






import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel






class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       def __init__(				self	:int,      snake_case	:Dict,      snake_case	:Union[str, Any]=99,      snake_case	:List[Any]=13,      snake_case	:Optional[int]=7,      snake_case	:str=9,      snake_case	:Any=True,      snake_case	:Tuple=True,      snake_case	:Dict=False,      snake_case	:Any=32,      snake_case	:Union[str, Any]=5,      snake_case	:Tuple=4,      snake_case	:Tuple=37,      snake_case	:Tuple=8,      snake_case	:Union[str, Any]=0.1,      snake_case	:Union[str, Any]=0.0_0_2,      snake_case	:Optional[int]=1,      snake_case	:List[Any]=0,      snake_case	:str=0,      snake_case	:Dict=None,      snake_case	:int=None,      ):



          """simple docstring"""

          _lowercase              =parent
          _lowercase              =batch_size
          _lowercase              =encoder_seq_length
          _lowercase              =decoder_seq_length
          # For common tests
          _lowercase              =self.decoder_seq_length
          _lowercase              =is_training
          _lowercase              =use_attention_mask
          _lowercase              =use_labels
          _lowercase              =vocab_size
          _lowercase              =hidden_size
          _lowercase              =num_hidden_layers
          _lowercase              =num_attention_heads
          _lowercase              =d_ff
          _lowercase              =relative_attention_num_buckets
          _lowercase              =dropout_rate
          _lowercase              =initializer_factor
          _lowercase              =eos_token_id
          _lowercase              =pad_token_id
          _lowercase              =decoder_start_token_id
          _lowercase              =None
          _lowercase              =decoder_layers





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          return TaConfig.from_pretrained('google/umt5-base')





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:Any,      snake_case	:List[str],      snake_case	:Optional[int],      snake_case	:Any=None,      snake_case	:int=None,      snake_case	:Optional[Any]=None,      snake_case	:Dict=None,      snake_case	:Optional[Any]=None,      ):



          """simple docstring"""

          if attention_mask is None:
             _lowercase              =input_ids.ne(config.pad_token_id)
          if decoder_attention_mask is None:
             _lowercase              =decoder_input_ids.ne(config.pad_token_id)
          if head_mask is None:
             _lowercase              =torch.ones(config.num_hidden_layers,      config.num_attention_heads,      device=snake_case)
          if decoder_head_mask is None:
             _lowercase              =torch.ones(config.num_decoder_layers,      config.num_attention_heads,      device=snake_case)
          if cross_attn_head_mask is None:
             _lowercase              =torch.ones(
                 config.num_decoder_layers,      config.num_attention_heads,      device=snake_case)
          return {
              "input_ids": input_ids,
              "decoder_input_ids": decoder_input_ids,
              "attention_mask": attention_mask,
              "decoder_attention_mask": decoder_attention_mask,
              "head_mask": head_mask,
              "decoder_head_mask": decoder_head_mask,
              "cross_attn_head_mask": cross_attn_head_mask,
          }





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

          _lowercase              =ids_tensor([self.batch_size, self.encoder_seq_length],      self.vocab_size)
          _lowercase              =ids_tensor([self.batch_size, self.decoder_seq_length],      self.vocab_size)

          # we need to clamp the input ids here to avoid having pad token in between
          # this is because for NllbMoe the position_ids are prepared such that
          # all pad tokens have pos id = 2 and rest are between 2..seq_length
          # and the seq_length here is seq_length - num_pad_tokens
          # but when using past, there is no way of knowing if the past input ids had
          # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
          # position_ids being off by num_pad_tokens in past input
          _lowercase              =input_ids.clamp(self.pad_token_id + 1)
          _lowercase              =decoder_input_ids.clamp(self.pad_token_id + 1)

          _lowercase              =self.get_config()
          _lowercase              =config.num_attention_heads
          _lowercase              =self.prepare_inputs_dict(snake_case,      snake_case,      snake_case)
          return config, input_dict





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          _lowercase       ,       _lowercase              =self.prepare_config_and_inputs()
          return config, inputs_dict





