





import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
					import faiss




@require_faiss
class   UpperCamelCase_       (     UpperCamelCase__    ):


							def      _snake_case  (							self    :int		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      tempfile.mkdtemp()
									SCREAMING_SNAKE_CASE__			=      8

									# DPR tok
									SCREAMING_SNAKE_CASE__			=      [
									    """[UNK]""",
									    """[CLS]""",
									    """[SEP]""",
									    """[PAD]""",
									    """[MASK]""",
									    """want""",
									    """##want""",
									    """##ed""",
									    """wa""",
									    """un""",
									    """runn""",
									    """##ing""",
									    """,""",
									    """low""",
									    """lowest""",
									]
									SCREAMING_SNAKE_CASE__			=      os.path.join(self.tmpdirname							,				"""dpr_tokenizer"""		)
									os.makedirs(__A							,				exist_ok=__A		)
									SCREAMING_SNAKE_CASE__			=      os.path.join(__A							,				DPR_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]		)		)

									# BART tok
									SCREAMING_SNAKE_CASE__			=      [
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """\u0120""",
									    """\u0120l""",
									    """\u0120n""",
									    """\u0120lo""",
									    """\u0120low""",
									    """er""",
									    """\u0120lowest""",
									    """\u0120newer""",
									    """\u0120wider""",
									    """<unk>""",
									]
									SCREAMING_SNAKE_CASE__			=      dict(zip(__A							,				range(len(__A		)		)		)		)
									SCREAMING_SNAKE_CASE__			=      ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
									SCREAMING_SNAKE_CASE__			=      {"""unk_token""": """<unk>"""}

									SCREAMING_SNAKE_CASE__			=      os.path.join(self.tmpdirname							,				"""bart_tokenizer"""		)
									os.makedirs(__A							,				exist_ok=__A		)
									SCREAMING_SNAKE_CASE__			=      os.path.join(__A							,				BART_VOCAB_FILES_NAMES["""vocab_file"""]		)
									SCREAMING_SNAKE_CASE__			=      os.path.join(__A							,				BART_VOCAB_FILES_NAMES["""merges_file"""]		)
									with open(self.vocab_file							,				"""w"""							,				encoding="""utf-8"""		) as fp:
											fp.write(json.dumps(__A		) + """\n"""		)
									with open(self.merges_file							,				"""w"""							,				encoding="""utf-8"""		) as fp:
											fp.write("""\n""".join(__A		)		)


							def      _snake_case  (							self    :Union[str, Any]		)   ->   DPRQuestionEncoderTokenizer:



									"""simple docstring"""



									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,				"""dpr_tokenizer"""		)		)


							def      _snake_case  (							self    :List[Any]		)   ->   DPRContextEncoderTokenizer:



									"""simple docstring"""



									return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,				"""dpr_tokenizer"""		)		)


							def      _snake_case  (							self    :Union[str, Any]		)   ->   BartTokenizer:



									"""simple docstring"""



									return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname							,				"""bart_tokenizer"""		)		)


							def      _snake_case  (							self    :Union[str, Any]		)   ->   List[Any]:



									"""simple docstring"""



									shutil.rmtree(self.tmpdirname		)


							def      _snake_case  (							self    :Tuple		)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      Dataset.from_dict(
									    {
									        """id""": ["""0""", """1"""],
									        """text""": ["""foo""", """bar"""],
									        """title""": ["""Foo""", """Bar"""],
									        """embeddings""": [np.ones(self.retrieval_vector_size		), 2 * np.ones(self.retrieval_vector_size		)],
									    }		)
									dataset.add_faiss_index("""embeddings"""							,				string_factory="""Flat"""							,				metric_type=faiss.METRIC_INNER_PRODUCT		)
									return dataset


							def      _snake_case  (							self    :str		)   ->   str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.get_dummy_dataset()
									SCREAMING_SNAKE_CASE__			=      RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size							,				question_encoder=DPRConfig().to_dict()							,				generator=BartConfig().to_dict()							,				)
									with patch("""transformers.models.rag.retrieval_rag.load_dataset"""		) as mock_load_dataset:
											SCREAMING_SNAKE_CASE__			=      dataset
											SCREAMING_SNAKE_CASE__			=      RagRetriever(
											    __A							,				question_encoder_tokenizer=self.get_dpr_tokenizer()							,				generator_tokenizer=self.get_bart_tokenizer()							,				)
									return retriever


