




'''simple docstring'''







import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_torch_available():
   from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
   A			   =False

if is_vision_available():
   from PIL import Image

   from transformers import (
       AutoProcessor,
       PixaStructImageProcessor,
       PixaStructProcessor,
       PreTrainedTokenizerFast,
       TaTokenizer,
   )


@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11							,							reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""							,							)
@require_vision
@require_torch
class       _a  (     unittest.TestCase			):
       def    A				(     self						:     Optional[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		tempfile.mkdtemp()

           UpperCAmelCase      	=		PixaStructImageProcessor()
           UpperCAmelCase      	=		TaTokenizer.from_pretrained('''t5-small'''    )

           UpperCAmelCase      	=		PixaStructProcessor(lowercase       ,    lowercase    )

           processor.save_pretrained(self.tmpdirname    )
       def    A				(     self						:     str       ,    **lowercase						:     Optional[int]    ):



           '''simple docstring'''

           return AutoProcessor.from_pretrained(self.tmpdirname       ,    **lowercase    ).tokenizer
       def    A				(     self						:     str       ,    **lowercase						:     List[Any]    ):



           '''simple docstring'''

           return AutoProcessor.from_pretrained(self.tmpdirname       ,    **lowercase    ).image_processor
       def    A				(     self						:     Optional[int]    ):



           '''simple docstring'''

           shutil.rmtree(self.tmpdirname    )
       def    A				(     self						:     Any    ):



           '''simple docstring'''

           UpperCAmelCase      	=		[np.random.randint(255       ,    size=(3, 30, 400)       ,    dtype=np.uinta    )]

           UpperCAmelCase      	=		[Image.fromarray(np.moveaxis(lowercase       ,    0       ,    -1    )    ) for x in image_inputs]

           return image_inputs
       def    A				(     self						:     str    ):



           '''simple docstring'''

           UpperCAmelCase      	=		PixaStructProcessor(tokenizer=self.get_tokenizer()       ,    image_processor=self.get_image_processor()    )
           processor.save_pretrained(self.tmpdirname    )

           UpperCAmelCase      	=		self.get_tokenizer(bos_token='''(BOS)'''       ,    eos_token='''(EOS)'''    )
           UpperCAmelCase      	=		self.get_image_processor(do_normalize=lowercase       ,    padding_value=1.0    )

           UpperCAmelCase      	=		PixaStructProcessor.from_pretrained(
               self.tmpdirname       ,    bos_token='''(BOS)'''       ,    eos_token='''(EOS)'''       ,    do_normalize=lowercase       ,    padding_value=1.0    )

           self.assertEqual(processor.tokenizer.get_vocab()       ,    tokenizer_add_kwargs.get_vocab()    )
           self.assertIsInstance(processor.tokenizer       ,    lowercase    )

           self.assertEqual(processor.image_processor.to_json_string()       ,    image_processor_add_kwargs.to_json_string()    )
           self.assertIsInstance(processor.image_processor       ,    lowercase    )
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.get_image_processor()
           UpperCAmelCase      	=		self.get_tokenizer()

           UpperCAmelCase      	=		PixaStructProcessor(tokenizer=lowercase       ,    image_processor=lowercase    )

           UpperCAmelCase      	=		self.prepare_image_inputs()

           UpperCAmelCase      	=		image_processor(lowercase       ,    return_tensors='''np'''    )
           UpperCAmelCase      	=		processor(images=lowercase       ,    return_tensors='''np'''    )

           for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()       ,    input_processor[key].sum()       ,    delta=1E-2    )
       def    A				(     self						:     List[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.get_image_processor()
           UpperCAmelCase      	=		self.get_tokenizer()

           UpperCAmelCase      	=		PixaStructProcessor(tokenizer=lowercase       ,    image_processor=lowercase    )

           UpperCAmelCase      	=		'''lower newer'''

           UpperCAmelCase      	=		processor(text=lowercase    )

           UpperCAmelCase      	=		tokenizer(lowercase       ,    return_token_type_ids=lowercase       ,    add_special_tokens=lowercase    )

           for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]       ,    encoded_processor[key]    )
       def    A				(     self						:     Union[str, Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.get_image_processor()
           UpperCAmelCase      	=		self.get_tokenizer()

           UpperCAmelCase      	=		PixaStructProcessor(tokenizer=lowercase       ,    image_processor=lowercase    )

           UpperCAmelCase      	=		'''lower newer'''
           UpperCAmelCase      	=		self.prepare_image_inputs()

           UpperCAmelCase      	=		processor(text=lowercase       ,    images=lowercase    )

           self.assertListEqual(
               list(inputs.keys()    )       ,    ['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']    )

