


import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil






class      UpperCamelCase(	_a ):


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




											'''simple docstring'''

											__snake_case				=		pa.array(TypedSequence([1, 2, 3]    )    )
											self.assertEqual(arr.type  ,     pa.intaa()    )


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




											'''simple docstring'''

											with self.assertRaises(SCREAMING_SNAKE_CASE    ):
																	__snake_case				=		pa.array(TypedSequence([1, 2, 3]    )  ,     type=pa.intaa()    )


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




											'''simple docstring'''

											with self.assertRaises(SCREAMING_SNAKE_CASE    ):
																	__snake_case				=		pa.array(TypedSequence([1, 2, 3]  ,     try_type=Value("bool"    )  ,     type=Value("int64"    )    )    )


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




											'''simple docstring'''

											__snake_case				=		pa.array(TypedSequence([1, 2, 3]  ,     type=Value("int32"    )    )    )
											self.assertEqual(arr.type  ,     pa.intaa()    )


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




											'''simple docstring'''

											with self.assertRaises((TypeError, pa.lib.ArrowInvalid)    ):
																	__snake_case				=		pa.array(TypedSequence(["foo", "bar"]  ,     type=Value("int64"    )    )    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		pa.array(TypedSequence([1, 2, 3]  ,     try_type=Value("int32"    )    )    )
											self.assertEqual(arr.type  ,     pa.intaa()    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						str:




											'''simple docstring'''

											__snake_case				=		pa.array(TypedSequence(["foo", "bar"]  ,     try_type=Value("int64"    )    )    )
											self.assertEqual(arr.type  ,     pa.string()    )


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




											'''simple docstring'''

											__snake_case				=		pa.array(TypedSequence([[[1, 2, 3]]]  ,     type=ArrayaD((1, 3)  ,     "int64"    )    )    )
											self.assertEqual(arr.type  ,     ArrayaDExtensionType((1, 3)  ,     "int64"    )    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int    )      ->						Tuple:




											'''simple docstring'''

											with self.assertRaises((TypeError, pa.lib.ArrowInvalid)    ):
																	__snake_case				=		pa.array(TypedSequence(["foo", "bar"]  ,     type=ArrayaD((1, 3)  ,     "int64"    )    )    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		pa.array(TypedSequence([[[1, 2, 3]]]  ,     try_type=ArrayaD((1, 3)  ,     "int64"    )    )    )
											self.assertEqual(arr.type  ,     ArrayaDExtensionType((1, 3)  ,     "int64"    )    )


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




											'''simple docstring'''

											__snake_case				=		pa.array(TypedSequence(["foo", "bar"]  ,     try_type=ArrayaD((1, 3)  ,     "int64"    )    )    )
											self.assertEqual(arr.type  ,     pa.string()    )






					@require_pil
					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]    )      ->						Union[str, Any]:




											'''simple docstring'''

											import PIL.Image

											__snake_case				=		PIL.Image.fromarray(np.arange(1_0  ,     dtype=np.uinta    ).reshape(2  ,     5    )    )
											with patch(
											    "datasets.arrow_writer.cast_to_python_objects"  ,     side_effect=SCREAMING_SNAKE_CASE    ) as mock_cast_to_python_objects:
																	__snake_case				=		pa.array(TypedSequence([{"path": None, "bytes": b"image_bytes"}, pil_image]  ,     type=Image()    )    )
																	__snake_case		,       __snake_case				=		mock_cast_to_python_objects.call_args_list[-1]
																	self.assertIn("optimize_list_casting"  ,     SCREAMING_SNAKE_CASE    )
																	self.assertFalse(kwargs["optimize_list_casting"]    )





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


						'''simple docstring'''






						__snake_case				=		pa.BufferReader(_lowerCAmelCase			) if isinstance(_lowerCAmelCase   ,						pa.Buffer			) else pa.memory_map(_lowerCAmelCase			)
						__snake_case				=		pa.ipc.open_stream(_lowerCAmelCase			)
						__snake_case				=		f.read_all()
						assert len(pa_table.to_batches()			) == expected_num_chunks
						assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
						del pa_table