							def      _snake_case  (							self    :Union[str, Any]							,				__A    :bool		)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.get_dummy_dataset()
									SCREAMING_SNAKE_CASE__			=      RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size							,				question_encoder=DPRConfig().to_dict()							,				generator=BartConfig().to_dict()							,				index_name="""custom"""							,				)
									if from_disk:
											SCREAMING_SNAKE_CASE__			=      os.path.join(self.tmpdirname							,				"""dataset"""		)
											SCREAMING_SNAKE_CASE__			=      os.path.join(self.tmpdirname							,				"""index.faiss"""		)
											dataset.get_index("""embeddings"""		).save(os.path.join(self.tmpdirname							,				"""index.faiss"""		)		)
											dataset.drop_index("""embeddings"""		)
											dataset.save_to_disk(os.path.join(self.tmpdirname							,				"""dataset"""		)		)
											del dataset
											SCREAMING_SNAKE_CASE__			=      RagRetriever(
											    __A							,				question_encoder_tokenizer=self.get_dpr_tokenizer()							,				generator_tokenizer=self.get_bart_tokenizer()							,				)
									else:
											SCREAMING_SNAKE_CASE__			=      RagRetriever(
											    __A							,				question_encoder_tokenizer=self.get_dpr_tokenizer()							,				generator_tokenizer=self.get_bart_tokenizer()							,				index=CustomHFIndex(config.retrieval_vector_size							,				__A		)							,				)
									return retriever


							def      _snake_case  (							self    :Tuple		)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      Dataset.from_dict(
									    {
									        """id""": ["""0""", """1"""],
									        """text""": ["""foo""", """bar"""],
									        """title""": ["""Foo""", """Bar"""],
									        """embeddings""": [np.ones(self.retrieval_vector_size + 1		), 2 * np.ones(self.retrieval_vector_size + 1		)],
									    }		)
									dataset.add_faiss_index("""embeddings"""							,				string_factory="""Flat"""							,				metric_type=faiss.METRIC_INNER_PRODUCT		)

									SCREAMING_SNAKE_CASE__			=      os.path.join(self.tmpdirname							,				"""hf_bert_base.hnswSQ8_correct_phi_128.c_index"""		)
									dataset.save_faiss_index("""embeddings"""							,				index_file_name + """.index.dpr"""		)
									pickle.dump(dataset["""id"""]							,				open(index_file_name + """.index_meta.dpr"""							,				"""wb"""		)		)

									SCREAMING_SNAKE_CASE__			=      os.path.join(self.tmpdirname							,				"""psgs_w100.tsv.pkl"""		)
									SCREAMING_SNAKE_CASE__			=      {sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
									pickle.dump(__A							,				open(__A							,				"""wb"""		)		)

									SCREAMING_SNAKE_CASE__			=      RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size							,				question_encoder=DPRConfig().to_dict()							,				generator=BartConfig().to_dict()							,				index_name="""legacy"""							,				index_path=self.tmpdirname							,				)
									SCREAMING_SNAKE_CASE__			=      RagRetriever(
									    __A							,				question_encoder_tokenizer=self.get_dpr_tokenizer()							,				generator_tokenizer=self.get_bart_tokenizer()		)
									return retriever


							def      _snake_case  (							self    :str		)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      1
									SCREAMING_SNAKE_CASE__			=      self.get_dummy_canonical_hf_index_retriever()
									SCREAMING_SNAKE_CASE__			=      np.array(
									    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]							,				dtype=np.floataa		)
									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      retriever.retrieve(__A							,				n_docs=__A		)
									self.assertEqual(retrieved_doc_embeds.shape							,				(2, n_docs, self.retrieval_vector_size)		)
									self.assertEqual(len(__A		)							,				2		)
									self.assertEqual(sorted(doc_dicts[0]		)							,				["""embeddings""", """id""", """text""", """title"""]		)
									self.assertEqual(len(doc_dicts[0]["""id"""]		)							,				__A		)
									self.assertEqual(doc_dicts[0]["""id"""][0]							,				"""1"""		)  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]["""id"""][0]							,				"""0"""		)  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist()							,				[[1], [0]]		)


