






import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


snake_case__   :						Optional[int]													=   get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")


@require_sentencepiece
@require_tokenizers
class     _a							(	__snake_case    ,							unittest.TestCase     ):






  """simple docstring"""





  A_						       = PegasusTokenizer
  A_						       = PegasusTokenizerFast
  A_						       = True
  A_						       = True




  def  _UpperCAmelCase  (  self			)      ->			Any:
     super().setUp()

     # We have a SentencePiece fixture for testing
     UpperCamelCase_						=  PegasusTokenizer(__lowerCamelCase			)
     tokenizer.save_pretrained(self.tmpdirname			)




  @cached_property
  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     return PegasusTokenizer.from_pretrained('google/pegasus-large'			)




  def  _UpperCAmelCase  (  self		,		**_UpperCAmelCase			)      ->			str:
     return PegasusTokenizer.from_pretrained(self.tmpdirname		,		**__lowerCamelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Tuple:
     return ("This is a test", "This is a test")




  def  _UpperCAmelCase  (  self			)      ->			Optional[Any]:
     UpperCamelCase_						=  '</s>'
     UpperCamelCase_						=  1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase			)		,		__lowerCamelCase			)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase			)		,		__lowerCamelCase			)




  def  _UpperCAmelCase  (  self			)      ->			Any:
     UpperCamelCase_						=  list(self.get_tokenizer().get_vocab().keys()			)

     self.assertEqual(vocab_keys[0]		,		'<pad>'			)
     self.assertEqual(vocab_keys[1]		,		'</s>'			)
     self.assertEqual(vocab_keys[-1]		,		'v'			)
     self.assertEqual(len(__lowerCamelCase			)		,		1103			)




  def  _UpperCAmelCase  (  self			)      ->			Dict:
     self.assertEqual(self.get_tokenizer().vocab_size		,		1103			)




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     UpperCamelCase_						=  self.rust_tokenizer_class.from_pretrained(self.tmpdirname			)
     UpperCamelCase_						=  self.tokenizer_class.from_pretrained(self.tmpdirname			)
     UpperCamelCase_						=  (
         'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
         ' </s> <pad> <pad> <pad>'
     )
     UpperCamelCase_						=  rust_tokenizer([raw_input_str]		,		return_tensors=__lowerCamelCase		,		add_special_tokens=__lowerCamelCase			).input_ids[0]
     UpperCamelCase_						=  py_tokenizer([raw_input_str]		,		return_tensors=__lowerCamelCase		,		add_special_tokens=__lowerCamelCase			).input_ids[0]
     self.assertListEqual(__lowerCamelCase		,		__lowerCamelCase			)




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     UpperCamelCase_						=  self._large_tokenizer
     # <mask_1> masks whole sentence while <mask_2> masks single word
     UpperCamelCase_						=  '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
     UpperCamelCase_						=  [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
     UpperCamelCase_						=  tokenizer([raw_input_str]		,		return_tensors=__lowerCamelCase			).input_ids[0]
     self.assertListEqual(__lowerCamelCase		,		__lowerCamelCase			)




  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     UpperCamelCase_						=  self._large_tokenizer
     # The tracebacks for the following asserts are **better** without messages or self.assertEqual
     assert tokenizer.vocab_size == 96103
     assert tokenizer.pad_token_id == 0
     assert tokenizer.eos_token_id == 1
     assert tokenizer.offset == 103
     assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
     assert tokenizer.unk_token == "<unk>"
     assert tokenizer.model_max_length == 1024
     UpperCamelCase_						=  'To ensure a smooth flow of bank resolutions.'
     UpperCamelCase_						=  [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
     UpperCamelCase_						=  tokenizer([raw_input_str]		,		return_tensors=__lowerCamelCase			).input_ids[0]
     self.assertListEqual(__lowerCamelCase		,		__lowerCamelCase			)
     assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]			) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]




  @require_torch
  def  _UpperCAmelCase  (  self			)      ->			str:
     UpperCamelCase_						=  ['This is going to be way too long.' * 150, 'short example']
     UpperCamelCase_						=  ['not super long but more than 5 tokens', 'tiny']
     UpperCamelCase_						=  self._large_tokenizer(__lowerCamelCase		,		padding=__lowerCamelCase		,		truncation=__lowerCamelCase		,		return_tensors='pt'			)
     UpperCamelCase_						=  self._large_tokenizer(
         text_target=__lowerCamelCase		,		max_length=5		,		padding=__lowerCamelCase		,		truncation=__lowerCamelCase		,		return_tensors='pt'			)

     assert batch.input_ids.shape == (2, 1024)
     assert batch.attention_mask.shape == (2, 1024)
     assert targets["input_ids"].shape == (2, 5)
     assert len(__lowerCamelCase			) == 2  # input_ids, attention_mask.




