






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


A_						:						str						      =					logging.get_logger(__name__)

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

A_						:						Optional[int]						      =					{
    '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'
        ),
    },
}

A_						:						Union[str, Any]						      =					{
    '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,
}

A_						:						Tuple						      =					{
    '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(			UpperCAmelCase_   ):





       """simple docstring"""







       a							:						str										=VOCAB_FILES_NAMES
       a							:						Any										=PRETRAINED_VOCAB_FILES_MAP
       a							:						List[Any]										=PRETRAINED_INIT_CONFIGURATION
       a							:						Optional[Any]										=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a							:						List[str]										=RealmTokenizer





       def __init__(   self    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     _lowerCamelCase=True    ,     _lowerCamelCase="[UNK]"    ,     _lowerCamelCase="[SEP]"    ,     _lowerCamelCase="[PAD]"    ,     _lowerCamelCase="[CLS]"    ,     _lowerCamelCase="[MASK]"    ,     _lowerCamelCase=True    ,     _lowerCamelCase=None    ,     **_lowerCamelCase    ,     ):
         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    ,     )

         UpperCamelCase_:   List[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
         ):
           UpperCamelCase_:   Tuple        =							getattr(_lowerCamelCase    ,     normalizer_state.pop('type'				)				)
           UpperCamelCase_:   Any        =							do_lower_case
           UpperCamelCase_:   Tuple        =							strip_accents
           UpperCamelCase_:   Optional[Any]        =							tokenize_chinese_chars
           UpperCamelCase_:   List[str]        =							normalizer_class(**_lowerCamelCase				)

         UpperCamelCase_:   Optional[Any]        =							do_lower_case





       def      _a       (   self    ,     _lowerCamelCase    ,     **_lowerCamelCase				):
         UpperCamelCase_:   Optional[Any]        =							PaddingStrategy.MAX_LENGTH

         UpperCamelCase_:   Any        =							text
         UpperCamelCase_:   List[Any]        =							kwargs.pop('text_pair'    ,     _lowerCamelCase				)
         UpperCamelCase_:   Optional[int]        =							kwargs.pop('return_tensors'    ,     _lowerCamelCase				)

         UpperCamelCase_:   Optional[int]        =							{
             'input_ids': [],
             'attention_mask': [],
             'token_type_ids': [],
         }

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

           UpperCamelCase_:   Any        =							super().__call__(_lowerCamelCase    ,     _lowerCamelCase    ,     return_tensors=_lowerCamelCase    ,     **_lowerCamelCase				)

           UpperCamelCase_:   Dict        =							encoded_candidates.get('input_ids'				)
           UpperCamelCase_:   str        =							encoded_candidates.get('attention_mask'				)
           UpperCamelCase_:   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				)

         UpperCamelCase_:   Dict        =							{key: item for key, item in output_data.items() if len(_lowerCamelCase				) != 0}

         return BatchEncoding(_lowerCamelCase    ,     tensor_type=_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase=None				):
         UpperCamelCase_:   Union[str, 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      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase = None				):
         UpperCamelCase_:   Union[str, Any]        =							[self.sep_token_id]
         UpperCamelCase_:   Dict        =							[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    ,     _lowerCamelCase    ,     _lowerCamelCase = None				):
         UpperCamelCase_:   Tuple        =							self._tokenizer.model.save(_lowerCamelCase    ,     name=_lowerCamelCase				)
         return tuple(_lowerCamelCase				)







"""simple docstring"""
import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
			import jax
			import jax.numpy as jnp
			from flax.jax_utils import replicate
			from flax.training.common_utils import shard


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







						def 			A      (    self							)		->							Any:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()







						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    FlaxStableDiffusionPipeline.from_pretrained(
										    'stabilityai/stable-diffusion-2'   ,				revision='bf16'   ,				dtype=jnp.bfloataa   ,				)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.device_count()
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_samples * [prompt]
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe.prepare_inputs(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    replicate(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    shard(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    jax.random.PRNGKey(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jax.random.split(lowerCAmelCase_   ,				jax.device_count()							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    sd_pipe(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				num_inference_steps=2_5   ,				jit=lowerCAmelCase_							)[0]
										assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

										_SCREAMING_SNAKE_CASE   :		int       		=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]							)
										print(F"""output_slice: {output_slice}"""							)
										assert jnp.abs(output_slice - expected_slice							).max() < 1e-2







						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		str       		=    'stabilityai/stable-diffusion-2'
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		int       		=    FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCAmelCase_   ,				subfolder='scheduler'							)
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[str]       		=    FlaxStableDiffusionPipeline.from_pretrained(
										    lowerCAmelCase_   ,				scheduler=lowerCAmelCase_   ,				revision='bf16'   ,				dtype=jnp.bfloataa   ,				)
										_SCREAMING_SNAKE_CASE   :		Any       		=    scheduler_params

										_SCREAMING_SNAKE_CASE   :		int       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jax.device_count()
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    num_samples * [prompt]
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe.prepare_inputs(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Dict       		=    replicate(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    shard(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    jax.random.PRNGKey(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.random.split(lowerCAmelCase_   ,				jax.device_count()							)

										_SCREAMING_SNAKE_CASE   :		int       		=    sd_pipe(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				num_inference_steps=2_5   ,				jit=lowerCAmelCase_							)[0]
										assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

										_SCREAMING_SNAKE_CASE   :		str       		=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
										_SCREAMING_SNAKE_CASE   :		str       		=    images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										_SCREAMING_SNAKE_CASE   :		int       		=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]							)
										print(F"""output_slice: {output_slice}"""							)
										assert jnp.abs(output_slice - expected_slice							).max() < 1e-2

