



'''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 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	def      A	(						self			:     Union[str, Any]		)			->		List[str]:
								UpperCAmelCase :       Dict 							=		tempfile.mkdtemp()
								UpperCAmelCase :       List[Any] 							=		8

								# DPR tok
								UpperCAmelCase :       Optional[int] 							=		[
								    '''[UNK]''',
								    '''[CLS]''',
								    '''[SEP]''',
								    '''[PAD]''',
								    '''[MASK]''',
								    '''want''',
								    '''##want''',
								    '''##ed''',
								    '''wa''',
								    '''un''',
								    '''runn''',
								    '''##ing''',
								    ''',''',
								    '''low''',
								    '''lowest''',
								]
								UpperCAmelCase :       Optional[int] 							=		os.path.join(self.tmpdirname		,			'''dpr_tokenizer'''		)
								os.makedirs(__snake_case		,			exist_ok=__snake_case		)
								UpperCAmelCase :       List[Any] 							=		os.path.join(__snake_case		,			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
								UpperCAmelCase :       Optional[Any] 							=		[
								    '''l''',
								    '''o''',
								    '''w''',
								    '''e''',
								    '''r''',
								    '''s''',
								    '''t''',
								    '''i''',
								    '''d''',
								    '''n''',
								    '''\u0120''',
								    '''\u0120l''',
								    '''\u0120n''',
								    '''\u0120lo''',
								    '''\u0120low''',
								    '''er''',
								    '''\u0120lowest''',
								    '''\u0120newer''',
								    '''\u0120wider''',
								    '''<unk>''',
								]
								UpperCAmelCase :       Any 							=		dict(zip(__snake_case		,			range(len(__snake_case		)		)		)		)
								UpperCAmelCase :       List[str] 							=		['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
								UpperCAmelCase :       str 							=		{'''unk_token''': '''<unk>'''}

								UpperCAmelCase :       List[str] 							=		os.path.join(self.tmpdirname		,			'''bart_tokenizer'''		)
								os.makedirs(__snake_case		,			exist_ok=__snake_case		)
								UpperCAmelCase :       Tuple 							=		os.path.join(__snake_case		,			BART_VOCAB_FILES_NAMES['''vocab_file''']		)
								UpperCAmelCase :       Optional[Any] 							=		os.path.join(__snake_case		,			BART_VOCAB_FILES_NAMES['''merges_file''']		)
								with open(self.vocab_file		,			'''w'''		,			encoding='''utf-8'''		) as fp:
															fp.write(json.dumps(__snake_case		) + '''\n'''		)
								with open(self.merges_file		,			'''w'''		,			encoding='''utf-8'''		) as fp:
															fp.write('''\n'''.join(__snake_case		)		)







	def      A	(						self			:     str		)			->		DPRQuestionEncoderTokenizer:
								return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,			'''dpr_tokenizer'''		)		)







	def      A	(						self			:     List[Any]		)			->		DPRContextEncoderTokenizer:
								return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,			'''dpr_tokenizer'''		)		)







	def      A	(						self			:     Dict		)			->		BartTokenizer:
								return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname		,			'''bart_tokenizer'''		)		)







	def      A	(						self			:     List[str]		)			->		Dict:
								shutil.rmtree(self.tmpdirname		)







	def      A	(						self			:     List[str]		)			->		List[str]:
								UpperCAmelCase :       Union[str, Any] 							=		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      A	(						self			:     Tuple		)			->		Union[str, Any]:
								UpperCAmelCase :       str 							=		self.get_dummy_dataset()
								UpperCAmelCase :       Optional[int] 							=		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:
															UpperCAmelCase :       List[str] 							=		dataset
															UpperCAmelCase :       str 							=		RagRetriever(
															    __snake_case		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		,			)
								return retriever







