




import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Tuple  =     old_name

     if "patch_embed" in old_name:
          UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				Tuple  =     old_name.split('''.''' )

          if layer == "0":
               UpperCAmelCase_     :				Dict  =     old_name.replace('''0'''					,		'''convolution1''' )
          elif layer == "1":
               UpperCAmelCase_     :				List[Any]  =     old_name.replace('''1'''					,		'''batchnorm_before''' )
          elif layer == "3":
               UpperCAmelCase_     :				Optional[Any]  =     old_name.replace('''3'''					,		'''convolution2''' )
          else:
               UpperCAmelCase_     :				Tuple  =     old_name.replace('''4'''					,		'''batchnorm_after''' )

     if "network" in old_name and re.search(r'''\d\.\d'''					,		lowerCAmelCase__ ):
          UpperCAmelCase_     :				Optional[int]  =     r'''\b\d{2}\b'''
          if bool(re.search(lowerCAmelCase__					,		lowerCAmelCase__ ) ):
               UpperCAmelCase_     :				Union[str, Any]  =     re.search(r'''\d\.\d\d.'''					,		lowerCAmelCase__ ).group()
          else:
               UpperCAmelCase_     :				Optional[Any]  =     re.search(r'''\d\.\d.'''					,		lowerCAmelCase__ ).group()
          if int(match[0] ) < 6:
               UpperCAmelCase_     :				List[str]  =     old_name.replace(lowerCAmelCase__					,		'''''' )
               UpperCAmelCase_     :				Tuple  =     trimmed_name.replace('''network'''					,		match[0] + '''.meta4D_layers.blocks.''' + match[2:-1] )
               UpperCAmelCase_     :				Optional[int]  =     '''intermediate_stages.''' + trimmed_name
          else:
               UpperCAmelCase_     :				Optional[Any]  =     old_name.replace(lowerCAmelCase__					,		'''''' )
               if int(match[2] ) < num_meta4D_last_stage:
                    UpperCAmelCase_     :				Dict  =     trimmed_name.replace('''network'''					,		'''meta4D_layers.blocks.''' + match[2] )
               else:
                    UpperCAmelCase_     :				str  =     str(int(match[2] ) - num_meta4D_last_stage )
                    UpperCAmelCase_     :				List[Any]  =     trimmed_name.replace('''network'''					,		'''meta3D_layers.blocks.''' + layer_index )
                    if "norm1" in old_name:
                         UpperCAmelCase_     :				Any  =     trimmed_name.replace('''norm1'''					,		'''layernorm1''' )
                    elif "norm2" in old_name:
                         UpperCAmelCase_     :				Optional[int]  =     trimmed_name.replace('''norm2'''					,		'''layernorm2''' )
                    elif "fc1" in old_name:
                         UpperCAmelCase_     :				Tuple  =     trimmed_name.replace('''fc1'''					,		'''linear_in''' )
                    elif "fc2" in old_name:
                         UpperCAmelCase_     :				List[Any]  =     trimmed_name.replace('''fc2'''					,		'''linear_out''' )

               UpperCAmelCase_     :				Union[str, Any]  =     '''last_stage.''' + trimmed_name

     elif "network" in old_name and re.search(r'''.\d.'''					,		lowerCAmelCase__ ):
          UpperCAmelCase_     :				Optional[int]  =     old_name.replace('''network'''					,		'''intermediate_stages''' )

     if "fc" in new_name:
          UpperCAmelCase_     :				Any  =     new_name.replace('''fc'''					,		'''convolution''' )
     elif ("norm1" in new_name) and ("layernorm1" not in new_name):
          UpperCAmelCase_     :				str  =     new_name.replace('''norm1'''					,		'''batchnorm_before''' )
     elif ("norm2" in new_name) and ("layernorm2" not in new_name):
          UpperCAmelCase_     :				Optional[Any]  =     new_name.replace('''norm2'''					,		'''batchnorm_after''' )
     if "proj" in new_name:
          UpperCAmelCase_     :				Tuple  =     new_name.replace('''proj'''					,		'''projection''' )
     if "dist_head" in new_name:
          UpperCAmelCase_     :				Any  =     new_name.replace('''dist_head'''					,		'''distillation_classifier''' )
     elif "head" in new_name:
          UpperCAmelCase_     :				Tuple  =     new_name.replace('''head'''					,		'''classifier''' )
     elif "patch_embed" in new_name:
          UpperCAmelCase_     :				int  =     '''efficientformer.''' + new_name
     elif new_name == "norm.weight" or new_name == "norm.bias":
          UpperCAmelCase_     :				Optional[Any]  =     new_name.replace('''norm'''					,		'''layernorm''' )
          UpperCAmelCase_     :				str  =     '''efficientformer.''' + new_name
     else:
          UpperCAmelCase_     :				Optional[Any]  =     '''efficientformer.encoder.''' + new_name

