





'''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     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[Any]:

   '''simple docstring'''







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

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

   if model_size == "tiny":
      __SCREAMING_SNAKE_CASE   				=							96
      __SCREAMING_SNAKE_CASE   				=							(2, 2, 6, 2)
      __SCREAMING_SNAKE_CASE   				=							(3, 6, 12, 24)
   elif model_size == "small":
      __SCREAMING_SNAKE_CASE   				=							96
      __SCREAMING_SNAKE_CASE   				=							(2, 2, 18, 2)
      __SCREAMING_SNAKE_CASE   				=							(3, 6, 12, 24)
   elif model_size == "base":
      __SCREAMING_SNAKE_CASE   				=							128
      __SCREAMING_SNAKE_CASE   				=							(2, 2, 18, 2)
      __SCREAMING_SNAKE_CASE   				=							(4, 8, 16, 32)
   else:
      __SCREAMING_SNAKE_CASE   				=							192
      __SCREAMING_SNAKE_CASE   				=							(2, 2, 18, 2)
      __SCREAMING_SNAKE_CASE   				=							(6, 12, 24, 48)

   if "in22k" in swin_name:
      __SCREAMING_SNAKE_CASE   				=							21841
   else:
      __SCREAMING_SNAKE_CASE   				=							1000
      __SCREAMING_SNAKE_CASE   				=							"""huggingface/label-files"""
      __SCREAMING_SNAKE_CASE   				=							"""imagenet-1k-id2label.json"""
      __SCREAMING_SNAKE_CASE   				=							json.load(open(hf_hub_download(__UpperCAmelCase				,						__UpperCAmelCase				,						repo_type="""dataset"""   )				,						"""r"""   )   )
      __SCREAMING_SNAKE_CASE   				=							{int(__UpperCAmelCase   ): v for k, v in idalabel.items()}
      __SCREAMING_SNAKE_CASE   				=							idalabel
      __SCREAMING_SNAKE_CASE   				=							{v: k for k, v in idalabel.items()}

   __SCREAMING_SNAKE_CASE   				=							img_size
   __SCREAMING_SNAKE_CASE   				=							num_classes
   __SCREAMING_SNAKE_CASE   				=							embed_dim
   __SCREAMING_SNAKE_CASE   				=							depths
   __SCREAMING_SNAKE_CASE   				=							num_heads
   __SCREAMING_SNAKE_CASE   				=							window_size

   return config

def     __magic_name__							(				__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







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

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

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

   return name

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Any:

   '''simple docstring'''







   for key in orig_state_dict.copy().keys():
      __SCREAMING_SNAKE_CASE   				=							orig_state_dict.pop(__UpperCAmelCase   )

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

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

   return orig_state_dict

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							timm.create_model(__UpperCAmelCase				,						pretrained=__UpperCAmelCase   )
   timm_model.eval()

   __SCREAMING_SNAKE_CASE   				=							get_swin_config(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							SwinForImageClassification(__UpperCAmelCase   )
   model.eval()

   __SCREAMING_SNAKE_CASE   				=							convert_state_dict(timm_model.state_dict()				,						__UpperCAmelCase   )
   model.load_state_dict(__UpperCAmelCase   )

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

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

   __SCREAMING_SNAKE_CASE   				=							timm_model(inputs["""pixel_values"""]   )
   __SCREAMING_SNAKE_CASE   				=							model(**__UpperCAmelCase   ).logits

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

   print(f"""Saving model {swin_name} to {pytorch_dump_folder_path}"""   )
   model.save_pretrained(__UpperCAmelCase   )

   print(f"""Saving image processor to {pytorch_dump_folder_path}"""   )
   image_processor.save_pretrained(__UpperCAmelCase   )


