


'''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      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]):
      A_						:  Optional[int]          =				SwinConfig()
      A_						:  Optional[Any]          =				swin_name.split("""_""")

      A_						:  int          =				name_split[1]
      A_						:  Optional[Any]          =				int(name_split[4])
      A_						:  int          =				int(name_split[3][-1])

      if model_size == "tiny":
            A_						:  str          =				96
            A_						:  Any          =				(2, 2, 6, 2)
            A_						:  Tuple          =				(3, 6, 12, 24)
      elif model_size == "small":
            A_						:  List[str]          =				96
            A_						:  int          =				(2, 2, 18, 2)
            A_						:  str          =				(3, 6, 12, 24)
      elif model_size == "base":
            A_						:  List[Any]          =				128
            A_						:  Optional[int]          =				(2, 2, 18, 2)
            A_						:  str          =				(4, 8, 16, 32)
      else:
            A_						:  Optional[Any]          =				192
            A_						:  Any          =				(2, 2, 18, 2)
            A_						:  Dict          =				(6, 12, 24, 48)

      if "in22k" in swin_name:
            A_						:  Any          =				2_1841
      else:
            A_						:  str          =				1000
            A_						:  Optional[int]          =				"""huggingface/label-files"""
            A_						:  Tuple          =				"""imagenet-1k-id2label.json"""
            A_						:  List[Any]          =				json.load(open(hf_hub_download(lowerCamelCase__							,						lowerCamelCase__							,						repo_type="""dataset""")							,						"""r"""))
            A_						:  str          =				{int(lowerCamelCase__): v for k, v in idalabel.items()}
            A_						:  Any          =				idalabel
            A_						:  Tuple          =				{v: k for k, v in idalabel.items()}

      A_						:  int          =				img_size
      A_						:  Dict          =				num_classes
      A_						:  Tuple          =				embed_dim
      A_						:  int          =				depths
      A_						:  List[str]          =				num_heads
      A_						:  Union[str, Any]          =				window_size

      return config




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

      if name == "norm.weight":
            A_						:  List[Any]          =				"""layernorm.weight"""
      if name == "norm.bias":
            A_						:  Optional[int]          =				"""layernorm.bias"""

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

      return name




def      lowerCamelCase					(      lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Optional[int]):
      for key in orig_state_dict.copy().keys():
            A_						:  Dict          =				orig_state_dict.pop(lowerCamelCase__)

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

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

      return orig_state_dict




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      Dict):
      A_						:  Any          =				timm.create_model(lowerCamelCase__							,						pretrained=lowerCamelCase__)
      timm_model.eval()

      A_						:  Optional[int]          =				get_swin_config(lowerCamelCase__)
      A_						:  int          =				SwinForImageClassification(lowerCamelCase__)
      model.eval()

      A_						:  Optional[Any]          =				convert_state_dict(timm_model.state_dict()							,						lowerCamelCase__)
      model.load_state_dict(lowerCamelCase__)

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

      A_						:  Union[str, Any]          =				AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""							,						"""-""")))
      A_						:  str          =				Image.open(requests.get(lowerCamelCase__							,						stream=lowerCamelCase__).raw)
      A_						:  int          =				image_processor(images=lowerCamelCase__							,						return_tensors="""pt""")

      A_						:  Union[str, Any]          =				timm_model(inputs["""pixel_values"""])
      A_						:  int          =				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__":
     __magic_name__			=      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.'
     )

     __magic_name__			=      parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)




'''simple docstring'''


import unittest

from transformers import 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
         OpenAIGPTConfig,
         OpenAIGPTDoubleHeadsModel,
         OpenAIGPTForSequenceClassification,
         OpenAIGPTLMHeadModel,
         OpenAIGPTModel,
     )



class 	__lowerCAmelCase		:


