


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import AlignProcessor, EfficientNetImageProcessor





@require_vision
class  lowerCAmelCase__(    unittest.TestCase						):






    '''simple docstring'''





    def 					_lowerCamelCase (	self				:   List[str]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[int]						=   tempfile.mkdtemp()

       UpperCAmelCase_							:      Optional[Any]						=   [
           '''[UNK]''',
           '''[CLS]''',
           '''[SEP]''',
           '''[PAD]''',
           '''[MASK]''',
           '''want''',
           '''##want''',
           '''##ed''',
           '''wa''',
           '''un''',
           '''runn''',
           '''##ing''',
           ''',''',
           '''low''',
           '''lowest''',
       ]
       UpperCAmelCase_							:      Dict						=   os.path.join(self.tmpdirname       ,   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]	)	)

       UpperCAmelCase_							:      Optional[int]						=   {
           '''do_resize''': True,
           '''size''': 20,
           '''do_center_crop''': True,
           '''crop_size''': 18,
           '''do_normalize''': True,
           '''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
           '''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
       }
       UpperCAmelCase_							:      Optional[Any]						=   os.path.join(self.tmpdirname       ,   UpperCamelCase__	)
       with open(self.image_processor_file       ,   '''w'''       ,   encoding='''utf-8'''	) as fp:
          json.dump(UpperCamelCase__       ,   UpperCamelCase__	)





    def 					_lowerCamelCase (	self				:   Dict       ,   **__snake_case				:   Any	):





       '''simple docstring'''
       return BertTokenizer.from_pretrained(self.tmpdirname       ,   **UpperCamelCase__	)





    def 					_lowerCamelCase (	self				:   Optional[int]       ,   **__snake_case				:   str	):





       '''simple docstring'''
       return BertTokenizerFast.from_pretrained(self.tmpdirname       ,   **UpperCamelCase__	)





    def 					_lowerCamelCase (	self				:   List[str]       ,   **__snake_case				:   Tuple	):





       '''simple docstring'''
       return EfficientNetImageProcessor.from_pretrained(self.tmpdirname       ,   **UpperCamelCase__	)





    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       shutil.rmtree(self.tmpdirname	)





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   [np.random.randint(255       ,   size=(3, 30, 400)       ,   dtype=np.uinta	)]
       UpperCAmelCase_							:      Optional[Any]						=   [Image.fromarray(np.moveaxis(UpperCamelCase__       ,   0       ,   -1	)	) for x in image_inputs]
       return image_inputs





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





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   self.get_tokenizer()
       UpperCAmelCase_							:      Any						=   self.get_rust_tokenizer()
       UpperCAmelCase_							:      Any						=   self.get_image_processor()

       UpperCAmelCase_							:      Optional[int]						=   AlignProcessor(tokenizer=UpperCamelCase__       ,   image_processor=UpperCamelCase__	)
       processor_slow.save_pretrained(self.tmpdirname	)
       UpperCAmelCase_							:      List[str]						=   AlignProcessor.from_pretrained(self.tmpdirname       ,   use_fast=UpperCamelCase__	)

       UpperCAmelCase_							:      Optional[Any]						=   AlignProcessor(tokenizer=UpperCamelCase__       ,   image_processor=UpperCamelCase__	)
       processor_fast.save_pretrained(self.tmpdirname	)
       UpperCAmelCase_							:      Any						=   AlignProcessor.from_pretrained(self.tmpdirname	)

       self.assertEqual(processor_slow.tokenizer.get_vocab()       ,   tokenizer_slow.get_vocab()	)
       self.assertEqual(processor_fast.tokenizer.get_vocab()       ,   tokenizer_fast.get_vocab()	)
       self.assertEqual(tokenizer_slow.get_vocab()       ,   tokenizer_fast.get_vocab()	)
       self.assertIsInstance(processor_slow.tokenizer       ,   UpperCamelCase__	)
       self.assertIsInstance(processor_fast.tokenizer       ,   UpperCamelCase__	)

       self.assertEqual(processor_slow.image_processor.to_json_string()       ,   image_processor.to_json_string()	)
       self.assertEqual(processor_fast.image_processor.to_json_string()       ,   image_processor.to_json_string()	)
       self.assertIsInstance(processor_slow.image_processor       ,   UpperCamelCase__	)
       self.assertIsInstance(processor_fast.image_processor       ,   UpperCamelCase__	)





    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Tuple						=   AlignProcessor(tokenizer=self.get_tokenizer()       ,   image_processor=self.get_image_processor()	)
       processor.save_pretrained(self.tmpdirname	)

       UpperCAmelCase_							:      Union[str, Any]						=   self.get_tokenizer(bos_token='''(BOS)'''       ,   eos_token='''(EOS)'''	)
       UpperCAmelCase_							:      str						=   self.get_image_processor(do_normalize=UpperCamelCase__       ,   padding_value=1.0	)