@pytest.mark.parametrize("writer_batch_size"   ,						[None, 1, 10]			)
@pytest.mark.parametrize(
    "fields"   ,						[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]			)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Any:


						'''simple docstring'''






						__snake_case				=		pa.BufferOutputStream()
						__snake_case				=		pa.schema(_lowerCAmelCase			) if fields else None
						with ArrowWriter(stream=_lowerCAmelCase   ,						schema=_lowerCAmelCase   ,						writer_batch_size=_lowerCAmelCase			) as writer:
												writer.write({"col_1": "foo", "col_2": 1}			)
												writer.write({"col_1": "bar", "col_2": 2}			)
												__snake_case		,       __snake_case				=		writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						if not fields:
												__snake_case				=		{"col_1": pa.string(), "col_2": pa.intaa()}
						assert writer._schema == pa.schema(_lowerCAmelCase   ,						metadata=writer._schema.metadata			)
						_check_output(output.getvalue()   ,						expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)





def 						_lowerCAmelCase							(     )    ->						str:


						'''simple docstring'''






						__snake_case				=		pa.BufferOutputStream()
						__snake_case				=		Features({"labels": ClassLabel(names=["neg", "pos"]			)}			)
						with ArrowWriter(stream=_lowerCAmelCase   ,						features=_lowerCAmelCase			) as writer:
												writer.write({"labels": 0}			)
												writer.write({"labels": 1}			)
												__snake_case		,       __snake_case				=		writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						assert writer._schema == features.arrow_schema
						assert writer._schema.metadata == features.arrow_schema.metadata
						__snake_case				=		pa.BufferReader(output.getvalue()			)
						__snake_case				=		pa.ipc.open_stream(_lowerCAmelCase			)
						__snake_case				=		f.read_all()
						__snake_case				=		pa_table.schema
						assert pa_table.num_rows == 2
						assert schema == features.arrow_schema
						assert schema.metadata == features.arrow_schema.metadata
						assert features == Features.from_arrow_schema(_lowerCAmelCase			)





@pytest.mark.parametrize("writer_batch_size"   ,						[None, 1, 10]			)
def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						Dict:


						'''simple docstring'''






						__snake_case				=		pa.BufferOutputStream()
						with ArrowWriter(
						    stream=_lowerCAmelCase   ,						writer_batch_size=_lowerCAmelCase   ,						hash_salt="split_name"   ,						check_duplicates=_lowerCAmelCase   ,						) as writer:
												with pytest.raises(_lowerCAmelCase			):
																		writer.write({"col_1": "foo", "col_2": 1}   ,						key=[1, 2]			)
																		__snake_case		,       __snake_case				=		writer.finalize()





@pytest.mark.parametrize("writer_batch_size"   ,						[None, 2, 10]			)
def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						Optional[int]:


						'''simple docstring'''






						__snake_case				=		pa.BufferOutputStream()
						with ArrowWriter(
						    stream=_lowerCAmelCase   ,						writer_batch_size=_lowerCAmelCase   ,						hash_salt="split_name"   ,						check_duplicates=_lowerCAmelCase   ,						) as writer:
												with pytest.raises(_lowerCAmelCase			):
																		writer.write({"col_1": "foo", "col_2": 1}   ,						key=10			)
																		writer.write({"col_1": "bar", "col_2": 2}   ,						key=10			)
																		__snake_case		,       __snake_case				=		writer.finalize()





@pytest.mark.parametrize("writer_batch_size"   ,						[None, 2, 10]			)
def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						Optional[int]:


						'''simple docstring'''