           # test if it raises when no input is passed
           with pytest.raises(lowercase    ):
               processor()
       def    A				(     self						:     Optional[int]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.get_image_processor()
           UpperCAmelCase      	=		self.get_tokenizer()

           UpperCAmelCase      	=		PixaStructProcessor(tokenizer=lowercase       ,    image_processor=lowercase    )

           UpperCAmelCase      	=		'''lower newer'''
           UpperCAmelCase      	=		self.prepare_image_inputs()

           UpperCAmelCase      	=		processor(text=lowercase       ,    images=lowercase    )

           UpperCAmelCase      	=		[512, 1_024, 2_048, 4_096]
           UpperCAmelCase      	=		[770, 770, 770, 770]
           # with text
           for i, max_patch in enumerate(lowercase    ):
               UpperCAmelCase      	=		processor(text=lowercase       ,    images=lowercase       ,    max_patches=lowercase    )
               self.assertEqual(inputs['''flattened_patches'''][0].shape[0]       ,    lowercase    )
               self.assertEqual(inputs['''flattened_patches'''][0].shape[1]       ,    expected_hidden_size[i]    )

           # without text input
           for i, max_patch in enumerate(lowercase    ):
               UpperCAmelCase      	=		processor(images=lowercase       ,    max_patches=lowercase    )
               self.assertEqual(inputs['''flattened_patches'''][0].shape[0]       ,    lowercase    )
               self.assertEqual(inputs['''flattened_patches'''][0].shape[1]       ,    expected_hidden_size[i]    )
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.get_image_processor()
           UpperCAmelCase      	=		self.get_tokenizer()

           UpperCAmelCase      	=		PixaStructProcessor(tokenizer=lowercase       ,    image_processor=lowercase    )

           UpperCAmelCase      	=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           UpperCAmelCase      	=		processor.batch_decode(lowercase    )
           UpperCAmelCase      	=		tokenizer.batch_decode(lowercase    )

           self.assertListEqual(lowercase       ,    lowercase    )







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



           '''simple docstring'''

           UpperCAmelCase      	=		self.get_image_processor()
           UpperCAmelCase      	=		self.get_tokenizer()

           UpperCAmelCase      	=		PixaStructProcessor(tokenizer=lowercase       ,    image_processor=lowercase    )

           UpperCAmelCase      	=		'''lower newer'''
           UpperCAmelCase      	=		self.prepare_image_inputs()

           UpperCAmelCase      	=		processor(text=lowercase       ,    images=lowercase    )

           # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
           self.assertListEqual(
               list(inputs.keys()    )       ,    ['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']    )

           UpperCAmelCase      	=		processor(text=lowercase    )

           # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
           self.assertListEqual(list(inputs.keys()    )       ,    ['''input_ids''', '''attention_mask''']    )










'''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


A			   =collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)





def 						snake_case_							(_a     :							str					):
    print(F"Reading saved model from {model_file}"					)
    UpperCAmelCase      	=		torch.load(_a   ,       map_location=lambda _a   ,       _a					: default_restore_location(_a   ,       '''cpu'''					)					)
    return CheckpointState(**_a					)


class       _a  :
       def __init__(     self						:     List[Any]       ,    lowercase						:     Path    ):



           '''simple docstring'''

           UpperCAmelCase      	=		src_file
       def    A				(     self						:     str    ):



           '''simple docstring'''

           raise NotImplementedError







       @staticmethod
       def    A				(     lowercase						:     str       ,    *lowercase						:     Tuple       ,    **lowercase						:     Union[str, Any]    ):



           '''simple docstring'''

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






class       _a  (     __a			):
       def    A				(     self						:     int    ):



           '''simple docstring'''

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






class       _a  (     __a			):
       def    A				(     self						:     int    ):



           '''simple docstring'''

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






class       _a  (     __a			):
       def    A				(     self						:     int    ):



           '''simple docstring'''

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






def 						snake_case_							(_a     :							str   ,       _a     :							Path   ,       _a     :							Path					):
    UpperCAmelCase      	=		Path(_a					)
    dest_dir.mkdir(exist_ok=_a					)

    UpperCAmelCase      	=		DPRState.from_type(_a   ,       src_file=_a					)
    UpperCAmelCase      	=		dpr_state.load_dpr_model()
    model.save_pretrained(_a					)
    model.from_pretrained(_a					)  # sanity check


if __name__ == "__main__":
   A			   =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.')
   A			   =parser.parse_args()

   A			   =Path(args.src)
   A			   =f"""converted-{src_file.name}""" if args.dest is None else args.dest
   A			   =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)




