





'''simple docstring'''

import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
    import faiss







@require_faiss
class __a (   _snake_case			):





   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       tempfile.mkdtemp()
      __SCREAMING_SNAKE_CASE      							=       8

      # DPR tok
      __SCREAMING_SNAKE_CASE      							=       [
          """[UNK]""",
          """[CLS]""",
          """[SEP]""",
          """[PAD]""",
          """[MASK]""",
          """want""",
          """##want""",
          """##ed""",
          """wa""",
          """un""",
          """runn""",
          """##ing""",
          """,""",
          """low""",
          """lowest""",
      ]
      __SCREAMING_SNAKE_CASE      							=       os.path.join(self.tmpdirname				,"""dpr_tokenizer"""    )
      os.makedirs(lowerCamelCase				,exist_ok=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       os.path.join(lowerCamelCase				,DPR_VOCAB_FILES_NAMES["""vocab_file"""]    )
      with open(self.vocab_file				,"""w"""				,encoding="""utf-8"""    ) as vocab_writer:
         vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]    )    )

      # BART tok
      __SCREAMING_SNAKE_CASE      							=       [
          """l""",
          """o""",
          """w""",
          """e""",
          """r""",
          """s""",
          """t""",
          """i""",
          """d""",
          """n""",
          """\u0120""",
          """\u0120l""",
          """\u0120n""",
          """\u0120lo""",
          """\u0120low""",
          """er""",
          """\u0120lowest""",
          """\u0120newer""",
          """\u0120wider""",
          """<unk>""",
      ]
      __SCREAMING_SNAKE_CASE      							=       dict(zip(lowerCamelCase				,range(len(lowerCamelCase    )    )    )    )
      __SCREAMING_SNAKE_CASE      							=       ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
      __SCREAMING_SNAKE_CASE      							=       {"""unk_token""": """<unk>"""}

      __SCREAMING_SNAKE_CASE      							=       os.path.join(self.tmpdirname				,"""bart_tokenizer"""    )
      os.makedirs(lowerCamelCase				,exist_ok=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       os.path.join(lowerCamelCase				,BART_VOCAB_FILES_NAMES["""vocab_file"""]    )
      __SCREAMING_SNAKE_CASE      							=       os.path.join(lowerCamelCase				,BART_VOCAB_FILES_NAMES["""merges_file"""]    )
      with open(self.vocab_file				,"""w"""				,encoding="""utf-8"""    ) as fp:
         fp.write(json.dumps(lowerCamelCase    ) + """\n"""    )
      with open(self.merges_file				,"""w"""				,encoding="""utf-8"""    ) as fp:
         fp.write("""\n""".join(lowerCamelCase    )    )





   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				,"""dpr_tokenizer"""    )    )





   def      UpperCAmelCase__     (					self				:			str    ):

      '''simple docstring'''

      return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				,"""dpr_tokenizer"""    )    )





   def      UpperCAmelCase__     (					self				:			str    ):

      '''simple docstring'''

      return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname				,"""bart_tokenizer"""    )    )





   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

      shutil.rmtree(self.tmpdirname    )





   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       Dataset.from_dict(
          {
              """id""": ["""0""", """1"""],
              """text""": ["""foo""", """bar"""],
              """title""": ["""Foo""", """Bar"""],
              """embeddings""": [np.ones(self.retrieval_vector_size    ), 2 * np.ones(self.retrieval_vector_size    )],
          }    )
      dataset.add_faiss_index("""embeddings"""				,string_factory="""Flat"""				,metric_type=faiss.METRIC_INNER_PRODUCT    )
      return dataset





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_dataset()
      __SCREAMING_SNAKE_CASE      							=       RagConfig(
          retrieval_vector_size=self.retrieval_vector_size				,question_encoder=DPRConfig().to_dict()				,generator=BartConfig().to_dict()				,)
      with patch("""transformers.models.rag.retrieval_rag.load_dataset"""    ) as mock_load_dataset:
         __SCREAMING_SNAKE_CASE      							=       dataset
         __SCREAMING_SNAKE_CASE      							=       RagRetriever(
             lowerCamelCase				,question_encoder_tokenizer=self.get_dpr_tokenizer()				,generator_tokenizer=self.get_bart_tokenizer()				,)
      return retriever