							def      _snake_case  (							self    :Union[str, Any]		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.get_dummy_canonical_hf_index_retriever()
									with tempfile.TemporaryDirectory() as tmp_dirname:
											with patch("""transformers.models.rag.retrieval_rag.load_dataset"""		) as mock_load_dataset:
													SCREAMING_SNAKE_CASE__			=      self.get_dummy_dataset()
													retriever.save_pretrained(__A		)
													SCREAMING_SNAKE_CASE__			=      RagRetriever.from_pretrained(__A		)
													self.assertIsInstance(__A							,				__A		)
													SCREAMING_SNAKE_CASE__			=      np.array(
													    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]							,				dtype=np.floataa		)
													SCREAMING_SNAKE_CASE__			=      retriever.retrieve(__A							,				n_docs=1		)
													self.assertTrue(out is not None		)


							def      _snake_case  (							self    :Tuple		)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      1
									SCREAMING_SNAKE_CASE__			=      self.get_dummy_custom_hf_index_retriever(from_disk=__A		)
									SCREAMING_SNAKE_CASE__			=      np.array(
									    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]							,				dtype=np.floataa		)
									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      retriever.retrieve(__A							,				n_docs=__A		)
									self.assertEqual(retrieved_doc_embeds.shape							,				(2, n_docs, self.retrieval_vector_size)		)
									self.assertEqual(len(__A		)							,				2		)
									self.assertEqual(sorted(doc_dicts[0]		)							,				["""embeddings""", """id""", """text""", """title"""]		)
									self.assertEqual(len(doc_dicts[0]["""id"""]		)							,				__A		)
									self.assertEqual(doc_dicts[0]["""id"""][0]							,				"""1"""		)  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]["""id"""][0]							,				"""0"""		)  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist()							,				[[1], [0]]		)


							def      _snake_case  (							self    :Any		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.get_dummy_custom_hf_index_retriever(from_disk=__A		)
									with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(__A		)
											SCREAMING_SNAKE_CASE__			=      RagRetriever.from_pretrained(__A		)
											self.assertIsInstance(__A							,				__A		)
											SCREAMING_SNAKE_CASE__			=      np.array(
											    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]							,				dtype=np.floataa		)
											SCREAMING_SNAKE_CASE__			=      retriever.retrieve(__A							,				n_docs=1		)
											self.assertTrue(out is not None		)


							def      _snake_case  (							self    :Any		)   ->   Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      1
									SCREAMING_SNAKE_CASE__			=      self.get_dummy_custom_hf_index_retriever(from_disk=__A		)
									SCREAMING_SNAKE_CASE__			=      np.array(
									    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]							,				dtype=np.floataa		)
									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      retriever.retrieve(__A							,				n_docs=__A		)
									self.assertEqual(retrieved_doc_embeds.shape							,				(2, n_docs, self.retrieval_vector_size)		)
									self.assertEqual(len(__A		)							,				2		)
									self.assertEqual(sorted(doc_dicts[0]		)							,				["""embeddings""", """id""", """text""", """title"""]		)
									self.assertEqual(len(doc_dicts[0]["""id"""]		)							,				__A		)
									self.assertEqual(doc_dicts[0]["""id"""][0]							,				"""1"""		)  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]["""id"""][0]							,				"""0"""		)  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist()							,				[[1], [0]]		)


							def      _snake_case  (							self    :Any		)   ->   List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.get_dummy_custom_hf_index_retriever(from_disk=__A		)
									with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(__A		)
											SCREAMING_SNAKE_CASE__			=      RagRetriever.from_pretrained(__A		)
											self.assertIsInstance(__A							,				__A		)
											SCREAMING_SNAKE_CASE__			=      np.array(
											    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]							,				dtype=np.floataa		)
											SCREAMING_SNAKE_CASE__			=      retriever.retrieve(__A							,				n_docs=1		)
											self.assertTrue(out is not None		)


							def      _snake_case  (							self    :Dict		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      1
									SCREAMING_SNAKE_CASE__			=      self.get_dummy_legacy_index_retriever()
									SCREAMING_SNAKE_CASE__			=      np.array(
									    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]							,				dtype=np.floataa		)
									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      retriever.retrieve(__A							,				n_docs=__A		)
									self.assertEqual(retrieved_doc_embeds.shape							,				(2, n_docs, self.retrieval_vector_size)		)
									self.assertEqual(len(__A		)							,				2		)
									self.assertEqual(sorted(doc_dicts[0]		)							,				["""text""", """title"""]		)
									self.assertEqual(len(doc_dicts[0]["""text"""]		)							,				__A		)
									self.assertEqual(doc_dicts[0]["""text"""][0]							,				"""bar"""		)  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]["""text"""][0]							,				"""foo"""		)  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist()							,				[[1], [0]]		)


