"""simple docstring"""






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     UpperCAmelCase__ (       lowerCAmelCase__  :Optional[Any]	)				->					Optional[Any]:
      '''simple docstring'''


      lowercase														=			SwinConfig()
      lowercase														=			swin_name.split("""_"""	)

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

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

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

      lowercase														=			img_size
      lowercase														=			num_classes
      lowercase														=			embed_dim
      lowercase														=			depths
      lowercase														=			num_heads
      lowercase														=			window_size

      return config


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[Any]	)				->					Any:
      '''simple docstring'''


      if "patch_embed.proj" in name:
            lowercase														=			name.replace("""patch_embed.proj"""      ,	"""embeddings.patch_embeddings.projection"""	)
      if "patch_embed.norm" in name:
            lowercase														=			name.replace("""patch_embed.norm"""      ,	"""embeddings.norm"""	)
      if "layers" in name:
            lowercase														=			"""encoder.""" + name
      if "attn.proj" in name:
            lowercase														=			name.replace("""attn.proj"""      ,	"""attention.output.dense"""	)
      if "attn" in name:
            lowercase														=			name.replace("""attn"""      ,	"""attention.self"""	)
      if "norm1" in name:
            lowercase														=			name.replace("""norm1"""      ,	"""layernorm_before"""	)
      if "norm2" in name:
            lowercase														=			name.replace("""norm2"""      ,	"""layernorm_after"""	)
      if "mlp.fc1" in name:
            lowercase														=			name.replace("""mlp.fc1"""      ,	"""intermediate.dense"""	)
      if "mlp.fc2" in name:
            lowercase														=			name.replace("""mlp.fc2"""      ,	"""output.dense"""	)

      if name == "norm.weight":
            lowercase														=			"""layernorm.weight"""
      if name == "norm.bias":
            lowercase														=			"""layernorm.bias"""

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

      return name


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[Any]      ,	lowerCAmelCase__  :str	)				->					Dict:
      '''simple docstring'''


      for key in orig_state_dict.copy().keys():
            lowercase														=			orig_state_dict.pop(lowerCAmelCase__	)

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

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

      return orig_state_dict


def     UpperCAmelCase__ (       lowerCAmelCase__  :Optional[int]      ,	lowerCAmelCase__  :Dict	)				->					Optional[int]:
      '''simple docstring'''


      lowercase														=			timm.create_model(lowerCAmelCase__      ,	pretrained=lowerCAmelCase__	)
      timm_model.eval()

      lowercase														=			get_swin_config(lowerCAmelCase__	)
      lowercase														=			SwinForImageClassification(lowerCAmelCase__	)
      model.eval()

      lowercase														=			convert_state_dict(timm_model.state_dict()      ,	lowerCAmelCase__	)
      model.load_state_dict(lowerCAmelCase__	)

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

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

      lowercase														=			timm_model(inputs["""pixel_values"""]	)
      lowercase														=			model(**lowerCAmelCase__	).logits

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

      print(f'Saving model {swin_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 __name__ == "__main__":
       __lowerCAmelCase					: int        =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."""
       )

       __lowerCAmelCase					: int        =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 RobertaConfig, 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():
       from transformers.models.roberta.modeling_flax_roberta import (
           FlaxRobertaForCausalLM,
           FlaxRobertaForMaskedLM,
           FlaxRobertaForMultipleChoice,
           FlaxRobertaForQuestionAnswering,
           FlaxRobertaForSequenceClassification,
           FlaxRobertaForTokenClassification,
           FlaxRobertaModel,
       )





class 				_A		( unittest.TestCase			):

      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=0.0_2	, __lowerCAmelCase=4	, ):







            """simple docstring"""



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

      def        A__ (		self		):







            """simple docstring"""



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

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

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

            lowercase														=			RobertaConfig(
                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        A__ (		self		):







            """simple docstring"""



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







      def        A__ (		self		):







            """simple docstring"""



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

            lowercase														=			True
            lowercase														=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
            lowercase														=			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
class 				_A		( lowerCAmelCase						,					unittest.TestCase			):
      snake_case__    :     Optional[int]													=			True

      snake_case__    :     List[str]													=			(
          (
              FlaxRobertaModel,
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			FlaxRobertaModelTester(self		)







      @slow
      def        A__ (		self		):







            """simple docstring"""



            for model_class_name in self.all_model_classes:
                  lowercase														=			model_class_name.from_pretrained("""roberta-base"""	, from_pt=__lowerCAmelCase		)
                  lowercase														=			model(np.ones((1, 1)		)		)
                  self.assertIsNotNone(__lowerCAmelCase		)



