




import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def 		snake_case_							(_a			:							List[Any]  ):
    UpperCAmelCase     =	SwinConfig()
    UpperCAmelCase     =	swin_name.split('''_'''  )

    UpperCAmelCase     =	name_split[1]
    UpperCAmelCase     =	int(name_split[4]  )
    UpperCAmelCase     =	int(name_split[3][-1]  )

    if model_size == "tiny":
        UpperCAmelCase     =	9_6
        UpperCAmelCase     =	(2, 2, 6, 2)
        UpperCAmelCase     =	(3, 6, 1_2, 2_4)
    elif model_size == "small":
        UpperCAmelCase     =	9_6
        UpperCAmelCase     =	(2, 2, 1_8, 2)
        UpperCAmelCase     =	(3, 6, 1_2, 2_4)
    elif model_size == "base":
        UpperCAmelCase     =	1_2_8
        UpperCAmelCase     =	(2, 2, 1_8, 2)
        UpperCAmelCase     =	(4, 8, 1_6, 3_2)
    else:
        UpperCAmelCase     =	1_9_2
        UpperCAmelCase     =	(2, 2, 1_8, 2)
        UpperCAmelCase     =	(6, 1_2, 2_4, 4_8)

    if "in22k" in swin_name:
        UpperCAmelCase     =	2_1_8_4_1
    else:
        UpperCAmelCase     =	1_0_0_0
        UpperCAmelCase     =	'''huggingface/label-files'''
        UpperCAmelCase     =	'''imagenet-1k-id2label.json'''
        UpperCAmelCase     =	json.load(open(hf_hub_download(snake_case__					,     snake_case__					,     repo_type='''dataset'''  )					,     '''r'''  )  )
        UpperCAmelCase     =	{int(snake_case__  ): v for k, v in idalabel.items()}
        UpperCAmelCase     =	idalabel
        UpperCAmelCase     =	{v: k for k, v in idalabel.items()}

    UpperCAmelCase     =	img_size
    UpperCAmelCase     =	num_classes
    UpperCAmelCase     =	embed_dim
    UpperCAmelCase     =	depths
    UpperCAmelCase     =	num_heads
    UpperCAmelCase     =	window_size

    return config





def 		snake_case_							(_a			:							List[str]  ):
    if "patch_embed.proj" in name:
        UpperCAmelCase     =	name.replace('''patch_embed.proj'''					,     '''embeddings.patch_embeddings.projection'''  )
    if "patch_embed.norm" in name:
        UpperCAmelCase     =	name.replace('''patch_embed.norm'''					,     '''embeddings.norm'''  )
    if "layers" in name:
        UpperCAmelCase     =	'''encoder.''' + name
    if "attn.proj" in name:
        UpperCAmelCase     =	name.replace('''attn.proj'''					,     '''attention.output.dense'''  )
    if "attn" in name:
        UpperCAmelCase     =	name.replace('''attn'''					,     '''attention.self'''  )
    if "norm1" in name:
        UpperCAmelCase     =	name.replace('''norm1'''					,     '''layernorm_before'''  )
    if "norm2" in name:
        UpperCAmelCase     =	name.replace('''norm2'''					,     '''layernorm_after'''  )
    if "mlp.fc1" in name:
        UpperCAmelCase     =	name.replace('''mlp.fc1'''					,     '''intermediate.dense'''  )
    if "mlp.fc2" in name:
        UpperCAmelCase     =	name.replace('''mlp.fc2'''					,     '''output.dense'''  )

    if name == "norm.weight":
        UpperCAmelCase     =	'''layernorm.weight'''
    if name == "norm.bias":
        UpperCAmelCase     =	'''layernorm.bias'''

    if "head" in name:
        UpperCAmelCase     =	name.replace('''head'''					,     '''classifier'''  )
    else:
        UpperCAmelCase     =	'''swin.''' + name

    return name





def 		snake_case_							(_a			:							Union[str, Any]					,     _a			:							Optional[int]  ):
    for key in orig_state_dict.copy().keys():
        UpperCAmelCase     =	orig_state_dict.pop(snake_case__  )

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

            if "weight" in key:
                UpperCAmelCase     =	val[:dim, :]
                UpperCAmelCase     =	val[
                    dim : dim * 2, :
                ]
                UpperCAmelCase     =	val[-dim:, :]
            else:
                UpperCAmelCase     =	val[
                    :dim
                ]
                UpperCAmelCase     =	val[
                    dim : dim * 2
                ]
                UpperCAmelCase     =	val[
                    -dim:
                ]
        else:
            UpperCAmelCase     =	val

    return orig_state_dict





def 		snake_case_							(_a			:							List[Any]					,     _a			:							str  ):
    UpperCAmelCase     =	timm.create_model(snake_case__					,     pretrained=snake_case__  )
    timm_model.eval()

    UpperCAmelCase     =	get_swin_config(snake_case__  )
    UpperCAmelCase     =	SwinForImageClassification(snake_case__  )
    model.eval()

    UpperCAmelCase     =	convert_state_dict(timm_model.state_dict()					,     snake_case__  )
    model.load_state_dict(snake_case__  )

    UpperCAmelCase     =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''

    UpperCAmelCase     =	AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''					,     '''-'''  )  )  )
    UpperCAmelCase     =	Image.open(requests.get(snake_case__					,     stream=snake_case__  ).raw  )
    UpperCAmelCase     =	image_processor(images=snake_case__					,     return_tensors='''pt'''  )