						__snake_case				=		pa.BufferOutputStream()
						with ArrowWriter(
						    stream=_lowerCAmelCase   ,						writer_batch_size=_lowerCAmelCase   ,						hash_salt="split_name"   ,						check_duplicates=_lowerCAmelCase   ,						) as writer:
												writer.write({"col_1": "foo", "col_2": 1}   ,						key=1			)
												writer.write({"col_1": "bar", "col_2": 2}   ,						key=2			)
												__snake_case		,       __snake_case				=		writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						_check_output(output.getvalue()   ,						expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)





@pytest.mark.parametrize("writer_batch_size"   ,						[None, 1, 10]			)
@pytest.mark.parametrize(
    "fields"   ,						[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]			)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						str:


						'''simple docstring'''






						__snake_case				=		pa.BufferOutputStream()
						__snake_case				=		pa.schema(_lowerCAmelCase			) if fields else None
						with ArrowWriter(stream=_lowerCAmelCase   ,						schema=_lowerCAmelCase   ,						writer_batch_size=_lowerCAmelCase			) as writer:
												writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}			)
												writer.write_batch({"col_1": [], "col_2": []}			)
												__snake_case		,       __snake_case				=		writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						if not fields:
												__snake_case				=		{"col_1": pa.string(), "col_2": pa.intaa()}
						assert writer._schema == pa.schema(_lowerCAmelCase   ,						metadata=writer._schema.metadata			)
						_check_output(output.getvalue()   ,						expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)





@pytest.mark.parametrize("writer_batch_size"   ,						[None, 1, 10]			)
@pytest.mark.parametrize(
    "fields"   ,						[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]			)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Tuple:


						'''simple docstring'''






						__snake_case				=		pa.BufferOutputStream()
						__snake_case				=		pa.schema(_lowerCAmelCase			) if fields else None
						with ArrowWriter(stream=_lowerCAmelCase   ,						schema=_lowerCAmelCase   ,						writer_batch_size=_lowerCAmelCase			) as writer:
												writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}			)			)
												__snake_case		,       __snake_case				=		writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						if not fields:
												__snake_case				=		{"col_1": pa.string(), "col_2": pa.intaa()}
						assert writer._schema == pa.schema(_lowerCAmelCase   ,						metadata=writer._schema.metadata			)
						_check_output(output.getvalue()   ,						expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)





@pytest.mark.parametrize("writer_batch_size"   ,						[None, 1, 10]			)
@pytest.mark.parametrize(
    "fields"   ,						[None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]			)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Any:


						'''simple docstring'''






						__snake_case				=		pa.BufferOutputStream()
						__snake_case				=		pa.schema(_lowerCAmelCase			) if fields else None
						with ArrowWriter(stream=_lowerCAmelCase   ,						schema=_lowerCAmelCase   ,						writer_batch_size=_lowerCAmelCase			) as writer:
												writer.write_row(pa.Table.from_pydict({"col_1": ["foo"], "col_2": [1]}			)			)
												writer.write_row(pa.Table.from_pydict({"col_1": ["bar"], "col_2": [2]}			)			)
												__snake_case		,       __snake_case				=		writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						if not fields:
												__snake_case				=		{"col_1": pa.string(), "col_2": pa.intaa()}
						assert writer._schema == pa.schema(_lowerCAmelCase   ,						metadata=writer._schema.metadata			)
						_check_output(output.getvalue()   ,						expected_num_chunks=num_examples if writer_batch_size == 1 else 1			)





def 						_lowerCAmelCase							(     )    ->						List[str]:


						'''simple docstring'''






						with tempfile.TemporaryDirectory() as tmp_dir:
												__snake_case				=		{"col_1": pa.string(), "col_2": pa.intaa()}
												__snake_case				=		os.path.join(_lowerCAmelCase   ,						"test.arrow"			)
												with ArrowWriter(path=_lowerCAmelCase   ,						schema=pa.schema(_lowerCAmelCase			)			) as writer:
																		writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}			)
																		__snake_case		,       __snake_case				=		writer.finalize()
												assert num_examples == 2
												assert num_bytes > 0
												assert writer._schema == pa.schema(_lowerCAmelCase   ,						metadata=writer._schema.metadata			)
												_check_output(_lowerCAmelCase   ,						1			)





