


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 					lowerCAmelCase_ (       _a):




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






									"""simple docstring"""






									a__ :List[Any]						  =    pa.array(TypedSequence([1, 2, 3]						)						)
									self.assertEqual(arr.type   ,     pa.intaa()						)




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






									"""simple docstring"""






									with self.assertRaises(__A						):
												a__ :List[str]						  =    pa.array(TypedSequence([1, 2, 3]						)   ,     type=pa.intaa()						)




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






									"""simple docstring"""






									with self.assertRaises(__A						):
												a__ :Optional[int]						  =    pa.array(TypedSequence([1, 2, 3]   ,     try_type=Value("bool"						)   ,     type=Value("int64"						)						)						)




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






									"""simple docstring"""






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




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






									"""simple docstring"""






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




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






									"""simple docstring"""






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




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






									"""simple docstring"""






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




						def  _snake_case					(	self						:   int						)      ->str:






									"""simple docstring"""






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




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






									"""simple docstring"""






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




						def  _snake_case					(	self						:   Tuple						)      ->Tuple:






									"""simple docstring"""






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




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






									"""simple docstring"""






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






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






									"""simple docstring"""






									import PIL.Image

									a__ :List[Any]						  =    PIL.Image.fromarray(np.arange(10   ,     dtype=np.uinta						).reshape(2   ,     5						)						)
									with patch(
									    "datasets.arrow_writer.cast_to_python_objects"   ,     side_effect=__A						) as mock_cast_to_python_objects:
												a__ :Optional[Any]						  =    pa.array(TypedSequence([{"path": None, "bytes": B"image_bytes"}, pil_image]   ,     type=Image()						)						)
												a__ :Tuple						  =    mock_cast_to_python_objects.call_args_list[-1]
												self.assertIn("optimize_list_casting"   ,     __A						)
												self.assertFalse(kwargs["optimize_list_casting"]						)






def 				lowerCamelCase__	(      a	:   List[Any]	,  a	:   int				)    ->	Any:





			"""simple docstring"""
			a__ :List[str]						  =    pa.BufferReader(a				) if isinstance(a	,  pa.Buffer				) else pa.memory_map(a				)
			a__ :int						  =    pa.ipc.open_stream(a				)
			a__ :pa.Table						  =    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__	(      a	:   str	,  a	:   List[Any]				)    ->	Tuple:





			"""simple docstring"""
			a__ :Any						  =    pa.BufferOutputStream()
			a__ :Any						  =    pa.schema(a				) if fields else None
			with ArrowWriter(stream=a	,  schema=a	,  writer_batch_size=a				) as writer:
						writer.write({"col_1": "foo", "col_2": 1}				)
						writer.write({"col_1": "bar", "col_2": 2}				)
						a__ :str						  =    writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						a__ :Union[str, Any]						  =    {"col_1": pa.string(), "col_2": pa.intaa()}
			assert writer._schema == pa.schema(a	,  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"""
			a__ :Optional[Any]						  =    pa.BufferOutputStream()
			a__ :Any						  =    Features({"labels": ClassLabel(names=["neg", "pos"]				)}				)
			with ArrowWriter(stream=a	,  features=a				) as writer:
						writer.write({"labels": 0}				)
						writer.write({"labels": 1}				)
						a__ :str						  =    writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			assert writer._schema == features.arrow_schema
			assert writer._schema.metadata == features.arrow_schema.metadata
			a__ :int						  =    pa.BufferReader(output.getvalue()				)
			a__ :Optional[Any]						  =    pa.ipc.open_stream(a				)
			a__ :pa.Table						  =    f.read_all()
			a__ :Tuple						  =    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(a				)


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





			"""simple docstring"""
			a__ :Union[str, Any]						  =    pa.BufferOutputStream()
			with ArrowWriter(
			    stream=a	,  writer_batch_size=a	,  hash_salt="split_name"	,  check_duplicates=a	,  ) as writer:
						with pytest.raises(a				):
									writer.write({"col_1": "foo", "col_2": 1}	,  key=[1, 2]				)
									a__ :Any						  =    writer.finalize()


@pytest.mark.parametrize("writer_batch_size"	,  [None, 2, 10]				)
def 				lowerCamelCase__	(      a	:   Dict				)    ->	List[Any]:





			"""simple docstring"""
			a__ :int						  =    pa.BufferOutputStream()
			with ArrowWriter(
			    stream=a	,  writer_batch_size=a	,  hash_salt="split_name"	,  check_duplicates=a	,  ) as writer:
						with pytest.raises(a				):
									writer.write({"col_1": "foo", "col_2": 1}	,  key=10				)
									writer.write({"col_1": "bar", "col_2": 2}	,  key=10				)
									a__ :List[Any]						  =    writer.finalize()


