






"""simple docstring"""



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def  _snake_case				(     lowerCamelCase__	:    str  )				->	Tuple:
							lowerCamelCase_       :							str									=SwinConfig(image_size=192  )

							if "base" in model_name:
														lowerCamelCase_       :							List[str]									=6
														lowerCamelCase_       :							Optional[int]									=128
														lowerCamelCase_       :							Tuple									=(2, 2, 18, 2)
														lowerCamelCase_       :							List[Any]									=(4, 8, 16, 32)
							elif "large" in model_name:
														lowerCamelCase_       :							Any									=12
														lowerCamelCase_       :							Optional[Any]									=192
														lowerCamelCase_       :							Any									=(2, 2, 18, 2)
														lowerCamelCase_       :							Optional[Any]									=(6, 12, 24, 48)
							else:
														raise ValueError("Model not supported, only supports base and large variants"  )

							lowerCamelCase_       :							str									=window_size
							lowerCamelCase_       :							str									=embed_dim
							lowerCamelCase_       :							Optional[int]									=depths
							lowerCamelCase_       :							Optional[Any]									=num_heads

							return config

def  _snake_case				(     lowerCamelCase__	:    List[str]  )				->	Any:






							if "encoder.mask_token" in name:
														lowerCamelCase_       :							Optional[int]									=name.replace("encoder.mask_token"				,						"embeddings.mask_token"  )
							if "encoder.patch_embed.proj" in name:
														lowerCamelCase_       :							int									=name.replace("encoder.patch_embed.proj"				,						"embeddings.patch_embeddings.projection"  )
							if "encoder.patch_embed.norm" in name:
														lowerCamelCase_       :							Union[str, Any]									=name.replace("encoder.patch_embed.norm"				,						"embeddings.norm"  )
							if "attn.proj" in name:
														lowerCamelCase_       :							List[Any]									=name.replace("attn.proj"				,						"attention.output.dense"  )
							if "attn" in name:
														lowerCamelCase_       :							List[str]									=name.replace("attn"				,						"attention.self"  )
							if "norm1" in name:
														lowerCamelCase_       :							str									=name.replace("norm1"				,						"layernorm_before"  )
							if "norm2" in name:
														lowerCamelCase_       :							Any									=name.replace("norm2"				,						"layernorm_after"  )
							if "mlp.fc1" in name:
														lowerCamelCase_       :							List[Any]									=name.replace("mlp.fc1"				,						"intermediate.dense"  )
							if "mlp.fc2" in name:
														lowerCamelCase_       :							Optional[Any]									=name.replace("mlp.fc2"				,						"output.dense"  )

							if name == "encoder.norm.weight":
														lowerCamelCase_       :							Dict									="""layernorm.weight"""
							if name == "encoder.norm.bias":
														lowerCamelCase_       :							List[str]									="""layernorm.bias"""

							if "decoder" in name:
														pass
							else:
														lowerCamelCase_       :							List[Any]									="""swin.""" + name

							return name

def  _snake_case				(     lowerCamelCase__	:    Optional[Any]				,						lowerCamelCase__	:    Tuple  )				->	str:






							for key in orig_state_dict.copy().keys():
														lowerCamelCase_       :							Optional[int]									=orig_state_dict.pop(_lowerCAmelCase  )

														if "attn_mask" in key:
																					pass
														elif "qkv" in key:
																					lowerCamelCase_       :							Optional[Any]									=key.split("."  )
																					lowerCamelCase_       :							Optional[int]									=int(key_split[2]  )
																					lowerCamelCase_       :							Optional[int]									=int(key_split[4]  )
																					lowerCamelCase_       :							str									=model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																					if "weight" in key:
																												lowerCamelCase_       :							int									=val[:dim, :]
																												lowerCamelCase_       :							List[str]									=val[
																												    dim : dim * 2, :
																												]
																												lowerCamelCase_       :							Optional[Any]									=val[-dim:, :]
																					else:
																												lowerCamelCase_       :							Union[str, Any]									=val[
																												    :dim
																												]
																												lowerCamelCase_       :							int									=val[
																												    dim : dim * 2
																												]
																												lowerCamelCase_       :							Optional[int]									=val[
																												    -dim:
																												]
														else:
																					lowerCamelCase_       :							int									=val

							return orig_state_dict

def  _snake_case				(     lowerCamelCase__	:    List[Any]				,						lowerCamelCase__	:    Dict				,						lowerCamelCase__	:    List[Any]				,						lowerCamelCase__	:    List[str]  )				->	int:
							lowerCamelCase_       :							List[Any]									=torch.load(_lowerCAmelCase				,						map_location="cpu"  )["""model"""]

							lowerCamelCase_       :							str									=get_swin_config(_lowerCAmelCase  )
							lowerCamelCase_       :							str									=SwinForMaskedImageModeling(_lowerCAmelCase  )
							model.eval()

							lowerCamelCase_       :							Optional[Any]									=convert_state_dict(_lowerCAmelCase				,						_lowerCAmelCase  )
							model.load_state_dict(_lowerCAmelCase  )

							lowerCamelCase_       :							Any									="""http://images.cocodataset.org/val2017/000000039769.jpg"""

							lowerCamelCase_       :							Dict									=ViTImageProcessor(size={"height": 192, "width": 192}  )
							lowerCamelCase_       :							int									=Image.open(requests.get(_lowerCAmelCase				,						stream=_lowerCAmelCase  ).raw  )
							lowerCamelCase_       :							Dict									=image_processor(images=_lowerCAmelCase				,						return_tensors="pt"  )

							with torch.no_grad():
														lowerCamelCase_       :							Optional[int]									=model(**_lowerCAmelCase  ).logits

							print(outputs.keys()  )
							print("Looks ok!"  )

							if pytorch_dump_folder_path is not None:
														print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""  )
														model.save_pretrained(_lowerCAmelCase  )

