import copy

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


_SCREAMING_SNAKE_CASE 		=      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(_UpperCamelCase		)
class SCREAMING_SNAKE_CASE_		(      _UpperCamelCase		):
   __lowerCAmelCase           =				'rag'
   __lowerCAmelCase           =				True







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





    """simple docstring"""


    super().__init__(
        bos_token_id=lowerCamelCase_   ,   pad_token_id=lowerCamelCase_   ,   eos_token_id=lowerCamelCase_   ,   decoder_start_token_id=lowerCamelCase_   ,   forced_eos_token_id=lowerCamelCase_   ,   is_encoder_decoder=lowerCamelCase_   ,   prefix=lowerCamelCase_   ,   vocab_size=lowerCamelCase_   ,   **lowerCamelCase_   ,   )
    assert (
        "question_encoder" in kwargs and "generator" in kwargs
    ), "Config has to be initialized with question_encoder and generator config"
    UpperCamelCase			  =      kwargs.pop("""question_encoder"""				)
    UpperCamelCase			  =      question_encoder_config.pop("""model_type"""				)
    UpperCamelCase			  =      kwargs.pop("""generator"""				)
    UpperCamelCase			  =      decoder_config.pop("""model_type"""				)

    from ..auto.configuration_auto import AutoConfig

    UpperCamelCase			  =      AutoConfig.for_model(lowerCamelCase_   ,   **lowerCamelCase_				)
    UpperCamelCase			  =      AutoConfig.for_model(lowerCamelCase_   ,   **lowerCamelCase_				)

    UpperCamelCase			  =      reduce_loss
    UpperCamelCase			  =      label_smoothing
    UpperCamelCase			  =      exclude_bos_score
    UpperCamelCase			  =      do_marginalize

    UpperCamelCase			  =      title_sep
    UpperCamelCase			  =      doc_sep
    UpperCamelCase			  =      n_docs
    UpperCamelCase			  =      max_combined_length

    UpperCamelCase			  =      dataset
    UpperCamelCase			  =      dataset_split
    UpperCamelCase			  =      index_name

    UpperCamelCase			  =      retrieval_vector_size
    UpperCamelCase			  =      retrieval_batch_size
    UpperCamelCase			  =      passages_path
    UpperCamelCase			  =      index_path
    UpperCamelCase			  =      use_dummy_dataset

    UpperCamelCase			  =      output_retrieved

    UpperCamelCase			  =      do_deduplication

    UpperCamelCase			  =      use_cache

    if self.forced_eos_token_id is None:
     UpperCamelCase			  =      getattr(self.generator   ,   """forced_eos_token_id"""   ,   lowerCamelCase_				)







   @classmethod
   def 						lowerCamelCase_	(     cls						:						Union[str, Any]   ,   lowerCamelCase_						:						PretrainedConfig   ,   lowerCamelCase_						:						PretrainedConfig   ,   **lowerCamelCase_						:						Optional[Any]				):





    """simple docstring"""


    return cls(question_encoder=question_encoder_config.to_dict()   ,   generator=generator_config.to_dict()   ,   **lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      copy.deepcopy(self.__dict__				)
    UpperCamelCase			  =      self.question_encoder.to_dict()
    UpperCamelCase			  =      self.generator.to_dict()
    UpperCamelCase			  =      self.__class__.model_type
    return output





"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPSegProcessor, ViTImageProcessor






@require_vision
class       UpperCAmelCase_    (							unittest.TestCase		):



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Tuple														=       tempfile.mkdtemp()

           # fmt: off
           _UpperCAmelCase     :      List[str]														=       ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
           # fmt: on
           _UpperCAmelCase     :      Tuple														=       dict(zip(A	,      range(len(A		)		)		)		)
           _UpperCAmelCase     :      Any														=       ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
           _UpperCAmelCase     :      Optional[Any]														=       {"unk_token": "<unk>"}

           _UpperCAmelCase     :      List[Any]														=       os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES["vocab_file"]		)
           _UpperCAmelCase     :      int														=       os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES["merges_file"]		)
           with open(self.vocab_file	,      "w"	,      encoding="utf-8"		) as fp:
                fp.write(json.dumps(A		) + "\n"		)
           with open(self.merges_file	,      "w"	,      encoding="utf-8"		) as fp:
                fp.write("\n".join(A		)		)