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





			"""simple docstring"""
			a__ :Dict						  =    pa.BufferOutputStream()
			with ArrowWriter(
			    stream=a	,  writer_batch_size=a	,  hash_salt="split_name"	,  check_duplicates=a	,  ) as writer:
						writer.write({"col_1": "foo", "col_2": 1}	,  key=1				)
						writer.write({"col_1": "bar", "col_2": 2}	,  key=2				)
						a__ :Optional[Any]						  =    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__	(      a	:   Tuple	,  a	:   int				)    ->	List[str]:





			"""simple docstring"""
			a__ :str						  =    pa.BufferOutputStream()
			a__ :Any						  =    pa.schema(a				) if fields else None
			with ArrowWriter(stream=a	,  schema=a	,  writer_batch_size=a				) as writer:
						writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}				)
						writer.write_batch({"col_1": [], "col_2": []}				)
						a__ :Tuple						  =    writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						a__ :List[str]						  =    {"col_1": pa.string(), "col_2": pa.intaa()}
			assert writer._schema == pa.schema(a	,  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__	(      a	:   List[str]	,  a	:   str				)    ->	List[str]:





			"""simple docstring"""
			a__ :str						  =    pa.BufferOutputStream()
			a__ :str						  =    pa.schema(a				) if fields else None
			with ArrowWriter(stream=a	,  schema=a	,  writer_batch_size=a				) as writer:
						writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}				)				)
						a__ :Union[str, Any]						  =    writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						a__ :Optional[int]						  =    {"col_1": pa.string(), "col_2": pa.intaa()}
			assert writer._schema == pa.schema(a	,  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__	(      a	:   int	,  a	:   Tuple				)    ->	List[Any]:





			"""simple docstring"""
			a__ :List[str]						  =    pa.BufferOutputStream()
			a__ :Dict						  =    pa.schema(a				) if fields else None
			with ArrowWriter(stream=a	,  schema=a	,  writer_batch_size=a				) 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]}				)				)
						a__ :Tuple						  =    writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						a__ :Any						  =    {"col_1": pa.string(), "col_2": pa.intaa()}
			assert writer._schema == pa.schema(a	,  metadata=writer._schema.metadata				)
			_check_output(output.getvalue()	,  expected_num_chunks=num_examples if writer_batch_size == 1 else 1				)


def 				lowerCamelCase__	(      )    ->	Optional[Any]:





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


def 				lowerCamelCase__	(      a	:   Optional[Any]				)    ->	Tuple:





			"""simple docstring"""
			if pa.types.is_list(a				):
						return get_base_dtype(arr_type.value_type				)
			else:
						return arr_type


def 				lowerCamelCase__	(      a	:   List[Any]	,  a	:   Tuple				)    ->	Optional[int]:





			"""simple docstring"""
			if isinstance(lst[0]	,  a				):
						change_first_primitive_element_in_list(lst[0]	,  a				)
			else:
						a__ :List[str]						  =    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__	(      a	:   Optional[Any]	,  a	:   str	,  a	:   Tuple				)    ->	Tuple:





			"""simple docstring"""
			a__ :Dict						  =    pa.array(TypedSequence(a	,  optimized_int_type=a				)				)
			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__	(      a	:   Any	,  a	:   Optional[Any]	,  a	:   Dict				)    ->	Dict:





			"""simple docstring"""
			a__ :int						  =    pa.array(OptimizedTypedSequence(a	,  col=a				)				)
			assert get_base_dtype(arr.type				) == expected_dtype

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


@pytest.mark.parametrize("raise_exception"	,  [False, True]				)
def 				lowerCamelCase__	(      a	:   Tuple	,  a	:   Tuple				)    ->	str:





			"""simple docstring"""
			a__ :List[Any]						  =    str(tmp_path / "dataset-train.arrow"				)
			try:
						with ArrowWriter(path=a				) 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__	(      a	:   Tuple				)    ->	Optional[int]:





			"""simple docstring"""
			a__ :List[str]						  =    "mock://dataset-train.arrow"
			with ArrowWriter(path=a	,  storage_options=mockfs.storage_options				) as writer:
						assert isinstance(writer._fs	,  type(a				)				)
						assert writer._fs.storage_options == mockfs.storage_options
						writer.write({"col_1": "foo", "col_2": 1}				)
						writer.write({"col_1": "bar", "col_2": 2}				)
						a__ :Optional[int]						  =    writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			assert mockfs.exists(a				)


def 				lowerCamelCase__	(      )    ->	Any:





