


"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__snake_case         =					{
    """vocab_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
        ),
        """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-openqa""": (
            """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-reader""": (
            """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-openqa""": (
            """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-reader""": (
            """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
        ),
    },
}

__snake_case         =					{
    """google/realm-cc-news-pretrained-embedder""": 512,
    """google/realm-cc-news-pretrained-encoder""": 512,
    """google/realm-cc-news-pretrained-scorer""": 512,
    """google/realm-cc-news-pretrained-openqa""": 512,
    """google/realm-orqa-nq-openqa""": 512,
    """google/realm-orqa-nq-reader""": 512,
    """google/realm-orqa-wq-openqa""": 512,
    """google/realm-orqa-wq-reader""": 512,
}

__snake_case         =					{
    """google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Dict													=						VOCAB_FILES_NAMES
   __UpperCAmelCase				: Optional[int]													=						PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase				: Union[str, Any]													=						PRETRAINED_INIT_CONFIGURATION
   __UpperCAmelCase				: Union[str, Any]													=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase				: Tuple													=						RealmTokenizer







   def __init__(			self		,			UpperCamelCase__=None		,			UpperCamelCase__=None		,			UpperCamelCase__=True		,			UpperCamelCase__="[UNK]"		,			UpperCamelCase__="[SEP]"		,			UpperCamelCase__="[PAD]"		,			UpperCamelCase__="[CLS]"		,			UpperCamelCase__="[MASK]"		,			UpperCamelCase__=True		,			UpperCamelCase__=None		,			**UpperCamelCase__		,			)			->	Dict:

       '''simple docstring'''




       super().__init__(
           UpperCamelCase__		,			tokenizer_file=UpperCamelCase__		,			do_lower_case=UpperCamelCase__		,			unk_token=UpperCamelCase__		,			sep_token=UpperCamelCase__		,			pad_token=UpperCamelCase__		,			cls_token=UpperCamelCase__		,			mask_token=UpperCamelCase__		,			tokenize_chinese_chars=UpperCamelCase__		,			strip_accents=UpperCamelCase__		,			**UpperCamelCase__		,			)

       snake_case					:   List[Any]         =					json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
       if (
           normalizer_state.get("lowercase"		,			UpperCamelCase__		) != do_lower_case
           or normalizer_state.get("strip_accents"		,			UpperCamelCase__		) != strip_accents
           or normalizer_state.get("handle_chinese_chars"		,			UpperCamelCase__		) != tokenize_chinese_chars
       ):
           snake_case					:   int         =					getattr(UpperCamelCase__		,			normalizer_state.pop("type"		)		)
           snake_case					:   str         =					do_lower_case
           snake_case					:   Any         =					strip_accents
           snake_case					:   List[str]         =					tokenize_chinese_chars
           snake_case					:   List[str]         =					normalizer_class(**UpperCamelCase__		)

       snake_case					:   Tuple         =					do_lower_case







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			**UpperCamelCase__		)			->	str:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					PaddingStrategy.MAX_LENGTH

       snake_case					:   Optional[Any]         =					text
       snake_case					:   Tuple         =					kwargs.pop("text_pair"		,			UpperCamelCase__		)
       snake_case					:   List[Any]         =					kwargs.pop("return_tensors"		,			UpperCamelCase__		)

       snake_case					:   Union[str, Any]         =					{
           "input_ids": [],
           "attention_mask": [],
           "token_type_ids": [],
       }

       for idx, candidate_text in enumerate(UpperCamelCase__		):
           if batch_text_pair is not None:
               snake_case					:   Union[str, Any]         =					batch_text_pair[idx]
           else:
               snake_case					:   Tuple         =					None

           snake_case					:   List[str]         =					super().__call__(UpperCamelCase__		,			UpperCamelCase__		,			return_tensors=UpperCamelCase__		,			**UpperCamelCase__		)

           snake_case					:   List[str]         =					encoded_candidates.get("input_ids"		)
           snake_case					:   Dict         =					encoded_candidates.get("attention_mask"		)
           snake_case					:   Optional[int]         =					encoded_candidates.get("token_type_ids"		)

           if encoded_input_ids is not None:
               output_data["input_ids"].append(UpperCamelCase__		)
           if encoded_attention_mask is not None:
               output_data["attention_mask"].append(UpperCamelCase__		)
           if encoded_token_type_ids is not None:
               output_data["token_type_ids"].append(UpperCamelCase__		)

       snake_case					:   List[Any]         =					{key: item for key, item in output_data.items() if len(UpperCamelCase__		) != 0}

       return BatchEncoding(UpperCamelCase__		,			tensor_type=UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__=None		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   Tuple         =					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
           output += token_ids_a + [self.sep_token_id]

       return output







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = None		)			->	List[int]:

       '''simple docstring'''




       snake_case					:   int         =					[self.sep_token_id]
       snake_case					:   int         =					[self.cls_token_id]
       if token_ids_a is None:
           return len(cls + token_ids_a + sep		) * [0]
       return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = None		)			->	Tuple[str]:

       '''simple docstring'''




       snake_case					:   List[str]         =					self._tokenizer.model.save(UpperCamelCase__		,			name=UpperCamelCase__		)
       return tuple(UpperCamelCase__		)









"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class _lowerCAmelCase       (   unittest.TestCase			):







   @property
   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       torch.manual_seed(0		)
       snake_case					:   Tuple         =					UNetaDModel(
           block_out_channels=(32, 64)		,			layers_per_block=2		,			sample_size=32		,			in_channels=3		,			out_channels=3		,			down_block_types=("DownBlock2D", "AttnDownBlock2D")		,			up_block_types=("AttnUpBlock2D", "UpBlock2D")		,			)
       return model







   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					self.dummy_uncond_unet
       snake_case					:   str         =					PNDMScheduler()

       snake_case					:   List[Any]         =					PNDMPipeline(unet=UpperCamelCase__		,			scheduler=UpperCamelCase__		)
       pndm.to(UpperCamelCase__		)
       pndm.set_progress_bar_config(disable=UpperCamelCase__		)

       snake_case					:   str         =					torch.manual_seed(0		)
       snake_case					:   Union[str, Any]         =					pndm(generator=UpperCamelCase__		,			num_inference_steps=20		,			output_type="numpy"		).images

       snake_case					:   Optional[int]         =					torch.manual_seed(0		)
       snake_case					:   Any         =					pndm(generator=UpperCamelCase__		,			num_inference_steps=20		,			output_type="numpy"		,			return_dict=UpperCamelCase__		)[0]

       snake_case					:   Optional[Any]         =					image[0, -3:, -3:, -1]
       snake_case					:   List[str]         =					image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case					:   int         =					np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]		)

       assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1e-2







@slow
@require_torch
class _lowerCAmelCase       (   unittest.TestCase			):







   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Dict         =					"google/ddpm-cifar10-32"

       snake_case					:   Optional[int]         =					UNetaDModel.from_pretrained(UpperCamelCase__		)
       snake_case					:   List[str]         =					PNDMScheduler()

       snake_case					:   Union[str, Any]         =					PNDMPipeline(unet=UpperCamelCase__		,			scheduler=UpperCamelCase__		)
       pndm.to(UpperCamelCase__		)
       pndm.set_progress_bar_config(disable=UpperCamelCase__		)
       snake_case					:   int         =					torch.manual_seed(0		)
       snake_case					:   str         =					pndm(generator=UpperCamelCase__		,			output_type="numpy"		).images

       snake_case					:   Union[str, Any]         =					image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case					:   Optional[Any]         =					np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]		)

       assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2





