'''simple docstring'''

import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch

import pyarrow as pa
import pytest
import requests
from packaging import version

from datasets import config


if config.PY_VERSION < version.parse('''3.8'''):
 import importlib_metadata
else:
 import importlib.metadata as importlib_metadata



def snake_case_      (					__snake_case  :							List[Any]	,   __snake_case  :							str=False)			->					Tuple:
   try:
      lowerCAmelCase_			=      os.environ[key]
   except KeyError:
      # KEY isn't set, default to `default`.
      lowerCAmelCase_			=      default
   else:
      # KEY is set, convert it to True or False.
      try:
         lowerCAmelCase_			=      strtobool(__snake_case)
      except ValueError:
         # More values are supported, but let's keep the message simple.
         raise ValueError(F'''If set, {key} must be yes or no.''')
   return _value


A_     :						Optional[Any]    							=parse_flag_from_env('''RUN_SLOW''', default=False)
A_     :						Optional[int]    							=parse_flag_from_env('''RUN_REMOTE''', default=False)
A_     :						Any    							=parse_flag_from_env('''RUN_LOCAL''', default=True)
A_     :						List[str]    							=parse_flag_from_env('''RUN_PACKAGED''', default=True)

# Compression
A_     :						Tuple    							=pytest.mark.skipif(not config.LZ4_AVAILABLE, reason='''test requires lz4''')
A_     :						int    							=pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason='''test requires py7zr''')
A_     :						List[str]    							=pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason='''test requires zstandard''')

# Audio
A_     :						Optional[int]    							=pytest.mark.skipif(
    # On Windows and OS X, soundfile installs sndfile
    find_spec('''soundfile''') is None or version.parse(importlib_metadata.version('''soundfile''')) < version.parse('''0.12.0'''),
    reason='''test requires sndfile>=0.12.1: \'pip install \"soundfile>=0.12.1\"\'; ''',
)

# Beam
A_     :						Any    							=pytest.mark.skipif(
    not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse('''0.3.2'''),
    reason='''test requires apache-beam and a compatible dill version''',
)

# Dill-cloudpickle compatibility
A_     :						Dict    							=pytest.mark.skipif(
    config.DILL_VERSION <= version.parse('''0.3.2'''),
    reason='''test requires dill>0.3.2 for cloudpickle compatibility''',
)

# Windows
A_     :						Dict    							=pytest.mark.skipif(
    sys.platform == '''win32''',
    reason='''test should not be run on Windows''',
)



def snake_case_      (					__snake_case  :							Optional[Any])			->					int:
   try:
      import faiss  # noqa
   except ImportError:
      lowerCAmelCase_			=      unittest.skip('''test requires faiss''')(__snake_case)
   return test_case



def snake_case_      (					__snake_case  :							List[str])			->					str:
   try:
      import regex  # noqa
   except ImportError:
      lowerCAmelCase_			=      unittest.skip('''test requires regex''')(__snake_case)
   return test_case



def snake_case_      (					__snake_case  :							Any)			->					int:
   try:
      import elasticsearch  # noqa
   except ImportError:
      lowerCAmelCase_			=      unittest.skip('''test requires elasticsearch''')(__snake_case)
   return test_case



def snake_case_      (					__snake_case  :							Optional[Any])			->					Tuple:
   try:
      import sqlalchemy  # noqa
   except ImportError:
      lowerCAmelCase_			=      unittest.skip('''test requires sqlalchemy''')(__snake_case)
   return test_case



def snake_case_      (					__snake_case  :							int)			->					Union[str, Any]:
   if not config.TORCH_AVAILABLE:
      lowerCAmelCase_			=      unittest.skip('''test requires PyTorch''')(__snake_case)
   return test_case



def snake_case_      (					__snake_case  :							Optional[int])			->					List[Any]:
   if not config.TF_AVAILABLE:
      lowerCAmelCase_			=      unittest.skip('''test requires TensorFlow''')(__snake_case)
   return test_case