			"""simple docstring"""
			a__ :List[str]						  =    pa.BufferOutputStream()
			with ParquetWriter(stream=a				) as writer:
						writer.write({"col_1": "foo", "col_2": 1}				)
						writer.write({"col_1": "bar", "col_2": 2}				)
						a__ :Optional[int]						  =    writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			a__ :Optional[int]						  =    pa.BufferReader(output.getvalue()				)
			a__ :pa.Table						  =    pq.read_table(a				)
			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__	(      a	:   Any	,  a	:   Tuple				)    ->	int:





			"""simple docstring"""
			import PIL.Image

			a__ :Optional[Any]						  =    str(tmp_path / "test_image_rgb.jpg"				)
			PIL.Image.fromarray(np.zeros((5, 5)	,  dtype=np.uinta				)				).save(a	,  format="png"				)
			a__ :Any						  =    pa.BufferOutputStream()
			with ParquetWriter(
			    stream=a	,  features=Features({"image": Image()}				)	,  embed_local_files=a				) as writer:
						writer.write({"image": image_path}				)
						writer.finalize()
			a__ :Any						  =    pa.BufferReader(output.getvalue()				)
			a__ :pa.Table						  =    pq.read_table(a				)
			a__ :List[str]						  =    pa_table.to_pydict()
			if embed_local_files:
						assert isinstance(out["image"][0]["path"]	,  a				)
						with open(a	,  "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"""
			a__ :Tuple						  =    pa.schema([pa.field("col_1"	,  pa.string()	,  nullable=a				)]				)
			a__ :Optional[Any]						  =    pa.BufferOutputStream()
			with ArrowWriter(stream=a				) as writer:
						writer._build_writer(inferred_schema=a				)
						assert writer._schema == pa.schema([pa.field("col_1"	,  pa.string()				)]				)






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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__			 =						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case__			 =						[]
for i in range(6):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
		)
		rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
		rename_keys.append(
		    (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
		)
		rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
		# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
		)
		rename_keys.append(
		    (
		        f'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
		        f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
		    )
		)
		rename_keys.append(
		    (
		        f'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
		        f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
		    )
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

		# q, k, v projections in self/cross-attention in decoder for conditional DETR
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', f'''decoder.layers.{i}.sa_qcontent_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', f'''decoder.layers.{i}.sa_kcontent_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', f'''decoder.layers.{i}.sa_qpos_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', f'''decoder.layers.{i}.sa_kpos_proj.weight''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.weight''', f'''decoder.layers.{i}.sa_v_proj.weight'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', f'''decoder.layers.{i}.ca_qcontent_proj.weight''')
		)
		# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', f'''decoder.layers.{i}.ca_kcontent_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', f'''decoder.layers.{i}.ca_kpos_proj.weight''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.weight''', f'''decoder.layers.{i}.ca_v_proj.weight'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', f'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
		)

		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', f'''decoder.layers.{i}.sa_qcontent_proj.bias''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', f'''decoder.layers.{i}.sa_kcontent_proj.bias''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', f'''decoder.layers.{i}.sa_qpos_proj.bias'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', f'''decoder.layers.{i}.sa_kpos_proj.bias'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.bias''', f'''decoder.layers.{i}.sa_v_proj.bias'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', f'''decoder.layers.{i}.ca_qcontent_proj.bias''')
		)
		# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', f'''decoder.layers.{i}.ca_kcontent_proj.bias''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', f'''decoder.layers.{i}.ca_kpos_proj.bias'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.bias''', f'''decoder.layers.{i}.ca_v_proj.bias'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', f'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
		)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)


def 				lowerCamelCase__	(      a	:   Any	,  a	:   Any	,  a	:   List[Any]				)    ->	Any:





			"""simple docstring"""
			a__ :Optional[Any]						  =    state_dict.pop(a				)
			a__ :Tuple						  =    val


def 				lowerCamelCase__	(      a	:   List[str]				)    ->	str:





			"""simple docstring"""
			a__ :Any						  =    OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									a__ :List[Any]						  =    key.replace("backbone.0.body"	,  "backbone.conv_encoder.model"				)
									a__ :List[Any]						  =    value
						else:
									a__ :Dict						  =    value

			return new_state_dict


def 				lowerCamelCase__	(      a	:   str	,  a	:   Dict=False				)    ->	List[Any]:





			"""simple docstring"""
			a__ :Optional[int]						  =    ""
			if is_panoptic:
						a__ :str						  =    "conditional_detr."

