




"""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   =       logging.get_logger(__name__)

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Any:


			"""simple docstring"""
			__UpperCAmelCase	: int		=					original_name.split("."							)[0]
			__UpperCAmelCase	: List[str]		=					key.split("."							)
			__UpperCAmelCase	: Union[str, Any]		=					int(key_list[key_list.index(UpperCamelCase							) - 2]							)
			__UpperCAmelCase	: int		=					int(key_list[key_list.index(UpperCamelCase							) - 1]							)
			__UpperCAmelCase	: Union[str, Any]		=					orig_block_num - offset

			__UpperCAmelCase	: Optional[Any]		=					key.replace(f"{orig_block_num}.{layer_num}.{original_name}"				,       f"block.{new_block_num}.{layer_num}.{new_name}"							)
			return key

def 					_UpperCamelCase    (			UpperCamelCase							)			->			int:


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

def 					_UpperCamelCase    (			)			->			List[Any]:


			"""simple docstring"""
			__UpperCAmelCase	: int		=					"http://images.cocodataset.org/val2017/000000039769.jpg"
			__UpperCAmelCase	: List[str]		=					Image.open(requests.get(UpperCamelCase				,       stream=UpperCamelCase							).raw							)

			return image

@torch.no_grad()
def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Any:


			"""simple docstring"""
			__UpperCAmelCase	: int		=					PoolFormerConfig()

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

			# set config attributes
			__UpperCAmelCase	: Optional[Any]		=					json.load(open(hf_hub_download(UpperCamelCase				,       UpperCamelCase				,       repo_type="dataset"							)				,       "r"							)							)
			__UpperCAmelCase	: List[Any]		=					{int(UpperCamelCase							): v for k, v in idalabel.items()}
			__UpperCAmelCase	: Dict		=					idalabel
			__UpperCAmelCase	: Optional[int]		=					{v: k for k, v in idalabel.items()}
			if size == "s12":
						__UpperCAmelCase	: List[Any]		=					[2, 2, 6, 2]
						__UpperCAmelCase	: int		=					[64, 128, 320, 512]
						__UpperCAmelCase	: Any		=					4.0
						__UpperCAmelCase	: Optional[int]		=					0.9
			elif size == "s24":
						__UpperCAmelCase	: Optional[int]		=					[4, 4, 12, 4]
						__UpperCAmelCase	: Tuple		=					[64, 128, 320, 512]
						__UpperCAmelCase	: List[str]		=					4.0
						__UpperCAmelCase	: Any		=					0.9
			elif size == "s36":
						__UpperCAmelCase	: Optional[Any]		=					[6, 6, 18, 6]
						__UpperCAmelCase	: Tuple		=					[64, 128, 320, 512]
						__UpperCAmelCase	: Dict		=					4.0
						__UpperCAmelCase	: Optional[Any]		=					1e-6
						__UpperCAmelCase	: List[str]		=					0.9
			elif size == "m36":
						__UpperCAmelCase	: Tuple		=					[6, 6, 18, 6]
						__UpperCAmelCase	: Dict		=					[96, 192, 384, 768]
						__UpperCAmelCase	: List[Any]		=					4.0
						__UpperCAmelCase	: Union[str, Any]		=					1e-6
						__UpperCAmelCase	: Any		=					0.95
			elif size == "m48":
						__UpperCAmelCase	: Optional[Any]		=					[8, 8, 24, 8]
						__UpperCAmelCase	: List[str]		=					[96, 192, 384, 768]
						__UpperCAmelCase	: Optional[Any]		=					4.0
						__UpperCAmelCase	: Any		=					1e-6
						__UpperCAmelCase	: str		=					0.95
			else:
						raise ValueError(f"Size {size} not supported"							)

			# load image processor
			__UpperCAmelCase	: Optional[int]		=					PoolFormerImageProcessor(crop_pct=UpperCamelCase							)

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

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

			# load original state dict
			__UpperCAmelCase	: str		=					torch.load(UpperCamelCase				,       map_location=torch.device("cpu"							)							)

			# rename keys
			__UpperCAmelCase	: List[str]		=					rename_keys(UpperCamelCase							)

			# create HuggingFace model and load state dict
			__UpperCAmelCase	: List[str]		=					PoolFormerForImageClassification(UpperCamelCase							)
			model.load_state_dict(UpperCamelCase							)
			model.eval()

			# Define image processor
			__UpperCAmelCase	: Tuple		=					PoolFormerImageProcessor(crop_pct=UpperCamelCase							)
			__UpperCAmelCase	: Any		=					image_processor(images=prepare_img()				,       return_tensors="pt"							).pixel_values

			# forward pass
			__UpperCAmelCase	: Optional[int]		=					model(UpperCamelCase							)
			__UpperCAmelCase	: Any		=					outputs.logits