  @slow
  def  _UpperCAmelCase  (  self			)      ->			str:
     # fmt: off
     UpperCamelCase_						=  {'input_ids': [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=__lowerCamelCase		,		model_name='google/bigbird-pegasus-large-arxiv'		,		revision='ba85d0851d708441f91440d509690f1ab6353415'		,		)




@require_sentencepiece
@require_tokenizers
class     _a							(	__snake_case    ,							unittest.TestCase     ):






  """simple docstring"""





  A_						       = PegasusTokenizer
  A_						       = PegasusTokenizerFast
  A_						       = True
  A_						       = True




  def  _UpperCAmelCase  (  self			)      ->			str:
     super().setUp()

     # We have a SentencePiece fixture for testing
     UpperCamelCase_						=  PegasusTokenizer(__lowerCamelCase		,		offset=0		,		mask_token_sent=__lowerCamelCase		,		mask_token='[MASK]'			)
     tokenizer.save_pretrained(self.tmpdirname			)




  @cached_property
  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'			)




  def  _UpperCAmelCase  (  self		,		**_UpperCAmelCase			)      ->			List[Any]:
     return PegasusTokenizer.from_pretrained(self.tmpdirname		,		**__lowerCamelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			List[Any]:
     return ("This is a test", "This is a test")




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     UpperCamelCase_						=  self.rust_tokenizer_class.from_pretrained(self.tmpdirname			)
     UpperCamelCase_						=  self.tokenizer_class.from_pretrained(self.tmpdirname			)
     UpperCamelCase_						=  (
         'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
         ' <pad> <pad> <pad>'
     )
     UpperCamelCase_						=  rust_tokenizer([raw_input_str]		,		return_tensors=__lowerCamelCase		,		add_special_tokens=__lowerCamelCase			).input_ids[0]
     UpperCamelCase_						=  py_tokenizer([raw_input_str]		,		return_tensors=__lowerCamelCase		,		add_special_tokens=__lowerCamelCase			).input_ids[0]
     self.assertListEqual(__lowerCamelCase		,		__lowerCamelCase			)




  @require_torch
  def  _UpperCAmelCase  (  self			)      ->			int:
     UpperCamelCase_						=  ['This is going to be way too long.' * 1000, 'short example']
     UpperCamelCase_						=  ['not super long but more than 5 tokens', 'tiny']
     UpperCamelCase_						=  self._large_tokenizer(__lowerCamelCase		,		padding=__lowerCamelCase		,		truncation=__lowerCamelCase		,		return_tensors='pt'			)
     UpperCamelCase_						=  self._large_tokenizer(
         text_target=__lowerCamelCase		,		max_length=5		,		padding=__lowerCamelCase		,		truncation=__lowerCamelCase		,		return_tensors='pt'			)

     assert batch.input_ids.shape == (2, 4096)
     assert batch.attention_mask.shape == (2, 4096)
     assert targets["input_ids"].shape == (2, 5)
     assert len(__lowerCamelCase			) == 2  # input_ids, attention_mask.




  def  _UpperCAmelCase  (  self			)      ->			Dict:
     UpperCamelCase_						=  (
         'This is an example string that is used to test the original TF implementation against the HF'
         ' implementation'
     )

     UpperCamelCase_						=  self._large_tokenizer(__lowerCamelCase			).input_ids

     self.assertListEqual(
         __lowerCamelCase		,		[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]		,		)