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						str:


						'''simple docstring'''






						if pa.types.is_list(_lowerCAmelCase			):
												return get_base_dtype(arr_type.value_type			)
						else:
												return arr_type





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


						'''simple docstring'''






						if isinstance(lst[0]   ,						_lowerCAmelCase			):
												change_first_primitive_element_in_list(lst[0]   ,						_lowerCAmelCase			)
						else:
												__snake_case				=		value





@pytest.mark.parametrize("optimized_int_type, expected_dtype"   ,						[(None, pa.intaa()), (Value("int32"			), pa.intaa())]			)
@pytest.mark.parametrize("sequence"   ,						[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]			)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Union[str, Any]:


						'''simple docstring'''






						__snake_case				=		pa.array(TypedSequence(_lowerCAmelCase   ,						optimized_int_type=_lowerCAmelCase			)			)
						assert get_base_dtype(arr.type			) == expected_dtype





@pytest.mark.parametrize(
    "col, expected_dtype"   ,						[
        ("attention_mask", pa.inta()),
        ("special_tokens_mask", pa.inta()),
        ("token_type_ids", pa.inta()),
        ("input_ids", pa.intaa()),
        ("other", pa.intaa()),
    ]   ,						)
@pytest.mark.parametrize("sequence"   ,						[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]			)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Tuple:


						'''simple docstring'''






						__snake_case				=		pa.array(OptimizedTypedSequence(_lowerCAmelCase   ,						col=_lowerCAmelCase			)			)
						assert get_base_dtype(arr.type			) == expected_dtype

						# not in range
						if col != "other":
												# avoids errors due to in-place modifications
												__snake_case				=		copy.deepcopy(_lowerCAmelCase			)
												__snake_case				=		np.iinfo(expected_dtype.to_pandas_dtype()			).max + 1
												change_first_primitive_element_in_list(_lowerCAmelCase   ,						_lowerCAmelCase			)
												__snake_case				=		pa.array(OptimizedTypedSequence(_lowerCAmelCase   ,						col=_lowerCAmelCase			)			)
												assert get_base_dtype(arr.type			) == pa.intaa()





@pytest.mark.parametrize("raise_exception"   ,						[False, True]			)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Dict:


						'''simple docstring'''






						__snake_case				=		str(tmp_path / "dataset-train.arrow"			)
						try:
												with ArrowWriter(path=_lowerCAmelCase			) as writer:
																		if raise_exception:
																								raise pa.lib.ArrowInvalid()
																		else:
																								writer.stream.close()
						except pa.lib.ArrowInvalid:
												pass
						finally:
												assert writer.stream.closed





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						Optional[Any]:


						'''simple docstring'''






						__snake_case				=		"mock://dataset-train.arrow"
						with ArrowWriter(path=_lowerCAmelCase   ,						storage_options=mockfs.storage_options			) as writer:
												assert isinstance(writer._fs   ,						type(_lowerCAmelCase			)			)
												assert writer._fs.storage_options == mockfs.storage_options
												writer.write({"col_1": "foo", "col_2": 1}			)
												writer.write({"col_1": "bar", "col_2": 2}			)
												__snake_case		,       __snake_case				=		writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						assert mockfs.exists(_lowerCAmelCase			)





def 						_lowerCAmelCase							(     )    ->						List[Any]:


						'''simple docstring'''






						__snake_case				=		pa.BufferOutputStream()
						with ParquetWriter(stream=_lowerCAmelCase			) as writer:
												writer.write({"col_1": "foo", "col_2": 1}			)
												writer.write({"col_1": "bar", "col_2": 2}			)
												__snake_case		,       __snake_case				=		writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						__snake_case				=		pa.BufferReader(output.getvalue()			)
						__snake_case				=		pq.read_table(_lowerCAmelCase			)
						assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}





