


import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase					:       Optional[Any]			    =     get_tests_dir("""fixtures/test_sentencepiece.model""")



@require_sentencepiece
@require_tokenizers
class A__     ( A__				,		unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		XGLMTokenizer
  _lowercase									=		XGLMTokenizerFast
  _lowercase									=		True
  _lowercase									=		True

  def    _UpperCamelCase( self			:  List[Any]       ):
        super().setUp()

        # We have a SentencePiece fixture for testing
        a__	:  str     =   XGLMTokenizer(lowerCamelCase__				,				keep_accents=lowerCamelCase__       )
        tokenizer.save_pretrained(self.tmpdirname       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  int     =   "<pad>"
        a__	:  Union[str, Any]     =   1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__       )				,				lowerCamelCase__       )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__       )				,				lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        a__	:  List[str]     =   list(self.get_tokenizer().get_vocab().keys()       )

        self.assertEqual(vocab_keys[0]				,				"<s>"       )
        self.assertEqual(vocab_keys[1]				,				"<pad>"       )
        self.assertEqual(len(lowerCamelCase__       )				,				1_008       )

  def    _UpperCamelCase( self			:  Dict       ):
        self.assertEqual(self.get_tokenizer().vocab_size				,				1_008       )

  def    _UpperCamelCase( self			:  Optional[int]       ):
        a__	:  str     =   XGLMTokenizer(lowerCamelCase__				,				keep_accents=lowerCamelCase__       )

        a__	:  List[str]     =   tokenizer.tokenize("This is a test"       )
        self.assertListEqual(lowerCamelCase__				,				["▁This", "▁is", "▁a", "▁t", "est"]       )

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(lowerCamelCase__       )				,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]				,				)

        a__	:  Any     =   tokenizer.tokenize("I was born in 92000, and this is falsé."       )
        self.assertListEqual(
            lowerCamelCase__				,				[
                SPIECE_UNDERLINE + "I",
                SPIECE_UNDERLINE + "was",
                SPIECE_UNDERLINE + "b",
                "or",
                "n",
                SPIECE_UNDERLINE + "in",
                SPIECE_UNDERLINE + "",
                "9",
                "2",
                "0",
                "0",
                "0",
                ",",
                SPIECE_UNDERLINE + "and",
                SPIECE_UNDERLINE + "this",
                SPIECE_UNDERLINE + "is",
                SPIECE_UNDERLINE + "f",
                "al",
                "s",
                "é",
                ".",
            ]				,				)
        a__	:  List[str]     =   tokenizer.convert_tokens_to_ids(lowerCamelCase__       )
        self.assertListEqual(
            lowerCamelCase__				,				[
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
            ]				,				)

        a__	:  Dict     =   tokenizer.convert_ids_to_tokens(lowerCamelCase__       )
        self.assertListEqual(
            lowerCamelCase__				,				[
                SPIECE_UNDERLINE + "I",
                SPIECE_UNDERLINE + "was",
                SPIECE_UNDERLINE + "b",
                "or",
                "n",
                SPIECE_UNDERLINE + "in",
                SPIECE_UNDERLINE + "",
                "<unk>",
                "2",
                "0",
                "0",
                "0",
                ",",
                SPIECE_UNDERLINE + "and",
                SPIECE_UNDERLINE + "this",
                SPIECE_UNDERLINE + "is",
                SPIECE_UNDERLINE + "f",
                "al",
                "s",
                "<unk>",
                ".",
            ]				,				)

  @cached_property
  def    _UpperCamelCase( self			:  Dict       ):
        return XGLMTokenizer.from_pretrained("facebook/xglm-564M"       )

  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        with tempfile.NamedTemporaryFile() as f:
              shutil.copyfile(lowerCamelCase__				,				f.name       )
              a__	:  Any     =   XGLMTokenizer(f.name				,				keep_accents=lowerCamelCase__       )
              a__	:  List[str]     =   pickle.dumps(lowerCamelCase__       )
        pickle.loads(lowerCamelCase__       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        if not self.test_rust_tokenizer:
              return

        a__	:  Any     =   self.get_tokenizer()
        a__	:  Optional[Any]     =   self.get_rust_tokenizer()

        a__	:  Tuple     =   "I was born in 92000, and this is falsé."

        a__	:  List[str]     =   tokenizer.tokenize(lowerCamelCase__       )
        a__	:  Union[str, Any]     =   rust_tokenizer.tokenize(lowerCamelCase__       )
        self.assertListEqual(lowerCamelCase__				,				lowerCamelCase__       )

        a__	:  Optional[int]     =   tokenizer.encode(lowerCamelCase__				,				add_special_tokens=lowerCamelCase__       )
        a__	:  Union[str, Any]     =   rust_tokenizer.encode(lowerCamelCase__				,				add_special_tokens=lowerCamelCase__       )
        self.assertListEqual(lowerCamelCase__				,				lowerCamelCase__       )

        a__	:  List[str]     =   self.get_rust_tokenizer()
        a__	:  Tuple     =   tokenizer.encode(lowerCamelCase__       )
        a__	:  Optional[Any]     =   rust_tokenizer.encode(lowerCamelCase__       )
        self.assertListEqual(lowerCamelCase__				,				lowerCamelCase__       )

  @slow
  def    _UpperCamelCase( self			:  List[str]       ):
        a__	:  Union[str, Any]     =   "Hello World!"
        a__	:  List[str]     =   [2, 31_227, 4_447, 35]

        self.assertListEqual(lowerCamelCase__				,				self.big_tokenizer.encode(lowerCamelCase__       )       )