def snake_case_      (					__snake_case  :							Optional[Any])			->					List[str]:
   if not config.JAX_AVAILABLE:
      lowerCAmelCase_			=      unittest.skip('''test requires JAX''')(__snake_case)
   return test_case



def snake_case_      (					__snake_case  :							Dict)			->					Tuple:
   if not config.PIL_AVAILABLE:
      lowerCAmelCase_			=      unittest.skip('''test requires Pillow''')(__snake_case)
   return test_case



def snake_case_      (					__snake_case  :							Optional[int])			->					Tuple:
   try:
      import transformers  # noqa F401
   except ImportError:
      return unittest.skip('''test requires transformers''')(__snake_case)
   else:
      return test_case



def snake_case_      (					__snake_case  :							List[Any])			->					Union[str, Any]:
   try:
      import tiktoken  # noqa F401
   except ImportError:
      return unittest.skip('''test requires tiktoken''')(__snake_case)
   else:
      return test_case



def snake_case_      (					__snake_case  :							Any)			->					str:
   try:
      import spacy  # noqa F401
   except ImportError:
      return unittest.skip('''test requires spacy''')(__snake_case)
   else:
      return test_case



def snake_case_      (					__snake_case  :							Dict)			->					Union[str, Any]:

   def _require_spacy_model(__snake_case  :							Optional[int]):
      try:
         import spacy  # noqa F401

         spacy.load(__snake_case)
      except ImportError:
         return unittest.skip('''test requires spacy''')(__snake_case)
      except OSError:
         return unittest.skip('''test requires spacy model \'{}\''''.format(__snake_case))(__snake_case)
      else:
         return test_case

   return _require_spacy_model



def snake_case_      (					__snake_case  :							Optional[int])			->					Optional[Any]:
   try:
      import pyspark  # noqa F401
   except ImportError:
      return unittest.skip('''test requires pyspark''')(__snake_case)
   else:
      return test_case



def snake_case_      (					__snake_case  :							List[str])			->					Union[str, Any]:
   try:
      import joblibspark  # noqa F401
   except ImportError:
      return unittest.skip('''test requires joblibspark''')(__snake_case)
   else:
      return test_case



def snake_case_      (					__snake_case  :							Optional[int])			->					Optional[int]:
   if not _run_slow_tests or _run_slow_tests == 0:
      lowerCAmelCase_			=      unittest.skip('''test is slow''')(__snake_case)
   return test_case



def snake_case_      (					__snake_case  :							Optional[Any])			->					List[str]:
   if not _run_local_tests or _run_local_tests == 0:
      lowerCAmelCase_			=      unittest.skip('''test is local''')(__snake_case)
   return test_case



def snake_case_      (					__snake_case  :							Union[str, Any])			->					Any:
   if not _run_packaged_tests or _run_packaged_tests == 0:
      lowerCAmelCase_			=      unittest.skip('''test is packaged''')(__snake_case)
   return test_case



def snake_case_      (					__snake_case  :							List[str])			->					Optional[int]:
   if not _run_remote_tests or _run_remote_tests == 0:
      lowerCAmelCase_			=      unittest.skip('''test requires remote''')(__snake_case)
   return test_case



def snake_case_      (					*__snake_case  :							List[Any])			->					Optional[int]:
   def decorate(cls  :							Optional[int]):
      for name, fn in cls.__dict__.items():
         if callable(__snake_case) and name.startswith('''test'''):
            for decorator in decorators:
               lowerCAmelCase_			=      decorator(__snake_case)
            setattr(cls	,   __snake_case	,   __snake_case)
      return cls

   return decorate


class     __UpperCAmelCase       ( __a		):
  pass






class     __UpperCAmelCase       ( __a		):
  __A :				List[str]											= 0
  __A :				Optional[int]											= 1
  __A :				List[Any]											= 2