			# first: transformer encoder
			for i in range(6				):
						# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
						a__ :str						  =    state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''				)
						a__ :Optional[int]						  =    state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''				)
						# next, add query, keys and values (in that order) to the state dict
						a__ :int						  =    in_proj_weight[:256, :]
						a__ :List[str]						  =    in_proj_bias[:256]
						a__ :List[str]						  =    in_proj_weight[256:512, :]
						a__ :Optional[Any]						  =    in_proj_bias[256:512]
						a__ :Dict						  =    in_proj_weight[-256:, :]
						a__ :Tuple						  =    in_proj_bias[-256:]


def 				lowerCamelCase__	(      )    ->	Optional[Any]:





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

			return im


@torch.no_grad()
def 				lowerCamelCase__	(      a	:   Tuple	,  a	:   Union[str, Any]				)    ->	List[Any]:





			"""simple docstring"""
			a__ :Optional[int]						  =    ConditionalDetrConfig()
			# set backbone and dilation attributes
			if "resnet101" in model_name:
						a__ :str						  =    "resnet101"
			if "dc5" in model_name:
						a__ :Any						  =    True
			a__ :Optional[int]						  =    "panoptic" in model_name
			if is_panoptic:
						a__ :Union[str, Any]						  =    250
			else:
						a__ :str						  =    91
						a__ :Dict						  =    "huggingface/label-files"
						a__ :Optional[Any]						  =    "coco-detection-id2label.json"
						a__ :Dict						  =    json.load(open(hf_hub_download(a	,  a	,  repo_type="dataset"				)	,  "r"				)				)
						a__ :str						  =    {int(a				): v for k, v in idalabel.items()}
						a__ :int						  =    idalabel
						a__ :Dict						  =    {v: k for k, v in idalabel.items()}

			# load image processor
			a__ :Union[str, Any]						  =    "coco_panoptic" if is_panoptic else "coco_detection"
			a__ :Any						  =    ConditionalDetrImageProcessor(format=a				)

			# prepare image
			a__ :int						  =    prepare_img()
			a__ :Optional[int]						  =    image_processor(images=a	,  return_tensors="pt"				)
			a__ :int						  =    encoding["pixel_values"]

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

			# load original model from torch hub
			a__ :str						  =    torch.hub.load("DeppMeng/ConditionalDETR"	,  a	,  pretrained=a				).eval()
			a__ :Optional[int]						  =    conditional_detr.state_dict()
			# rename keys
			for src, dest in rename_keys:
						if is_panoptic:
									a__ :Optional[Any]						  =    "conditional_detr." + src
						rename_key(a	,  a	,  a				)
			a__ :Dict						  =    rename_backbone_keys(a				)
			# query, key and value matrices need special treatment
			read_in_q_k_v(a	,  is_panoptic=a				)
			# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
			a__ :str						  =    "conditional_detr.model." if is_panoptic else "model."
			for key in state_dict.copy().keys():
						if is_panoptic:
									if (
									    key.startswith("conditional_detr"				)
									    and not key.startswith("class_labels_classifier"				)
									    and not key.startswith("bbox_predictor"				)
									):
												a__ :List[Any]						  =    state_dict.pop(a				)
												a__ :str						  =    val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												a__ :str						  =    state_dict.pop(a				)
												a__ :str						  =    val
									elif key.startswith("bbox_attention"				) or key.startswith("mask_head"				):
												continue
									else:
												a__ :int						  =    state_dict.pop(a				)
												a__ :str						  =    val
						else:
									if not key.startswith("class_labels_classifier"				) and not key.startswith("bbox_predictor"				):
												a__ :str						  =    state_dict.pop(a				)
												a__ :Optional[Any]						  =    val
    # finally, create HuggingFace model and load state dict
			a__ :str						  =    ConditionalDetrForSegmentation(a				) if is_panoptic else ConditionalDetrForObjectDetection(a				)
			model.load_state_dict(a				)
			model.eval()
			model.push_to_hub(repo_id=a	,  organization="DepuMeng"	,  commit_message="Add model"				)
			# verify our conversion
			a__ :Union[str, Any]						  =    conditional_detr(a				)
			a__ :Optional[int]						  =    model(a				)
			assert torch.allclose(outputs.logits	,  original_outputs["pred_logits"]	,  atol=1e-4				)
			assert torch.allclose(outputs.pred_boxes	,  original_outputs["pred_boxes"]	,  atol=1e-4				)
			if is_panoptic:
						assert torch.allclose(outputs.pred_masks	,  original_outputs["pred_masks"]	,  atol=1e-4				)

			# Save model and image processor
			logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''				)
			Path(a				).mkdir(exist_ok=a				)
			model.save_pretrained(a				)
			image_processor.save_pretrained(a				)


if __name__ == "__main__":
		snake_case__			 =						argparse.ArgumentParser()

		parser.add_argument(
		    '''--model_name''',
		    default='''conditional_detr_resnet50''',
		    type=str,
		    help='''Name of the CONDITIONAL_DETR model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
		)
		snake_case__			 =						parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