           _UpperCAmelCase     :      List[Any]														=       {
               "do_resize": True,
               "size": 2_0,
               "do_center_crop": True,
               "crop_size": 1_8,
               "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     :      Tuple														=       os.path.join(self.tmpdirname	,      A		)
           with open(self.image_processor_file	,      "w"	,      encoding="utf-8"		) as fp:
                json.dump(A	,      A		)



      def 		snake_case_   (	self	:      int	,      **A	:      Any		):
           return CLIPTokenizer.from_pretrained(self.tmpdirname	,      **A		)



      def 		snake_case_   (	self	:      List[str]	,      **A	:      Tuple		):
           return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,      **A		)



      def 		snake_case_   (	self	:      Any	,      **A	:      Optional[Any]		):
           return ViTImageProcessor.from_pretrained(self.tmpdirname	,      **A		)



      def 		snake_case_   (	self	:      Dict		):
           shutil.rmtree(self.tmpdirname		)



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      int														=       [np.random.randint(2_5_5	,      size=(3, 3_0, 4_0_0)	,      dtype=np.uinta		)]

           _UpperCAmelCase     :      str														=       [Image.fromarray(np.moveaxis(A	,      0	,      -1		)		) for x in image_inputs]

           return image_inputs



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      str														=       self.get_tokenizer()
           _UpperCAmelCase     :      int														=       self.get_rust_tokenizer()
           _UpperCAmelCase     :      List[str]														=       self.get_image_processor()

           _UpperCAmelCase     :      Union[str, Any]														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)
           processor_slow.save_pretrained(self.tmpdirname		)
           _UpperCAmelCase     :      Dict														=       CLIPSegProcessor.from_pretrained(self.tmpdirname	,      use_fast=A		)

           _UpperCAmelCase     :      Any														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)
           processor_fast.save_pretrained(self.tmpdirname		)
           _UpperCAmelCase     :      int														=       CLIPSegProcessor.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	,      A		)
           self.assertIsInstance(processor_fast.tokenizer	,      A		)

           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	,      A		)
           self.assertIsInstance(processor_fast.image_processor	,      A		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Union[str, Any]														=       CLIPSegProcessor(tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()		)
           processor.save_pretrained(self.tmpdirname		)

           _UpperCAmelCase     :      List[str]														=       self.get_tokenizer(bos_token="(BOS)"	,      eos_token="(EOS)"		)
           _UpperCAmelCase     :      int														=       self.get_image_processor(do_normalize=A	,      padding_value=1.0		)

           _UpperCAmelCase     :      Optional[Any]														=       CLIPSegProcessor.from_pretrained(
               self.tmpdirname	,      bos_token="(BOS)"	,      eos_token="(EOS)"	,      do_normalize=A	,      padding_value=1.0		)

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

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



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Any														=       self.get_image_processor()
           _UpperCAmelCase     :      Optional[int]														=       self.get_tokenizer()

           _UpperCAmelCase     :      Dict														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      Dict														=       self.prepare_image_inputs()

           _UpperCAmelCase     :      int														=       image_processor(A	,      return_tensors="np"		)
           _UpperCAmelCase     :      int														=       processor(images=A	,      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 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      List[Any]														=       self.get_image_processor()
           _UpperCAmelCase     :      Dict														=       self.get_tokenizer()

           _UpperCAmelCase     :      Optional[Any]														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      Tuple														=       "lower newer"

           _UpperCAmelCase     :      Any														=       processor(text=A		)

           _UpperCAmelCase     :      int														=       tokenizer(A		)

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



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      List[str]														=       self.get_image_processor()
           _UpperCAmelCase     :      Union[str, Any]														=       self.get_tokenizer()

           _UpperCAmelCase     :      Tuple														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      List[Any]														=       "lower newer"
           _UpperCAmelCase     :      List[Any]														=       self.prepare_image_inputs()

           _UpperCAmelCase     :      str														=       processor(text=A	,      images=A		)

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

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



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Dict														=       self.get_image_processor()
           _UpperCAmelCase     :      Optional[int]														=       self.get_tokenizer()

           _UpperCAmelCase     :      str														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      List[Any]														=       self.prepare_image_inputs()
           _UpperCAmelCase     :      int														=       self.prepare_image_inputs()

           _UpperCAmelCase     :      Any														=       processor(images=A	,      visual_prompt=A		)

           self.assertListEqual(list(inputs.keys()		)	,      ["pixel_values", "conditional_pixel_values"]		)

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



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Dict														=       self.get_image_processor()
           _UpperCAmelCase     :      Optional[Any]														=       self.get_tokenizer()

           _UpperCAmelCase     :      Optional[Any]														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      str														=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _UpperCAmelCase     :      Union[str, Any]														=       processor.batch_decode(A		)
           _UpperCAmelCase     :      List[Any]														=       tokenizer.batch_decode(A		)

           self.assertListEqual(A	,      A		)
