






import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM
@require_torch
class     a    :



      """simple docstring"""


      def __init__(      self					:			Any		,      __lowercase					:			List[Any]		,      __lowercase					:			List[Any]=99		,      __lowercase					:			Tuple=13		,      __lowercase					:			Union[str, Any]=16		,      __lowercase					:			Dict=7		,      __lowercase					:			List[Any]=True		,      __lowercase					:			List[str]=True		,      __lowercase					:			int=True		,      __lowercase					:			str=False		,      __lowercase					:			Dict=True		,      __lowercase					:			Dict=2		,      __lowercase					:			Tuple=32		,      __lowercase					:			str=4		,      __lowercase					:			Optional[Any]=4		,      __lowercase					:			Tuple=30		,      __lowercase					:			Dict=0		,      __lowercase					:			Optional[int]=1		,      __lowercase					:			List[Any]=2		,      __lowercase					:			int=None		,      )							->      List[Any]:
        __UpperCAmelCase							:	List[Any]           =  parent
        __UpperCAmelCase							:	int           =  batch_size
        __UpperCAmelCase							:	List[str]           =  decoder_seq_length
        # For common tests
        __UpperCAmelCase							:	Any           =  self.decoder_seq_length
        __UpperCAmelCase							:	List[str]           =  is_training
        __UpperCAmelCase							:	Dict           =  use_attention_mask
        __UpperCAmelCase							:	List[str]           =  use_labels

        __UpperCAmelCase							:	Dict           =  vocab_size
        __UpperCAmelCase							:	Tuple           =  d_model
        __UpperCAmelCase							:	Optional[int]           =  d_model
        __UpperCAmelCase							:	Dict           =  decoder_layers
        __UpperCAmelCase							:	Union[str, Any]           =  decoder_layers
        __UpperCAmelCase							:	int           =  decoder_ffn_dim
        __UpperCAmelCase							:	Optional[int]           =  decoder_attention_heads
        __UpperCAmelCase							:	Any           =  decoder_attention_heads
        __UpperCAmelCase							:	Optional[Any]           =  eos_token_id
        __UpperCAmelCase							:	Tuple           =  bos_token_id
        __UpperCAmelCase							:	Tuple           =  pad_token_id
        __UpperCAmelCase							:	Optional[Any]           =  decoder_start_token_id
        __UpperCAmelCase							:	str           =  use_cache
        __UpperCAmelCase							:	Tuple           =  max_position_embeddings

        __UpperCAmelCase							:	Optional[int]           =  None
        __UpperCAmelCase							:	Any           =  decoder_seq_length
        __UpperCAmelCase							:	Tuple           =  2
        __UpperCAmelCase							:	Optional[int]           =  1
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      Union[str, Any]:
        __UpperCAmelCase							:	List[Any]           =  ids_tensor([self.batch_size, self.decoder_seq_length]		,      self.vocab_size					)

        __UpperCAmelCase							:	List[str]           =  None
        if self.use_attention_mask:
          __UpperCAmelCase							:	Dict           =  ids_tensor([self.batch_size, self.decoder_seq_length]		,      vocab_size=2					)

        __UpperCAmelCase							:	Tuple           =  None
        if self.use_labels:
          __UpperCAmelCase							:	List[Any]           =  ids_tensor([self.batch_size, self.decoder_seq_length]		,      self.vocab_size					)

        __UpperCAmelCase							:	Dict           =  TrOCRConfig(
            vocab_size=self.vocab_size		,      d_model=self.d_model		,      decoder_layers=self.decoder_layers		,      decoder_ffn_dim=self.decoder_ffn_dim		,      decoder_attention_heads=self.decoder_attention_heads		,      eos_token_id=self.eos_token_id		,      bos_token_id=self.bos_token_id		,      use_cache=self.use_cache		,      pad_token_id=self.pad_token_id		,      decoder_start_token_id=self.decoder_start_token_id		,      max_position_embeddings=self.max_position_embeddings		,      )

        return (config, input_ids, attention_mask, lm_labels)
      def 	UpperCAmelCase      (      self					:			Union[str, Any]		,      __lowercase					:			List[str]		,      __lowercase					:			Any		,      __lowercase					:			List[Any]		,      __lowercase					:			Optional[int]		,      )							->      str:
        __UpperCAmelCase							:	Dict           =  True
        __UpperCAmelCase							:	Any           =  TrOCRDecoder(config=__lowerCamelCase					).to(__lowerCamelCase					).eval()
        __UpperCAmelCase							:	int           =  input_ids[:2]

        input_ids[input_ids == 0] += 1
        # first forward pass
        __UpperCAmelCase							:	List[Any]           =  model(__lowerCamelCase		,      use_cache=__lowerCamelCase					)
        __UpperCAmelCase							:	Optional[Any]           =  model(__lowerCamelCase					)
        __UpperCAmelCase							:	Optional[int]           =  model(__lowerCamelCase		,      use_cache=__lowerCamelCase					)

        self.parent.assertTrue(len(__lowerCamelCase					) == len(__lowerCamelCase					)					)
        self.parent.assertTrue(len(__lowerCamelCase					) == len(__lowerCamelCase					) + 1					)

