



'''simple docstring'''







import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


SCREAMING_SNAKE_CASE__     : List[Any]				=   collections.namedtuple(
    """CheckpointState""", ["""model_dict""", """optimizer_dict""", """scheduler_dict""", """offset""", """epoch""", """encoder_params"""]
)







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		print(F'Reading saved model from {model_file}'     )
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      torch.load(SCREAMING_SNAKE_CASE ,		map_location=lambda SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     : default_restore_location(SCREAMING_SNAKE_CASE ,		'cpu'     )     )
		return CheckpointState(**SCREAMING_SNAKE_CASE     )




class 				__lowerCAmelCase:






			def __init__(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      src_file






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					raise NotImplementedError






			@staticmethod
			def 				_lowercase	(   SCREAMING_SNAKE_CASE  :							Tuple      ,       *SCREAMING_SNAKE_CASE  :							Union[str, Any]      ,       **SCREAMING_SNAKE_CASE  :							List[str]	):




					"""simple docstring"""







					if comp_type.startswith('c'	):
							return DPRContextEncoderState(*SCREAMING_SNAKE_CASE      ,       **SCREAMING_SNAKE_CASE	)
					if comp_type.startswith('q'	):
							return DPRQuestionEncoderState(*SCREAMING_SNAKE_CASE      ,       **SCREAMING_SNAKE_CASE	)
					if comp_type.startswith('r'	):
							return DPRReaderState(*SCREAMING_SNAKE_CASE      ,       **SCREAMING_SNAKE_CASE	)
					else:
							raise ValueError('Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'	)




class 				__lowerCAmelCase(							UpperCamelCase__      ):






			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'	)[0]	)	)
					print(f'Loading DPR biencoder from {self.src_file}'	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      load_states_from_checkpoint(self.src_file	)
					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[int]										=      model.ctx_encoder, 'ctx_model.'
					# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
					SCREAMING_SNAKE_CASE_						:str										=      {'bert_model.embeddings.position_ids': model.ctx_encoder.bert_model.embeddings.position_ids}
					for key, value in saved_state.model_dict.items():
							if key.startswith(SCREAMING_SNAKE_CASE	):
									SCREAMING_SNAKE_CASE_						:Dict										=      key[len(SCREAMING_SNAKE_CASE	) :]
									if not key.startswith('encode_proj.'	):
											SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      'bert_model.' + key
									SCREAMING_SNAKE_CASE_						:int										=      value
					encoder.load_state_dict(SCREAMING_SNAKE_CASE	)
					return model




class 				__lowerCAmelCase(							UpperCamelCase__      ):






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:str										=      DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'	)[0]	)	)
					print(f'Loading DPR biencoder from {self.src_file}'	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      load_states_from_checkpoint(self.src_file	)
					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:str										=      model.question_encoder, 'question_model.'
					# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
					SCREAMING_SNAKE_CASE_						:str										=      {'bert_model.embeddings.position_ids': model.question_encoder.bert_model.embeddings.position_ids}
					for key, value in saved_state.model_dict.items():
							if key.startswith(SCREAMING_SNAKE_CASE	):
									SCREAMING_SNAKE_CASE_						:Optional[int]										=      key[len(SCREAMING_SNAKE_CASE	) :]
									if not key.startswith('encode_proj.'	):
											SCREAMING_SNAKE_CASE_						:int										=      'bert_model.' + key
									SCREAMING_SNAKE_CASE_						:int										=      value
					encoder.load_state_dict(SCREAMING_SNAKE_CASE	)
					return model




class 				__lowerCAmelCase(							UpperCamelCase__      ):






			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      DPRReader(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'	)[0]	)	)
					print(f'Loading DPR reader from {self.src_file}'	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      load_states_from_checkpoint(self.src_file	)
					# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      {
					    'encoder.bert_model.embeddings.position_ids': model.span_predictor.encoder.bert_model.embeddings.position_ids
					}
					for key, value in saved_state.model_dict.items():
							if key.startswith('encoder.'	) and not key.startswith('encoder.encode_proj'	):
									SCREAMING_SNAKE_CASE_						:Tuple										=      'encoder.bert_model.' + key[len('encoder.'	) :]
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      value
					model.span_predictor.load_state_dict(SCREAMING_SNAKE_CASE	)
					return model







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Any										=      Path(SCREAMING_SNAKE_CASE     )
		dest_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE     )

		SCREAMING_SNAKE_CASE_						:List[str]										=      DPRState.from_type(SCREAMING_SNAKE_CASE ,		src_file=SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:Tuple										=      dpr_state.load_dpr_model()
		model.save_pretrained(SCREAMING_SNAKE_CASE     )
		model.from_pretrained(SCREAMING_SNAKE_CASE     )  # sanity check


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__     : List[Any]				=   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--type""", type=str, help="""Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'."""
							)
							parser.add_argument(
							    """--src""",
							    type=str,
							    help=(
							        """Path to the dpr checkpoint file. They can be downloaded from the official DPR repo"""
							        """ https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the"""
							        """ \'retriever\' checkpoints."""
							    ),
							)
							parser.add_argument("""--dest""", type=str, default=None, help="""Path to the output PyTorch model directory.""")
							SCREAMING_SNAKE_CASE__     : str				=   parser.parse_args()

							SCREAMING_SNAKE_CASE__     : List[str]				=   Path(args.src)
							SCREAMING_SNAKE_CASE__     : List[str]				=   f"""converted-{src_file.name}""" if args.dest is None else args.dest
							SCREAMING_SNAKE_CASE__     : Tuple				=   Path(dest_dir)
							assert src_file.exists()
							assert (
							    args.type is not None
							), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
							convert(args.type, src_file, dest_dir)





'''simple docstring'''



import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
	from PIL import Image






class        UpperCAmelCase__						(		unittest.TestCase				,							UpperCamelCase__			):



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						__lowerCAmelCase       				=	load_tool("image-question-answering"  )
						self.tool.setup()
						__lowerCAmelCase       				=	load_tool("image-question-answering"      ,					remote=UpperCamelCase  )



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.tool(UpperCamelCase      ,					"How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.remote_tool(UpperCamelCase      ,					"How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )



					def 							UpperCAmelCase_			(     self  )  ->      Optional[Any]:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.tool(image=UpperCamelCase      ,					question="How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.remote_tool(image=UpperCamelCase      ,					question="How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )