


'''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 		UpperCAmelCase_      (  unittest.TestCase		):





     def __init__( self       :      str    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      List[Any]=1_3    ,      UpperCAmelCase__       :      Tuple=7    ,      UpperCAmelCase__       :      List[Any]=True    ,      UpperCAmelCase__       :      Dict=True    ,      UpperCAmelCase__       :      Optional[Any]=True    ,      UpperCAmelCase__       :      int=True    ,      UpperCAmelCase__       :      int=9_9    ,      UpperCAmelCase__       :      List[str]=3_2    ,      UpperCAmelCase__       :      Tuple=5    ,      UpperCAmelCase__       :      List[str]=4    ,      UpperCAmelCase__       :      Tuple=3_7    ,      UpperCAmelCase__       :      Union[str, Any]="gelu"    ,      UpperCAmelCase__       :      Any=0.1    ,      UpperCAmelCase__       :      Tuple=0.1    ,      UpperCAmelCase__       :      int=5_1_2    ,      UpperCAmelCase__       :      int=1_6    ,      UpperCAmelCase__       :      Optional[int]=2    ,      UpperCAmelCase__       :      Tuple=0.02    ,      UpperCAmelCase__       :      Any=4    ,      )				->  int:
        lowerCAmelCase     =			parent
        lowerCAmelCase     =			batch_size
        lowerCAmelCase     =			seq_length
        lowerCAmelCase     =			is_training
        lowerCAmelCase     =			use_attention_mask
        lowerCAmelCase     =			use_token_type_ids
        lowerCAmelCase     =			use_labels
        lowerCAmelCase     =			vocab_size
        lowerCAmelCase     =			hidden_size
        lowerCAmelCase     =			num_hidden_layers
        lowerCAmelCase     =			num_attention_heads
        lowerCAmelCase     =			intermediate_size
        lowerCAmelCase     =			hidden_act
        lowerCAmelCase     =			hidden_dropout_prob
        lowerCAmelCase     =			attention_probs_dropout_prob
        lowerCAmelCase     =			max_position_embeddings
        lowerCAmelCase     =			type_vocab_size
        lowerCAmelCase     =			type_sequence_label_size
        lowerCAmelCase     =			initializer_range
        lowerCAmelCase     =			num_choices





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  Optional[Any]:
        lowerCAmelCase     =			ids_tensor([self.batch_size, self.seq_length]    ,      self.vocab_size    )

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

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

        lowerCAmelCase     =			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=UpperCAmelCase__    ,      initializer_range=self.initializer_range    ,      )

        return config, input_ids, token_type_ids, attention_mask





     def 							__UpperCAmelCase      ( self       :      Any    )				->  str:
        lowerCAmelCase     =			self.prepare_config_and_inputs()
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			config_and_inputs
        lowerCAmelCase     =			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
        return config, inputs_dict




@require_flax
class 		UpperCAmelCase_      (  __lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	Optional[Any]		=     True

     lowerCamelCase				:	Tuple		=     (
         (
             FlaxRoFormerModel,
             FlaxRoFormerForMaskedLM,
             FlaxRoFormerForSequenceClassification,
             FlaxRoFormerForTokenClassification,
             FlaxRoFormerForMultipleChoice,
             FlaxRoFormerForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )





     def 							__UpperCAmelCase      ( self       :      str    )				->  str:
        lowerCAmelCase     =			FlaxRoFormerModelTester(self    )





     @slow
     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  List[Any]:
        for model_class_name in self.all_model_classes:
           lowerCAmelCase     =			model_class_name.from_pretrained('junnyu/roformer_chinese_small'    ,      from_pt=UpperCAmelCase__    )
           lowerCAmelCase     =			model(np.ones((1, 1)    )    )
           self.assertIsNotNone(UpperCAmelCase__    )




@require_flax
class 		UpperCAmelCase_      (  unittest.TestCase		):





     @slow
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  str:
        lowerCAmelCase     =			FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'    )
        lowerCAmelCase     =			jnp.array([[0, 1, 2, 3, 4, 5]]    )
        lowerCAmelCase     =			model(UpperCAmelCase__    )[0]

        lowerCAmelCase     =			5_0_0_0_0

        lowerCAmelCase     =			(1, 6, vocab_size)
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			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]    ,      UpperCAmelCase__    ,      atol=1E-4    )    )










'''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 transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


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

a   :			Union[str, Any]					  =    torch.device("""cpu""")



def       __lowerCamelCase  (					)      ->			Any:
							UpperCAmelCase     :		Optional[Any]							     =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase     :		Dict							     =		Image.open(requests.get(_lowercase	,				stream=_lowercase		).raw		)
							return im



def       __lowerCamelCase  (					_lowercase		)      ->			Dict:
							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]		)

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]		)

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]		)

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]		)