        __UpperCAmelCase							:	Any           =  outputs['''past_key_values''']

        # create hypothetical next token and extent to next_input_ids
        __UpperCAmelCase							:	Dict           =  ids_tensor((2, 1)		,      config.vocab_size - 1					) + 1

        # append to next input_ids and
        __UpperCAmelCase							:	List[Any]           =  torch.cat([input_ids, next_tokens]		,      dim=-1					)

        __UpperCAmelCase							:	Dict           =  model(__lowerCamelCase					)['''last_hidden_state''']
        __UpperCAmelCase							:	List[str]           =  model(__lowerCamelCase		,      past_key_values=__lowerCamelCase					)['''last_hidden_state''']

        # select random slice
        __UpperCAmelCase							:	List[str]           =  ids_tensor((1,)		,      output_from_past.shape[-1]					).item()
        __UpperCAmelCase							:	Tuple           =  output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
        __UpperCAmelCase							:	List[str]           =  output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        assert torch.allclose(__lowerCamelCase		,      __lowerCamelCase		,      atol=1e-3					)




      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      int:
        __UpperCAmelCase							:	Union[str, Any]           =  self.prepare_config_and_inputs()
        __UpperCAmelCase							:	List[str]           =  config_and_inputs

        __UpperCAmelCase							:	Dict           =  {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict


@require_torch
class     a    ( SCREAMING_SNAKE_CASE__							,       SCREAMING_SNAKE_CASE__							,       SCREAMING_SNAKE_CASE__							,       unittest.TestCase   ):



      """simple docstring"""


      a  : int							       =	(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
      a  : Any							       =	(TrOCRForCausalLM,) if is_torch_available() else ()
      a  : Any							       =	{'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
      a  : Tuple							       =	True
      a  : int							       =	False
      def 	UpperCAmelCase      (      self					:			List[str]					)							->      Union[str, Any]:
        __UpperCAmelCase							:	str           =  TrOCRStandaloneDecoderModelTester(self		,      is_training=__lowerCamelCase					)
        __UpperCAmelCase							:	Dict           =  ConfigTester(self		,      config_class=__lowerCamelCase					)
      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      List[Any]:
        pass
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      List[str]:
        pass
      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      str:
        pass
      def 	UpperCAmelCase      (      self					:			Any					)							->      Dict:
        self.config_tester.run_common_tests()
      def 	UpperCAmelCase      (      self					:			Optional[int]					)							->      Tuple:
        __UpperCAmelCase							:	Optional[int]           =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past(*__lowerCamelCase					)
      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      Any:
        return




      @unittest.skip("""The model doesn\'t support left padding"""					)  # and it's not used enough to be worth fixing :)
      def 	UpperCAmelCase      (      self					:			Optional[int]					)							->      Optional[int]:
        pass









"""simple docstring"""



import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


a_ =			logging.getLogger(__name__)
torch.set_grad_enabled(False)
a_ =			"""cuda""" if torch.cuda.is_available() else """cpu"""







def 		__lowercase     (   snake_case_  :  str				,snake_case_  :  Tuple=100				,snake_case_  :  int=" "						)   ->List[str]:



    '''simple docstring'''







    __A    :						Dict						 =     text.split(snake_case_						)
    return [character.join(text[i : i + n]						).strip() for i in range(0				,len(snake_case_						)				,snake_case_						)]







def 		__lowercase     (   snake_case_  :  dict						)   ->dict:



    '''simple docstring'''







    __A ,       __A    :						Optional[int]						 =     [], []
    for title, text in zip(documents['''title''']				,documents['''text''']						):
        if text is not None:
            for passage in split_text(snake_case_						):
                titles.append(title if title is not None else ''''''						)
                texts.append(snake_case_						)
    return {"title": titles, "text": texts}







def 		__lowercase     (   snake_case_  :  dict				,snake_case_  :  DPRContextEncoder				,snake_case_  :  DPRContextEncoderTokenizerFast						)   ->dict:



    '''simple docstring'''