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          return TaConfig(
              vocab_size=166,      d_model=self.hidden_size,      d_ff=self.d_ff,      d_kv=self.hidden_size // self.num_attention_heads,      num_layers=self.num_hidden_layers,      num_decoder_layers=self.decoder_layers,      num_heads=self.num_attention_heads,      relative_attention_num_buckets=self.relative_attention_num_buckets,      dropout_rate=self.dropout_rate,      initializer_factor=self.initializer_factor,      eos_token_id=self.eos_token_id,      bos_token_id=self.pad_token_id,      pad_token_id=self.pad_token_id,      decoder_start_token_id=self.decoder_start_token_id,      )





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          return TaConfig(
              vocab_size=self.vocab_size,      d_model=self.hidden_size,      d_ff=self.d_ff,      d_kv=self.hidden_size // self.num_attention_heads,      num_layers=self.num_hidden_layers,      num_decoder_layers=self.decoder_layers,      num_heads=self.num_attention_heads,      relative_attention_num_buckets=self.relative_attention_num_buckets,      dropout_rate=self.dropout_rate,      initializer_factor=self.initializer_factor,      eos_token_id=self.eos_token_id,      bos_token_id=self.pad_token_id,      pad_token_id=self.pad_token_id,      decoder_start_token_id=self.decoder_start_token_id,      )





       def 				UpperCamelCase__						(				self	:str,      snake_case	:Tuple,      snake_case	:Any,      snake_case	:List[str],      snake_case	:Tuple,      snake_case	:Dict,      snake_case	:List[str],      ):



          """simple docstring"""

          _lowercase              =UMTaModel(config=snake_case)
          model.to(snake_case)
          model.eval()
          _lowercase              =model(
              input_ids=snake_case,      decoder_input_ids=snake_case,      attention_mask=snake_case,      decoder_attention_mask=snake_case,      )
          _lowercase              =model(input_ids=snake_case,      decoder_input_ids=snake_case)
          _lowercase              =result.last_hidden_state
          _lowercase              =result.past_key_values
          _lowercase              =result.encoder_last_hidden_state

          self.parent.assertEqual(encoder_output.size(),      (self.batch_size, self.encoder_seq_length, self.hidden_size))
          self.parent.assertEqual(decoder_output.size(),      (self.batch_size, self.decoder_seq_length, self.hidden_size))
          # There should be `num_layers` key value embeddings stored in decoder_past
          self.parent.assertEqual(len(snake_case),      config.num_layers)
          # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
          self.parent.assertEqual(len(decoder_past[0]),      4)





       def 				UpperCamelCase__						(				self	:Optional[Any],      snake_case	:Any,      snake_case	:List[Any],      snake_case	:Tuple,      snake_case	:List[Any],      snake_case	:Union[str, Any],      snake_case	:Optional[int],      ):



          """simple docstring"""

          _lowercase              =UMTaModel(config=snake_case).get_decoder().to(snake_case).eval()
          # first forward pass
          _lowercase              =model(snake_case,      use_cache=snake_case)
          _lowercase              =model(snake_case)
          _lowercase              =model(snake_case,      use_cache=snake_case)

          self.parent.assertTrue(len(snake_case) == len(snake_case))
          self.parent.assertTrue(len(snake_case) == len(snake_case) + 1)

          _lowercase       ,       _lowercase              =outputs.to_tuple()

          # create hypothetical next token and extent to next_input_ids
          _lowercase              =ids_tensor((self.batch_size, 1),      config.vocab_size)

          # append to next input_ids and
          _lowercase              =torch.cat([input_ids, next_tokens],      dim=-1)

          _lowercase              =model(snake_case)['last_hidden_state']
          _lowercase              =model(snake_case,      past_key_values=snake_case)['last_hidden_state']

          # select random slice
          _lowercase              =ids_tensor((1,),      output_from_past.shape[-1]).item()
          _lowercase              =output_from_no_past[:, -1, random_slice_idx].detach()
          _lowercase              =output_from_past[:, 0, random_slice_idx].detach()

          # test that outputs are equal for slice
          self.parent.assertTrue(torch.allclose(snake_case,      snake_case,      atol=1e-3))





       def 				UpperCamelCase__						(				self	:int,      snake_case	:List[str],      snake_case	:str,      ):



          """simple docstring"""