			# define expected logit slices for different models
			if size == "s12":
						__UpperCAmelCase	: Tuple		=					torch.tensor([-0.3045, -0.6758, -0.4869]							)
			elif size == "s24":
						__UpperCAmelCase	: int		=					torch.tensor([0.4402, -0.1374, -0.8045]							)
			elif size == "s36":
						__UpperCAmelCase	: Optional[Any]		=					torch.tensor([-0.6080, -0.5133, -0.5898]							)
			elif size == "m36":
						__UpperCAmelCase	: Optional[int]		=					torch.tensor([0.3952, 0.2263, -1.2668]							)
			elif size == "m48":
						__UpperCAmelCase	: Any		=					torch.tensor([0.1167, -0.0656, -0.3423]							)
			else:
						raise ValueError(f"Size {size} not supported"							)

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

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


if __name__ == "__main__":
					A   =       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   =       parser.parse_args()
					convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)








"""simple docstring"""





import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list



@require_torchaudio
@require_sentencepiece
class      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					"laion/clap-htsat-unfused"
						__UpperCAmelCase	: List[str]		=					tempfile.mkdtemp()


			def       a_     (      self      :    Optional[Any]      ,  **UpperCamelCase_      :    Union[str, Any]):


						"""simple docstring"""




						return RobertaTokenizer.from_pretrained(self.checkpoint      ,  **UpperCamelCase_)


			def       a_     (      self      :    Union[str, Any]      ,  **UpperCamelCase_      :    Optional[int]):


						"""simple docstring"""




						return ClapFeatureExtractor.from_pretrained(self.checkpoint      ,  **UpperCamelCase_)


			def       a_     (      self      :    List[Any]):


						"""simple docstring"""




						shutil.rmtree(self.tmpdirname)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					self.get_tokenizer()
						__UpperCAmelCase	: str		=					self.get_feature_extractor()

						__UpperCAmelCase	: List[Any]		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						processor.save_pretrained(self.tmpdirname)
						__UpperCAmelCase	: Union[str, Any]		=					ClapProcessor.from_pretrained(self.tmpdirname)

						self.assertEqual(processor.tokenizer.get_vocab()      ,  tokenizer.get_vocab())
						self.assertIsInstance(processor.tokenizer      ,  UpperCamelCase_)

						self.assertEqual(processor.feature_extractor.to_json_string()      ,  feature_extractor.to_json_string())
						self.assertIsInstance(processor.feature_extractor      ,  UpperCamelCase_)


			def       a_     (      self      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					ClapProcessor(tokenizer=self.get_tokenizer()      ,  feature_extractor=self.get_feature_extractor())
						processor.save_pretrained(self.tmpdirname)

						__UpperCAmelCase	: int		=					self.get_tokenizer(bos_token="(BOS)"      ,  eos_token="(EOS)")
						__UpperCAmelCase	: Any		=					self.get_feature_extractor(do_normalize=UpperCamelCase_      ,  padding_value=1.0)

						__UpperCAmelCase	: Tuple		=					ClapProcessor.from_pretrained(
						    self.tmpdirname      ,  bos_token="(BOS)"      ,  eos_token="(EOS)"      ,  do_normalize=UpperCamelCase_      ,  padding_value=1.0)

						self.assertEqual(processor.tokenizer.get_vocab()      ,  tokenizer_add_kwargs.get_vocab())
						self.assertIsInstance(processor.tokenizer      ,  UpperCamelCase_)

						self.assertEqual(processor.feature_extractor.to_json_string()      ,  feature_extractor_add_kwargs.to_json_string())
						self.assertIsInstance(processor.feature_extractor      ,  UpperCamelCase_)


			def       a_     (      self      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					self.get_feature_extractor()
						__UpperCAmelCase	: int		=					self.get_tokenizer()

						__UpperCAmelCase	: Any		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						__UpperCAmelCase	: Optional[int]		=					floats_list((3, 1000))

						__UpperCAmelCase	: Tuple		=					feature_extractor(UpperCamelCase_      ,  return_tensors="np")
						__UpperCAmelCase	: Any		=					processor(audios=UpperCamelCase_      ,  return_tensors="np")

						for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()      ,  input_processor[key].sum()      ,  delta=1e-2)


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					self.get_feature_extractor()
						__UpperCAmelCase	: Optional[int]		=					self.get_tokenizer()

						__UpperCAmelCase	: str		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						__UpperCAmelCase	: Any		=					"This is a test string"

						__UpperCAmelCase	: List[Any]		=					processor(text=UpperCamelCase_)

						__UpperCAmelCase	: Dict		=					tokenizer(UpperCamelCase_)

						for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]      ,  encoded_processor[key])


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: int		=					self.get_feature_extractor()
						__UpperCAmelCase	: List[str]		=					self.get_tokenizer()

						__UpperCAmelCase	: List[str]		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						__UpperCAmelCase	: Tuple		=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__UpperCAmelCase	: int		=					processor.batch_decode(UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					tokenizer.batch_decode(UpperCamelCase_)

						self.assertListEqual(UpperCamelCase_      ,  UpperCamelCase_)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					self.get_feature_extractor()
						__UpperCAmelCase	: int		=					self.get_tokenizer()

						__UpperCAmelCase	: int		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						self.assertListEqual(
						    processor.model_input_names[2:]      ,  feature_extractor.model_input_names      ,  msg="`processor` and `feature_extractor` model input names do not match"      ,  )


