

"""simple docstring"""

import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
    from transformers.models.rag.configuration_rag import RagConfig
    from transformers.models.rag.tokenization_rag import RagTokenizer






@require_faiss
@require_torch
class 			__a					(						__magic_name__       ):





       """simple docstring"""


       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				tempfile.mkdtemp()
        lowerCAmelCase__   :				Optional[Any] 			=				8

        # DPR tok
        lowerCAmelCase__   :				Optional[int] 			=				[
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "[PAD]",
            "[MASK]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        lowerCAmelCase__   :				str 			=				os.path.join(self.tmpdirname			,					"dpr_tokenizer"   )
        os.makedirs(snake_case			,					exist_ok=snake_case   )
        lowerCAmelCase__   :				Optional[int] 			=				os.path.join(snake_case			,					DPR_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]   )   )

        # BART tok
        lowerCAmelCase__   :				Any 			=				[
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "\u0120",
            "\u0120l",
            "\u0120n",
            "\u0120lo",
            "\u0120low",
            "er",
            "\u0120lowest",
            "\u0120newer",
            "\u0120wider",
            "<unk>",
        ]
        lowerCAmelCase__   :				List[Any] 			=				dict(zip(snake_case			,					range(len(snake_case   )   )   )   )
        lowerCAmelCase__   :				List[Any] 			=				["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        lowerCAmelCase__   :				Optional[int] 			=				{"unk_token": "<unk>"}

        lowerCAmelCase__   :				Any 			=				os.path.join(self.tmpdirname			,					"bart_tokenizer"   )
        os.makedirs(snake_case			,					exist_ok=snake_case   )
        lowerCAmelCase__   :				Union[str, Any] 			=				os.path.join(snake_case			,					BART_VOCAB_FILES_NAMES["vocab_file"]   )
        lowerCAmelCase__   :				Optional[int] 			=				os.path.join(snake_case			,					BART_VOCAB_FILES_NAMES["merges_file"]   )
        with open(self.vocab_file			,					"w"			,					encoding="utf-8"   ) as fp:
         fp.write(json.dumps(snake_case   ) + "\n"   )
        with open(self.merges_file			,					"w"			,					encoding="utf-8"   ) as fp:
         fp.write("\n".join(snake_case   )   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,					"dpr_tokenizer"   )   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname			,					"bart_tokenizer"   )   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        shutil.rmtree(self.tmpdirname   )






       @require_tokenizers
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				os.path.join(self.tmpdirname			,					"rag_tokenizer"   )
        lowerCAmelCase__   :				Any 			=				RagConfig(question_encoder=DPRConfig().to_dict()			,					generator=BartConfig().to_dict()   )
        lowerCAmelCase__   :				Any 			=				RagTokenizer(question_encoder=self.get_dpr_tokenizer()			,					generator=self.get_bart_tokenizer()   )
        rag_config.save_pretrained(snake_case   )
        rag_tokenizer.save_pretrained(snake_case   )
        lowerCAmelCase__   :				Union[str, Any] 			=				RagTokenizer.from_pretrained(snake_case			,					config=snake_case   )
        self.assertIsInstance(new_rag_tokenizer.question_encoder			,					snake_case   )
        self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()			,					rag_tokenizer.question_encoder.get_vocab()   )
        self.assertIsInstance(new_rag_tokenizer.generator			,					snake_case   )
        self.assertEqual(new_rag_tokenizer.generator.get_vocab()			,					rag_tokenizer.generator.get_vocab()   )






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				RagTokenizer.from_pretrained("facebook/rag-token-nq"   )
        lowerCAmelCase__   :				List[Any] 			=				[
            "who got the first nobel prize in physics",
            "when is the next deadpool movie being released",
            "which mode is used for short wave broadcast service",
            "who is the owner of reading football club",
            "when is the next scandal episode coming out",
            "when is the last time the philadelphia won the superbowl",
            "what is the most current adobe flash player version",
            "how many episodes are there in dragon ball z",
            "what is the first step in the evolution of the eye",
            "where is gall bladder situated in human body",
            "what is the main mineral in lithium batteries",
            "who is the president of usa right now",
            "where do the greasers live in the outsiders",
            "panda is a national animal of which country",
            "what is the name of manchester united stadium",
        ]
        lowerCAmelCase__   :				Dict 			=				tokenizer(snake_case   )
        self.assertIsNotNone(snake_case   )






