
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


_lowerCamelCase       :				str       =			logging.get_logger(__name__)

_lowerCamelCase       :				str       =			{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase       :				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'''
        ),
    },
}

_lowerCamelCase       :				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,
}

_lowerCamelCase       :				Optional[Any]       =			{
    '''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    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		List[Any]       =					VOCAB_FILES_NAMES
	_UpperCAmelCase       :		Tuple       =					PRETRAINED_VOCAB_FILES_MAP
	_UpperCAmelCase       :		Union[str, Any]       =					PRETRAINED_INIT_CONFIGURATION
	_UpperCAmelCase       :		Tuple       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_UpperCAmelCase       :		Tuple       =					RealmTokenizer



	def __init__(  self	:    Optional[Any]			,  lowercase	:    str=None			,  lowercase	:    List[str]=None			,  lowercase	:    Union[str, Any]=True			,  lowercase	:    str="[UNK]"			,  lowercase	:    List[Any]="[SEP]"			,  lowercase	:    Optional[Any]="[PAD]"			,  lowercase	:    Optional[int]="[CLS]"			,  lowercase	:    Any="[MASK]"			,  lowercase	:    Any=True			,  lowercase	:    Union[str, Any]=None			,  **lowercase	:    int			,  ):


				'''simple docstring'''


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

				_snake_case		=			json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
				if (
				    normalizer_state.get('lowercase'			,  lowercase	) != do_lower_case
				    or normalizer_state.get('strip_accents'			,  lowercase	) != strip_accents
				    or normalizer_state.get('handle_chinese_chars'			,  lowercase	) != tokenize_chinese_chars
				):
							_snake_case		=			getattr(lowercase			,  normalizer_state.pop('type'	)	)
							_snake_case		=			do_lower_case
							_snake_case		=			strip_accents
							_snake_case		=			tokenize_chinese_chars
							_snake_case		=			normalizer_class(**lowercase	)

				_snake_case		=			do_lower_case



	def  A							(  self	:    Optional[int]			,  lowercase	:    int			,  **lowercase	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			PaddingStrategy.MAX_LENGTH

				_snake_case		=			text
				_snake_case		=			kwargs.pop('text_pair'			,  lowercase	)
				_snake_case		=			kwargs.pop('return_tensors'			,  lowercase	)

				_snake_case		=			{
				    'input_ids': [],
				    'attention_mask': [],
				    'token_type_ids': [],
				}

				for idx, candidate_text in enumerate(lowercase	):
							if batch_text_pair is not None:
										_snake_case		=			batch_text_pair[idx]
							else:
										_snake_case		=			None

							_snake_case		=			super().__call__(lowercase			,  lowercase			,  return_tensors=lowercase			,  **lowercase	)

							_snake_case		=			encoded_candidates.get('input_ids'	)
							_snake_case		=			encoded_candidates.get('attention_mask'	)
							_snake_case		=			encoded_candidates.get('token_type_ids'	)

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

				_snake_case		=			{key: item for key, item in output_data.items() if len(lowercase	) != 0}

				return BatchEncoding(lowercase			,  tensor_type=lowercase	)



	def  A							(  self	:    str			,  lowercase	:    List[Any]			,  lowercase	:    int=None	):


				'''simple docstring'''


				_snake_case		=			[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  A							(  self	:    Any			,  lowercase	:    List[int]			,  lowercase	:    Optional[List[int]] = None	):


				'''simple docstring'''


				_snake_case		=			[self.sep_token_id]
				_snake_case		=			[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  A							(  self	:    Any			,  lowercase	:    str			,  lowercase	:    Optional[str] = None	):


				'''simple docstring'''


				_snake_case		=			self._tokenizer.model.save(lowercase			,  name=lowercase	)
				return tuple(lowercase	)

import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch




@require_torch
class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	@property
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				return self.get_dummy_input()



	@property
	def  A							(  self	:    Any	):


				'''simple docstring'''


				if self.block_type == "down":
							return (4, 32, 16, 16)
				elif self.block_type == "mid":
							return (4, 32, 32, 32)
				elif self.block_type == "up":
							return (4, 32, 64, 64)

				raise ValueError(f'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'''	)



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    Any=True			,  lowercase	:    List[Any]=False			,  lowercase	:    List[str]=False			,  lowercase	:    Dict=False			,  ):


				'''simple docstring'''


				_snake_case		=			4
				_snake_case		=			32
				_snake_case		=			(32, 32)

				_snake_case		=			torch.manual_seed(0	)
				_snake_case		=			torch.device(lowercase	)
				_snake_case		=			(batch_size, num_channels) + sizes
				_snake_case		=			randn_tensor(lowercase			,  generator=lowercase			,  device=lowercase	)
				_snake_case		=			{'hidden_states': hidden_states}

				if include_temb:
							_snake_case		=			128
							_snake_case		=			randn_tensor((batch_size, temb_channels)			,  generator=lowercase			,  device=lowercase	)

				if include_res_hidden_states_tuple:
							_snake_case		=			torch.manual_seed(1	)
							_snake_case		=			(randn_tensor(lowercase			,  generator=lowercase			,  device=lowercase	),)

				if include_encoder_hidden_states:
							_snake_case		=			floats_tensor((batch_size, 32, 32)	).to(lowercase	)

				if include_skip_sample:
							_snake_case		=			randn_tensor(((batch_size, 3) + sizes)			,  generator=lowercase			,  device=lowercase	)

				return dummy_input



	def  A							(  self	:    Any	):


				'''simple docstring'''


				_snake_case		=			{
				    'in_channels': 32,
				    'out_channels': 32,
				    'temb_channels': 128,
				}
				if self.block_type == "up":
							_snake_case		=			32

				if self.block_type == "mid":
							init_dict.pop('out_channels'	)

				_snake_case		=			self.dummy_input
				return init_dict, inputs_dict



	def  A							(  self	:    Dict			,  lowercase	:    Optional[int]	):


				'''simple docstring'''


				_snake_case	,					_snake_case		=			self.prepare_init_args_and_inputs_for_common()
				_snake_case		=			self.block_class(**lowercase	)
				unet_block.to(lowercase	)
				unet_block.eval()

				with torch.no_grad():
							_snake_case		=			unet_block(**lowercase	)

				if isinstance(lowercase			,  lowercase	):
							_snake_case		=			output[0]

				self.assertEqual(output.shape			,  self.output_shape	)

				_snake_case		=			output[0, -1, -3:, -3:]
				_snake_case		=			torch.tensor(lowercase	).to(lowercase	)
				assert torch_all_close(output_slice.flatten()			,  lowercase			,  atol=5E-3	)





	@unittest.skipIf(torch_device == 'mps'			,  'Training is not supported in mps'	)
	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case	,					_snake_case		=			self.prepare_init_args_and_inputs_for_common()
				_snake_case		=			self.block_class(**lowercase	)
				model.to(lowercase	)
				model.train()
				_snake_case		=			model(**lowercase	)

				if isinstance(lowercase			,  lowercase	):
							_snake_case		=			output[0]

				_snake_case		=			torch.device(lowercase	)
				_snake_case		=			randn_tensor(output.shape			,  device=lowercase	)
				_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  lowercase	)
				loss.backward()