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

							if push_to_hub:
														print(F"""Pushing model and image processor for {model_name} to hub"""  )
														model.push_to_hub(F"""microsoft/{model_name}"""  )
														image_processor.push_to_hub(F"""microsoft/{model_name}"""  )


if __name__ == "__main__":
						A__       :				Union[str, Any] 					=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='swin-base-simmim-window6-192',
						    type=str,
						    choices=['swin-base-simmim-window6-192', 'swin-large-simmim-window12-192'],
						    help='Name of the Swin SimMIM model you\'d like to convert.',
						)
						parser.add_argument(
						    '--checkpoint_path',
						    default='/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth',
						    type=str,
						    help='Path to the original PyTorch checkpoint (.pth file).',
						)
						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 or not to push the converted model to the 🤗 hub.'
						)

						A__       :				Dict 					=					parser.parse_args()
						convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








import unittest

import numpy as np

from transformers import AlbertConfig, 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.albert.modeling_flax_albert import (
         FlaxAlbertForMaskedLM,
         FlaxAlbertForMultipleChoice,
         FlaxAlbertForPreTraining,
         FlaxAlbertForQuestionAnswering,
         FlaxAlbertForSequenceClassification,
         FlaxAlbertForTokenClassification,
         FlaxAlbertModel,
     )






class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__=13,		lowerCamelCase__=7,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=99,		lowerCamelCase__=32,		lowerCamelCase__=5,		lowerCamelCase__=4,		lowerCamelCase__=37,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=16,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=4,		):
        A :       List[str]          =   parent
        A :       Optional[int]          =   batch_size
        A :       Union[str, Any]          =   seq_length
        A :       Any          =   is_training
        A :       List[str]          =   use_attention_mask
        A :       Union[str, Any]          =   use_token_type_ids
        A :       Any          =   use_labels
        A :       str          =   vocab_size
        A :       Union[str, Any]          =   hidden_size
        A :       str          =   num_hidden_layers
        A :       List[Any]          =   num_attention_heads
        A :       Optional[int]          =   intermediate_size
        A :       Optional[Any]          =   hidden_act
        A :       Dict          =   hidden_dropout_prob
        A :       List[Any]          =   attention_probs_dropout_prob
        A :       Optional[int]          =   max_position_embeddings
        A :       int          =   type_vocab_size
        A :       str          =   type_sequence_label_size
        A :       List[Any]          =   initializer_range
        A :       str          =   num_choices






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[int]          =   ids_tensor([self.batch_size, self.seq_length],		self.vocab_size		)

        A :       Union[str, Any]          =   None
        if self.use_attention_mask:
            A :       Optional[int]          =   random_attention_mask([self.batch_size, self.seq_length]		)

        A :       int          =   None
        if self.use_token_type_ids:
            A :       List[Any]          =   ids_tensor([self.batch_size, self.seq_length],		self.type_vocab_size		)

        A :       Optional[int]          =   AlbertConfig(
            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=lowerCamelCase__,		initializer_range=self.initializer_range,		)

        return config, input_ids, token_type_ids, attention_mask






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   self.prepare_config_and_inputs()
        A     ,							A     ,							A     ,							A :       str          =   config_and_inputs
        A :       Union[str, Any]          =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
        return config, inputs_dict






@require_flax
class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__ ,						unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: Any					=      (
        (
            FlaxAlbertModel,
            FlaxAlbertForPreTraining,
            FlaxAlbertForMaskedLM,
            FlaxAlbertForMultipleChoice,
            FlaxAlbertForQuestionAnswering,
            FlaxAlbertForSequenceClassification,
            FlaxAlbertForTokenClassification,
            FlaxAlbertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   FlaxAlbertModelTester(self		)






    @slow
    def 			_lowerCAmelCase					(       self		):
        for model_class_name in self.all_model_classes:
            A :       Dict          =   model_class_name.from_pretrained("""albert-base-v2"""		)
            A :       Optional[int]          =   model(np.ones((1, 1)		)		)
            self.assertIsNotNone(lowerCamelCase__		)






@require_flax
class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   FlaxAlbertModel.from_pretrained("""albert-base-v2"""		)
        A :       List[str]          =   np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]		)
        A :       str          =   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
        A :       Optional[int]          =   model(lowerCamelCase__,		attention_mask=lowerCamelCase__		)[0]
        A :       str          =   (1, 11, 768)
        self.assertEqual(output.shape,		lowerCamelCase__		)
        A :       Optional[int]          =   np.array(
            [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]		)

        self.assertTrue(jnp.allclose(output[:, 1:4, 1:4],		lowerCamelCase__,		atol=1e-4		)		)