   def      UpperCAmelCase__     (					self				:			Optional[Any]				,lowerCamelCase				:			bool    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_dataset()
      __SCREAMING_SNAKE_CASE      							=       RagConfig(
          retrieval_vector_size=self.retrieval_vector_size				,question_encoder=DPRConfig().to_dict()				,generator=BartConfig().to_dict()				,index_name="""custom"""				,)
      if from_disk:
         __SCREAMING_SNAKE_CASE      							=       os.path.join(self.tmpdirname				,"""dataset"""    )
         __SCREAMING_SNAKE_CASE      							=       os.path.join(self.tmpdirname				,"""index.faiss"""    )
         dataset.get_index("""embeddings"""    ).save(os.path.join(self.tmpdirname				,"""index.faiss"""    )    )
         dataset.drop_index("""embeddings"""    )
         dataset.save_to_disk(os.path.join(self.tmpdirname				,"""dataset"""    )    )
         del dataset
         __SCREAMING_SNAKE_CASE      							=       RagRetriever(
             lowerCamelCase				,question_encoder_tokenizer=self.get_dpr_tokenizer()				,generator_tokenizer=self.get_bart_tokenizer()				,)
      else:
         __SCREAMING_SNAKE_CASE      							=       RagRetriever(
             lowerCamelCase				,question_encoder_tokenizer=self.get_dpr_tokenizer()				,generator_tokenizer=self.get_bart_tokenizer()				,index=CustomHFIndex(config.retrieval_vector_size				,lowerCamelCase    )				,)
      return retriever





   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       Dataset.from_dict(
          {
              """id""": ["""0""", """1"""],
              """text""": ["""foo""", """bar"""],
              """title""": ["""Foo""", """Bar"""],
              """embeddings""": [np.ones(self.retrieval_vector_size + 1    ), 2 * np.ones(self.retrieval_vector_size + 1    )],
          }    )
      dataset.add_faiss_index("""embeddings"""				,string_factory="""Flat"""				,metric_type=faiss.METRIC_INNER_PRODUCT    )

      __SCREAMING_SNAKE_CASE      							=       os.path.join(self.tmpdirname				,"""hf_bert_base.hnswSQ8_correct_phi_128.c_index"""    )
      dataset.save_faiss_index("""embeddings"""				,index_file_name + """.index.dpr"""    )
      pickle.dump(dataset["""id"""]				,open(index_file_name + """.index_meta.dpr"""				,"""wb"""    )    )

      __SCREAMING_SNAKE_CASE      							=       os.path.join(self.tmpdirname				,"""psgs_w100.tsv.pkl"""    )
      __SCREAMING_SNAKE_CASE      							=       {sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
      pickle.dump(lowerCamelCase				,open(lowerCamelCase				,"""wb"""    )    )

      __SCREAMING_SNAKE_CASE      							=       RagConfig(
          retrieval_vector_size=self.retrieval_vector_size				,question_encoder=DPRConfig().to_dict()				,generator=BartConfig().to_dict()				,index_name="""legacy"""				,index_path=self.tmpdirname				,)
      __SCREAMING_SNAKE_CASE      							=       RagRetriever(
          lowerCamelCase				,question_encoder_tokenizer=self.get_dpr_tokenizer()				,generator_tokenizer=self.get_bart_tokenizer()    )
      return retriever





   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       1
      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_canonical_hf_index_retriever()
      __SCREAMING_SNAKE_CASE      							=       np.array(
          [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]				,dtype=np.floataa    )
      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       retriever.retrieve(lowerCamelCase				,n_docs=lowerCamelCase    )
      self.assertEqual(retrieved_doc_embeds.shape				,(2, n_docs, self.retrieval_vector_size)    )
      self.assertEqual(len(lowerCamelCase    )				,2    )
      self.assertEqual(sorted(doc_dicts[0]    )				,["""embeddings""", """id""", """text""", """title"""]    )
      self.assertEqual(len(doc_dicts[0]["""id"""]    )				,lowerCamelCase    )
      self.assertEqual(doc_dicts[0]["""id"""][0]				,"""1"""    )  # max inner product is reached with second doc
      self.assertEqual(doc_dicts[1]["""id"""][0]				,"""0"""    )  # max inner product is reached with first doc
      self.assertListEqual(doc_ids.tolist()				,[[1], [0]]    )





