"""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()
SCREAMING_SNAKE_CASE__   :      Tuple							=logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   :      Any							=torch.device('cpu')



def 					UpperCamelCase					(       )      ->Union[str, Any]:
    _lowerCamelCase	: Optional[int]       =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    _lowerCamelCase	: str       =       Image.open(requests.get(SCREAMING_SNAKE_CASE_						,	stream=SCREAMING_SNAKE_CASE_ ).raw )
    return im



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_ )      ->Optional[int]:
    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 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->List[str]:
    _lowerCamelCase	: Optional[int]       =       dct.pop(SCREAMING_SNAKE_CASE_ )
    _lowerCamelCase	: List[str]       =       val



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



@torch.no_grad()
def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->Tuple:
    _lowerCamelCase	: str       =       SwiftFormerConfig()

    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    _lowerCamelCase	: Optional[int]       =       1000
    _lowerCamelCase	: int       =       '''huggingface/label-files'''
    _lowerCamelCase	: List[Any]       =       '''imagenet-1k-id2label.json'''
    _lowerCamelCase	: Any       =       json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	repo_type='''dataset''' )						,	'''r''' ) )
    _lowerCamelCase	: Optional[Any]       =       {int(SCREAMING_SNAKE_CASE_ ): v for k, v in idalabel.items()}
    _lowerCamelCase	: Optional[int]       =       idalabel
    _lowerCamelCase	: str       =       {v: k for k, v in idalabel.items()}

    # size of the architecture
    if swiftformer_name == "swiftformer_xs":
        _lowerCamelCase	: Any       =       [3, 3, 6, 4]
        _lowerCamelCase	: List[str]       =       [48, 56, 112, 220]

    elif swiftformer_name == "swiftformer_s":
        _lowerCamelCase	: Union[str, Any]       =       [3, 3, 9, 6]
        _lowerCamelCase	: Tuple       =       [48, 64, 168, 224]

    elif swiftformer_name == "swiftformer_l1":
        _lowerCamelCase	: Optional[int]       =       [4, 3, 10, 5]
        _lowerCamelCase	: Optional[Any]       =       [48, 96, 192, 384]

    elif swiftformer_name == "swiftformer_l3":
        _lowerCamelCase	: List[str]       =       [4, 4, 12, 6]
        _lowerCamelCase	: int       =       [64, 128, 320, 512]

    # load state_dict of original model, remove and rename some keys
    if original_ckpt:
        if original_ckpt.startswith('''https''' ):
            _lowerCamelCase	: int       =       torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_						,	map_location='''cpu'''						,	check_hash=SCREAMING_SNAKE_CASE_ )
        else:
            _lowerCamelCase	: Union[str, Any]       =       torch.load(SCREAMING_SNAKE_CASE_						,	map_location='''cpu''' )
    _lowerCamelCase	: Any       =       checkpoint

    _lowerCamelCase	: List[str]       =       create_rename_keys(SCREAMING_SNAKE_CASE_ )
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )

    # load HuggingFace model
    _lowerCamelCase	: Any       =       SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_ ).eval()
    hf_model.load_state_dict(SCREAMING_SNAKE_CASE_ )

    # prepare test inputs
    _lowerCamelCase	: Tuple       =       prepare_img()
    _lowerCamelCase	: Tuple       =       ViTImageProcessor.from_pretrained('''preprocessor_config''' )
    _lowerCamelCase	: int       =       processor(images=SCREAMING_SNAKE_CASE_						,	return_tensors='''pt''' )

    # compare outputs from both models
    _lowerCamelCase	: List[Any]       =       get_expected_output(SCREAMING_SNAKE_CASE_ )
    _lowerCamelCase	: int       =       hf_model(inputs['''pixel_values'''] ).logits

    assert hf_logits.shape == torch.Size([1, 1000] )
    assert torch.allclose(hf_logits[0, 0:5]						,	SCREAMING_SNAKE_CASE_						,	atol=1e-3 )

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__   :      Optional[Any]							=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.')

  SCREAMING_SNAKE_CASE__   :      Dict							=parser.parse_args()
  convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)








'''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 						a_				:




	def __init__(       self     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[int]=1_3       ,		__lowerCAmelCase     :  Tuple=7       ,		__lowerCAmelCase     :  Optional[int]=True       ,		__lowerCAmelCase     :  Any=True       ,		__lowerCAmelCase     :  List[Any]=True       ,		__lowerCAmelCase     :  Union[str, Any]=9_9       ,		__lowerCAmelCase     :  Any=3_2       ,		__lowerCAmelCase     :  Tuple=5       ,		__lowerCAmelCase     :  int=4       ,		__lowerCAmelCase     :  List[str]=3_7       ,		__lowerCAmelCase     :  List[Any]="gelu"       ,		__lowerCAmelCase     :  Union[str, Any]=0.1       ,		__lowerCAmelCase     :  int=0.1       ,		__lowerCAmelCase     :  Any=5_1_2       ,		__lowerCAmelCase     :  Dict=1_6       ,		__lowerCAmelCase     :  Dict=2       ,		__lowerCAmelCase     :  Union[str, Any]=0.02       ,		__lowerCAmelCase     :  Optional[Any]=3       ,		__lowerCAmelCase     :  str=4       ,		__lowerCAmelCase     :  List[str]=None       ,		):
							__snake_case						 =   parent
							__snake_case						 =   batch_size
							__snake_case						 =   seq_length
							__snake_case						 =   is_training
							__snake_case						 =   use_token_type_ids
							__snake_case						 =   use_labels
							__snake_case						 =   vocab_size
							__snake_case						 =   hidden_size
							__snake_case						 =   num_hidden_layers
							__snake_case						 =   num_attention_heads
							__snake_case						 =   intermediate_size
							__snake_case						 =   hidden_act
							__snake_case						 =   hidden_dropout_prob
							__snake_case						 =   attention_probs_dropout_prob
							__snake_case						 =   max_position_embeddings
							__snake_case						 =   type_vocab_size
							__snake_case						 =   type_sequence_label_size
							__snake_case						 =   initializer_range
							__snake_case						 =   num_labels
							__snake_case						 =   num_choices
							__snake_case						 =   scope
							__snake_case						 =   self.vocab_size - 1




	def   lowercase__   (       self     :  Union[str, Any]      ):
							__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size      )