@contextmanager
def snake_case_      (					__snake_case  :							Optional[Any]=OfflineSimulationMode.CONNECTION_FAILS	,   __snake_case  :							Optional[Any]=1E-1_6)			->					str:
   lowerCAmelCase_			=      requests.Session().request

   def timeout_request(__snake_case  :							Union[str, Any]	,   __snake_case  :							Optional[int]	,   __snake_case  :							Tuple	,   **__snake_case  :							int):
      # Change the url to an invalid url so that the connection hangs
      lowerCAmelCase_			=      '''https://10.255.255.1'''
      if kwargs.get('''timeout''') is None:
         raise RequestWouldHangIndefinitelyError(
             F'''Tried a call to {url} in offline mode with no timeout set. Please set a timeout.''')
      lowerCAmelCase_			=      timeout
      try:
         return online_request(__snake_case	,   __snake_case	,   **__snake_case)
      except Exception as e:
         # The following changes in the error are just here to make the offline timeout error prettier
         lowerCAmelCase_			=      url
         lowerCAmelCase_			=      e.args[0]
         lowerCAmelCase_			=      (max_retry_error.args[0].replace('''10.255.255.1'''	,   F'''OfflineMock[{url}]'''),)
         lowerCAmelCase_			=      (max_retry_error,)
         raise

   def raise_connection_error(__snake_case  :							Dict	,   __snake_case  :							str	,   **__snake_case  :							Union[str, Any]):
      raise requests.ConnectionError('''Offline mode is enabled.'''	,   request=__snake_case)

   if mode is OfflineSimulationMode.CONNECTION_FAILS:
      with patch('''requests.Session.send'''	,   __snake_case):
         yield
   elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
      # inspired from https://stackoverflow.com/a/904609
      with patch('''requests.Session.request'''	,   __snake_case):
         yield
   elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
      with patch('''datasets.config.HF_DATASETS_OFFLINE'''	,   __snake_case):
         yield
   else:
      raise ValueError('''Please use a value from the OfflineSimulationMode enum.''')



@contextmanager
def snake_case_      (					*__snake_case  :							Any	,   **__snake_case  :							Optional[int])			->					int:
   lowerCAmelCase_			=      str(Path().resolve())
   with tempfile.TemporaryDirectory(*__snake_case	,   **__snake_case) as tmp_dir:
      try:
         os.chdir(__snake_case)
         yield
      finally:
         os.chdir(__snake_case)



@contextmanager
def snake_case_      (					)			->					Tuple:
   import gc

   gc.collect()
   lowerCAmelCase_			=      pa.total_allocated_bytes()
   yield
   assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."



@contextmanager
def snake_case_      (					)			->					Dict:
   import gc

   gc.collect()
   lowerCAmelCase_			=      pa.total_allocated_bytes()
   yield
   assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							str)			->					Any:
   return deepcopy(__snake_case).integers(0	,   100	,   10).tolist() == deepcopy(__snake_case).integers(0	,   100	,   10).tolist()



def snake_case_      (					__snake_case  :							Dict)			->					Dict:
   import decorator
   from requests.exceptions import HTTPError

   def _wrapper(__snake_case  :							Tuple	,   *__snake_case  :							int	,   **__snake_case  :							List[str]):
      try:
         return func(*__snake_case	,   **__snake_case)
      except HTTPError as err:
         if str(__snake_case).startswith('''500''') or str(__snake_case).startswith('''502'''):
            pytest.xfail(str(__snake_case))
         raise err

   return decorator.decorator(_wrapper	,   __snake_case)


class     __UpperCAmelCase       :





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      returncode
     lowerCAmelCase_			=      stdout
     lowerCAmelCase_			=      stderr






async def snake_case_      (					__snake_case  :							str	,   __snake_case  :							Tuple)			->					List[Any]:
   while True:
      lowerCAmelCase_			=      await stream.readline()
      if line:
         callback(__snake_case)
      else:
         break



async def snake_case_      (					__snake_case  :							int	,   __snake_case  :							Any=None	,   __snake_case  :							Optional[Any]=None	,   __snake_case  :							Optional[Any]=None	,   __snake_case  :							Dict=False	,   __snake_case  :							Tuple=False)			->					_RunOutput:
   if echo:
      print('''\nRunning: '''	,   ''' '''.join(__snake_case))

   lowerCAmelCase_			=      await asyncio.create_subprocess_exec(
       cmd[0]	,   *cmd[1:]	,   stdin=__snake_case	,   stdout=asyncio.subprocess.PIPE	,   stderr=asyncio.subprocess.PIPE	,   env=__snake_case	,   )

   # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
   # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
   #
   # If it starts hanging, will need to switch to the following code. The problem is that no data
   # will be seen until it's done and if it hangs for example there will be no debug info.
   # out, err = await p.communicate()
   # return _RunOutput(p.returncode, out, err)

   lowerCAmelCase_			=      []
   lowerCAmelCase_			=      []

   def tee(__snake_case  :							Optional[int]	,   __snake_case  :							Dict	,   __snake_case  :							Optional[int]	,   __snake_case  :							str=""):
      lowerCAmelCase_			=      line.decode('''utf-8''').rstrip()
      sink.append(__snake_case)
      if not quiet:
         print(__snake_case	,   __snake_case	,   file=__snake_case)

    # XXX: the timeout doesn't seem to make any difference here
   await asyncio.wait(
       [
           _read_stream(p.stdout	,   lambda __snake_case: tee(__snake_case	,   __snake_case	,   sys.stdout	,   label='''stdout:''')),
           _read_stream(p.stderr	,   lambda __snake_case: tee(__snake_case	,   __snake_case	,   sys.stderr	,   label='''stderr:''')),
       ]	,   timeout=__snake_case	,   )
   return _RunOutput(await p.wait()	,   __snake_case	,   __snake_case)



def snake_case_      (					__snake_case  :							Tuple	,   __snake_case  :							Any=None	,   __snake_case  :							Any=None	,   __snake_case  :							Optional[int]=180	,   __snake_case  :							List[Any]=False	,   __snake_case  :							str=True)			->					_RunOutput:
   lowerCAmelCase_			=      asyncio.get_event_loop()
   lowerCAmelCase_			=      loop.run_until_complete(
       _stream_subprocess(__snake_case	,   env=__snake_case	,   stdin=__snake_case	,   timeout=__snake_case	,   quiet=__snake_case	,   echo=__snake_case))

   lowerCAmelCase_			=      ''' '''.join(__snake_case)
   if result.returncode > 0:
      lowerCAmelCase_			=      '''\n'''.join(result.stderr)
      raise RuntimeError(
          F'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
          F'''The combined stderr from workers follows:\n{stderr}''')

   # check that the subprocess actually did run and produced some output, should the test rely on
   # the remote side to do the testing
   if not result.stdout and not result.stderr:
      raise RuntimeError(F'''\'{cmd_str}\' produced no output.''')

   return result



def snake_case_      (					)			->					Dict:
   lowerCAmelCase_			=      os.environ.get('''PYTEST_XDIST_WORKER'''	,   '''gw0''')
   lowerCAmelCase_			=      re.sub(R'''^gw'''	,   ''''''	,   __snake_case	,   0	,   re.M)
   return int(__snake_case)






def snake_case_      (					)			->					Dict:
   lowerCAmelCase_			=      29500
   lowerCAmelCase_			=      pytest_xdist_worker_id()
   return port + uniq_delta






'''simple docstring'''

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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
A_     :						Dict    							=logging.get_logger(__name__)



def snake_case_      (					__snake_case  :							Any)			->					Tuple:
   # initialize config
   if "resnet-50" in model_name:
      lowerCAmelCase_			=      ResNetConfig.from_pretrained('''microsoft/resnet-50''')
   elif "resnet-101" in model_name:
      lowerCAmelCase_			=      ResNetConfig.from_pretrained('''microsoft/resnet-101''')
   else:
      raise ValueError('''Model name should include either resnet50 or resnet101''')

   lowerCAmelCase_			=      DetrConfig(use_timm_backbone=__snake_case	,   backbone_config=__snake_case)

   # set label attributes
   lowerCAmelCase_			=      '''panoptic''' in model_name
   if is_panoptic:
      lowerCAmelCase_			=      250
   else:
      lowerCAmelCase_			=      91
      lowerCAmelCase_			=      '''huggingface/label-files'''
      lowerCAmelCase_			=      '''coco-detection-id2label.json'''
      lowerCAmelCase_			=      json.load(open(hf_hub_download(__snake_case	,   __snake_case	,   repo_type='''dataset''')	,   '''r'''))
      lowerCAmelCase_			=      {int(__snake_case): v for k, v in idalabel.items()}
      lowerCAmelCase_			=      idalabel
      lowerCAmelCase_			=      {v: k for k, v in idalabel.items()}

   return config, is_panoptic



def snake_case_      (					__snake_case  :							Optional[Any])			->					Tuple:
   # here we list all keys to be renamed (original name on the left, our name on the right)
   lowerCAmelCase_			=      []

   # stem
   # fmt: off
   rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight'''))
   rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight'''))
   rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias'''))
   rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean'''))
   rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var'''))
   # stages
   for stage_idx in range(len(config.backbone_config.depths)):
      for layer_idx in range(config.backbone_config.depths[stage_idx]):
         # shortcut
         if layer_idx == 0:
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
                ))
         # 3 convs
         for i in range(3):
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
                ))
    # fmt: on

   for i in range(config.encoder_layers):
      # 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}.multihead_attn.out_proj.weight''',
              F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
          ))
      rename_keys.append(
          (
              F'''transformer.decoder.layers.{i}.multihead_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'''))

   # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
   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'''),
       ])

   return rename_keys



def snake_case_      (					__snake_case  :							List[str]	,   __snake_case  :							Tuple	,   __snake_case  :							Optional[Any])			->					List[str]:
   lowerCAmelCase_			=      state_dict.pop(__snake_case)
   lowerCAmelCase_			=      val



def snake_case_      (					__snake_case  :							List[Any]	,   __snake_case  :							Optional[int]=False)			->					str:
   lowerCAmelCase_			=      ''''''
   if is_panoptic:
      lowerCAmelCase_			=      '''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)
      lowerCAmelCase_			=      state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''')
      lowerCAmelCase_			=      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
      lowerCAmelCase_			=      in_proj_weight[:256, :]
      lowerCAmelCase_			=      in_proj_bias[:256]
      lowerCAmelCase_			=      in_proj_weight[256:512, :]
      lowerCAmelCase_			=      in_proj_bias[256:512]
      lowerCAmelCase_			=      in_proj_weight[-256:, :]
      lowerCAmelCase_			=      in_proj_bias[-256:]
   # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
   for i in range(6):
      # read in weights + bias of input projection layer of self-attention
      lowerCAmelCase_			=      state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''')
      lowerCAmelCase_			=      state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''')
      # next, add query, keys and values (in that order) to the state dict
      lowerCAmelCase_			=      in_proj_weight[:256, :]
      lowerCAmelCase_			=      in_proj_bias[:256]
      lowerCAmelCase_			=      in_proj_weight[256:512, :]
      lowerCAmelCase_			=      in_proj_bias[256:512]
      lowerCAmelCase_			=      in_proj_weight[-256:, :]
      lowerCAmelCase_			=      in_proj_bias[-256:]
      # read in weights + bias of input projection layer of cross-attention
      lowerCAmelCase_			=      state_dict.pop(
          F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''')
      lowerCAmelCase_			=      state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''')
      # next, add query, keys and values (in that order) of cross-attention to the state dict
      lowerCAmelCase_			=      in_proj_weight_cross_attn[:256, :]
      lowerCAmelCase_			=      in_proj_bias_cross_attn[:256]
      lowerCAmelCase_			=      in_proj_weight_cross_attn[256:512, :]
      lowerCAmelCase_			=      in_proj_bias_cross_attn[256:512]
      lowerCAmelCase_			=      in_proj_weight_cross_attn[-256:, :]
      lowerCAmelCase_			=      in_proj_bias_cross_attn[-256:]



def snake_case_      (					)			->					str:
   lowerCAmelCase_			=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   lowerCAmelCase_			=      Image.open(requests.get(__snake_case	,   stream=__snake_case).raw)

   return im



@torch.no_grad()
def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							List[Any]=None	,   __snake_case  :							Any=False)			->					Optional[Any]:
   lowerCAmelCase_	,lowerCAmelCase_			=      get_detr_config(__snake_case)