  '''simple docstring'''







  def __init__(							self :       Optional[int]	,_a :       List[Any]	,_a :       Dict=13	,_a :       List[Any]=7	,_a :       Optional[Any]=True	,_a :       Any=True	,_a :       Optional[int]=True	,_a :       Union[str, Any]=99	,_a :       Union[str, Any]=32	,_a :       List[str]=5	,_a :       List[str]=4	,_a :       Dict=37	,_a :       List[Any]="gelu"	,_a :       int=0.1	,_a :       Optional[int]=0.1	,_a :       Tuple=512	,_a :       Union[str, Any]=16	,_a :       Optional[Any]=2	,_a :       Optional[Any]=0.02	,_a :       Optional[int]=3	,_a :       str=4	,_a :       Optional[Any]=None	,):

        '''simple docstring'''




        A_						:  Optional[Any]          =				parent
        A_						:  str          =				batch_size
        A_						:  int          =				seq_length
        A_						:  Union[str, Any]          =				is_training
        A_						:  Optional[Any]          =				use_token_type_ids
        A_						:  int          =				use_labels
        A_						:  Dict          =				vocab_size
        A_						:  List[Any]          =				hidden_size
        A_						:  Tuple          =				num_hidden_layers
        A_						:  Optional[int]          =				num_attention_heads
        A_						:  int          =				intermediate_size
        A_						:  Tuple          =				hidden_act
        A_						:  int          =				hidden_dropout_prob
        A_						:  Dict          =				attention_probs_dropout_prob
        A_						:  Any          =				max_position_embeddings
        A_						:  Optional[Any]          =				type_vocab_size
        A_						:  Tuple          =				type_sequence_label_size
        A_						:  int          =				initializer_range
        A_						:  Optional[Any]          =				num_labels
        A_						:  str          =				num_choices
        A_						:  Optional[Any]          =				scope
        A_						:  List[Any]          =				self.vocab_size - 1






  def        _a							(							self :       Any ):

        '''simple docstring'''




        A_						:  List[Any]          =				ids_tensor([self.batch_size, self.seq_length]	,self.vocab_size )

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

        A_						:  int          =				None
        A_						:  str          =				None
        A_						:  Union[str, Any]          =				None
        if self.use_labels:
              A_						:  Optional[int]          =				ids_tensor([self.batch_size]	,self.type_sequence_label_size )
              A_						:  str          =				ids_tensor([self.batch_size, self.seq_length]	,self.num_labels )
              A_						:  Any          =				ids_tensor([self.batch_size]	,self.num_choices )

        A_						:  List[Any]          =				OpenAIGPTConfig(
            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	,)

        A_						:  Tuple          =				ids_tensor([self.num_hidden_layers, self.num_attention_heads]	,2 )

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






  def        _a							(							self :       Optional[int]	,_a :       List[str]	,_a :       str	,_a :       int	,_a :       int	,*_a :       Union[str, Any] ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				OpenAIGPTModel(config=_a )
        model.to(_a )
        model.eval()

        A_						:  Optional[int]          =				model(_a	,token_type_ids=_a	,head_mask=_a )
        A_						:  str          =				model(_a	,token_type_ids=_a )
        A_						:  Dict          =				model(_a )

        self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size) )






  def        _a							(							self :       Dict	,_a :       Optional[int]	,_a :       Union[str, Any]	,_a :       Dict	,_a :       List[str]	,*_a :       str ):

        '''simple docstring'''




        A_						:  str          =				OpenAIGPTLMHeadModel(_a )
        model.to(_a )
        model.eval()

        A_						:  Any          =				model(_a	,token_type_ids=_a	,labels=_a )
        self.parent.assertEqual(result.loss.shape	,() )
        self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length, self.vocab_size) )






  def        _a							(							self :       Any	,_a :       Dict	,_a :       List[Any]	,_a :       Dict	,_a :       Union[str, Any]	,*_a :       str ):

        '''simple docstring'''




        A_						:  Any          =				OpenAIGPTDoubleHeadsModel(_a )
        model.to(_a )
        model.eval()