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_canonical_hf_index_retriever()
      with tempfile.TemporaryDirectory() as tmp_dirname:
         with patch("""transformers.models.rag.retrieval_rag.load_dataset"""    ) as mock_load_dataset:
            __SCREAMING_SNAKE_CASE      							=       self.get_dummy_dataset()
            retriever.save_pretrained(lowerCamelCase    )
            __SCREAMING_SNAKE_CASE      							=       RagRetriever.from_pretrained(lowerCamelCase    )
            self.assertIsInstance(lowerCamelCase				,lowerCamelCase    )
            __SCREAMING_SNAKE_CASE      							=       np.array(
                [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]				,dtype=np.floataa    )
            __SCREAMING_SNAKE_CASE      							=       retriever.retrieve(lowerCamelCase				,n_docs=1    )
            self.assertTrue(out is not None    )





   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       1
      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       np.array(
          [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]				,dtype=np.floataa    )
      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       retriever.retrieve(lowerCamelCase				,n_docs=lowerCamelCase    )
      self.assertEqual(retrieved_doc_embeds.shape				,(2, n_docs, self.retrieval_vector_size)    )
      self.assertEqual(len(lowerCamelCase    )				,2    )
      self.assertEqual(sorted(doc_dicts[0]    )				,["""embeddings""", """id""", """text""", """title"""]    )
      self.assertEqual(len(doc_dicts[0]["""id"""]    )				,lowerCamelCase    )
      self.assertEqual(doc_dicts[0]["""id"""][0]				,"""1"""    )  # max inner product is reached with second doc
      self.assertEqual(doc_dicts[1]["""id"""][0]				,"""0"""    )  # max inner product is reached with first doc
      self.assertListEqual(doc_ids.tolist()				,[[1], [0]]    )





   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase    )
      with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(lowerCamelCase    )
         __SCREAMING_SNAKE_CASE      							=       RagRetriever.from_pretrained(lowerCamelCase    )
         self.assertIsInstance(lowerCamelCase				,lowerCamelCase    )
         __SCREAMING_SNAKE_CASE      							=       np.array(
             [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]				,dtype=np.floataa    )
         __SCREAMING_SNAKE_CASE      							=       retriever.retrieve(lowerCamelCase				,n_docs=1    )
         self.assertTrue(out is not None    )





   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       1
      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       np.array(
          [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]				,dtype=np.floataa    )
      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       retriever.retrieve(lowerCamelCase				,n_docs=lowerCamelCase    )
      self.assertEqual(retrieved_doc_embeds.shape				,(2, n_docs, self.retrieval_vector_size)    )
      self.assertEqual(len(lowerCamelCase    )				,2    )
      self.assertEqual(sorted(doc_dicts[0]    )				,["""embeddings""", """id""", """text""", """title"""]    )
      self.assertEqual(len(doc_dicts[0]["""id"""]    )				,lowerCamelCase    )
      self.assertEqual(doc_dicts[0]["""id"""][0]				,"""1"""    )  # max inner product is reached with second doc
      self.assertEqual(doc_dicts[1]["""id"""][0]				,"""0"""    )  # max inner product is reached with first doc
      self.assertListEqual(doc_ids.tolist()				,[[1], [0]]    )





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase    )
      with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(lowerCamelCase    )
         __SCREAMING_SNAKE_CASE      							=       RagRetriever.from_pretrained(lowerCamelCase    )
         self.assertIsInstance(lowerCamelCase				,lowerCamelCase    )
         __SCREAMING_SNAKE_CASE      							=       np.array(
             [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]				,dtype=np.floataa    )
         __SCREAMING_SNAKE_CASE      							=       retriever.retrieve(lowerCamelCase				,n_docs=1    )
         self.assertTrue(out is not None    )





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       1
      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_legacy_index_retriever()
      __SCREAMING_SNAKE_CASE      							=       np.array(
          [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]				,dtype=np.floataa    )
      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       retriever.retrieve(lowerCamelCase				,n_docs=lowerCamelCase    )
      self.assertEqual(retrieved_doc_embeds.shape				,(2, n_docs, self.retrieval_vector_size)    )
      self.assertEqual(len(lowerCamelCase    )				,2    )
      self.assertEqual(sorted(doc_dicts[0]    )				,["""text""", """title"""]    )
      self.assertEqual(len(doc_dicts[0]["""text"""]    )				,lowerCamelCase    )
      self.assertEqual(doc_dicts[0]["""text"""][0]				,"""bar"""    )  # max inner product is reached with second doc
      self.assertEqual(doc_dicts[1]["""text"""][0]				,"""foo"""    )  # max inner product is reached with first doc
      self.assertListEqual(doc_ids.tolist()				,[[1], [0]]    )