   # load original model from torch hub
   lowerCAmelCase_			=      {
       '''detr-resnet-50''': '''detr_resnet50''',
       '''detr-resnet-101''': '''detr_resnet101''',
   }
   logger.info(F'''Converting model {model_name}...''')
   lowerCAmelCase_			=      torch.hub.load('''facebookresearch/detr'''	,   model_name_to_original_name[model_name]	,   pretrained=__snake_case).eval()
   lowerCAmelCase_			=      detr.state_dict()
   # rename keys
   for src, dest in create_rename_keys(__snake_case):
      if is_panoptic:
         lowerCAmelCase_			=      '''detr.''' + src
      rename_key(__snake_case	,   __snake_case	,   __snake_case)
   # query, key and value matrices need special treatment
   read_in_q_k_v(__snake_case	,   is_panoptic=__snake_case)
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   lowerCAmelCase_			=      '''detr.model.''' if is_panoptic else '''model.'''
   for key in state_dict.copy().keys():
      if is_panoptic:
         if (
             key.startswith('''detr''')
             and not key.startswith('''class_labels_classifier''')
             and not key.startswith('''bbox_predictor''')
         ):
            lowerCAmelCase_			=      state_dict.pop(__snake_case)
            lowerCAmelCase_			=      val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            lowerCAmelCase_			=      state_dict.pop(__snake_case)
            lowerCAmelCase_			=      val
         elif key.startswith('''bbox_attention''') or key.startswith('''mask_head'''):
            continue
         else:
            lowerCAmelCase_			=      state_dict.pop(__snake_case)
            lowerCAmelCase_			=      val
      else:
         if not key.startswith('''class_labels_classifier''') and not key.startswith('''bbox_predictor'''):
            lowerCAmelCase_			=      state_dict.pop(__snake_case)
            lowerCAmelCase_			=      val

    # finally, create HuggingFace model and load state dict
   lowerCAmelCase_			=      DetrForSegmentation(__snake_case) if is_panoptic else DetrForObjectDetection(__snake_case)
   model.load_state_dict(__snake_case)
   model.eval()

   # verify our conversion on an image
   lowerCAmelCase_			=      '''coco_panoptic''' if is_panoptic else '''coco_detection'''
   lowerCAmelCase_			=      DetrImageProcessor(format=__snake_case)

   lowerCAmelCase_			=      processor(images=prepare_img()	,   return_tensors='''pt''')
   lowerCAmelCase_			=      encoding['''pixel_values''']

   lowerCAmelCase_			=      detr(__snake_case)
   lowerCAmelCase_			=      model(__snake_case)

   assert torch.allclose(outputs.logits	,   original_outputs['''pred_logits''']	,   atol=1E-3)
   assert torch.allclose(outputs.pred_boxes	,   original_outputs['''pred_boxes''']	,   atol=1E-3)
   if is_panoptic:
      assert torch.allclose(outputs.pred_masks	,   original_outputs['''pred_masks''']	,   atol=1E-4)
   print('''Looks ok!''')

   if pytorch_dump_folder_path is not None:
      # Save model and image processor
      logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''')
      Path(__snake_case).mkdir(exist_ok=__snake_case)
      model.save_pretrained(__snake_case)
      processor.save_pretrained(__snake_case)

   if push_to_hub:
      # Upload model and image processor to the hub
      logger.info('''Uploading PyTorch model and image processor to the hub...''')
      model.push_to_hub(F'''nielsr/{model_name}''')
      processor.push_to_hub(F'''nielsr/{model_name}''')


if __name__ == "__main__":
 A_     :						Any    							=argparse.ArgumentParser()

 parser.add_argument(
     '''--model_name''',
     default='''detr-resnet-50''',
     type=str,
     choices=['''detr-resnet-50''', '''detr-resnet-101'''],
     help='''Name of the 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.'''
 )
 parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
 A_     :						Optional[int]    							=parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





