



'''simple docstring'''






import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A_							: Tuple						 =	logging.get_logger(__name__)




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Dict:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[Any]			     = original_name.split("""."""		)[0]
       _UpperCAmelCase  :						Any			     = key.split("""."""		)
       _UpperCAmelCase  :						List[str]			     = int(key_list[key_list.index(lowerCAmelCase_		) - 2]		)
       _UpperCAmelCase  :						Tuple			     = int(key_list[key_list.index(lowerCAmelCase_		) - 1]		)
       _UpperCAmelCase  :						str			     = orig_block_num - offset

       _UpperCAmelCase  :						Optional[int]			     = key.replace(F'''{orig_block_num}.{layer_num}.{original_name}'''       ,   F'''block.{new_block_num}.{layer_num}.{new_name}'''		)
       return key




def     snake_case_					(     lowerCAmelCase_		)->		Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = OrderedDict()
       _UpperCAmelCase  :						Tuple			     = 0, 0
       for key, value in state_dict.items():
              if key.startswith("""network"""		):
                     _UpperCAmelCase  :						Optional[Any]			     = key.replace("""network"""       ,   """poolformer.encoder"""		)
              if "proj" in key:
                     # Works for the first embedding as well as the internal embedding layers
                     if key.endswith("""bias"""		) and "patch_embed" not in key:
                            patch_emb_offset += 1
                     _UpperCAmelCase  :						Any			     = key[: key.find("""proj"""		)]
                     _UpperCAmelCase  :						Any			     = key.replace(lowerCAmelCase_       ,   F'''patch_embeddings.{total_embed_found}.'''		)
                     _UpperCAmelCase  :						int			     = key.replace("""proj"""       ,   """projection"""		)
                     if key.endswith("""bias"""		):
                            total_embed_found += 1
              if "patch_embeddings" in key:
                     _UpperCAmelCase  :						Optional[int]			     = "poolformer.encoder." + key
              if "mlp.fc1" in key:
                     _UpperCAmelCase  :						Optional[int]			     = replace_key_with_offset(lowerCAmelCase_       ,   lowerCAmelCase_       ,   """mlp.fc1"""       ,   """output.conv1"""		)
              if "mlp.fc2" in key:
                     _UpperCAmelCase  :						List[str]			     = replace_key_with_offset(lowerCAmelCase_       ,   lowerCAmelCase_       ,   """mlp.fc2"""       ,   """output.conv2"""		)
              if "norm1" in key:
                     _UpperCAmelCase  :						Optional[Any]			     = replace_key_with_offset(lowerCAmelCase_       ,   lowerCAmelCase_       ,   """norm1"""       ,   """before_norm"""		)
              if "norm2" in key:
                     _UpperCAmelCase  :						int			     = replace_key_with_offset(lowerCAmelCase_       ,   lowerCAmelCase_       ,   """norm2"""       ,   """after_norm"""		)
              if "layer_scale_1" in key:
                     _UpperCAmelCase  :						Tuple			     = replace_key_with_offset(lowerCAmelCase_       ,   lowerCAmelCase_       ,   """layer_scale_1"""       ,   """layer_scale_1"""		)
              if "layer_scale_2" in key:
                     _UpperCAmelCase  :						List[Any]			     = replace_key_with_offset(lowerCAmelCase_       ,   lowerCAmelCase_       ,   """layer_scale_2"""       ,   """layer_scale_2"""		)
              if "head" in key:
                     _UpperCAmelCase  :						Optional[int]			     = key.replace("""head"""       ,   """classifier"""		)
              _UpperCAmelCase  :						List[str]			     = value
       return new_state_dict




def     snake_case_					(     )->		Optional[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						str			     = "http://images.cocodataset.org/val2017/000000039769.jpg"
       _UpperCAmelCase  :						Optional[Any]			     = Image.open(requests.get(lowerCAmelCase_       ,   stream=lowerCAmelCase_		).raw		)

       return image




@torch.no_grad()
def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		int:





       '''simple docstring'''


       _UpperCAmelCase  :						str			     = PoolFormerConfig()

       # set attributes based on model_name
       _UpperCAmelCase  :						List[str]			     = "huggingface/label-files"
       _UpperCAmelCase  :						str			     = model_name[-3:]
       _UpperCAmelCase  :						List[Any]			     = 1000
       _UpperCAmelCase  :						Optional[int]			     = "imagenet-1k-id2label.json"
       _UpperCAmelCase  :						int			     = (1, 1000)

       # set config attributes
       _UpperCAmelCase  :						int			     = json.load(open(hf_hub_download(lowerCAmelCase_       ,   lowerCAmelCase_       ,   repo_type="""dataset"""		)       ,   """r"""		)		)
       _UpperCAmelCase  :						List[Any]			     = {int(lowerCAmelCase_		): v for k, v in idalabel.items()}
       _UpperCAmelCase  :						List[Any]			     = idalabel
       _UpperCAmelCase  :						Union[str, Any]			     = {v: k for k, v in idalabel.items()}
       if size == "s12":
              _UpperCAmelCase  :						Union[str, Any]			     = [2, 2, 6, 2]
              _UpperCAmelCase  :						Tuple			     = [64, 128, 320, 512]
              _UpperCAmelCase  :						str			     = 4.0
              _UpperCAmelCase  :						Dict			     = 0.9
       elif size == "s24":
              _UpperCAmelCase  :						Dict			     = [4, 4, 12, 4]
              _UpperCAmelCase  :						Dict			     = [64, 128, 320, 512]
              _UpperCAmelCase  :						Optional[int]			     = 4.0
              _UpperCAmelCase  :						List[str]			     = 0.9
       elif size == "s36":
              _UpperCAmelCase  :						List[Any]			     = [6, 6, 18, 6]
              _UpperCAmelCase  :						int			     = [64, 128, 320, 512]
              _UpperCAmelCase  :						Dict			     = 4.0
              _UpperCAmelCase  :						int			     = 1e-6
              _UpperCAmelCase  :						Union[str, Any]			     = 0.9
       elif size == "m36":
              _UpperCAmelCase  :						List[Any]			     = [6, 6, 18, 6]
              _UpperCAmelCase  :						Dict			     = [96, 192, 384, 768]
              _UpperCAmelCase  :						Tuple			     = 4.0
              _UpperCAmelCase  :						int			     = 1e-6
              _UpperCAmelCase  :						Optional[Any]			     = 0.9_5
       elif size == "m48":
              _UpperCAmelCase  :						Tuple			     = [8, 8, 24, 8]
              _UpperCAmelCase  :						Tuple			     = [96, 192, 384, 768]
              _UpperCAmelCase  :						str			     = 4.0
              _UpperCAmelCase  :						Optional[Any]			     = 1e-6
              _UpperCAmelCase  :						Tuple			     = 0.9_5
       else:
              raise ValueError(F'''Size {size} not supported'''		)

       # load image processor
       _UpperCAmelCase  :						Optional[Any]			     = PoolFormerImageProcessor(crop_pct=lowerCAmelCase_		)

       # Prepare image
       _UpperCAmelCase  :						Dict			     = prepare_img()
       _UpperCAmelCase  :						str			     = image_processor(images=lowerCAmelCase_       ,   return_tensors="""pt"""		).pixel_values

       logger.info(F'''Converting model {model_name}...'''		)

       # load original state dict
       _UpperCAmelCase  :						Dict			     = torch.load(lowerCAmelCase_       ,   map_location=torch.device("""cpu"""		)		)

       # rename keys
       _UpperCAmelCase  :						str			     = rename_keys(lowerCAmelCase_		)

       # create HuggingFace model and load state dict
       _UpperCAmelCase  :						List[Any]			     = PoolFormerForImageClassification(lowerCAmelCase_		)
       model.load_state_dict(lowerCAmelCase_		)
       model.eval()

       # Define image processor
       _UpperCAmelCase  :						Any			     = PoolFormerImageProcessor(crop_pct=lowerCAmelCase_		)
       _UpperCAmelCase  :						List[Any]			     = image_processor(images=prepare_img()       ,   return_tensors="""pt"""		).pixel_values

       # forward pass
       _UpperCAmelCase  :						List[Any]			     = model(lowerCAmelCase_		)
       _UpperCAmelCase  :						Dict			     = outputs.logits

       # define expected logit slices for different models
       if size == "s12":
              _UpperCAmelCase  :						Optional[Any]			     = torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]		)
       elif size == "s24":
              _UpperCAmelCase  :						Any			     = torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]		)
       elif size == "s36":
              _UpperCAmelCase  :						int			     = torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]		)
       elif size == "m36":
              _UpperCAmelCase  :						str			     = torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]		)
       elif size == "m48":
              _UpperCAmelCase  :						Optional[Any]			     = torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3]		)
       else:
              raise ValueError(F'''Size {size} not supported'''		)

       # verify logits
       assert logits.shape == expected_shape
       assert torch.allclose(logits[0, :3]       ,   lowerCAmelCase_       ,   atol=1e-2		)

       # finally, save model and image processor
       logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''		)
       Path(lowerCAmelCase_		).mkdir(exist_ok=lowerCAmelCase_		)
       model.save_pretrained(lowerCAmelCase_		)
       print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
       image_processor.save_pretrained(lowerCAmelCase_		)


if __name__ == "__main__":
   A_							: Dict						 =	argparse.ArgumentParser()

   parser.add_argument(
       """--model_name""",
       default="""poolformer_s12""",
       type=str,
       help="""Name of the model you\'d like to convert.""",
   )
   parser.add_argument(
       """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
   )
   A_							: str						 =	parser.parse_args()
   convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""







import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY


@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       a  :				int   =MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							hf_hub_download(
             repo_id="nateraw/video-demo"		,     filename="archery.mp4"		,     repo_type="dataset"						)
         lowerCAmelCase     :     Dict								=							VideoClassificationPipeline(model=snake_case__		,     image_processor=snake_case__		,     top_k=2						)
         lowerCAmelCase     :     Any								=							[
             example_video_filepath,
             "https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
         ]
         return video_classifier, examples

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         for example in examples:
           lowerCAmelCase     :     str								=							video_classifier(snake_case__						)

           self.assertEqual(
               snake_case__		,     [
                   {"score": ANY(snake_case__						), "label": ANY(snake_case__						)},
                   {"score": ANY(snake_case__						), "label": ANY(snake_case__						)},
               ]		,     )

       @require_torch
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
         lowerCAmelCase     :     str								=							VideoMAEFeatureExtractor(
             size={"shortest_edge": 10}		,     crop_size={"height": 10, "width": 10}						)
         lowerCAmelCase     :     int								=							pipeline(
             "video-classification"		,     model=snake_case__		,     feature_extractor=snake_case__		,     frame_sampling_rate=4						)

         lowerCAmelCase     :     Optional[int]								=							hf_hub_download(repo_id="nateraw/video-demo"		,     filename="archery.mp4"		,     repo_type="dataset"						)
         lowerCAmelCase     :     Union[str, Any]								=							video_classifier(snake_case__		,     top_k=2						)
         self.assertEqual(
             nested_simplify(snake_case__		,     decimals=4						)		,     [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}]		,     )

         lowerCAmelCase     :     Tuple								=							video_classifier(
             [
                 video_file_path,
                 video_file_path,
             ]		,     top_k=2		,     )
         self.assertEqual(
             nested_simplify(snake_case__		,     decimals=4						)		,     [
                 [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
                 [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
             ]		,     )







       @require_tf
       def        lowercase__							(						self						):






         """simple docstring"""





         pass