@require_pil
@pytest.mark.parametrize("embed_local_files"   ,						[False, True]			)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Any:


						'''simple docstring'''






						import PIL.Image

						__snake_case				=		str(tmp_path / "test_image_rgb.jpg"			)
						PIL.Image.fromarray(np.zeros((5, 5)   ,						dtype=np.uinta			)			).save(_lowerCAmelCase   ,						format="png"			)
						__snake_case				=		pa.BufferOutputStream()
						with ParquetWriter(
						    stream=_lowerCAmelCase   ,						features=Features({"image": Image()}			)   ,						embed_local_files=_lowerCAmelCase			) as writer:
												writer.write({"image": image_path}			)
												writer.finalize()
						__snake_case				=		pa.BufferReader(output.getvalue()			)
						__snake_case				=		pq.read_table(_lowerCAmelCase			)
						__snake_case				=		pa_table.to_pydict()
						if embed_local_files:
												assert isinstance(out["image"][0]["path"]   ,						_lowerCAmelCase			)
												with open(_lowerCAmelCase   ,						"rb"			) as f:
																		assert out["image"][0]["bytes"] == f.read()
						else:
												assert out["image"][0]["path"] == image_path
												assert out["image"][0]["bytes"] is None





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


						'''simple docstring'''






						__snake_case				=		pa.schema([pa.field("col_1"   ,						pa.string()   ,						nullable=_lowerCAmelCase			)]			)
						__snake_case				=		pa.BufferOutputStream()
						with ArrowWriter(stream=_lowerCAmelCase			) as writer:
												writer._build_writer(inferred_schema=_lowerCAmelCase			)
												assert writer._schema == pa.schema([pa.field("col_1"   ,						pa.string()			)]			)









import argparse
import json
from pathlib import Path

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

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
A				:       int      =  logging.get_logger(__name__)





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase=False			)    ->						Optional[int]:


						'''simple docstring'''






						__snake_case				=		[]
						for i in range(config.num_hidden_layers			):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')			)
												rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')			)
												rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')			)
												rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')			)
												rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')			)
												rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')			)
												rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')			)
												rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')			)
												rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')			)
												rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')			)

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        ("cls_token", "vit.embeddings.cls_token"),
						        ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
						        ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
						        ("pos_embed", "vit.embeddings.position_embeddings"),
						    ]			)

						if base_model:
												# layernorm + pooler
												rename_keys.extend(
												    [
												        ("norm.weight", "layernorm.weight"),
												        ("norm.bias", "layernorm.bias"),
												    ]			)

												# if just the base model, we should remove "vit" from all keys that start with "vit"
												__snake_case				=		[(pair[0], pair[1][4:]) if pair[1].startswith("vit"			) else pair for pair in rename_keys]
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ("norm.weight", "vit.layernorm.weight"),
												        ("norm.bias", "vit.layernorm.bias"),
												        ("head.weight", "classifier.weight"),
												        ("head.bias", "classifier.bias"),
												    ]			)

						return rename_keys





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase=False			)    ->						int:


						'''simple docstring'''






						for i in range(config.num_hidden_layers			):
												if base_model:
																		__snake_case				=		""
												else:
																		__snake_case				=		"vit."
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												__snake_case				=		state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''			)
												__snake_case				=		state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''			)
												# next, add query, keys and values (in that order) to the state dict
												__snake_case				=		in_proj_weight[
												    : config.hidden_size, :
												]
												__snake_case				=		in_proj_bias[: config.hidden_size]
												__snake_case				=		in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												__snake_case				=		in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												__snake_case				=		in_proj_weight[
												    -config.hidden_size :, :
												]
												__snake_case				=		in_proj_bias[-config.hidden_size :]





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						List[str]:


						'''simple docstring'''