  @slow
  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        a__	:  Optional[int]     =   (
            "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
            " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
        )
        # fmt: off
        a__	:  Union[str, Any]     =   [2, 1_018, 67, 11, 1_988, 2_617, 5_631, 278, 11, 3_407, 48, 71_630, 28_085, 4, 3_234, 157, 13, 6, 5, 6, 4, 3_526, 768, 15, 659, 57, 298, 3_983, 864, 129, 21, 6, 5, 13_675, 377, 652, 7_580, 10_341, 155, 2_817, 422, 1_666, 7, 1_674, 53, 113, 202_277, 17_892, 33, 60, 87, 4, 3_234, 157, 61, 2_667, 52_376, 19, 88, 23, 735]
        # fmt: on

        self.assertListEqual(lowerCamelCase__				,				self.big_tokenizer.encode(lowerCamelCase__       )       )

  @slow
  def    _UpperCamelCase( self			:  List[Any]       ):
        # fmt: off
        a__	:  Optional[int]     =   {
            "input_ids": [[2, 108_825, 1_163, 15, 88_010, 473, 15_898, 157, 13_672, 1_857, 312, 8, 238_021, 1_163, 53, 13_672, 1_857, 312, 8, 53_283, 182_396, 8, 18_566, 16, 36_733, 4_101, 8, 230, 244_017, 122_553, 7, 15, 132_597, 4, 293, 12_511, 7_610, 4, 3_414, 132_597, 9, 4, 32_361, 362, 4, 734, 28_512, 32_569, 18, 4, 32_361, 26_096, 14_982, 73, 18_715, 21_433, 235_261, 15, 492, 12_427, 16, 53, 18_715, 21_433, 65_454, 15, 23_659, 563, 16, 278, 597, 2_843, 595, 7_931, 182_396, 64_186, 22, 886, 595, 132_981, 53, 25_540, 3_449, 43_982, 39_901, 5_951, 878, 330, 4, 27_694, 80_269, 312, 53, 6_517, 11_780, 611, 20_408, 5], [2, 6, 132_597, 67, 42_897, 33, 592, 8, 163_729, 25_540, 361, 136_997, 109_514, 173_230, 7, 501, 60, 102_913, 196, 5_631, 235, 63_243, 473, 6, 231_757, 74, 5_277, 7_905, 53, 3_095, 37_317, 22, 454, 183_874, 5], [2, 268, 31_298, 46_530, 6, 132_935, 43_831, 7, 597, 32, 24, 3_688, 9_865, 5]],
            "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
        }  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=lowerCamelCase__				,				model_name="facebook/xglm-564M"				,				padding=lowerCamelCase__				,				)





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()
_lowerCAmelCase:						List[str]   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Tuple   				=       torch.device('cpu')






def   _lowercase(					):
							a__      ='http://images.cocodataset.org/val2017/000000039769.jpg'
							a__      =Image.open(requests.get(__a    ,     stream=__a     ).raw     )
							return im






def   _lowercase(					__a				:						Optional[Any]     ):
							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01]     )

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01]     )

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02]     )

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02]     )






def   _lowercase(					__a				:						int    ,     __a				:						int    ,     __a				:						Optional[Any]     ):
							a__      =dct.pop(__a     )
							a__      =val






def   _lowercase(					__a				:						Optional[Any]     ):
							a__      =[]
							for k in state_dict.keys():
														a__      =k
														if ".pwconv" in k:
																					a__      =k_new.replace('.pwconv'    ,     '.point_wise_conv'     )
														if ".dwconv" in k:
																					a__      =k_new.replace('.dwconv'    ,     '.depth_wise_conv'     )
														if ".Proj." in k:
																					a__      =k_new.replace('.Proj.'    ,     '.proj.'     )
														if "patch_embed" in k_new:
																					a__      =k_new.replace('patch_embed'    ,     'swiftformer.patch_embed.patch_embedding'     )
														if "network" in k_new:
																					a__      =k_new.split('.'     )
																					if ls[2].isdigit():
																												a__      ='swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]     )
																					else:
																												a__      =k_new.replace('network'    ,     'swiftformer.encoder.network'     )
														rename_keys.append((k, k_new)     )
							return rename_keys






@torch.no_grad()
def   _lowercase(					__a				:						Union[str, Any]    ,     __a				:						int    ,     __a				:						str     ):
							a__      =SwiftFormerConfig()

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

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

							elif swiftformer_name == "swiftformer_s":
														a__      =[3, 3, 9, 6]
														a__      =[48, 64, 168, 224]

							elif swiftformer_name == "swiftformer_l1":
														a__      =[4, 3, 10, 5]
														a__      =[48, 96, 192, 384]

							elif swiftformer_name == "swiftformer_l3":
														a__      =[4, 4, 12, 6]
														a__      =[64, 128, 320, 512]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith('https'     ):
																					a__      =torch.hub.load_state_dict_from_url(__a    ,     map_location='cpu'    ,     check_hash=__a     )
														else:
																					a__      =torch.load(__a    ,     map_location='cpu'     )
							a__      =checkpoint

							a__      =create_rename_keys(__a     )
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(__a    ,     __a    ,     __a     )

							# load HuggingFace model
							a__      =SwiftFormerForImageClassification(__a     ).eval()
							hf_model.load_state_dict(__a     )

							# prepare test inputs
							a__      =prepare_img()
							a__      =ViTImageProcessor.from_pretrained('preprocessor_config'     )
							a__      =processor(images=__a    ,     return_tensors='pt'     )

							# compare outputs from both models
							a__      =get_expected_output(__a     )
							a__      =hf_model(inputs['pixel_values']     ).logits

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

							Path(__a     ).mkdir(exist_ok=__a     )
							print(f"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""     )
							hf_model.save_pretrained(__a     )


if __name__ == "__main__":
	_lowerCAmelCase:						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.')

	_lowerCAmelCase:						Optional[int]   				=       parser.parse_args()
	convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)