    __A    :						Tuple						 =     ctx_tokenizer(
        documents['''title''']				,documents['''text''']				,truncation=snake_case_				,padding='''longest'''				,return_tensors='''pt'''						)['''input_ids''']
    __A    :						int						 =     ctx_encoder(input_ids.to(device=snake_case_						)				,return_dict=snake_case_						).pooler_output
    return {"embeddings": embeddings.detach().cpu().numpy()}







def 		__lowercase     (   snake_case_  :  "RagExampleArguments"				,snake_case_  :  "ProcessingArguments"				,snake_case_  :  "IndexHnswArguments"				,)   ->Tuple:



    '''simple docstring'''







    logger.info('''Step 1 - Create the dataset'''						)
    ######################################

    # The dataset needed for RAG must have three columns:
    # - title (string): title of the document
    # - text (string): text of a passage of the document
    # - embeddings (array of dimension d): DPR representation of the passage
    # Let's say you have documents in tab-separated csv files with columns "title" and "text"
    assert os.path.isfile(rag_example_args.csv_path						), "Please provide a valid path to a csv file"

    # You can load a Dataset object this way
    __A    :						Optional[Any]						 =     load_dataset(
        '''csv'''				,data_files=[rag_example_args.csv_path]				,split='''train'''				,delimiter='''\t'''				,column_names=['''title''', '''text''']						)

    # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

    # Then split the documents into passages of 100 words
    __A    :						str						 =     dataset.map(snake_case_				,batched=snake_case_				,num_proc=processing_args.num_proc						)

    # And compute the embeddings
    __A    :						Union[str, Any]						 =     DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=snake_case_						)
    __A    :						str						 =     DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
    __A    :						Optional[int]						 =     Features(
        {'''text''': Value('''string'''						), '''title''': Value('''string'''						), '''embeddings''': Sequence(Value('''float32'''						)						)}						)  # optional, save as float32 instead of float64 to save space
    __A    :						List[Any]						 =     dataset.map(
        partial(snake_case_				,ctx_encoder=snake_case_				,ctx_tokenizer=snake_case_						)				,batched=snake_case_				,batch_size=processing_args.batch_size				,features=snake_case_				,)

    # And finally save your dataset
    __A    :						int						 =     os.path.join(rag_example_args.output_dir				,'''my_knowledge_dataset'''						)
    dataset.save_to_disk(snake_case_						)
    # from datasets import load_from_disk
    # dataset = load_from_disk(passages_path)  # to reload the dataset

    ######################################
    logger.info('''Step 2 - Index the dataset'''						)
    ######################################

    # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
    __A    :						Tuple						 =     faiss.IndexHNSWFlat(index_hnsw_args.d				,index_hnsw_args.m				,faiss.METRIC_INNER_PRODUCT						)
    dataset.add_faiss_index('''embeddings'''				,custom_index=snake_case_						)

    # And save the index
    __A    :						List[str]						 =     os.path.join(rag_example_args.output_dir				,'''my_knowledge_dataset_hnsw_index.faiss'''						)
    dataset.get_index('''embeddings'''						).save(snake_case_						)
    # dataset.load_faiss_index("embeddings", index_path)  # to reload the index






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     field(
     default=str(Path(SCREAMING_SNAKE_CASE__     ).parent / """test_run""" / """dummy-kb""" / """my_knowledge_dataset.csv"""     )							,							metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""}							,							)
 _lowerCamelCase        =     field(
     default=SCREAMING_SNAKE_CASE__							,							metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}							,							)
 _lowerCamelCase        =     field(
     default="""facebook/rag-sequence-nq"""							,							metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""}							,							)
 _lowerCamelCase        =     field(
     default="""facebook/dpr-ctx_encoder-multiset-base"""							,							metadata={
         """help""": (
             """The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"""
             """ 'facebook/dpr-ctx_encoder-multiset-base'"""
         )
     }							,							)
 _lowerCamelCase        =     field(
     default=str(Path(SCREAMING_SNAKE_CASE__     ).parent / """test_run""" / """dummy-kb"""     )							,							metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}							,							)






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     field(
     default=SCREAMING_SNAKE_CASE__							,							metadata={
         """help""": """The number of processes to use to split the documents into passages. Default is single process."""
     }							,							)
 _lowerCamelCase        =     field(
     default=16							,							metadata={
         """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
     }							,							)






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     field(
     default=7_68							,							metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}							,							)
 _lowerCamelCase        =     field(
     default=1_28							,							metadata={
         """help""": (
             """The number of bi-directional links created for every new element during the HNSW index construction."""
         )
     }							,							)


if __name__ == "__main__":
       logging.basicConfig(level=logging.WARNING)
       logger.setLevel(logging.INFO)

       a_ =			HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
       a_ ,			a_ ,			a_ =			parser.parse_args_into_dataclasses()
       with TemporaryDirectory() as tmp_dir:
              a_ =			rag_example_args.output_dir or tmp_dir
              main(rag_example_args, processing_args, index_hnsw_args)