          _lowercase              =UMTaModel(config=snake_case).to(snake_case).half().eval()
          _lowercase              =model(**snake_case)['last_hidden_state']
          self.parent.assertFalse(torch.isnan(snake_case).any().item())






@require_torch
class 					SCREAMING_SNAKE_CASE_  (       _a  ,						_a  ,						_a  ,						unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		Dict					=(
           (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
       )
       __lowerCAmelCase    :		Optional[int]					=(UMTaForConditionalGeneration,) if is_torch_available() else ()
       __lowerCAmelCase    :		Optional[int]					=(
           {
               '''conversational''': UMTaForConditionalGeneration,
               '''feature-extraction''': UMTaModel,
               '''summarization''': UMTaForConditionalGeneration,
               '''text2text-generation''': UMTaForConditionalGeneration,
               '''translation''': UMTaForConditionalGeneration,
               '''question-answering''': UMTaForQuestionAnswering,
           }
           if is_torch_available()
           else {}
       )
       __lowerCAmelCase    :		Any					=True
       __lowerCAmelCase    :		str					=False
       __lowerCAmelCase    :		Any					=False
       __lowerCAmelCase    :		Tuple					=True
       __lowerCAmelCase    :		Optional[int]					=True
       # The small UMT5 model needs higher percentages for CPU/MP tests
       __lowerCAmelCase    :		List[Any]					=[0.8, 0.9]





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          _lowercase              =UMTaModelTester(self)





       @unittest.skip('Test has a segmentation fault on torch 1.8.0')
       def 				UpperCamelCase__						(				self	:List[str]):



          """simple docstring"""

          _lowercase              =self.model_tester.prepare_config_and_inputs()
          _lowercase              =UMTaModel(config_and_inputs[0]).to(snake_case)
          with tempfile.TemporaryDirectory() as tmpdirname:
             torch.onnx.export(
                 snake_case,      (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]),      f'''{tmpdirname}/t5_test.onnx''',      export_params=snake_case,      opset_version=9,      input_names=['input_ids', 'decoder_input_ids'],      )





       @unittest.skipIf(torch_device == 'cpu',      'Cant do half precision')
       def 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

          _lowercase              =self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model_fpaa_forward(*snake_case)





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          _lowercase              =['encoder_attentions', 'decoder_attentions', 'cross_attentions']
          _lowercase              =self.model_tester.prepare_config_and_inputs()
          _lowercase              =config_and_inputs[0]
          _lowercase              =UMTaForConditionalGeneration(snake_case).eval()
          model.to(snake_case)

          _lowercase              ={
              'head_mask': torch.zeros(config.num_layers,      config.num_heads,      device=snake_case),
              'decoder_head_mask': torch.zeros(config.num_decoder_layers,      config.num_heads,      device=snake_case),
              'cross_attn_head_mask': torch.zeros(config.num_decoder_layers,      config.num_heads,      device=snake_case),
          }

          for attn_name, (name, mask) in zip(snake_case,      head_masking.items()):
             _lowercase              ={name: mask}
             # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
             if name == "head_mask":
                _lowercase              =torch.ones(
                    config.num_decoder_layers,      config.num_heads,      device=snake_case)

             _lowercase              =model.generate(
                 config_and_inputs[1]['input_ids'],      num_beams=1,      max_length=3,      output_attentions=snake_case,      return_dict_in_generate=snake_case,      **snake_case,      )
             # We check the state of decoder_attentions and cross_attentions just from the last step
             _lowercase              =out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
             self.assertEqual(sum([w.sum().item() for w in attn_weights]),      0.0)





       @unittest.skip('Does not work on the tiny model as we keep hitting edge cases.')
       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          pass






@require_torch
@require_sentencepiece
@require_tokenizers
class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       @slow
       @unittest.skip(
           'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged')
       def 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

          _lowercase              =UMTaForConditionalGeneration.from_pretrained('google/umt5-small',      return_dict=snake_case).to(snake_case)
          _lowercase              =AutoTokenizer.from_pretrained('google/umt5-small',      use_fast=snake_case,      legacy=snake_case)
          _lowercase              =[
              'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
              'No se como puedo <extra_id_0>.',
              'This is the reason why we <extra_id_0> them.',
              'The <extra_id_0> walks in <extra_id_1>, seats',
              'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
          ]
          _lowercase              =tokenizer(snake_case,      return_tensors='pt',      padding=snake_case).input_ids
          # fmt: off
          _lowercase              =torch.tensor(
              [
                  [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                  [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
              ])
          # fmt: on
          torch.testing.assert_allclose(snake_case,      snake_case)