    UpperCAmelCase     =	timm_model(inputs['''pixel_values''']  )
    UpperCAmelCase     =	model(**snake_case__  ).logits

    assert torch.allclose(snake_case__					,     snake_case__					,     atol=1E-3  )

    print(F"Saving model {swin_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__  )


if __name__ == "__main__":
   A									=argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--swin_name',
       default='swin_tiny_patch4_window7_224',
       type=str,
       help='Name of the Swin 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_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)










'''simple docstring'''







import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   import jax.numpy as jnp

   from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
       FlaxRobertaPreLayerNormForCausalLM,
       FlaxRobertaPreLayerNormForMaskedLM,
       FlaxRobertaPreLayerNormForMultipleChoice,
       FlaxRobertaPreLayerNormForQuestionAnswering,
       FlaxRobertaPreLayerNormForSequenceClassification,
       FlaxRobertaPreLayerNormForTokenClassification,
       FlaxRobertaPreLayerNormModel,
   )


class     _a						(			unittest.TestCase				):
       def __init__(			self		:			List[Any]     ,       lowercase		:			Dict     ,       lowercase		:			List[str]=13     ,       lowercase		:			str=7     ,       lowercase		:			List[str]=True     ,       lowercase		:			List[str]=True     ,       lowercase		:			Optional[Any]=True     ,       lowercase		:			Optional[Any]=True     ,       lowercase		:			Any=99     ,       lowercase		:			Any=32     ,       lowercase		:			Any=5     ,       lowercase		:			Tuple=4     ,       lowercase		:			List[Any]=37     ,       lowercase		:			List[Any]="gelu"     ,       lowercase		:			int=0.1     ,       lowercase		:			Any=0.1     ,       lowercase		:			Optional[int]=512     ,       lowercase		:			List[str]=16     ,       lowercase		:			Union[str, Any]=2     ,       lowercase		:			int=0.02     ,       lowercase		:			int=4     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	parent
           UpperCAmelCase     =	batch_size
           UpperCAmelCase     =	seq_length
           UpperCAmelCase     =	is_training
           UpperCAmelCase     =	use_attention_mask
           UpperCAmelCase     =	use_token_type_ids
           UpperCAmelCase     =	use_labels
           UpperCAmelCase     =	vocab_size
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	num_hidden_layers
           UpperCAmelCase     =	num_attention_heads
           UpperCAmelCase     =	intermediate_size
           UpperCAmelCase     =	hidden_act
           UpperCAmelCase     =	hidden_dropout_prob
           UpperCAmelCase     =	attention_probs_dropout_prob
           UpperCAmelCase     =	max_position_embeddings
           UpperCAmelCase     =	type_vocab_size
           UpperCAmelCase     =	type_sequence_label_size
           UpperCAmelCase     =	initializer_range
           UpperCAmelCase     =	num_choices
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

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

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

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

           UpperCAmelCase     =	RobertaPreLayerNormConfig(
               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=lowercase     ,       initializer_range=self.initializer_range     ,       )

           return config, input_ids, token_type_ids, attention_mask
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

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







       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.prepare_config_and_inputs()
           UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	config_and_inputs

           UpperCAmelCase     =	True
           UpperCAmelCase     =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
           UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       vocab_size=2       )

           return (
               config,
               input_ids,
               token_type_ids,
               encoder_hidden_states,
               encoder_attention_mask,
           )






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class     _a						(			__a							,							unittest.TestCase				):
       __a       :  Any            =      True

       __a       :  str            =      (
           (
               FlaxRobertaPreLayerNormModel,
               FlaxRobertaPreLayerNormForCausalLM,
               FlaxRobertaPreLayerNormForMaskedLM,
               FlaxRobertaPreLayerNormForSequenceClassification,
               FlaxRobertaPreLayerNormForTokenClassification,
               FlaxRobertaPreLayerNormForMultipleChoice,
               FlaxRobertaPreLayerNormForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	FlaxRobertaPreLayerNormModelTester(self       )







       @slow
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           for model_class_name in self.all_model_classes:
               UpperCAmelCase     =	model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''     ,       from_pt=lowercase       )
               UpperCAmelCase     =	model(np.ones((1, 1)       )       )
               self.assertIsNotNone(lowercase       )






@require_flax
class     _a						(			unittest.TestCase				):
       @slow
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''     ,       from_pt=lowercase       )

           UpperCAmelCase     =	np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]     ,       dtype=jnp.intaa       )
           UpperCAmelCase     =	model(lowercase       )[0]
           UpperCAmelCase     =	[1, 11, 50_265]
           self.assertEqual(list(output.shape       )     ,       lowercase       )
           # compare the actual values for a slice.
           UpperCAmelCase     =	np.array(
               [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]     ,       dtype=np.floataa       )
           self.assertTrue(np.allclose(output[:, :3, :3]     ,       lowercase     ,       atol=1E-4       )       )







       @slow
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''     ,       from_pt=lowercase       )

           UpperCAmelCase     =	np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]     ,       dtype=jnp.intaa       )
           UpperCAmelCase     =	model(lowercase       )[0]
           # compare the actual values for a slice.
           UpperCAmelCase     =	np.array(
               [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]     ,       dtype=np.floataa       )
           self.assertTrue(np.allclose(output[:, :3, :3]     ,       lowercase     ,       atol=1E-4       )       )




