


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__   	=							logging.get_logger(__name__)

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

UpperCAmelCase__   	=							{
    '''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__   	=							{
    '''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__   	=							{
    '''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__   (       lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__      			=			PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE__      			=			PRETRAINED_INIT_CONFIGURATION
     SCREAMING_SNAKE_CASE__      			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE__      			=			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		,     )     ->   Optional[Any]:
      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		,     )

      _lowercase							      =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
      ):
       _lowercase							      =getattr(UpperCAmelCase		,     normalizer_state.pop('''type'''       )       )
       _lowercase							      =do_lower_case
       _lowercase							      =strip_accents
       _lowercase							      =tokenize_chinese_chars
       _lowercase							      =normalizer_class(**UpperCAmelCase       )

      _lowercase							      =do_lower_case





     def      __A					(self		,     UpperCAmelCase		,     **UpperCAmelCase       )     ->   Any:
      _lowercase							      =PaddingStrategy.MAX_LENGTH

      _lowercase							      =text
      _lowercase							      =kwargs.pop('''text_pair'''		,     UpperCAmelCase       )
      _lowercase							      =kwargs.pop('''return_tensors'''		,     UpperCAmelCase       )

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

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

       _lowercase							      =super().__call__(UpperCAmelCase		,     UpperCAmelCase		,     return_tensors=UpperCAmelCase		,     **UpperCAmelCase       )

       _lowercase							      =encoded_candidates.get('''input_ids'''       )
       _lowercase							      =encoded_candidates.get('''attention_mask'''       )
       _lowercase							      =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       )

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

      return BatchEncoding(UpperCAmelCase		,     tensor_type=UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase=None       )     ->   Dict:
      _lowercase							      =[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		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   List[int]:
      _lowercase							      =[self.sep_token_id]
      _lowercase							      =[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		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   Tuple[str]:
      _lowercase							      =self._tokenizer.model.save(UpperCAmelCase		,     name=UpperCAmelCase       )
      return tuple(UpperCAmelCase       )








import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						(UnCLIPScheduler,)





			def 		__lowerCamelCase				(    self       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		{
				    '''num_train_timesteps''': 1_0_0_0,
				    '''variance_type''': '''fixed_small_log''',
				    '''clip_sample''': True,
				    '''clip_sample_range''': 1.0,
				    '''prediction_type''': '''epsilon''',
				}

				config.update(**__lowerCAmelCase							)
				return config





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
					self.check_over_configs(num_train_timesteps=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for variance in ["fixed_small_log", "learned_range"]:
					self.check_over_configs(variance_type=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for clip_sample in [True, False]:
					self.check_over_configs(clip_sample=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for clip_sample_range in [1, 5, 1_0, 2_0]:
					self.check_over_configs(clip_sample_range=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for prediction_type in ["epsilon", "sample"]:
					self.check_over_configs(prediction_type=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for time_step in [0, 5_0_0, 9_9_9]:
					for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
						if prev_timestep is not None and prev_timestep >= time_step:
							continue

						self.check_over_forward(time_step=__lowerCAmelCase       ,  prev_timestep=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config(variance_type='''fixed_small_log'''							)
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				assert torch.sum(torch.abs(scheduler._get_variance(0							) - 1.0000E-10							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.054_9625							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.999_4987							)							) < 1E-5





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config(variance_type='''learned_range'''							)
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				lowerCamelCase__								=		0.5

				assert scheduler._get_variance(1       ,  predicted_variance=__lowerCAmelCase							) - -10.171_2790 < 1E-5
				assert scheduler._get_variance(4_8_7       ,  predicted_variance=__lowerCAmelCase							) - -5.799_8052 < 1E-5
				assert scheduler._get_variance(9_9_9       ,  predicted_variance=__lowerCAmelCase							) - -0.001_0011 < 1E-5





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config()
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				lowerCamelCase__								=		scheduler.timesteps

				lowerCamelCase__								=		self.dummy_model()
				lowerCamelCase__								=		self.dummy_sample_deter
				lowerCamelCase__								=		torch.manual_seed(0							)

				for i, t in enumerate(__lowerCAmelCase							):
					# 1. predict noise residual
					lowerCamelCase__								=		model(__lowerCAmelCase       ,  __lowerCAmelCase							)

					# 2. predict previous mean of sample x_t-1
					lowerCamelCase__								=		scheduler.step(__lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  generator=__lowerCAmelCase							).prev_sample

					lowerCamelCase__								=		pred_prev_sample

				lowerCamelCase__								=		torch.sum(torch.abs(__lowerCAmelCase							)							)
				lowerCamelCase__								=		torch.mean(torch.abs(__lowerCAmelCase							)							)

				assert abs(result_sum.item() - 252.268_2495							) < 1E-2
				assert abs(result_mean.item() - 0.328_4743							) < 1E-3





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config()
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				scheduler.set_timesteps(2_5							)

				lowerCamelCase__								=		scheduler.timesteps

				lowerCamelCase__								=		self.dummy_model()
				lowerCamelCase__								=		self.dummy_sample_deter
				lowerCamelCase__								=		torch.manual_seed(0							)

				for i, t in enumerate(__lowerCAmelCase							):
					# 1. predict noise residual
					lowerCamelCase__								=		model(__lowerCAmelCase       ,  __lowerCAmelCase							)

					if i + 1 == timesteps.shape[0]:
						lowerCamelCase__								=		None
					else:
						lowerCamelCase__								=		timesteps[i + 1]

					# 2. predict previous mean of sample x_t-1
					lowerCamelCase__								=		scheduler.step(
					    __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  prev_timestep=__lowerCAmelCase       ,  generator=__lowerCAmelCase							).prev_sample

					lowerCamelCase__								=		pred_prev_sample

				lowerCamelCase__								=		torch.sum(torch.abs(__lowerCAmelCase							)							)
				lowerCamelCase__								=		torch.mean(torch.abs(__lowerCAmelCase							)							)

				assert abs(result_sum.item() - 258.204_4983							) < 1E-2
				assert abs(result_mean.item() - 0.336_2038							) < 1E-3





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass



