




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()
UpperCAmelCase_		:					Union[str, Any]          =  logging.get_logger(__name__)




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

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




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						Tuple										=OrderedDict()
				__magic_name__					,			__magic_name__						:						Any										=0, 0
				for key, value in state_dict.items():
								if key.startswith("""network""" ):
												__magic_name__						:						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
												__magic_name__						:						Union[str, Any]										=key[: key.find("""proj""" )]
												__magic_name__						:						Optional[int]										=key.replace(__lowerCAmelCase						,  F"patch_embeddings.{total_embed_found}." )
												__magic_name__						:						Any										=key.replace("""proj"""						,  """projection""" )
												if key.endswith("""bias""" ):
																total_embed_found += 1
								if "patch_embeddings" in key:
												__magic_name__						:						Dict										="""poolformer.encoder.""" + key
								if "mlp.fc1" in key:
												__magic_name__						:						str										=replace_key_with_offset(__lowerCAmelCase						,  __lowerCAmelCase						,  """mlp.fc1"""						,  """output.conv1""" )
								if "mlp.fc2" in key:
												__magic_name__						:						Optional[Any]										=replace_key_with_offset(__lowerCAmelCase						,  __lowerCAmelCase						,  """mlp.fc2"""						,  """output.conv2""" )
								if "norm1" in key:
												__magic_name__						:						List[Any]										=replace_key_with_offset(__lowerCAmelCase						,  __lowerCAmelCase						,  """norm1"""						,  """before_norm""" )
								if "norm2" in key:
												__magic_name__						:						Dict										=replace_key_with_offset(__lowerCAmelCase						,  __lowerCAmelCase						,  """norm2"""						,  """after_norm""" )
								if "layer_scale_1" in key:
												__magic_name__						:						List[str]										=replace_key_with_offset(__lowerCAmelCase						,  __lowerCAmelCase						,  """layer_scale_1"""						,  """layer_scale_1""" )
								if "layer_scale_2" in key:
												__magic_name__						:						str										=replace_key_with_offset(__lowerCAmelCase						,  __lowerCAmelCase						,  """layer_scale_2"""						,  """layer_scale_2""" )
								if "head" in key:
												__magic_name__						:						Any										=key.replace("""head"""						,  """classifier""" )
								__magic_name__						:						int										=value
				return new_state_dict




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

				return image




@torch.no_grad()
def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Optional[int]										=PoolFormerConfig()

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

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

				# load image processor
				__magic_name__						:						List[str]										=PoolFormerImageProcessor(crop_pct=__lowerCAmelCase )

				# Prepare image
				__magic_name__						:						Union[str, Any]										=prepare_img()
				__magic_name__						:						Any										=image_processor(images=__lowerCAmelCase						,  return_tensors="""pt""" ).pixel_values

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

				# load original state dict
				__magic_name__						:						Any										=torch.load(__lowerCAmelCase						,  map_location=torch.device("""cpu""" ) )

				# rename keys
				__magic_name__						:						Tuple										=rename_keys(__lowerCAmelCase )

				# create HuggingFace model and load state dict
				__magic_name__						:						Tuple										=PoolFormerForImageClassification(__lowerCAmelCase )
				model.load_state_dict(__lowerCAmelCase )
				model.eval()

				# Define image processor
				__magic_name__						:						List[str]										=PoolFormerImageProcessor(crop_pct=__lowerCAmelCase )
				__magic_name__						:						Optional[int]										=image_processor(images=prepare_img()						,  return_tensors="""pt""" ).pixel_values

				# forward pass
				__magic_name__						:						List[Any]										=model(__lowerCAmelCase )
				__magic_name__						:						str										=outputs.logits

				# define expected logit slices for different models
				if size == "s12":
								__magic_name__						:						Union[str, Any]										=torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9] )
				elif size == "s24":
								__magic_name__						:						int										=torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5] )
				elif size == "s36":
								__magic_name__						:						Optional[int]										=torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8] )
				elif size == "m36":
								__magic_name__						:						Optional[Any]										=torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8] )
				elif size == "m48":
								__magic_name__						:						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__":
							UpperCAmelCase_		:					Any          =  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."
							)
							UpperCAmelCase_		:					int          =  parser.parse_args()
							convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
     from transformers import TvltImageProcessor

if is_speech_available():
     from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class        _SCREAMING_SNAKE_CASE	(   unittest.TestCase			):
      def A_      (      self			):
          snake_case__    =					"ZinengTang/tvlt-base"
          snake_case__    =					tempfile.mkdtemp()
      def A_      (      self		,						**lowerCamelCase			):
          return TvltImageProcessor.from_pretrained(self.checkpoint		,						**lowerCamelCase			)
      def A_      (      self		,						**lowerCamelCase			):
          return TvltFeatureExtractor.from_pretrained(self.checkpoint		,						**lowerCamelCase			)
      def A_      (      self			):
          shutil.rmtree(self.tmpdirname			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)
          processor.save_pretrained(self.tmpdirname			)
          snake_case__    =					TvltProcessor.from_pretrained(self.tmpdirname			)

          self.assertIsInstance(processor.feature_extractor		,						lowerCamelCase			)
          self.assertIsInstance(processor.image_processor		,						lowerCamelCase			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([1_20_00]			)

          snake_case__    =					feature_extractor(lowerCamelCase		,						return_tensors="np"			)
          snake_case__    =					processor(audio=lowerCamelCase		,						return_tensors="np"			)

          for key in audio_dict.keys():
              self.assertAlmostEqual(audio_dict[key].sum()		,						input_processor[key].sum()		,						delta=1e-2			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([3, 2_24, 2_24]			)

          snake_case__    =					image_processor(lowerCamelCase		,						return_tensors="np"			)
          snake_case__    =					processor(images=lowerCamelCase		,						return_tensors="np"			)

          for key in image_dict.keys():
              self.assertAlmostEqual(image_dict[key].sum()		,						input_processor[key].sum()		,						delta=1e-2			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([1_20_00]			)
          snake_case__    =					np.ones([3, 2_24, 2_24]			)

          snake_case__    =					processor(audio=lowerCamelCase		,						images=lowerCamelCase			)

          self.assertListEqual(list(inputs.keys()			)		,						["audio_values", "audio_mask", "pixel_values", "pixel_mask"]			)

          # test if it raises when no input is passed
          with pytest.raises(lowerCamelCase			):
              processor()



      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          self.assertListEqual(
              processor.model_input_names		,						image_processor.model_input_names + feature_extractor.model_input_names		,						msg="`processor` and `image_processor`+`feature_extractor` model input names do not match"		,						)