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				RagTokenizer.from_pretrained("facebook/rag-sequence-nq"   )
        lowerCAmelCase__   :				Dict 			=				[
            "who got the first nobel prize in physics",
            "when is the next deadpool movie being released",
            "which mode is used for short wave broadcast service",
            "who is the owner of reading football club",
            "when is the next scandal episode coming out",
            "when is the last time the philadelphia won the superbowl",
            "what is the most current adobe flash player version",
            "how many episodes are there in dragon ball z",
            "what is the first step in the evolution of the eye",
            "where is gall bladder situated in human body",
            "what is the main mineral in lithium batteries",
            "who is the president of usa right now",
            "where do the greasers live in the outsiders",
            "panda is a national animal of which country",
            "what is the name of manchester united stadium",
        ]
        lowerCAmelCase__   :				Tuple 			=				tokenizer(snake_case   )
        self.assertIsNotNone(snake_case   )









"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    {
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      Union[str, Any]    =						'beit'






       def __init__(   self			,					snake_case=8_192			,					snake_case=768			,					snake_case=12			,					snake_case=12			,					snake_case=3_072			,					snake_case="gelu"			,					snake_case=0.0			,					snake_case=0.0			,					snake_case=0.02			,					snake_case=1e-12			,					snake_case=224			,					snake_case=16			,					snake_case=3			,					snake_case=False			,					snake_case=False			,					snake_case=False			,					snake_case=False			,					snake_case=0.1			,					snake_case=0.1			,					snake_case=True			,					snake_case=[3, 5, 7, 11]			,					snake_case=[1, 2, 3, 6]			,					snake_case=True			,					snake_case=0.4			,					snake_case=256			,					snake_case=1			,					snake_case=False			,					snake_case=255			,					**snake_case			,					):





        """simple docstring"""

        super().__init__(**snake_case   )

        lowerCAmelCase__   :				Union[str, Any] 			=				vocab_size
        lowerCAmelCase__   :				int 			=				hidden_size
        lowerCAmelCase__   :				str 			=				num_hidden_layers
        lowerCAmelCase__   :				Any 			=				num_attention_heads
        lowerCAmelCase__   :				List[str] 			=				intermediate_size
        lowerCAmelCase__   :				int 			=				hidden_act
        lowerCAmelCase__   :				Optional[Any] 			=				hidden_dropout_prob
        lowerCAmelCase__   :				str 			=				attention_probs_dropout_prob
        lowerCAmelCase__   :				Optional[int] 			=				initializer_range
        lowerCAmelCase__   :				Dict 			=				layer_norm_eps

        lowerCAmelCase__   :				int 			=				image_size
        lowerCAmelCase__   :				Union[str, Any] 			=				patch_size
        lowerCAmelCase__   :				Dict 			=				num_channels
        lowerCAmelCase__   :				Optional[Any] 			=				use_mask_token
        lowerCAmelCase__   :				Dict 			=				use_absolute_position_embeddings
        lowerCAmelCase__   :				Any 			=				use_relative_position_bias
        lowerCAmelCase__   :				List[Any] 			=				use_shared_relative_position_bias
        lowerCAmelCase__   :				Dict 			=				layer_scale_init_value
        lowerCAmelCase__   :				Optional[int] 			=				drop_path_rate
        lowerCAmelCase__   :				Optional[Any] 			=				use_mean_pooling
        # decode head attributes (semantic segmentation)
        lowerCAmelCase__   :				Optional[int] 			=				out_indices
        lowerCAmelCase__   :				List[Any] 			=				pool_scales
        # auxiliary head attributes (semantic segmentation)
        lowerCAmelCase__   :				List[Any] 			=				use_auxiliary_head
        lowerCAmelCase__   :				Optional[int] 			=				auxiliary_loss_weight
        lowerCAmelCase__   :				List[str] 			=				auxiliary_channels
        lowerCAmelCase__   :				Optional[Any] 			=				auxiliary_num_convs
        lowerCAmelCase__   :				Union[str, Any] 			=				auxiliary_concat_input
        lowerCAmelCase__   :				List[str] 			=				semantic_loss_ignore_index






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      List[str]    =						version.parse('1.11'       )






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]   )






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return 1e-4