							def      _snake_case  (							self    :int		)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.get_dummy_legacy_index_retriever()
									with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(__A		)
											SCREAMING_SNAKE_CASE__			=      RagRetriever.from_pretrained(__A		)
											self.assertIsInstance(__A							,				__A		)
											SCREAMING_SNAKE_CASE__			=      np.array(
											    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]							,				dtype=np.floataa		)
											SCREAMING_SNAKE_CASE__			=      retriever.retrieve(__A							,				n_docs=1		)
											self.assertTrue(out is not None		)


							@require_torch
							@require_tokenizers
							@require_sentencepiece
							def      _snake_case  (							self    :Dict		)   ->   Tuple:



									"""simple docstring"""



									import torch

									SCREAMING_SNAKE_CASE__			=      1
									SCREAMING_SNAKE_CASE__			=      self.get_dummy_canonical_hf_index_retriever()
									SCREAMING_SNAKE_CASE__			=      [[5, 7], [10, 11]]
									SCREAMING_SNAKE_CASE__			=      np.array(
									    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]							,				dtype=np.floataa		)
									SCREAMING_SNAKE_CASE__			=      retriever(__A							,				__A							,				prefix=retriever.config.generator.prefix							,				n_docs=__A		)
									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      (
									    out["""context_input_ids"""],
									    out["""context_attention_mask"""],
									    out["""retrieved_doc_embeds"""],
									)
									self.assertEqual(retrieved_doc_embeds.shape							,				(2, n_docs, self.retrieval_vector_size)		)
									self.assertIsInstance(__A							,				__A		)
									self.assertIsInstance(__A							,				__A		)
									self.assertIsInstance(__A							,				np.ndarray		)

									SCREAMING_SNAKE_CASE__			=      retriever(
									    __A							,				__A							,				prefix=retriever.config.generator.prefix							,				n_docs=__A							,				return_tensors="""pt"""							,				)
									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      (  # noqa: F841
									    out["""context_input_ids"""],
									    out["""context_attention_mask"""],
									    out["""retrieved_doc_embeds"""],
									    out["""doc_ids"""],
									)
									self.assertEqual(retrieved_doc_embeds.shape							,				(2, n_docs, self.retrieval_vector_size)		)
									self.assertIsInstance(__A							,				torch.Tensor		)
									self.assertIsInstance(__A							,				torch.Tensor		)
									self.assertIsInstance(__A							,				torch.Tensor		)


							@require_torch
							@require_tokenizers
							@require_sentencepiece
							def      _snake_case  (							self    :List[str]		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.get_dpr_ctx_encoder_tokenizer()
									SCREAMING_SNAKE_CASE__			=      1
									SCREAMING_SNAKE_CASE__			=      self.get_dummy_custom_hf_index_retriever(from_disk=__A		)
									retriever.set_ctx_encoder_tokenizer(__A		)

									SCREAMING_SNAKE_CASE__			=      [[5, 7], [10, 11]]
									SCREAMING_SNAKE_CASE__			=      np.array(
									    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]							,				dtype=np.floataa		)
									SCREAMING_SNAKE_CASE__			=      retriever(__A							,				__A							,				prefix=retriever.config.generator.prefix							,				n_docs=__A		)

									self.assertEqual(
									    len(__A		)							,				6		)  # check whether the retriever output consist of 6 attributes including tokenized docs
									self.assertEqual(
									    all(k in out for k in ("""tokenized_doc_ids""", """tokenized_doc_attention_mask""")		)							,				__A		)  # check for doc token related keys in dictionary.