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 gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
        CTRLForSequenceClassification,
        CTRLLMHeadModel,
        CTRLModel,
    )







class __a			:





   def __init__(       self					:      Union[str, Any]					,lowerCamelCase					:      Optional[int]					,lowerCamelCase					:      Tuple=14					,lowerCamelCase					:      Optional[Any]=7					,lowerCamelCase					:      str=True					,lowerCamelCase					:      List[str]=True					,lowerCamelCase					:      Dict=True					,lowerCamelCase					:      Any=True					,lowerCamelCase					:      int=True					,lowerCamelCase					:      Dict=99					,lowerCamelCase					:      Dict=32					,lowerCamelCase					:      Optional[Any]=5					,lowerCamelCase					:      Tuple=4					,lowerCamelCase					:      Optional[int]=37					,lowerCamelCase					:      Optional[int]="gelu"					,lowerCamelCase					:      Optional[Any]=0.1					,lowerCamelCase					:      Tuple=0.1					,lowerCamelCase					:      Dict=512					,lowerCamelCase					:      int=16					,lowerCamelCase					:      Union[str, Any]=2					,lowerCamelCase					:      Tuple=0.02					,lowerCamelCase					:      str=3					,lowerCamelCase					:      Union[str, Any]=4					,lowerCamelCase					:      Any=None					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							parent
      __SCREAMING_SNAKE_CASE   				=							batch_size
      __SCREAMING_SNAKE_CASE   				=							seq_length
      __SCREAMING_SNAKE_CASE   				=							is_training
      __SCREAMING_SNAKE_CASE   				=							use_token_type_ids
      __SCREAMING_SNAKE_CASE   				=							use_input_mask
      __SCREAMING_SNAKE_CASE   				=							use_labels
      __SCREAMING_SNAKE_CASE   				=							use_mc_token_ids
      __SCREAMING_SNAKE_CASE   				=							vocab_size
      __SCREAMING_SNAKE_CASE   				=							hidden_size
      __SCREAMING_SNAKE_CASE   				=							num_hidden_layers
      __SCREAMING_SNAKE_CASE   				=							num_attention_heads
      __SCREAMING_SNAKE_CASE   				=							intermediate_size
      __SCREAMING_SNAKE_CASE   				=							hidden_act
      __SCREAMING_SNAKE_CASE   				=							hidden_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							max_position_embeddings
      __SCREAMING_SNAKE_CASE   				=							type_vocab_size
      __SCREAMING_SNAKE_CASE   				=							type_sequence_label_size
      __SCREAMING_SNAKE_CASE   				=							initializer_range
      __SCREAMING_SNAKE_CASE   				=							num_labels
      __SCREAMING_SNAKE_CASE   				=							num_choices
      __SCREAMING_SNAKE_CASE   				=							scope
      __SCREAMING_SNAKE_CASE   				=							self.vocab_size - 1





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE   				=							None
      if self.use_input_mask:
         __SCREAMING_SNAKE_CASE   				=							random_attention_mask([self.batch_size, self.seq_length]     )

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

      __SCREAMING_SNAKE_CASE   				=							None
      if self.use_mc_token_ids:
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size, self.num_choices]					,self.seq_length     )

      __SCREAMING_SNAKE_CASE   				=							None
      __SCREAMING_SNAKE_CASE   				=							None
      __SCREAMING_SNAKE_CASE   				=							None
      if self.use_labels:
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size]					,self.type_sequence_label_size     )
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size, self.seq_length]					,self.num_labels     )
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size]					,self.num_choices     )

      __SCREAMING_SNAKE_CASE   				=							self.get_config()

      __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.num_hidden_layers, self.num_attention_heads]					,2     )

      return (
          config,
          input_ids,
          input_mask,
          head_mask,
          token_type_ids,
          mc_token_ids,
          sequence_labels,
          token_labels,
          choice_labels,
      )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      return CTRLConfig(
          vocab_size=self.vocab_size					,n_embd=self.hidden_size					,n_layer=self.num_hidden_layers					,n_head=self.num_attention_heads					,n_positions=self.max_position_embeddings					,pad_token_id=self.pad_token_id					,)





   def   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Any					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      str					,*lowerCamelCase					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							CTRLModel(config=lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()

      model(lowerCamelCase					,token_type_ids=lowerCamelCase					,head_mask=lowerCamelCase     )
      model(lowerCamelCase					,token_type_ids=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase     )
      self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)     )
      self.parent.assertEqual(len(result.past_key_values     )					,config.n_layer     )





   def   UpperCAmelCase__  (       self					:      Optional[int]					,lowerCamelCase					:      int					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      int					,lowerCamelCase					:      Dict					,*lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							CTRLLMHeadModel(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()