	def      A	(						self			:     List[Any]		,			__snake_case			:     bool		)			->		List[str]:
								UpperCAmelCase :       Optional[int] 							=		self.get_dummy_dataset()
								UpperCAmelCase :       Dict 							=		RagConfig(
								    retrieval_vector_size=self.retrieval_vector_size		,			question_encoder=DPRConfig().to_dict()		,			generator=BartConfig().to_dict()		,			index_name='''custom'''		,			)
								if from_disk:
															UpperCAmelCase :       int 							=		os.path.join(self.tmpdirname		,			'''dataset'''		)
															UpperCAmelCase :       Optional[Any] 							=		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
															UpperCAmelCase :       str 							=		RagRetriever(
															    __snake_case		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		,			)
								else:
															UpperCAmelCase :       Union[str, Any] 							=		RagRetriever(
															    __snake_case		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		,			index=CustomHFIndex(config.retrieval_vector_size		,			__snake_case		)		,			)
								return retriever







	def      A	(						self			:     Optional[Any]		)			->		Optional[Any]:
								UpperCAmelCase :       Optional[int] 							=		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		)

								UpperCAmelCase :       Dict 							=		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'''		)		)

								UpperCAmelCase :       List[Any] 							=		os.path.join(self.tmpdirname		,			'''psgs_w100.tsv.pkl'''		)
								UpperCAmelCase :       int 							=		{sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
								pickle.dump(__snake_case		,			open(__snake_case		,			'''wb'''		)		)

								UpperCAmelCase :       List[str] 							=		RagConfig(
								    retrieval_vector_size=self.retrieval_vector_size		,			question_encoder=DPRConfig().to_dict()		,			generator=BartConfig().to_dict()		,			index_name='''legacy'''		,			index_path=self.tmpdirname		,			)
								UpperCAmelCase :       Dict 							=		RagRetriever(
								    __snake_case		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		)
								return retriever







	def      A	(						self			:     Dict		)			->		Optional[Any]:
								UpperCAmelCase :       List[Any] 							=		1
								UpperCAmelCase :       Optional[int] 							=		self.get_dummy_canonical_hf_index_retriever()
								UpperCAmelCase :       Union[str, Any] 							=		np.array(
								    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Any 							=		retriever.retrieve(__snake_case		,			n_docs=__snake_case		)
								self.assertEqual(retrieved_doc_embeds.shape		,			(2, n_docs, self.retrieval_vector_size)		)
								self.assertEqual(len(__snake_case		)		,			2		)
								self.assertEqual(sorted(doc_dicts[0]		)		,			['''embeddings''', '''id''', '''text''', '''title''']		)
								self.assertEqual(len(doc_dicts[0]['''id''']		)		,			__snake_case		)
								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      A	(						self			:     Any		)			->		List[str]:
								UpperCAmelCase :       List[Any] 							=		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:
																						UpperCAmelCase :       List[Any] 							=		self.get_dummy_dataset()
																						retriever.save_pretrained(__snake_case		)
																						UpperCAmelCase :       Tuple 							=		RagRetriever.from_pretrained(__snake_case		)
																						self.assertIsInstance(__snake_case		,			__snake_case		)
																						UpperCAmelCase :       List[Any] 							=		np.array(
																						    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
																						UpperCAmelCase :       Optional[int] 							=		retriever.retrieve(__snake_case		,			n_docs=1		)
																						self.assertTrue(out is not None		)







	def      A	(						self			:     Union[str, Any]		)			->		Any:
								UpperCAmelCase :       Any 							=		1
								UpperCAmelCase :       Optional[Any] 							=		self.get_dummy_custom_hf_index_retriever(from_disk=__snake_case		)
								UpperCAmelCase :       str 							=		np.array(
								    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       List[str] 							=		retriever.retrieve(__snake_case		,			n_docs=__snake_case		)
								self.assertEqual(retrieved_doc_embeds.shape		,			(2, n_docs, self.retrieval_vector_size)		)
								self.assertEqual(len(__snake_case		)		,			2		)
								self.assertEqual(sorted(doc_dicts[0]		)		,			['''embeddings''', '''id''', '''text''', '''title''']		)
								self.assertEqual(len(doc_dicts[0]['''id''']		)		,			__snake_case		)
								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      A	(						self			:     Tuple		)			->		Any:
								UpperCAmelCase :       Dict 							=		self.get_dummy_custom_hf_index_retriever(from_disk=__snake_case		)
								with tempfile.TemporaryDirectory() as tmp_dirname:
															retriever.save_pretrained(__snake_case		)
															UpperCAmelCase :       Dict 							=		RagRetriever.from_pretrained(__snake_case		)
															self.assertIsInstance(__snake_case		,			__snake_case		)
															UpperCAmelCase :       Tuple 							=		np.array(
															    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
															UpperCAmelCase :       List[Any] 							=		retriever.retrieve(__snake_case		,			n_docs=1		)
															self.assertTrue(out is not None		)