     return new_name







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     for key in checkpoint.copy().keys():
          UpperCAmelCase_     :				List[str]  =     checkpoint.pop(lowerCAmelCase__ )
          UpperCAmelCase_     :				List[Any]  =     val

     return checkpoint







def lowerCamelCase__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[Any]  =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     UpperCAmelCase_     :				int  =     Image.open(requests.get(lowerCAmelCase__					,		stream=lowerCAmelCase__ ).raw )

     return image







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Tuple  =     torch.load(lowerCAmelCase__					,		map_location='''cpu''' )['''model''']
     UpperCAmelCase_     :				str  =     EfficientFormerConfig.from_json_file(lowerCAmelCase__ )
     UpperCAmelCase_     :				int  =     EfficientFormerForImageClassificationWithTeacher(lowerCAmelCase__ )
     UpperCAmelCase_     :				Any  =     '''_'''.join(checkpoint_path.split('''/''' )[-1].split('''.''' )[0].split('''_''' )[:-1] )

     UpperCAmelCase_     :				List[Any]  =     config.depths[-1] - config.num_metaad_blocks + 1
     UpperCAmelCase_     :				str  =     convert_torch_checkpoint(lowerCAmelCase__					,		lowerCAmelCase__ )

     model.load_state_dict(lowerCAmelCase__ )
     model.eval()

     UpperCAmelCase_     :				Tuple  =     {
         '''bilinear''': PILImageResampling.BILINEAR,
         '''bicubic''': PILImageResampling.BICUBIC,
         '''nearest''': PILImageResampling.NEAREST,
     }

     # prepare image
     UpperCAmelCase_     :				Any  =     prepare_img()
     UpperCAmelCase_     :				Optional[Any]  =     256
     UpperCAmelCase_     :				str  =     224
     UpperCAmelCase_     :				Optional[Any]  =     EfficientFormerImageProcessor(
         size={'''shortest_edge''': image_size}					,		crop_size={'''height''': crop_size, '''width''': crop_size}					,		resample=pillow_resamplings['''bicubic''']					,		)
     UpperCAmelCase_     :				Optional[Any]  =     processor(images=lowerCAmelCase__					,		return_tensors='''pt''' ).pixel_values

     # original processing pipeline
     UpperCAmelCase_     :				int  =     Compose(
         [
             Resize(lowerCAmelCase__					,		interpolation=pillow_resamplings['''bicubic'''] ),
             CenterCrop(lowerCAmelCase__ ),
             ToTensor(),
             Normalize(lowerCAmelCase__					,		lowerCAmelCase__ ),
         ] )
     UpperCAmelCase_     :				Optional[int]  =     image_transforms(lowerCAmelCase__ ).unsqueeze(0 )

     assert torch.allclose(lowerCAmelCase__					,		lowerCAmelCase__ )

     UpperCAmelCase_     :				Optional[Any]  =     model(lowerCAmelCase__ )
     UpperCAmelCase_     :				int  =     outputs.logits

     UpperCAmelCase_     :				Optional[Any]  =     (1, 1000)

     if "l1" in model_name:
          UpperCAmelCase_     :				List[str]  =     torch.Tensor(
              [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328] )
          assert torch.allclose(logits[0, :10]					,		lowerCAmelCase__					,		atol=1E-3 )
          assert logits.shape == expected_shape
     elif "l3" in model_name:
          UpperCAmelCase_     :				Tuple  =     torch.Tensor(
              [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127] )
          assert torch.allclose(logits[0, :10]					,		lowerCAmelCase__					,		atol=1E-3 )
          assert logits.shape == expected_shape
     elif "l7" in model_name:
          UpperCAmelCase_     :				List[str]  =     torch.Tensor(
              [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878] )
          assert logits.shape == expected_shape
     else:
          raise ValueError(
              f'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7''' )