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

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

							__snake_case						 =   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       ,		)

							__snake_case						 =   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   lowercase__   (       self     :  List[str]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  Union[str, Any]       ,		*__lowerCAmelCase     :  List[str]      ):
							__snake_case						 =   OpenAIGPTModel(config=__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()

							__snake_case						 =   model(__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase       ,		head_mask=__lowerCAmelCase      )
							__snake_case						 =   model(__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase      )
							__snake_case						 =   model(__lowerCAmelCase      )

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




	def   lowercase__   (       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Dict       ,		*__lowerCAmelCase     :  str      ):
							__snake_case						 =   OpenAIGPTLMHeadModel(__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()

							__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   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  List[Any]       ,		*__lowerCAmelCase     :  Tuple      ):
							__snake_case						 =   OpenAIGPTDoubleHeadsModel(__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()

							__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   lowercase__   (       self     :  Optional[int]       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  int       ,		*__lowerCAmelCase     :  int      ):
							__snake_case						 =   self.num_labels
							__snake_case						 =   OpenAIGPTForSequenceClassification(__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()

							__snake_case						 =   ids_tensor([self.batch_size]       ,		self.type_sequence_label_size      )
							__snake_case						 =   model(__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase       ,		labels=__lowerCAmelCase      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)      )




	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   self.prepare_config_and_inputs()
							(
							    (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       
							)						 =   config_and_inputs
							__snake_case						 =   {
							    'input_ids': input_ids,
							    'token_type_ids': token_type_ids,
							    'head_mask': head_mask,
							}

							return config, inputs_dict





@require_torch
class 						a_				(			UpperCAmelCase__     ,						UpperCAmelCase__     ,						UpperCAmelCase__     ,						unittest.TestCase						):
	lowercase_       :     List[str]			  =      (
	    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
	    if is_torch_available()
	    else ()
	)
	lowercase_       :     Union[str, Any]			  =      (
	    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
	)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
	lowercase_       :     str			  =      (
	    {
	        '''feature-extraction''': OpenAIGPTModel,
	        '''text-classification''': OpenAIGPTForSequenceClassification,
	        '''text-generation''': OpenAIGPTLMHeadModel,
	        '''zero-shot''': OpenAIGPTForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Any      ):
							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   lowercase__   (       self     :  Tuple       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  List[str]=False      ):
							__snake_case						 =   super()._prepare_for_class(__lowerCAmelCase       ,		__lowerCAmelCase       ,		return_labels=__lowerCAmelCase      )

							if return_labels:
													if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																			__snake_case						 =   torch.zeros(
																			    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)       ,		dtype=torch.long       ,		device=__lowerCAmelCase       ,		)
																			__snake_case						 =   inputs_dict['labels']
																			__snake_case						 =   inputs_dict['labels']
																			__snake_case						 =   torch.zeros(
																			    (self.model_tester.batch_size, self.model_tester.num_choices)       ,		dtype=torch.long       ,		device=__lowerCAmelCase       ,		)
																			__snake_case						 =   torch.zeros(
																			    self.model_tester.batch_size       ,		dtype=torch.long       ,		device=__lowerCAmelCase      )
							return inputs_dict




	def   lowercase__   (       self     :  Tuple      ):
							__snake_case						 =   OpenAIGPTModelTester(self      )
							__snake_case						 =   ConfigTester(self       ,		config_class=__lowerCAmelCase       ,		n_embd=3_7      )




	def   lowercase__   (       self     :  int      ):
							self.config_tester.run_common_tests()




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_openai_gpt_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_lm_head_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  Any      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_double_lm_head_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  str      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__lowerCAmelCase      )




	@slow
	def   lowercase__   (       self     :  str      ):
							for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__snake_case						 =   OpenAIGPTModel.from_pretrained(__lowerCAmelCase      )
													self.assertIsNotNone(__lowerCAmelCase      )





@require_torch
class 						a_				(			unittest.TestCase						):




	@slow
	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   OpenAIGPTLMHeadModel.from_pretrained('openai-gpt'      )
							model.to(__lowerCAmelCase      )
							__snake_case						 =   torch.tensor([[4_8_1, 4_7_3_5, 5_4_4]]       ,		dtype=torch.long       ,		device=__lowerCAmelCase      )  # the president is
							__snake_case						 =   [
							    4_8_1,
							    4_7_3_5,
							    5_4_4,
							    2_4_6,
							    9_6_3,
							    8_7_0,
							    7_6_2,
							    2_3_9,
							    2_4_4,
							    4_0_4_7_7,
							    2_4_4,
							    2_4_9,
							    7_1_9,
							    8_8_1,
							    4_8_7,
							    5_4_4,
							    2_4_0,
							    2_4_4,
							    6_0_3,
							    4_8_1,
							]  # the president is a very good man. " \n " i\'m sure he is, " said the

							__snake_case						 =   model.generate(__lowerCAmelCase       ,		do_sample=__lowerCAmelCase      )
							self.assertListEqual(output_ids[0].tolist()       ,		__lowerCAmelCase      )