	def      A	(						self			:     Any		)			->		Optional[Any]:
								UpperCAmelCase :       Optional[int] 							=		1
								UpperCAmelCase :       int 							=		self.get_dummy_custom_hf_index_retriever(from_disk=__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		np.array(
								    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       int 							=		retriever.retrieve(__snake_case		,			n_docs=__snake_case		)
								self.assertEqual(retrieved_doc_embeds.shape		,			(2, n_docs, self.retrieval_vector_size)		)
								self.assertEqual(len(__snake_case		)		,			2		)
								self.assertEqual(sorted(doc_dicts[0]		)		,			['''embeddings''', '''id''', '''text''', '''title''']		)
								self.assertEqual(len(doc_dicts[0]['''id''']		)		,			__snake_case		)
								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      A	(						self			:     List[Any]		)			->		Optional[Any]:
								UpperCAmelCase :       Dict 							=		self.get_dummy_custom_hf_index_retriever(from_disk=__snake_case		)
								with tempfile.TemporaryDirectory() as tmp_dirname:
															retriever.save_pretrained(__snake_case		)
															UpperCAmelCase :       Optional[Any] 							=		RagRetriever.from_pretrained(__snake_case		)
															self.assertIsInstance(__snake_case		,			__snake_case		)
															UpperCAmelCase :       Union[str, Any] 							=		np.array(
															    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
															UpperCAmelCase :       List[Any] 							=		retriever.retrieve(__snake_case		,			n_docs=1		)
															self.assertTrue(out is not None		)







	def      A	(						self			:     Optional[Any]		)			->		List[Any]:
								UpperCAmelCase :       List[str] 							=		1
								UpperCAmelCase :       Union[str, Any] 							=		self.get_dummy_legacy_index_retriever()
								UpperCAmelCase :       Optional[Any] 							=		np.array(
								    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		retriever.retrieve(__snake_case		,			n_docs=__snake_case		)
								self.assertEqual(retrieved_doc_embeds.shape		,			(2, n_docs, self.retrieval_vector_size)		)
								self.assertEqual(len(__snake_case		)		,			2		)
								self.assertEqual(sorted(doc_dicts[0]		)		,			['''text''', '''title''']		)
								self.assertEqual(len(doc_dicts[0]['''text''']		)		,			__snake_case		)
								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      A	(						self			:     List[str]		)			->		List[str]:
								UpperCAmelCase :       int 							=		self.get_dummy_legacy_index_retriever()
								with tempfile.TemporaryDirectory() as tmp_dirname:
															retriever.save_pretrained(__snake_case		)
															UpperCAmelCase :       int 							=		RagRetriever.from_pretrained(__snake_case		)
															self.assertIsInstance(__snake_case		,			__snake_case		)
															UpperCAmelCase :       int 							=		np.array(
															    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
															UpperCAmelCase :       int 							=		retriever.retrieve(__snake_case		,			n_docs=1		)
															self.assertTrue(out is not None		)







	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def      A	(						self			:     int		)			->		Tuple:
								import torch

								UpperCAmelCase :       List[str] 							=		1
								UpperCAmelCase :       List[str] 							=		self.get_dummy_canonical_hf_index_retriever()
								UpperCAmelCase :       str 							=		[[5, 7], [10, 11]]
								UpperCAmelCase :       List[Any] 							=		np.array(
								    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
								UpperCAmelCase :       Optional[int] 							=		retriever(__snake_case		,			__snake_case		,			prefix=retriever.config.generator.prefix		,			n_docs=__snake_case		)
								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       List[Any] 							=		(
								    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(__snake_case		,			__snake_case		)
								self.assertIsInstance(__snake_case		,			__snake_case		)
								self.assertIsInstance(__snake_case		,			np.ndarray		)

								UpperCAmelCase :       Tuple 							=		retriever(
								    __snake_case		,			__snake_case		,			prefix=retriever.config.generator.prefix		,			n_docs=__snake_case		,			return_tensors='''pt'''		,			)
								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Optional[int] 							=		(  # 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(__snake_case		,			torch.Tensor		)
								self.assertIsInstance(__snake_case		,			torch.Tensor		)
								self.assertIsInstance(__snake_case		,			torch.Tensor		)







	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def      A	(						self			:     Optional[int]		)			->		Optional[Any]:
								UpperCAmelCase :       Optional[Any] 							=		self.get_dpr_ctx_encoder_tokenizer()
								UpperCAmelCase :       List[Any] 							=		1
								UpperCAmelCase :       Optional[int] 							=		self.get_dummy_custom_hf_index_retriever(from_disk=__snake_case		)
								retriever.set_ctx_encoder_tokenizer(__snake_case		)

								UpperCAmelCase :       Union[str, Any] 							=		[[5, 7], [10, 11]]
								UpperCAmelCase :       Optional[int] 							=		np.array(
								    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
								UpperCAmelCase :       Optional[int] 							=		retriever(__snake_case		,			__snake_case		,			prefix=retriever.config.generator.prefix		,			n_docs=__snake_case		)

								self.assertEqual(
								    len(__snake_case		)		,			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''')		)		,			__snake_case		)  # check for doc token related keys in dictionary.













import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowerCamelCase	:							Tuple				=logging.get_logger(__name__)

lowerCamelCase	:							Optional[Any]				={
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}





class 			__a  (    A__       ):
 _lowerCAmelCase       :       Optional[Any]        = '''conditional_detr'''
 _lowerCAmelCase       :       List[Any]        = ['''past_key_values''']
 _lowerCAmelCase       :       List[str]        = {
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }







 def __init__(		self					:   str							,   SCREAMING_SNAKE_CASE					:   Tuple=True							,   SCREAMING_SNAKE_CASE					:   Optional[int]=None							,   SCREAMING_SNAKE_CASE					:   Tuple=3							,   SCREAMING_SNAKE_CASE					:   Any=3_00							,   SCREAMING_SNAKE_CASE					:   Tuple=6							,   SCREAMING_SNAKE_CASE					:   int=20_48							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=8							,   SCREAMING_SNAKE_CASE					:   int=6							,   SCREAMING_SNAKE_CASE					:   Dict=20_48							,   SCREAMING_SNAKE_CASE					:   Optional[int]=8							,   SCREAMING_SNAKE_CASE					:   Dict=0.0							,   SCREAMING_SNAKE_CASE					:   List[Any]=0.0							,   SCREAMING_SNAKE_CASE					:   int=True							,   SCREAMING_SNAKE_CASE					:   int="relu"							,   SCREAMING_SNAKE_CASE					:   Any=2_56							,   SCREAMING_SNAKE_CASE					:   int=0.1							,   SCREAMING_SNAKE_CASE					:   int=0.0							,   SCREAMING_SNAKE_CASE					:   Optional[int]=0.0							,   SCREAMING_SNAKE_CASE					:   List[str]=0.0_2							,   SCREAMING_SNAKE_CASE					:   Optional[int]=1.0							,   SCREAMING_SNAKE_CASE					:   List[str]=False							,   SCREAMING_SNAKE_CASE					:   int="sine"							,   SCREAMING_SNAKE_CASE					:   str="resnet50"							,   SCREAMING_SNAKE_CASE					:   int=True							,   SCREAMING_SNAKE_CASE					:   Tuple=False							,   SCREAMING_SNAKE_CASE					:   Tuple=2							,   SCREAMING_SNAKE_CASE					:   Tuple=5							,   SCREAMING_SNAKE_CASE					:   List[str]=2							,   SCREAMING_SNAKE_CASE					:   List[str]=1							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=1							,   SCREAMING_SNAKE_CASE					:   str=2							,   SCREAMING_SNAKE_CASE					:   Any=5							,   SCREAMING_SNAKE_CASE					:   int=2							,   SCREAMING_SNAKE_CASE					:   List[Any]=0.2_5							,   **SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   ):


      '''simple docstring'''






      if backbone_config is not None and use_timm_backbone:
           raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."						)

      if not use_timm_backbone:
           if backbone_config is None:
                logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."						)
                UpperCamelCase__			:   Tuple       				=  CONFIG_MAPPING["resnet"](out_features=["stage4"]						)
           elif isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
                UpperCamelCase__			:   Union[str, Any]       				=  backbone_config.get("model_type"						)
                UpperCamelCase__			:   Tuple       				=  CONFIG_MAPPING[backbone_model_type]
                UpperCamelCase__			:   Any       				=  config_class.from_dict(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[Any]       				=  use_timm_backbone
      UpperCamelCase__			:   List[Any]       				=  backbone_config
      UpperCamelCase__			:   Tuple       				=  num_channels
      UpperCamelCase__			:   List[Any]       				=  num_queries
      UpperCamelCase__			:   Dict       				=  d_model
      UpperCamelCase__			:   Any       				=  encoder_ffn_dim
      UpperCamelCase__			:   List[str]       				=  encoder_layers
      UpperCamelCase__			:   List[str]       				=  encoder_attention_heads
      UpperCamelCase__			:   Optional[int]       				=  decoder_ffn_dim
      UpperCamelCase__			:   str       				=  decoder_layers
      UpperCamelCase__			:   Optional[Any]       				=  decoder_attention_heads
      UpperCamelCase__			:   int       				=  dropout
      UpperCamelCase__			:   Optional[int]       				=  attention_dropout
      UpperCamelCase__			:   Any       				=  activation_dropout
      UpperCamelCase__			:   int       				=  activation_function
      UpperCamelCase__			:   int       				=  init_std
      UpperCamelCase__			:   List[Any]       				=  init_xavier_std
      UpperCamelCase__			:   List[str]       				=  encoder_layerdrop
      UpperCamelCase__			:   List[Any]       				=  decoder_layerdrop
      UpperCamelCase__			:   List[Any]       				=  encoder_layers
      UpperCamelCase__			:   Optional[Any]       				=  auxiliary_loss
      UpperCamelCase__			:   List[str]       				=  position_embedding_type
      UpperCamelCase__			:   Optional[Any]       				=  backbone
      UpperCamelCase__			:   Optional[int]       				=  use_pretrained_backbone
      UpperCamelCase__			:   List[Any]       				=  dilation
      # Hungarian matcher
      UpperCamelCase__			:   List[str]       				=  class_cost
      UpperCamelCase__			:   Union[str, Any]       				=  bbox_cost
      UpperCamelCase__			:   int       				=  giou_cost
      # Loss coefficients
      UpperCamelCase__			:   str       				=  mask_loss_coefficient
      UpperCamelCase__			:   List[Any]       				=  dice_loss_coefficient
      UpperCamelCase__			:   int       				=  cls_loss_coefficient
      UpperCamelCase__			:   Tuple       				=  bbox_loss_coefficient
      UpperCamelCase__			:   Any       				=  giou_loss_coefficient
      UpperCamelCase__			:   List[Any]       				=  focal_alpha
      super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







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


      '''simple docstring'''






      return self.encoder_attention_heads







 @property
 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      return self.d_model







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  copy.deepcopy(self.__dict__						)
      if self.backbone_config is not None:
           UpperCamelCase__			:   List[str]       				=  self.backbone_config.to_dict()
      UpperCamelCase__			:   int       				=  self.__class__.model_type
      return output







class 			__a  (    A__       ):
 _lowerCAmelCase       :       Tuple        = version.parse('''1.11'''       )







 @property
 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      return OrderedDict(
          [
              ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ("pixel_mask", {0: "batch"}),
          ]						)







 @property
 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      return 1e-5







 @property
 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      return 12