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()
__A			:					Union[str, Any]						=			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__A			:					List[Any]						=			[]
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 	__a     (      A__			:  Dict			,  A__			:  Dict			,  A__			:  Any       ):
    SCREAMING_SNAKE_CASE										=   state_dict.pop(A__       )
    SCREAMING_SNAKE_CASE										=   val





def 	__a     (      A__			:  Optional[int]       ):
    SCREAMING_SNAKE_CASE										=   OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            SCREAMING_SNAKE_CASE										=   key.replace("backbone.0.body"			,  "backbone.conv_encoder.model"       )
            SCREAMING_SNAKE_CASE										=   value
        else:
            SCREAMING_SNAKE_CASE										=   value

    return new_state_dict





def 	__a     (      A__			:  Optional[Any]			,  A__			:  Tuple=False       ):
    SCREAMING_SNAKE_CASE										=   ""
    if is_panoptic:
        SCREAMING_SNAKE_CASE										=   "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)
        SCREAMING_SNAKE_CASE										=   state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"       )
        SCREAMING_SNAKE_CASE										=   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
        SCREAMING_SNAKE_CASE										=   in_proj_weight[:256, :]
        SCREAMING_SNAKE_CASE										=   in_proj_bias[:256]
        SCREAMING_SNAKE_CASE										=   in_proj_weight[256:512, :]
        SCREAMING_SNAKE_CASE										=   in_proj_bias[256:512]
        SCREAMING_SNAKE_CASE										=   in_proj_weight[-256:, :]
        SCREAMING_SNAKE_CASE										=   in_proj_bias[-256:]





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

    return im





@torch.no_grad()
def 	__a     (      A__			:  List[str]			,  A__			:  Union[str, Any]       ):
    SCREAMING_SNAKE_CASE										=   ConditionalDetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        SCREAMING_SNAKE_CASE										=   "resnet101"
    if "dc5" in model_name:
        SCREAMING_SNAKE_CASE										=   True
    SCREAMING_SNAKE_CASE										=   "panoptic" in model_name
    if is_panoptic:
        SCREAMING_SNAKE_CASE										=   250
    else:
        SCREAMING_SNAKE_CASE										=   91
        SCREAMING_SNAKE_CASE										=   "huggingface/label-files"
        SCREAMING_SNAKE_CASE										=   "coco-detection-id2label.json"
        SCREAMING_SNAKE_CASE										=   json.load(open(hf_hub_download(A__			,  A__			,  repo_type="dataset"       )			,  "r"       )       )
        SCREAMING_SNAKE_CASE										=   {int(A__       ): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE										=   idalabel
        SCREAMING_SNAKE_CASE										=   {v: k for k, v in idalabel.items()}

    # load image processor
    SCREAMING_SNAKE_CASE										=   "coco_panoptic" if is_panoptic else "coco_detection"
    SCREAMING_SNAKE_CASE										=   ConditionalDetrImageProcessor(format=A__       )

    # prepare image
    SCREAMING_SNAKE_CASE										=   prepare_img()
    SCREAMING_SNAKE_CASE										=   image_processor(images=A__			,  return_tensors="pt"       )
    SCREAMING_SNAKE_CASE										=   encoding["pixel_values"]

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

    # load original model from torch hub
    SCREAMING_SNAKE_CASE										=   torch.hub.load("DeppMeng/ConditionalDETR"			,  A__			,  pretrained=A__       ).eval()
    SCREAMING_SNAKE_CASE										=   conditional_detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            SCREAMING_SNAKE_CASE										=   "conditional_detr." + src
        rename_key(A__			,  A__			,  A__       )
    SCREAMING_SNAKE_CASE										=   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
    SCREAMING_SNAKE_CASE										=   "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"       )
            ):
                SCREAMING_SNAKE_CASE										=   state_dict.pop(A__       )
                SCREAMING_SNAKE_CASE										=   val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                SCREAMING_SNAKE_CASE										=   state_dict.pop(A__       )
                SCREAMING_SNAKE_CASE										=   val
            elif key.startswith("bbox_attention"       ) or key.startswith("mask_head"       ):
                continue
            else:
                SCREAMING_SNAKE_CASE										=   state_dict.pop(A__       )
                SCREAMING_SNAKE_CASE										=   val
        else:
            if not key.startswith("class_labels_classifier"       ) and not key.startswith("bbox_predictor"       ):
                SCREAMING_SNAKE_CASE										=   state_dict.pop(A__       )
                SCREAMING_SNAKE_CASE										=   val
    # finally, create HuggingFace model and load state dict
    SCREAMING_SNAKE_CASE										=   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
    SCREAMING_SNAKE_CASE										=   conditional_detr(A__       )
    SCREAMING_SNAKE_CASE										=   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__":
    __A			:					str						=			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.'
    )
    __A			:					int						=			parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)