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()
_lowerCamelCase   				=			logging.get_logger(__name__)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[str, Any]					,   UpperCamelCase__:							List[Any]					,   UpperCamelCase__:							Optional[Any]					,   UpperCamelCase__:							Optional[Any]    ):
		SCREAMING_SNAKE_CASE__			=      original_name.split("""."""    )[0]
		SCREAMING_SNAKE_CASE__			=      key.split("""."""    )
		SCREAMING_SNAKE_CASE__			=      int(key_list[key_list.index(UpperCamelCase__    ) - 2]    )
		SCREAMING_SNAKE_CASE__			=      int(key_list[key_list.index(UpperCamelCase__    ) - 1]    )
		SCREAMING_SNAKE_CASE__			=      orig_block_num - offset

		SCREAMING_SNAKE_CASE__			=      key.replace(f'''{orig_block_num}.{layer_num}.{original_name}'''					,   f'''block.{new_block_num}.{layer_num}.{new_name}'''    )
		return key






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							int    ):
		SCREAMING_SNAKE_CASE__			=      OrderedDict()
		SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      0, 0
		for key, value in state_dict.items():
				if key.startswith("""network"""    ):
						SCREAMING_SNAKE_CASE__			=      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
						SCREAMING_SNAKE_CASE__			=      key[: key.find("""proj"""    )]
						SCREAMING_SNAKE_CASE__			=      key.replace(UpperCamelCase__					,   f'''patch_embeddings.{total_embed_found}.'''    )
						SCREAMING_SNAKE_CASE__			=      key.replace("""proj"""					,   """projection"""    )
						if key.endswith("""bias"""    ):
								total_embed_found += 1
				if "patch_embeddings" in key:
						SCREAMING_SNAKE_CASE__			=      """poolformer.encoder.""" + key
				if "mlp.fc1" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """mlp.fc1"""					,   """output.conv1"""    )
				if "mlp.fc2" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """mlp.fc2"""					,   """output.conv2"""    )
				if "norm1" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """norm1"""					,   """before_norm"""    )
				if "norm2" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """norm2"""					,   """after_norm"""    )
				if "layer_scale_1" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """layer_scale_1"""					,   """layer_scale_1"""    )
				if "layer_scale_2" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """layer_scale_2"""					,   """layer_scale_2"""    )
				if "head" in key:
						SCREAMING_SNAKE_CASE__			=      key.replace("""head"""					,   """classifier"""    )
				SCREAMING_SNAKE_CASE__			=      value
		return new_state_dict






def 				SCREAMING_SNAKE_CASE__							(       ):
		SCREAMING_SNAKE_CASE__			=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
		SCREAMING_SNAKE_CASE__			=      Image.open(requests.get(UpperCamelCase__					,   stream=UpperCamelCase__    ).raw    )

		return image






@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]					,   UpperCamelCase__:							Optional[int]					,   UpperCamelCase__:							Any    ):
		SCREAMING_SNAKE_CASE__			=      PoolFormerConfig()

		# set attributes based on model_name
		SCREAMING_SNAKE_CASE__			=      """huggingface/label-files"""
		SCREAMING_SNAKE_CASE__			=      model_name[-3:]
		SCREAMING_SNAKE_CASE__			=      1_000
		SCREAMING_SNAKE_CASE__			=      """imagenet-1k-id2label.json"""
		SCREAMING_SNAKE_CASE__			=      (1, 1_000)

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

		# load image processor
		SCREAMING_SNAKE_CASE__			=      PoolFormerImageProcessor(crop_pct=UpperCamelCase__    )

		# Prepare image
		SCREAMING_SNAKE_CASE__			=      prepare_img()
		SCREAMING_SNAKE_CASE__			=      image_processor(images=UpperCamelCase__					,   return_tensors="""pt"""    ).pixel_values

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

		# load original state dict
		SCREAMING_SNAKE_CASE__			=      torch.load(UpperCamelCase__					,   map_location=torch.device("""cpu"""    )    )

		# rename keys
		SCREAMING_SNAKE_CASE__			=      rename_keys(UpperCamelCase__    )

		# create HuggingFace model and load state dict
		SCREAMING_SNAKE_CASE__			=      PoolFormerForImageClassification(UpperCamelCase__    )
		model.load_state_dict(UpperCamelCase__    )
		model.eval()

		# Define image processor
		SCREAMING_SNAKE_CASE__			=      PoolFormerImageProcessor(crop_pct=UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      image_processor(images=prepare_img()					,   return_tensors="""pt"""    ).pixel_values

		# forward pass
		SCREAMING_SNAKE_CASE__			=      model(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      outputs.logits

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