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			str:
							UpperCAmelCase     :		Union[str, Any]							     =		dct.pop(_lowercase		)
							UpperCAmelCase     :		str							     =		val



def       __lowerCamelCase  (					_lowercase		)      ->			List[str]:
							UpperCAmelCase     :		Tuple							     =		[]
							for k in state_dict.keys():
														UpperCAmelCase     :		Dict							     =		k
														if ".pwconv" in k:
																					UpperCAmelCase     :		Union[str, Any]							     =		k_new.replace(""".pwconv"""	,				""".point_wise_conv"""		)
														if ".dwconv" in k:
																					UpperCAmelCase     :		Dict							     =		k_new.replace(""".dwconv"""	,				""".depth_wise_conv"""		)
														if ".Proj." in k:
																					UpperCAmelCase     :		str							     =		k_new.replace(""".Proj."""	,				""".proj."""		)
														if "patch_embed" in k_new:
																					UpperCAmelCase     :		Dict							     =		k_new.replace("""patch_embed"""	,				"""swiftformer.patch_embed.patch_embedding"""		)
														if "network" in k_new:
																					UpperCAmelCase     :		Optional[Any]							     =		k_new.split("""."""		)
																					if ls[2].isdigit():
																												UpperCAmelCase     :		Any							     =		"""swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]		)
																					else:
																												UpperCAmelCase     :		Any							     =		k_new.replace("""network"""	,				"""swiftformer.encoder.network"""		)
														rename_keys.append((k, k_new)		)
							return rename_keys



@torch.no_grad()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		Optional[Any]							     =		SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							UpperCAmelCase     :		List[Any]							     =		1_0_0_0
							UpperCAmelCase     :		List[str]							     =		"""huggingface/label-files"""
							UpperCAmelCase     :		Tuple							     =		"""imagenet-1k-id2label.json"""
							UpperCAmelCase     :		Dict							     =		json.load(open(hf_hub_download(_lowercase	,				_lowercase	,				repo_type="""dataset"""		)	,				"""r"""		)		)
							UpperCAmelCase     :		Tuple							     =		{int(_lowercase		): v for k, v in idalabel.items()}
							UpperCAmelCase     :		Tuple							     =		idalabel
							UpperCAmelCase     :		List[Any]							     =		{v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														UpperCAmelCase     :		List[Any]							     =		[3, 3, 6, 4]
														UpperCAmelCase     :		int							     =		[4_8, 5_6, 1_1_2, 2_2_0]

							elif swiftformer_name == "swiftformer_s":
														UpperCAmelCase     :		str							     =		[3, 3, 9, 6]
														UpperCAmelCase     :		str							     =		[4_8, 6_4, 1_6_8, 2_2_4]

							elif swiftformer_name == "swiftformer_l1":
														UpperCAmelCase     :		List[Any]							     =		[4, 3, 1_0, 5]
														UpperCAmelCase     :		Union[str, Any]							     =		[4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														UpperCAmelCase     :		Any							     =		[4, 4, 1_2, 6]
														UpperCAmelCase     :		List[Any]							     =		[6_4, 1_2_8, 3_2_0, 5_1_2]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith("""https"""		):
																					UpperCAmelCase     :		Dict							     =		torch.hub.load_state_dict_from_url(_lowercase	,				map_location="""cpu"""	,				check_hash=_lowercase		)
														else:
																					UpperCAmelCase     :		Dict							     =		torch.load(_lowercase	,				map_location="""cpu"""		)
							UpperCAmelCase     :		str							     =		checkpoint

							UpperCAmelCase     :		Tuple							     =		create_rename_keys(_lowercase		)
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(_lowercase	,				_lowercase	,				_lowercase		)

							# load HuggingFace model
							UpperCAmelCase     :		str							     =		SwiftFormerForImageClassification(_lowercase		).eval()
							hf_model.load_state_dict(_lowercase		)

							# prepare test inputs
							UpperCAmelCase     :		Any							     =		prepare_img()
							UpperCAmelCase     :		List[Any]							     =		ViTImageProcessor.from_pretrained("""preprocessor_config"""		)
							UpperCAmelCase     :		List[str]							     =		processor(images=_lowercase	,				return_tensors="""pt"""		)

							# compare outputs from both models
							UpperCAmelCase     :		List[str]							     =		get_expected_output(_lowercase		)
							UpperCAmelCase     :		Dict							     =		hf_model(inputs["""pixel_values"""]		).logits

							assert hf_logits.shape == torch.Size([1, 1_0_0_0]		)
							assert torch.allclose(hf_logits[0, 0:5]	,				_lowercase	,				atol=1e-3		)

							Path(_lowercase		).mkdir(exist_ok=_lowercase		)
							print(F'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''		)
							hf_model.save_pretrained(_lowercase		)


if __name__ == "__main__":
							a   :			Tuple					  =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--swiftformer_name""",
							    default="""swiftformer_xs""",
							    choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
							    type=str,
							    help="""Name of the SwiftFormer model you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default="""./converted_outputs/""",
							    type=str,
							    help="""Path to the output PyTorch model directory.""",
							)
							parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

							a   :			Union[str, Any]					  =    parser.parse_args()
							convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)