        A_						:  Optional[int]          =				model(_a	,token_type_ids=_a	,labels=_a )
        self.parent.assertEqual(result.loss.shape	,() )
        self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length, self.vocab_size) )






  def        _a							(							self :       List[str]	,_a :       str	,_a :       Tuple	,_a :       Dict	,_a :       Tuple	,*_a :       Dict ):

        '''simple docstring'''




        A_						:  List[str]          =				self.num_labels
        A_						:  int          =				OpenAIGPTForSequenceClassification(_a )
        model.to(_a )
        model.eval()

        A_						:  Dict          =				ids_tensor([self.batch_size]	,self.type_sequence_label_size )
        A_						:  Optional[Any]          =				model(_a	,token_type_ids=_a	,labels=_a )
        self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_labels) )






  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				self.prepare_config_and_inputs()
        (
            (
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					
        )						:  str          =				config_and_inputs
        A_						:  int          =				{
            """input_ids""": input_ids,
            """token_type_ids""": token_type_ids,
            """head_mask""": head_mask,
        }

        return config, inputs_dict







@require_torch
class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE  , __SCREAMING_SNAKE_CASE  , __SCREAMING_SNAKE_CASE  , unittest.TestCase    ):


  '''simple docstring'''







  a_  					=							(
      (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
      if is_torch_available()
      else ()
  )
  a_  					=							(
      (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
  )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
  a_  					=							(
      {
          """feature-extraction""": OpenAIGPTModel,
          """text-classification""": OpenAIGPTForSequenceClassification,
          """text-generation""": OpenAIGPTLMHeadModel,
          """zero-shot""": OpenAIGPTForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )






  def        _a							(							self :       Tuple	,_a :       Optional[int]	,_a :       str	,_a :       List[str]	,_a :       List[str]	,_a :       Any ):

        '''simple docstring'''




        if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
              # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
              # `OpenAIGPTConfig` 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        _a							(							self :       Optional[int]	,_a :       str	,_a :       Dict	,_a :       Optional[int]=False ):

        '''simple docstring'''




        A_						:  Any          =				super()._prepare_for_class(_a	,_a	,return_labels=_a )

        if return_labels:
              if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
                    A_						:  Union[str, Any]          =				torch.zeros(
                        (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)	,dtype=torch.long	,device=_a	,)
                    A_						:  Any          =				inputs_dict["""labels"""]
                    A_						:  Any          =				inputs_dict["""labels"""]
                    A_						:  Tuple          =				torch.zeros(
                        (self.model_tester.batch_size, self.model_tester.num_choices)	,dtype=torch.long	,device=_a	,)
                    A_						:  int          =				torch.zeros(
                        self.model_tester.batch_size	,dtype=torch.long	,device=_a )
        return inputs_dict






  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        A_						:  Tuple          =				OpenAIGPTModelTester(self )
        A_						:  Optional[int]          =				ConfigTester(self	,config_class=_a	,n_embd=37 )






  def        _a							(							self :       Any ):

        '''simple docstring'''




        self.config_tester.run_common_tests()






  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        A_						:  int          =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_openai_gpt_model(*_a )






  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  Dict          =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(*_a )






  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  str          =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_double_lm_head_model(*_a )






  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        A_						:  List[Any]          =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_a )






  @slow
  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              A_						:  Union[str, Any]          =				OpenAIGPTModel.from_pretrained(_a )
              self.assertIsNotNone(_a )







@require_torch
class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







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

        '''simple docstring'''




        A_						:  Dict          =				OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt""" )
        model.to(_a )
        A_						:  Dict          =				torch.tensor([[481, 4735, 544]]	,dtype=torch.long	,device=_a )  # the president is
        A_						:  Dict          =				[
            481,
            4735,
            544,
            246,
            963,
            870,
            762,
            239,
            244,
            40477,
            244,
            249,
            719,
            881,
            487,
            544,
            240,
            244,
            603,
            481,
        ]  # the president is a very good man. " \n " i\'m sure he is, " said the

        A_						:  int          =				model.generate(_a	,do_sample=_a )
        self.assertListEqual(output_ids[0].tolist()	,_a )