          _lowercase              =model.generate(input_ids.to(snake_case))
          _lowercase              =[
              '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
              '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
              '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
              '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
              '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
          ]
          _lowercase              =tokenizer.batch_decode(snake_case)
          self.assertEqual(snake_case,      snake_case)










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()
_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_SCREAMING_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        _snake_case   (_snake_case				:       Optional[int]      ,    _snake_case				:       Dict      ,    _snake_case				:       Dict)					->	str:
   _lowercase              =state_dict.pop(_snake_case)
   _lowercase              =val





def        _snake_case   (_snake_case				:       Any)					->	Union[str, Any]:
   _lowercase              =OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         _lowercase              =key.replace('backbone.0.body'      ,    'backbone.conv_encoder.model')
         _lowercase              =value
      else:
         _lowercase              =value

   return new_state_dict





def        _snake_case   (_snake_case				:       Tuple      ,    _snake_case				:       Tuple=False)					->	str:
   _lowercase              =''
   if is_panoptic:
      _lowercase              ='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)
      _lowercase              =state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''')
      _lowercase              =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
      _lowercase              =in_proj_weight[:256, :]
      _lowercase              =in_proj_bias[:256]
      _lowercase              =in_proj_weight[256:512, :]
      _lowercase              =in_proj_bias[256:512]
      _lowercase              =in_proj_weight[-256:, :]
      _lowercase              =in_proj_bias[-256:]





def        _snake_case   ()					->	List[Any]:
   _lowercase              ='http://images.cocodataset.org/val2017/000000039769.jpg'
   _lowercase              =Image.open(requests.get(_snake_case      ,    stream=_snake_case).raw)

   return im





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

   # load image processor
   _lowercase              ='coco_panoptic' if is_panoptic else 'coco_detection'
   _lowercase              =ConditionalDetrImageProcessor(format=_snake_case)

   # prepare image
   _lowercase              =prepare_img()
   _lowercase              =image_processor(images=_snake_case      ,    return_tensors='pt')
   _lowercase              =encoding['pixel_values']

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

   # load original model from torch hub
   _lowercase              =torch.hub.load('DeppMeng/ConditionalDETR'      ,    _snake_case      ,    pretrained=_snake_case).eval()
   _lowercase              =conditional_detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         _lowercase              ='conditional_detr.' + src
      rename_key(_snake_case      ,    _snake_case      ,    _snake_case)
   _lowercase              =rename_backbone_keys(_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
   _lowercase              ='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')
         ):
            _lowercase              =state_dict.pop(_snake_case)
            _lowercase              =val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            _lowercase              =state_dict.pop(_snake_case)
            _lowercase              =val
         elif key.startswith('bbox_attention') or key.startswith('mask_head'):
            continue
         else:
            _lowercase              =state_dict.pop(_snake_case)
            _lowercase              =val
      else:
         if not key.startswith('class_labels_classifier') and not key.startswith('bbox_predictor'):
            _lowercase              =state_dict.pop(_snake_case)
            _lowercase              =val
    # finally, create HuggingFace model and load state dict
   _lowercase              =ConditionalDetrForSegmentation(_snake_case) if is_panoptic else ConditionalDetrForObjectDetection(_snake_case)
   model.load_state_dict(_snake_case)
   model.eval()
   model.push_to_hub(repo_id=_snake_case      ,    organization='DepuMeng'      ,    commit_message='Add model')
   # verify our conversion
   _lowercase              =conditional_detr(_snake_case)
   _lowercase              =model(_snake_case)
   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(_snake_case).mkdir(exist_ok=_snake_case)
   model.save_pretrained(_snake_case)
   image_processor.save_pretrained(_snake_case)


if __name__ == "__main__":
    _SCREAMING_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."
    )
    _SCREAMING_SNAKE_CASE   =  parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