       UpperCAmelCase_							:      Dict						=   AlignProcessor.from_pretrained(
           self.tmpdirname       ,   bos_token='''(BOS)'''       ,   eos_token='''(EOS)'''       ,   do_normalize=UpperCamelCase__       ,   padding_value=1.0	)

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

       self.assertEqual(processor.image_processor.to_json_string()       ,   image_processor_add_kwargs.to_json_string()	)
       self.assertIsInstance(processor.image_processor       ,   UpperCamelCase__	)





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





       '''simple docstring'''
       UpperCAmelCase_							:      str						=   self.get_image_processor()
       UpperCAmelCase_							:      Tuple						=   self.get_tokenizer()

       UpperCAmelCase_							:      Dict						=   AlignProcessor(tokenizer=UpperCamelCase__       ,   image_processor=UpperCamelCase__	)

       UpperCAmelCase_							:      List[str]						=   self.prepare_image_inputs()

       UpperCAmelCase_							:      List[str]						=   image_processor(UpperCamelCase__       ,   return_tensors='''np'''	)
       UpperCAmelCase_							:      Tuple						=   processor(images=UpperCamelCase__       ,   return_tensors='''np'''	)

       for key in input_image_proc.keys():
          self.assertAlmostEqual(input_image_proc[key].sum()       ,   input_processor[key].sum()       ,   delta=1E-2	)





    def 					_lowerCamelCase (	self				:   List[str]	):





       '''simple docstring'''
       UpperCAmelCase_							:      str						=   self.get_image_processor()
       UpperCAmelCase_							:      Any						=   self.get_tokenizer()

       UpperCAmelCase_							:      Dict						=   AlignProcessor(tokenizer=UpperCamelCase__       ,   image_processor=UpperCamelCase__	)

       UpperCAmelCase_							:      List[Any]						=   '''lower newer'''

       UpperCAmelCase_							:      Tuple						=   processor(text=UpperCamelCase__	)

       UpperCAmelCase_							:      Optional[int]						=   tokenizer(UpperCamelCase__       ,   padding='''max_length'''       ,   max_length=64	)

       for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]       ,   encoded_processor[key]	)





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





       '''simple docstring'''
       UpperCAmelCase_							:      Any						=   self.get_image_processor()
       UpperCAmelCase_							:      Optional[Any]						=   self.get_tokenizer()

       UpperCAmelCase_							:      Any						=   AlignProcessor(tokenizer=UpperCamelCase__       ,   image_processor=UpperCamelCase__	)

       UpperCAmelCase_							:      int						=   '''lower newer'''
       UpperCAmelCase_							:      str						=   self.prepare_image_inputs()

       UpperCAmelCase_							:      Tuple						=   processor(text=UpperCamelCase__       ,   images=UpperCamelCase__	)

       self.assertListEqual(list(inputs.keys()	)       ,   ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']	)

       # test if it raises when no input is passed
       with pytest.raises(UpperCamelCase__	):
          processor()





    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Dict						=   self.get_image_processor()
       UpperCAmelCase_							:      Optional[int]						=   self.get_tokenizer()

       UpperCAmelCase_							:      List[Any]						=   AlignProcessor(tokenizer=UpperCamelCase__       ,   image_processor=UpperCamelCase__	)

       UpperCAmelCase_							:      Tuple						=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       UpperCAmelCase_							:      str						=   processor.batch_decode(UpperCamelCase__	)
       UpperCAmelCase_							:      List[str]						=   tokenizer.batch_decode(UpperCamelCase__	)

       self.assertListEqual(UpperCamelCase__       ,   UpperCamelCase__	)





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   self.get_image_processor()
       UpperCAmelCase_							:      Any						=   self.get_tokenizer()

       UpperCAmelCase_							:      Union[str, Any]						=   AlignProcessor(tokenizer=UpperCamelCase__       ,   image_processor=UpperCamelCase__	)

       UpperCAmelCase_							:      str						=   '''lower newer'''
       UpperCAmelCase_							:      str						=   self.prepare_image_inputs()

       UpperCAmelCase_							:      Optional[Any]						=   processor(text=UpperCamelCase__       ,   images=UpperCamelCase__	)

       self.assertListEqual(list(inputs.keys()	)       ,   processor.model_input_names	)





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


__UpperCamelCase				:  Dict      					=							R'\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `" / "`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `" // "`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `"train"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `"compressed"`)\n            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and\n            `"compressed"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a "dummy" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n'





@add_start_docstrings(snake_case__						)
class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     Union[str, Any]			= 'rag'
    A_  :     Tuple			= True





    def __init__(	self				:   int       ,   __snake_case				:   List[str]=None       ,   __snake_case				:   List[Any]=True       ,   __snake_case				:   Optional[int]=None       ,   __snake_case				:   Union[str, Any]=None       ,   __snake_case				:   int=None       ,   __snake_case				:   Any=None       ,   __snake_case				:   Optional[int]=None       ,   __snake_case				:   Optional[int]=" / "       ,   __snake_case				:   Any=" // "       ,   __snake_case				:   Tuple=5       ,   __snake_case				:   Union[str, Any]=300       ,   __snake_case				:   Any=768       ,   __snake_case				:   Tuple=8       ,   __snake_case				:   int="wiki_dpr"       ,   __snake_case				:   Optional[int]="train"       ,   __snake_case				:   Tuple="compressed"       ,   __snake_case				:   Optional[int]=None       ,   __snake_case				:   List[Any]=None       ,   __snake_case				:   Optional[int]=False       ,   __snake_case				:   str=False       ,   __snake_case				:   Dict=0.0       ,   __snake_case				:   Union[str, Any]=True       ,   __snake_case				:   Union[str, Any]=False       ,   __snake_case				:   str=False       ,   __snake_case				:   str=False       ,   __snake_case				:   Optional[Any]=True       ,   __snake_case				:   int=None       ,   **__snake_case				:   str       ,   ):





       '''simple docstring'''
       super().__init__(
           bos_token_id=__snake_case       ,   pad_token_id=__snake_case       ,   eos_token_id=__snake_case       ,   decoder_start_token_id=__snake_case       ,   forced_eos_token_id=__snake_case       ,   is_encoder_decoder=__snake_case       ,   prefix=__snake_case       ,   vocab_size=__snake_case       ,   **__snake_case       ,   )
       assert (
           "question_encoder" in kwargs and "generator" in kwargs
       ), "Config has to be initialized with question_encoder and generator config"
       UpperCAmelCase_							:      Dict						=   kwargs.pop('''question_encoder'''	)
       UpperCAmelCase_							:      List[Any]						=   question_encoder_config.pop('''model_type'''	)
       UpperCAmelCase_							:      Any						=   kwargs.pop('''generator'''	)
       UpperCAmelCase_							:      Union[str, Any]						=   decoder_config.pop('''model_type'''	)

       from ..auto.configuration_auto import AutoConfig

       UpperCAmelCase_							:      Any						=   AutoConfig.for_model(__snake_case       ,   **__snake_case	)
       UpperCAmelCase_							:      int						=   AutoConfig.for_model(__snake_case       ,   **__snake_case	)

       UpperCAmelCase_							:      Any						=   reduce_loss
       UpperCAmelCase_							:      List[Any]						=   label_smoothing
       UpperCAmelCase_							:      Tuple						=   exclude_bos_score
       UpperCAmelCase_							:      int						=   do_marginalize

       UpperCAmelCase_							:      Tuple						=   title_sep
       UpperCAmelCase_							:      Union[str, Any]						=   doc_sep
       UpperCAmelCase_							:      Any						=   n_docs
       UpperCAmelCase_							:      Optional[int]						=   max_combined_length

       UpperCAmelCase_							:      Any						=   dataset
       UpperCAmelCase_							:      List[Any]						=   dataset_split
       UpperCAmelCase_							:      Union[str, Any]						=   index_name

       UpperCAmelCase_							:      List[str]						=   retrieval_vector_size
       UpperCAmelCase_							:      Optional[Any]						=   retrieval_batch_size
       UpperCAmelCase_							:      Optional[int]						=   passages_path
       UpperCAmelCase_							:      Optional[Any]						=   index_path
       UpperCAmelCase_							:      List[Any]						=   use_dummy_dataset

       UpperCAmelCase_							:      int						=   output_retrieved

       UpperCAmelCase_							:      int						=   do_deduplication

       UpperCAmelCase_							:      Optional[int]						=   use_cache

       if self.forced_eos_token_id is None:
          UpperCAmelCase_							:      int						=   getattr(self.generator       ,   '''forced_eos_token_id'''       ,   __snake_case	)





    @classmethod
    def 					_lowerCamelCase (	cls				:   Optional[Any]       ,   __snake_case				:   PretrainedConfig       ,   __snake_case				:   PretrainedConfig       ,   **__snake_case				:   str	):





       '''simple docstring'''
       return cls(question_encoder=question_encoder_config.to_dict()       ,   generator=generator_config.to_dict()       ,   **__snake_case	)





    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   copy.deepcopy(self.__dict__	)
       UpperCAmelCase_							:      Optional[int]						=   self.question_encoder.to_dict()
       UpperCAmelCase_							:      Dict						=   self.generator.to_dict()
       UpperCAmelCase_							:      Optional[Any]						=   self.__class__.model_type
       return output