


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


UpperCamelCase					:       List[str]			    =     logging.get_logger(__name__)

UpperCamelCase					:       List[Any]			    =     {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase					:       Union[str, Any]			    =     {
    """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"""
        ),
    },
}

UpperCamelCase					:       Optional[int]			    =     {
    """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,
}

UpperCamelCase					:       List[str]			    =     {
    """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 A__     ( A__  ):



  """simple docstring"""



  _lowercase									=		VOCAB_FILES_NAMES
  _lowercase									=		PRETRAINED_VOCAB_FILES_MAP
  _lowercase									=		PRETRAINED_INIT_CONFIGURATION
  _lowercase									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase									=		RealmTokenizer

  def __init__( self			:  str				,				lowerCamelCase__			:  str=None				,				lowerCamelCase__			:  int=None				,				lowerCamelCase__			:  Any=True				,				lowerCamelCase__			:  Any="[UNK]"				,				lowerCamelCase__			:  Optional[int]="[SEP]"				,				lowerCamelCase__			:  Optional[Any]="[PAD]"				,				lowerCamelCase__			:  Optional[int]="[CLS]"				,				lowerCamelCase__			:  Tuple="[MASK]"				,				lowerCamelCase__			:  int=True				,				lowerCamelCase__			:  Optional[int]=None				,				**lowerCamelCase__			:  str				,				):
        super().__init__(
            lowerCamelCase__				,				tokenizer_file=lowerCamelCase__				,				do_lower_case=lowerCamelCase__				,				unk_token=lowerCamelCase__				,				sep_token=lowerCamelCase__				,				pad_token=lowerCamelCase__				,				cls_token=lowerCamelCase__				,				mask_token=lowerCamelCase__				,				tokenize_chinese_chars=lowerCamelCase__				,				strip_accents=lowerCamelCase__				,				**lowerCamelCase__				,				)

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

        a__	:  Union[str, Any]     =   do_lower_case

  def    _UpperCamelCase( self			:  Any				,				lowerCamelCase__			:  int				,				**lowerCamelCase__			:  int       ):
        a__	:  List[str]     =   PaddingStrategy.MAX_LENGTH

        a__	:  Dict     =   text
        a__	:  Optional[int]     =   kwargs.pop("text_pair"				,				lowerCamelCase__       )
        a__	:  Dict     =   kwargs.pop("return_tensors"				,				lowerCamelCase__       )

        a__	:  List[Any]     =   {
            "input_ids": [],
            "attention_mask": [],
            "token_type_ids": [],
        }

        for idx, candidate_text in enumerate(lowerCamelCase__       ):
              if batch_text_pair is not None:
                    a__	:  Dict     =   batch_text_pair[idx]
              else:
                    a__	:  Union[str, Any]     =   None

              a__	:  int     =   super().__call__(lowerCamelCase__				,				lowerCamelCase__				,				return_tensors=lowerCamelCase__				,				**lowerCamelCase__       )

              a__	:  Union[str, Any]     =   encoded_candidates.get("input_ids"       )
              a__	:  List[Any]     =   encoded_candidates.get("attention_mask"       )
              a__	:  int     =   encoded_candidates.get("token_type_ids"       )

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

        a__	:  Union[str, Any]     =   {key: item for key, item in output_data.items() if len(lowerCamelCase__       ) != 0}

        return BatchEncoding(lowerCamelCase__				,				tensor_type=lowerCamelCase__       )

  def    _UpperCamelCase( self			:  List[str]				,				lowerCamelCase__			:  Optional[Any]				,				lowerCamelCase__			:  str=None       ):
        a__	:  Optional[Any]     =   [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    _UpperCamelCase( self			:  Dict				,				lowerCamelCase__			:  List[int]				,				lowerCamelCase__			:  Optional[List[int]] = None       ):
        a__	:  List[Any]     =   [self.sep_token_id]
        a__	:  Optional[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    _UpperCamelCase( self			:  List[Any]				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  Optional[str] = None       ):
        a__	:  Tuple     =   self._tokenizer.model.save(lowerCamelCase__				,				name=lowerCamelCase__       )
        return tuple(lowerCamelCase__       )








"""simple docstring"""






import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


__UpperCAmelCase						=			False



class 					__UpperCAmelCase							(						unittest.TestCase  ):
				def       UpperCAmelCase      (		self    :			Tuple	, a_    :			Optional[Any]=32    )	->	Union[str, Any]:



							'''simple docstring'''



							set_seed(0    )
							a__  :	Any		 =			UNetaDModel(sample_size=a_	, in_channels=3	, out_channels=3    )
							a__  :	Union[str, Any]		 =			torch.optim.SGD(model.parameters()	, lr=0.0001    )
							return model, optimizer






				@slow
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Any:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
							a__  :	Dict		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )
							a__  :	Optional[Any]		 =			DDIMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )

							assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

							# shared batches for DDPM and DDIM
							set_seed(0    )
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).clip(-1	, 1    ).to(a_    ) for _ in range(4    )]
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).to(a_    ) for _ in range(4    )]
							a__  :	Optional[Any]		 =			[torch.randint(0	, 10_00	, (4,)    ).long().to(a_    ) for _ in range(4    )]

							# train with a DDPM scheduler
							a__ ,	a__  :	Union[str, Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	Optional[int]		 =			ddpm_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	List[str]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Optional[int]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							# recreate the model and optimizer, and retry with DDIM
							a__ ,	a__  :	Optional[Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	List[str]		 =			ddim_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	Optional[int]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Union[str, Any]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )
							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )