"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ViltImageProcessor






class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=7 ,				_SCREAMING_SNAKE_CASE=3 ,				_SCREAMING_SNAKE_CASE=18 ,				_SCREAMING_SNAKE_CASE=30 ,				_SCREAMING_SNAKE_CASE=400 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=None ,				_SCREAMING_SNAKE_CASE=2 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] ,				_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					size if size is not None else {'shortest_edge': 30}
      __lowerCamelCase    		=					parent
      __lowerCamelCase    		=					batch_size
      __lowerCamelCase    		=					num_channels
      __lowerCamelCase    		=					image_size
      __lowerCamelCase    		=					min_resolution
      __lowerCamelCase    		=					max_resolution
      __lowerCamelCase    		=					do_resize
      __lowerCamelCase    		=					size
      __lowerCamelCase    		=					size_divisor
      __lowerCamelCase    		=					do_normalize
      __lowerCamelCase    		=					image_mean
      __lowerCamelCase    		=					image_std







   def    _A				(	self	):


      '''simple docstring'''

      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=False	):


      '''simple docstring'''

      if not batched:
         __lowerCamelCase    		=					self.size['shortest_edge']
         __lowerCamelCase    		=					image_inputs[0]
         if isinstance(_SCREAMING_SNAKE_CASE ,				Image.Image	):
            __lowerCamelCase		,__lowerCamelCase    		=					image.size
         else:
            __lowerCamelCase		,__lowerCamelCase    		=					image.shape[1], image.shape[2]
         __lowerCamelCase    		=					size / min(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
         if h < w:
            __lowerCamelCase		,__lowerCamelCase    		=					size, scale * w
         else:
            __lowerCamelCase		,__lowerCamelCase    		=					scale * h, size

         __lowerCamelCase    		=					int((1333 / 800) * size	)
         if max(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	) > max_size:
            __lowerCamelCase    		=					max_size / max(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
            __lowerCamelCase    		=					newh * scale
            __lowerCamelCase    		=					neww * scale

         __lowerCamelCase		,__lowerCamelCase    		=					int(newh + 0.5	), int(neww + 0.5	)
         __lowerCamelCase		,__lowerCamelCase    		=					(
             newh // self.size_divisor * self.size_divisor,
             neww // self.size_divisor * self.size_divisor,
         )

      else:
         __lowerCamelCase    		=					[]
         for image in image_inputs:
            __lowerCamelCase		,__lowerCamelCase    		=					self.get_expected_values([image]	)
            expected_values.append((expected_height, expected_width)	)
         __lowerCamelCase    		=					max(_SCREAMING_SNAKE_CASE ,				key=lambda _SCREAMING_SNAKE_CASE	: item[0]	)[0]
         __lowerCamelCase    		=					max(_SCREAMING_SNAKE_CASE ,				key=lambda _SCREAMING_SNAKE_CASE	: item[1]	)[1]

      return expected_height, expected_width






@require_torch
@require_vision
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				unittest.TestCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    ViltImageProcessor if is_vision_available() else None







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					ViltImageProcessingTester(self	)







   @property
   def    _A				(	self	):


      '''simple docstring'''

      return self.image_processor_tester.prepare_image_processor_dict()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.image_processing_class(**self.image_processor_dict	)
      self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'image_mean'	)	)
      self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'image_std'	)	)
      self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'do_normalize'	)	)
      self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'do_resize'	)	)
      self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'size'	)	)
      self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'size_divisor'	)	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.image_processing_class.from_dict(self.image_processor_dict	)
      self.assertEqual(image_processor.size ,				{'shortest_edge': 30}	)

      __lowerCamelCase    		=					self.image_processing_class.from_dict(self.image_processor_dict ,				size=42	)
      self.assertEqual(image_processor.size ,				{'shortest_edge': 42}	)







   def    _A				(	self	):


      '''simple docstring'''

      pass







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.image_processing_class(**self.image_processor_dict	)
      # create random PIL images
      __lowerCamelCase    		=					prepare_image_inputs(self.image_processor_tester ,				equal_resolution=_SCREAMING_SNAKE_CASE	)
      for image in image_inputs:
         self.assertIsInstance(_SCREAMING_SNAKE_CASE ,				Image.Image	)

      # Test not batched input
      __lowerCamelCase    		=					image_processing(image_inputs[0] ,				return_tensors='pt'	).pixel_values

      __lowerCamelCase		,__lowerCamelCase    		=					self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE	)
      self.assertEqual(
          encoded_images.shape ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,				)

      # Test batched
      __lowerCamelCase    		=					image_processing(_SCREAMING_SNAKE_CASE ,				return_tensors='pt'	).pixel_values

      __lowerCamelCase		,__lowerCamelCase    		=					self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE ,				batched=_SCREAMING_SNAKE_CASE	)
      self.assertEqual(
          encoded_images.shape ,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          ) ,				)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.image_processing_class(**self.image_processor_dict	)
      # create random numpy tensors
      __lowerCamelCase    		=					prepare_image_inputs(self.image_processor_tester ,				equal_resolution=_SCREAMING_SNAKE_CASE ,				numpify=_SCREAMING_SNAKE_CASE	)
      for image in image_inputs:
         self.assertIsInstance(_SCREAMING_SNAKE_CASE ,				np.ndarray	)

      # Test not batched input
      __lowerCamelCase    		=					image_processing(image_inputs[0] ,				return_tensors='pt'	).pixel_values

      __lowerCamelCase		,__lowerCamelCase    		=					self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE	)
      self.assertEqual(
          encoded_images.shape ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,				)

      # Test batched
      __lowerCamelCase    		=					image_processing(_SCREAMING_SNAKE_CASE ,				return_tensors='pt'	).pixel_values

      __lowerCamelCase		,__lowerCamelCase    		=					self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE ,				batched=_SCREAMING_SNAKE_CASE	)
      self.assertEqual(
          encoded_images.shape ,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          ) ,				)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.image_processing_class(**self.image_processor_dict	)
      # create random PyTorch tensors
      __lowerCamelCase    		=					prepare_image_inputs(self.image_processor_tester ,				equal_resolution=_SCREAMING_SNAKE_CASE ,				torchify=_SCREAMING_SNAKE_CASE	)
      for image in image_inputs:
         self.assertIsInstance(_SCREAMING_SNAKE_CASE ,				torch.Tensor	)

      # Test not batched input
      __lowerCamelCase    		=					image_processing(image_inputs[0] ,				return_tensors='pt'	).pixel_values

      __lowerCamelCase		,__lowerCamelCase    		=					self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE	)
      self.assertEqual(
          encoded_images.shape ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,				)

      # Test batched
      __lowerCamelCase    		=					image_processing(_SCREAMING_SNAKE_CASE ,				return_tensors='pt'	).pixel_values

      __lowerCamelCase		,__lowerCamelCase    		=					self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE ,				batched=_SCREAMING_SNAKE_CASE	)
      self.assertEqual(
          encoded_images.shape ,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          ) ,				)










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


__UpperCamelCase							:							Tuple    							=					collections.namedtuple(
    "CheckpointState", ["model_dict", "optimizer_dict", "scheduler_dict", "offset", "epoch", "encoder_params"]
)

def       _a	(					SCREAMING_SNAKE_CASE						:					str  ):







 """simple docstring"""



 print(F"Reading saved model from {model_file}"  )
 UpperCamelCase__       :     int 							= 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    __magic_name__						:


     def __init__(   self				:  str				,						lowerCamelCase__				:  Path      )	->   Tuple:





      '''simple docstring'''






      UpperCamelCase__       :     List[str] 							= src_file


     def   UpperCAmelCase__						(   self				:  Dict      )	->   int:





      '''simple docstring'''






      raise NotImplementedError


     @staticmethod
     def   UpperCAmelCase__						(   lowerCamelCase__				:  str				,						*lowerCamelCase__				:  Union[str, Any]				,						**lowerCamelCase__				:  Optional[int]      )	->   "DPRState":





      '''simple docstring'''






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



class    __magic_name__						(  __lowerCAmelCase):


     def   UpperCAmelCase__						(   self				:  List[str]      )	->   Dict:





      '''simple docstring'''






      UpperCamelCase__       :     Union[str, Any] 							= DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''      )[0]      )      )
      print(F"Loading DPR biencoder from {self.src_file}"      )
      UpperCamelCase__       :     int 							= load_states_from_checkpoint(self.src_file      )
      UpperCamelCase__							,					UpperCamelCase__       :     Any 							= model.ctx_encoder, '''ctx_model.'''
      # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
      UpperCamelCase__       :     Union[str, Any] 							= {'''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(lowerCamelCase__      ):
        UpperCamelCase__       :     int 							= key[len(lowerCamelCase__      ) :]
        if not key.startswith('''encode_proj.'''      ):
         UpperCamelCase__       :     Union[str, Any] 							= '''bert_model.''' + key
        UpperCamelCase__       :     Dict 							= value
      encoder.load_state_dict(lowerCamelCase__      )
      return model



class    __magic_name__						(  __lowerCAmelCase):


     def   UpperCAmelCase__						(   self				:  Any      )	->   Dict:





      '''simple docstring'''






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



class    __magic_name__						(  __lowerCAmelCase):


     def   UpperCAmelCase__						(   self				:  int      )	->   Any:





      '''simple docstring'''






      UpperCamelCase__       :     int 							= DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''      )[0]      )      )
      print(F"Loading DPR reader from {self.src_file}"      )
      UpperCamelCase__       :     List[str] 							= load_states_from_checkpoint(self.src_file      )
      # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
      UpperCamelCase__       :     Tuple 							= {
          '''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'''      ):
        UpperCamelCase__       :     List[str] 							= '''encoder.bert_model.''' + key[len('''encoder.'''      ) :]
       UpperCamelCase__       :     List[Any] 							= value
      model.span_predictor.load_state_dict(lowerCamelCase__      )
      return model



def       _a	(					SCREAMING_SNAKE_CASE						:					str     ,			SCREAMING_SNAKE_CASE						:					Path     ,			SCREAMING_SNAKE_CASE						:					Path  ):







 """simple docstring"""



 UpperCamelCase__       :     Any 							= Path(SCREAMING_SNAKE_CASE  )
 dest_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE  )

 UpperCamelCase__       :     str 							= DPRState.from_type(SCREAMING_SNAKE_CASE     ,			src_file=SCREAMING_SNAKE_CASE  )
 UpperCamelCase__       :     str 							= dpr_state.load_dpr_model()
 model.save_pretrained(SCREAMING_SNAKE_CASE  )
 model.from_pretrained(SCREAMING_SNAKE_CASE  )  # sanity check


if __name__ == "__main__":
       __UpperCamelCase							:							Optional[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.")
       __UpperCamelCase							:							Union[str, Any]    							=					parser.parse_args()

       __UpperCamelCase							:							Optional[int]    							=					Path(args.src)
       __UpperCamelCase							:							Dict    							=					f"converted-{src_file.name}" if args.dest is None else args.dest
       __UpperCamelCase							:							str    							=					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)


