     model_class(__lowerCamelCase  )

         __A    :						Any						 =     model_class.__name__[4:]  # Skip the "Flax" at the beginning
         __A    :						Dict						 =     getattr(__lowerCamelCase					,						__lowerCamelCase  )
         __A    :						Tuple						 =     pt_model_class(__lowerCamelCase  ).eval()
         __A    :						Optional[Any]						 =     load_flax_weights_in_pytorch_model(__lowerCamelCase					,						flax_model.params  )

         __A    :						str						 =     flax_model.generate(__lowerCamelCase  ).sequences
         __A    :						List[Any]						 =     pt_model.generate(torch.tensor(__lowerCamelCase					,						dtype=torch.long  )  )

         if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
             __A    :						Any						 =     flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

         self.assertListEqual(pt_generation_outputs.numpy().tolist()					,						flax_generation_outputs.tolist()  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						Union[str, Any]						 =     self._get_input_ids_and_config()
     __A    :						List[str]						 =     False
     __A    :						Optional[Any]						 =     max_length

     for model_class in self.all_generative_model_classes:
         __A    :						Optional[int]						 =     model_class(__lowerCamelCase  )

         __A    :						Dict						 =     model.generate(__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						Union[str, Any]						 =     jit(model.generate  )
         __A    :						str						 =     jit_generate(__lowerCamelCase  ).sequences

         self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						int						 =     self._get_input_ids_and_config()
     __A    :						List[Any]						 =     True
     __A    :						Union[str, Any]						 =     max_length

     for model_class in self.all_generative_model_classes:
         __A    :						Dict						 =     model_class(__lowerCamelCase  )

         __A    :						List[Any]						 =     model.generate(__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						List[Any]						 =     jit(model.generate  )
         __A    :						List[str]						 =     jit_generate(__lowerCamelCase  ).sequences

         self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						List[Any]						 =     self._get_input_ids_and_config()
     __A    :						List[str]						 =     False
     __A    :						Any						 =     max_length
     __A    :						List[Any]						 =     2

     for model_class in self.all_generative_model_classes:
         __A    :						Tuple						 =     model_class(__lowerCamelCase  )

         __A    :						Optional[Any]						 =     model.generate(__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						Union[str, Any]						 =     jit(model.generate  )
         __A    :						Optional[int]						 =     jit_generate(__lowerCamelCase  ).sequences

         self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						Tuple						 =     self._get_input_ids_and_config()
     __A    :						str						 =     False
     __A    :						Union[str, Any]						 =     max_length
     __A    :						List[Any]						 =     2
     __A    :						Any						 =     2

     for model_class in self.all_generative_model_classes:
         __A    :						List[str]						 =     model_class(__lowerCamelCase  )

         __A    :						Optional[int]						 =     model.generate(__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[0]					,						input_ids.shape[0] * config.num_return_sequences  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						Any						 =     self._get_input_ids_and_config()
     __A    :						Optional[Any]						 =     True
     __A    :						Union[str, Any]						 =     max_length
     __A    :						List[str]						 =     0.8
     __A    :						List[str]						 =     10
     __A    :						Union[str, Any]						 =     0.3
     __A    :						Union[str, Any]						 =     1
     __A    :						Optional[Any]						 =     8
     __A    :						Dict						 =     9

     for model_class in self.all_generative_model_classes:
         __A    :						List[Any]						 =     model_class(__lowerCamelCase  )

         __A    :						List[str]						 =     model.generate(__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						Any						 =     jit(model.generate  )
         __A    :						str						 =     jit_generate(__lowerCamelCase  ).sequences

         self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						Union[str, Any]						 =     self._get_input_ids_and_config()
     __A    :						Union[str, Any]						 =     max_length
     __A    :						List[str]						 =     1
     __A    :						str						 =     8
     __A    :						Any						 =     9

     for model_class in self.all_generative_model_classes:
         __A    :						Union[str, Any]						 =     model_class(__lowerCamelCase  )

         __A    :						Tuple						 =     model.generate(__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						Optional[Any]						 =     jit(model.generate  )
         __A    :						Optional[Any]						 =     jit_generate(__lowerCamelCase  ).sequences

         self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						int						 =     self._get_input_ids_and_config()
     __A    :						Optional[int]						 =     max_length
     __A    :						List[str]						 =     2
     __A    :						List[Any]						 =     1
     __A    :						Optional[Any]						 =     8
     __A    :						str						 =     9

     for model_class in self.all_generative_model_classes:
         __A    :						Optional[int]						 =     model_class(__lowerCamelCase  )

         __A    :						int						 =     model.generate(__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						str						 =     jit(model.generate  )
         __A    :						Any						 =     jit_generate(__lowerCamelCase  ).sequences

         self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						int						 =     self._get_input_ids_and_config()

     # pad attention mask on the left
     __A    :						Dict						 =     attention_mask.at[(0, 0)].set(0  )

     __A    :						Optional[Any]						 =     False
     __A    :						Union[str, Any]						 =     max_length

     for model_class in self.all_generative_model_classes:
         __A    :						int						 =     model_class(__lowerCamelCase  )

         __A    :						Union[str, Any]						 =     model.generate(__lowerCamelCase					,						attention_mask=__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						Optional[int]						 =     jit(model.generate  )
         __A    :						Any						 =     jit_generate(__lowerCamelCase					,						attention_mask=__lowerCamelCase  ).sequences

         self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						Optional[int]						 =     self._get_input_ids_and_config()

     # pad attention mask on the left
     __A    :						str						 =     attention_mask.at[(0, 0)].set(0  )

     __A    :						List[Any]						 =     True
     __A    :						Any						 =     max_length

     for model_class in self.all_generative_model_classes:
         __A    :						str						 =     model_class(__lowerCamelCase  )

         __A    :						List[Any]						 =     model.generate(__lowerCamelCase					,						attention_mask=__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						List[str]						 =     jit(model.generate  )
         __A    :						Optional[int]						 =     jit_generate(__lowerCamelCase					,						attention_mask=__lowerCamelCase  ).sequences

         self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()  )







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						Optional[Any]						 =     self._get_input_ids_and_config()

     # pad attention mask on the left
     __A    :						List[str]						 =     attention_mask.at[(0, 0)].set(0  )

     __A    :						Optional[int]						 =     2
     __A    :						Dict						 =     max_length

     for model_class in self.all_generative_model_classes:
         __A    :						Any						 =     model_class(__lowerCamelCase  )

         __A    :						int						 =     model.generate(__lowerCamelCase					,						attention_mask=__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						str						 =     jit(model.generate  )
         __A    :						str						 =     jit_generate(__lowerCamelCase					,						attention_mask=__lowerCamelCase  ).sequences

         self.assertListEqual(generation_outputs.tolist()					,						jit_generation_outputs.tolist()  )






@require_flax
class 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Optional[int]						 =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''  )
     __A    :						Optional[Any]						 =     FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''  )

     __A    :						Any						 =     '''Hello world'''
     __A    :						Dict						 =     tokenizer(__lowerCamelCase					,						return_tensors='''np'''  ).input_ids

     # typos are quickly detected (the correct argument is `do_sample`)
     with self.assertRaisesRegex(__lowerCamelCase					,						'''do_samples'''  ):
         model.generate(__lowerCamelCase					,						do_samples=__lowerCamelCase  )

     # arbitrary arguments that will not be used anywhere are also not accepted
     with self.assertRaisesRegex(__lowerCamelCase					,						'''foo'''  ):
         __A    :						Any						 =     {'''foo''': '''bar'''}
         model.generate(__lowerCamelCase					,						**__lowerCamelCase  )