						__snake_case				=		["head.weight", "head.bias"]
						for k in ignore_keys:
												state_dict.pop(_lowerCAmelCase   ,						_lowerCAmelCase			)





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Dict:


						'''simple docstring'''






						__snake_case				=		dct.pop(_lowerCAmelCase			)
						__snake_case				=		val





def 						_lowerCAmelCase							(     )    ->						Any:


						'''simple docstring'''






						__snake_case				=		"http://images.cocodataset.org/val2017/000000039769.jpg"
						__snake_case				=		Image.open(requests.get(_lowerCAmelCase   ,						stream=_lowerCAmelCase			).raw			)
						return im





@torch.no_grad()
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase=True			)    ->						int:


						'''simple docstring'''






						__snake_case				=		ViTConfig()
						# patch_size
						if model_name[-1] == "8":
												__snake_case				=		8
						# set labels if required
						if not base_model:
												__snake_case				=		1000
												__snake_case				=		"huggingface/label-files"
												__snake_case				=		"imagenet-1k-id2label.json"
												__snake_case				=		json.load(open(hf_hub_download(_lowerCAmelCase   ,						_lowerCAmelCase   ,						repo_type="dataset"			)   ,						"r"			)			)
												__snake_case				=		{int(_lowerCAmelCase			): v for k, v in idalabel.items()}
												__snake_case				=		idalabel
												__snake_case				=		{v: k for k, v in idalabel.items()}
						# size of the architecture
						if model_name in ["dino_vits8", "dino_vits16"]:
												__snake_case				=		384
												__snake_case				=		1536
												__snake_case				=		12
												__snake_case				=		6

						# load original model from torch hub
						__snake_case				=		torch.hub.load("facebookresearch/dino:main"   ,						_lowerCAmelCase			)
						original_model.eval()

						# load state_dict of original model, remove and rename some keys
						__snake_case				=		original_model.state_dict()
						if base_model:
												remove_classification_head_(_lowerCAmelCase			)
						__snake_case				=		create_rename_keys(_lowerCAmelCase   ,						base_model=_lowerCAmelCase			)
						for src, dest in rename_keys:
												rename_key(_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)
						read_in_q_k_v(_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)

						# load HuggingFace model
						if base_model:
												__snake_case				=		ViTModel(_lowerCAmelCase   ,						add_pooling_layer=_lowerCAmelCase			).eval()
						else:
												__snake_case				=		ViTForImageClassification(_lowerCAmelCase			).eval()
						model.load_state_dict(_lowerCAmelCase			)

						# Check outputs on an image, prepared by ViTImageProcessor
						__snake_case				=		ViTImageProcessor()
						__snake_case				=		image_processor(images=prepare_img()   ,						return_tensors="pt"			)
						__snake_case				=		encoding["pixel_values"]
						__snake_case				=		model(_lowerCAmelCase			)

						if base_model:
												__snake_case				=		original_model(_lowerCAmelCase			)
												assert torch.allclose(_lowerCAmelCase   ,						outputs.last_hidden_state[:, 0, :]   ,						atol=1E-1			)
						else:
												__snake_case				=		original_model(_lowerCAmelCase			)
												assert logits.shape == outputs.logits.shape
												assert torch.allclose(_lowerCAmelCase   ,						outputs.logits   ,						atol=1E-3			)

						Path(_lowerCAmelCase			).mkdir(exist_ok=_lowerCAmelCase			)
						print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''			)
						model.save_pretrained(_lowerCAmelCase			)
						print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
						image_processor.save_pretrained(_lowerCAmelCase			)


if __name__ == "__main__":
	A				:       str      =  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default='dino_vitb16',
	    type=str,
	    help='Name of the model trained with DINO you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument(
	    '--base_model',
	    action='store_true',
	    help='Whether to only convert the base model (no projection head weights).',
	)

	parser.set_defaults(base_model=True)
	A				:       Any      =  parser.parse_args()
	convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)