   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_legacy_index_retriever()
      with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(lowerCamelCase    )
         __SCREAMING_SNAKE_CASE      							=       RagRetriever.from_pretrained(lowerCamelCase    )
         self.assertIsInstance(lowerCamelCase				,lowerCamelCase    )
         __SCREAMING_SNAKE_CASE      							=       np.array(
             [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]				,dtype=np.floataa    )
         __SCREAMING_SNAKE_CASE      							=       retriever.retrieve(lowerCamelCase				,n_docs=1    )
         self.assertTrue(out is not None    )





   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      import torch

      __SCREAMING_SNAKE_CASE      							=       1
      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_canonical_hf_index_retriever()
      __SCREAMING_SNAKE_CASE      							=       [[5, 7], [10, 11]]
      __SCREAMING_SNAKE_CASE      							=       np.array(
          [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]				,dtype=np.floataa    )
      __SCREAMING_SNAKE_CASE      							=       retriever(lowerCamelCase				,lowerCamelCase				,prefix=retriever.config.generator.prefix				,n_docs=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       (
          out["""context_input_ids"""],
          out["""context_attention_mask"""],
          out["""retrieved_doc_embeds"""],
      )
      self.assertEqual(retrieved_doc_embeds.shape				,(2, n_docs, self.retrieval_vector_size)    )
      self.assertIsInstance(lowerCamelCase				,lowerCamelCase    )
      self.assertIsInstance(lowerCamelCase				,lowerCamelCase    )
      self.assertIsInstance(lowerCamelCase				,np.ndarray    )

      __SCREAMING_SNAKE_CASE      							=       retriever(
          lowerCamelCase				,lowerCamelCase				,prefix=retriever.config.generator.prefix				,n_docs=lowerCamelCase				,return_tensors="""pt"""				,)
      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       (  # noqa: F841
          out["""context_input_ids"""],
          out["""context_attention_mask"""],
          out["""retrieved_doc_embeds"""],
          out["""doc_ids"""],
      )
      self.assertEqual(retrieved_doc_embeds.shape				,(2, n_docs, self.retrieval_vector_size)    )
      self.assertIsInstance(lowerCamelCase				,torch.Tensor    )
      self.assertIsInstance(lowerCamelCase				,torch.Tensor    )
      self.assertIsInstance(lowerCamelCase				,torch.Tensor    )





   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_dpr_ctx_encoder_tokenizer()
      __SCREAMING_SNAKE_CASE      							=       1
      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase    )
      retriever.set_ctx_encoder_tokenizer(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       [[5, 7], [10, 11]]
      __SCREAMING_SNAKE_CASE      							=       np.array(
          [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]				,dtype=np.floataa    )
      __SCREAMING_SNAKE_CASE      							=       retriever(lowerCamelCase				,lowerCamelCase				,prefix=retriever.config.generator.prefix				,n_docs=lowerCamelCase    )

      self.assertEqual(
          len(lowerCamelCase    )				,6    )  # check whether the retriever output consist of 6 attributes including tokenized docs
      self.assertEqual(
          all(k in out for k in ("""tokenized_doc_ids""", """tokenized_doc_attention_mask""")    )				,lowerCamelCase    )  # check for doc token related keys in dictionary.












'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a        =						logging.get_logger(__name__)

a        =						{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}







class __a (   _snake_case			):
   __UpperCamelCase   : Dict	      =	'deta'
   __UpperCamelCase   : List[str]	      =	{
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
   }





   def __init__(					self				:			Tuple				,lowerCamelCase				:			List[Any]=None				,lowerCamelCase				:			Any=900				,lowerCamelCase				:			int=2048				,lowerCamelCase				:			Any=6				,lowerCamelCase				:			Optional[Any]=2048				,lowerCamelCase				:			str=8				,lowerCamelCase				:			Union[str, Any]=6				,lowerCamelCase				:			List[str]=1024				,lowerCamelCase				:			int=8				,lowerCamelCase				:			Any=0.0				,lowerCamelCase				:			Any=True				,lowerCamelCase				:			Optional[int]="relu"				,lowerCamelCase				:			int=256				,lowerCamelCase				:			Tuple=0.1				,lowerCamelCase				:			Optional[Any]=0.0				,lowerCamelCase				:			Tuple=0.0				,lowerCamelCase				:			List[str]=0.02				,lowerCamelCase				:			Any=1.0				,lowerCamelCase				:			Optional[int]=True				,lowerCamelCase				:			int=False				,lowerCamelCase				:			Optional[Any]="sine"				,lowerCamelCase				:			Dict=5				,lowerCamelCase				:			List[Any]=4				,lowerCamelCase				:			Optional[Any]=4				,lowerCamelCase				:			Any=True				,lowerCamelCase				:			int=300				,lowerCamelCase				:			Any=True				,lowerCamelCase				:			Tuple=True				,lowerCamelCase				:			int=1				,lowerCamelCase				:			Tuple=5				,lowerCamelCase				:			Union[str, Any]=2				,lowerCamelCase				:			Tuple=1				,lowerCamelCase				:			int=1				,lowerCamelCase				:			str=5				,lowerCamelCase				:			Optional[Any]=2				,lowerCamelCase				:			List[Any]=0.1				,lowerCamelCase				:			Union[str, Any]=0.25				,**lowerCamelCase				:			int				,):

      '''simple docstring'''

      if backbone_config is None:
         logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""    )
         __SCREAMING_SNAKE_CASE      							=       CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]    )
      else:
         if isinstance(lowerCamelCase				,lowerCamelCase    ):
            __SCREAMING_SNAKE_CASE      							=       backbone_config.pop("""model_type"""    )
            __SCREAMING_SNAKE_CASE      							=       CONFIG_MAPPING[backbone_model_type]
            __SCREAMING_SNAKE_CASE      							=       config_class.from_dict(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       backbone_config
      __SCREAMING_SNAKE_CASE      							=       num_queries
      __SCREAMING_SNAKE_CASE      							=       max_position_embeddings
      __SCREAMING_SNAKE_CASE      							=       d_model
      __SCREAMING_SNAKE_CASE      							=       encoder_ffn_dim
      __SCREAMING_SNAKE_CASE      							=       encoder_layers
      __SCREAMING_SNAKE_CASE      							=       encoder_attention_heads
      __SCREAMING_SNAKE_CASE      							=       decoder_ffn_dim
      __SCREAMING_SNAKE_CASE      							=       decoder_layers
      __SCREAMING_SNAKE_CASE      							=       decoder_attention_heads
      __SCREAMING_SNAKE_CASE      							=       dropout
      __SCREAMING_SNAKE_CASE      							=       attention_dropout
      __SCREAMING_SNAKE_CASE      							=       activation_dropout
      __SCREAMING_SNAKE_CASE      							=       activation_function
      __SCREAMING_SNAKE_CASE      							=       init_std
      __SCREAMING_SNAKE_CASE      							=       init_xavier_std
      __SCREAMING_SNAKE_CASE      							=       encoder_layerdrop
      __SCREAMING_SNAKE_CASE      							=       auxiliary_loss
      __SCREAMING_SNAKE_CASE      							=       position_embedding_type
      # deformable attributes
      __SCREAMING_SNAKE_CASE      							=       num_feature_levels
      __SCREAMING_SNAKE_CASE      							=       encoder_n_points
      __SCREAMING_SNAKE_CASE      							=       decoder_n_points
      __SCREAMING_SNAKE_CASE      							=       two_stage
      __SCREAMING_SNAKE_CASE      							=       two_stage_num_proposals
      __SCREAMING_SNAKE_CASE      							=       with_box_refine
      __SCREAMING_SNAKE_CASE      							=       assign_first_stage
      if two_stage is True and with_box_refine is False:
         raise ValueError("""If two_stage is True, with_box_refine must be True."""    )
      # Hungarian matcher
      __SCREAMING_SNAKE_CASE      							=       class_cost
      __SCREAMING_SNAKE_CASE      							=       bbox_cost
      __SCREAMING_SNAKE_CASE      							=       giou_cost
      # Loss coefficients
      __SCREAMING_SNAKE_CASE      							=       mask_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       dice_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       bbox_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       giou_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       eos_coefficient
      __SCREAMING_SNAKE_CASE      							=       focal_alpha
      super().__init__(is_encoder_decoder=lowerCamelCase				,**lowerCamelCase    )





   @property
   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      return self.encoder_attention_heads





   @property
   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      return self.d_model





   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       copy.deepcopy(self.__dict__    )
      __SCREAMING_SNAKE_CASE      							=       self.backbone_config.to_dict()
      __SCREAMING_SNAKE_CASE      							=       self.__class__.model_type
      return output