     # Save Checkpoints
     Path(lowerCAmelCase__ ).mkdir(exist_ok=lowerCAmelCase__ )
     model.save_pretrained(lowerCAmelCase__ )
     print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''' )
     processor.save_pretrained(lowerCAmelCase__ )
     print(f'''Processor successfuly saved at {pytorch_dump_path}''' )

     if push_to_hub:
          print('''Pushing model to the hub...''' )

          model.push_to_hub(
              repo_id=f'''Bearnardd/{pytorch_dump_path}'''					,		commit_message='''Add model'''					,		use_temp_dir=lowerCAmelCase__					,		)
          processor.push_to_hub(
              repo_id=f'''Bearnardd/{pytorch_dump_path}'''					,		commit_message='''Add image processor'''					,		use_temp_dir=lowerCAmelCase__					,		)


if __name__ == "__main__":
     __a							     =					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--pytorch_model_path',
         default=None,
         type=str,
         required=True,
         help='Path to EfficientFormer pytorch checkpoint.',
     )
     parser.add_argument(
         '--config_file',
         default=None,
         type=str,
         required=True,
         help='The json file for EfficientFormer model config.',
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )

     parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
     parser.add_argument(
         '--no-push_to_hub',
         dest='push_to_hub',
         action='store_false',
         help='Do not push model and image processor to the hub',
     )
     parser.set_defaults(push_to_hub=True)

     __a							     =					parser.parse_args()
     convert_efficientformer_checkpoint(
         checkpoint_path=args.pytorch_model_path,
         efficientformer_config_file=args.config_file,
         pytorch_dump_path=args.pytorch_dump_path,
         push_to_hub=args.push_to_hub,
     )
"""simple docstring"""






import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class 					_A		(			lowerCAmelCase    ,       unittest.TestCase      ):
      snake_case__     :  str				    =      TransfoXLTokenizer
      snake_case__     :  Union[str, Any]				    =      False
      snake_case__     :  Union[str, Any]				    =      False

      def 							A__			(		self		):







            """simple docstring"""



            super().setUp()

            lowercase								=      [
                """<unk>""",
                """[CLS]""",
                """[SEP]""",
                """want""",
                """unwanted""",
                """wa""",
                """un""",
                """running""",
                """,""",
                """low""",
                """l""",
            ]
            lowercase								=      os.path.join(self.tmpdirname						,			VOCAB_FILES_NAMES["""vocab_file"""]		)
            with open(self.vocab_file						,			"""w"""						,			encoding="""utf-8"""		) as vocab_writer:
                  vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)

      def 							A__			(		self						,			**__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      True
            return TransfoXLTokenizer.from_pretrained(self.tmpdirname						,			**__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      """<unk> UNwanted , running"""
            lowercase								=      """<unk> unwanted, running"""
            return input_text, output_text

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      TransfoXLTokenizer(vocab_file=self.vocab_file						,			lower_case=__lowerCAmelCase		)

            lowercase								=      tokenizer.tokenize("""<unk> UNwanted , running"""		)
            self.assertListEqual(__lowerCAmelCase						,			["""<unk>""", """unwanted""", """,""", """running"""]		)

            self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase		)						,			[0, 4, 8, 7]		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      TransfoXLTokenizer(lower_case=__lowerCAmelCase		)

            self.assertListEqual(
                tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """		)						,			["""hello""", """!""", """how""", """are""", """you""", """?"""]		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      TransfoXLTokenizer(lower_case=__lowerCAmelCase		)

            self.assertListEqual(
                tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """		)						,			["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      TransfoXLTokenizer(lower_case=__lowerCAmelCase		)
            lowercase								=      """Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"""
            lowercase								=      [
                """Hello""",
                """(""",
                """bracket""",
                """)""",
                """and""",
                """side""",
                """@-@""",
                """scrolled""",
                """[""",
                """and""",
                """]""",
                """Henry""",
                """'s""",
                """$""",
                """5""",
                """@,@""",
                """000""",
                """with""",
                """3""",
                """@.@""",
                """34""",
                """m""",
                """.""",
                """What""",
                """'s""",
                """up""",
                """!""",
                """?""",
            ]

            self.assertListEqual(tokenizer.tokenize(__lowerCAmelCase		)						,			__lowerCAmelCase		)

            self.assertEqual(tokenizer.convert_tokens_to_string(__lowerCAmelCase		)						,			__lowerCAmelCase		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.get_tokenizer()
            lowercase								=      len(__lowerCAmelCase		)

            tokenizer.add_tokens(["""new1""", """new2"""]		)
            tokenizer.move_added_token("""new1"""						,			1		)

            # Check that moved token is not copied (duplicate)
            self.assertEqual(len(__lowerCAmelCase		)						,			original_len + 2		)
            # Check that token is moved to specified id
            self.assertEqual(tokenizer.encode("""new1"""		)						,			[1]		)
            self.assertEqual(tokenizer.decode([1]		)						,			"""new1"""		)