      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,token_type_ids=lowerCamelCase					,labels=lowerCamelCase     )
      self.parent.assertEqual(result.loss.shape					,()     )
      self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.vocab_size)     )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.prepare_config_and_inputs()

      (
          (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  
      )   				=							config_and_inputs

      __SCREAMING_SNAKE_CASE   				=							{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """head_mask""": head_mask}

      return config, inputs_dict





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      int					,lowerCamelCase					:      str					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      Optional[Any]					,*lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.num_labels
      __SCREAMING_SNAKE_CASE   				=							CTRLForSequenceClassification(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size]					,self.type_sequence_label_size     )
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,token_type_ids=lowerCamelCase					,labels=lowerCamelCase     )
      self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)     )







@require_torch
class __a			( _snake_case, _snake_case, _snake_case, unittest.TestCase      ):
   __UpperCamelCase :			str						   =						(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
   __UpperCamelCase :			Dict						   =						(CTRLLMHeadModel,) if is_torch_available() else ()
   __UpperCamelCase :			int						   =						(
       {
           'feature-extraction': CTRLModel,
           'text-classification': CTRLForSequenceClassification,
           'text-generation': CTRLLMHeadModel,
           'zero-shot': CTRLForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __UpperCamelCase :			List[str]						   =						True
   __UpperCamelCase :			Dict						   =						False
   __UpperCamelCase :			Tuple						   =						False





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      Any					,lowerCamelCase					:      str					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      int					,lowerCamelCase					:      Dict     ):

      '''simple docstring'''

      if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
         # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
         # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
         # config could not be created.
         return True

      return False





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							CTRLModelTester(self     )
      __SCREAMING_SNAKE_CASE   				=							ConfigTester(self					,config_class=lowerCamelCase					,n_embd=37     )





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      super().tearDown()
      # clean-up as much as possible GPU memory occupied by PyTorch
      gc.collect()
      torch.cuda.empty_cache()





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      self.config_tester.run_common_tests()





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_model(*lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_lm_head_model(*lowerCamelCase     )





   @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""     )
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      pass





   @slow
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __SCREAMING_SNAKE_CASE   				=							CTRLModel.from_pretrained(lowerCamelCase     )
         self.assertIsNotNone(lowerCamelCase     )





   @unittest.skip("""The model doesn't support left padding"""     )  # and it's not used enough to be worth fixing :)
   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      pass







@require_torch
class __a			( unittest.TestCase      ):





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      super().tearDown()
      # clean-up as much as possible GPU memory occupied by PyTorch
      gc.collect()
      torch.cuda.empty_cache()





   @slow
   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							CTRLLMHeadModel.from_pretrained("""ctrl"""     )
      model.to(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							torch.tensor(
          [[1_1859, 0, 1611, 8]]					,dtype=torch.long					,device=lowerCamelCase     )  # Legal the president is
      __SCREAMING_SNAKE_CASE   				=							[
          1_1859,
          0,
          1611,
          8,
          5,
          150,
          2_6449,
          2,
          19,
          348,
          469,
          3,
          2595,
          48,
          2_0740,
          24_6533,
          24_6533,
          19,
          30,
          5,
      ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

      __SCREAMING_SNAKE_CASE   				=							model.generate(lowerCamelCase					,do_sample=lowerCamelCase     )
      self.assertListEqual(output_ids[0].tolist()					,lowerCamelCase     )





