






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


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

__UpperCamelCase      :      Optional[int]     						=						{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__UpperCamelCase      :      Any     						=						{
    """vocab_file""": {"""mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"""},
    """tokenizer_file""": {
        """mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"""
    },
}

__UpperCamelCase      :      List[Any]     						=						{"""mobilebert-uncased""": 512}


__UpperCamelCase      :      Optional[Any]     						=						{}


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:Optional[int]   =       VOCAB_FILES_NAMES
	__snake_case	:List[Any]   =       PRETRAINED_VOCAB_FILES_MAP
	__snake_case	:Tuple   =       PRETRAINED_INIT_CONFIGURATION
	__snake_case	:Union[str, Any]   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__snake_case	:Optional[int]   =       MobileBertTokenizer







	def __init__(  self      :							List[Any]       ,				_lowerCAmelCase      :							Union[str, Any]=None       ,				_lowerCAmelCase      :							Any=None       ,				_lowerCAmelCase      :							int=True       ,				_lowerCAmelCase      :							Union[str, Any]="[UNK]"       ,				_lowerCAmelCase      :							List[str]="[SEP]"       ,				_lowerCAmelCase      :							Dict="[PAD]"       ,				_lowerCAmelCase      :							str="[CLS]"       ,				_lowerCAmelCase      :							List[str]="[MASK]"       ,				_lowerCAmelCase      :							str=True       ,				_lowerCAmelCase      :							Tuple=None       ,				**_lowerCAmelCase      :							List[Any]       ,				)		-> Optional[Any]:
		"""simple docstring"""







		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       ,				)

		__lowercase             =  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
		):
			__lowercase             =  getattr(_lowerCAmelCase       ,				normalizer_state.pop("""type"""			)			)
			__lowercase             =  do_lower_case
			__lowercase             =  strip_accents
			__lowercase             =  tokenize_chinese_chars
			__lowercase             =  normalizer_class(**_lowerCAmelCase			)

		__lowercase             =  do_lower_case







	def  _a   (  self      :							Any       ,				_lowerCAmelCase      :							Optional[Any]       ,				_lowerCAmelCase      :							Optional[int]=None			)		-> Tuple:
		"""simple docstring"""







		__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      :							Dict       ,				_lowerCAmelCase      :							List[int]       ,				_lowerCAmelCase      :							Optional[List[int]] = None			)		-> List[int]:
		"""simple docstring"""







		__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      :							Tuple       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							Optional[str] = None			)		-> Tuple[str]:
		"""simple docstring"""







		__lowercase             =  self._tokenizer.model.save(_lowerCAmelCase       ,				name=_lowerCAmelCase			)
		return tuple(_lowerCAmelCase			)









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        __UpperCamelCase    (     unittest.TestCase   ):







	def  _a   (  self      :							Union[str, Any]			)		-> Any:
		"""simple docstring"""







		super().tearDown()
		gc.collect()







	def  _a   (  self      :							List[str]			)		-> int:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  FlaxStableDiffusionPipeline.from_pretrained(
		    """stabilityai/stable-diffusion-2"""       ,				revision="""bf16"""       ,				dtype=jnp.bfloataa       ,				)

		__lowercase             =  """A painting of a squirrel eating a burger"""
		__lowercase             =  jax.device_count()
		__lowercase             =  num_samples * [prompt]
		__lowercase             =  sd_pipe.prepare_inputs(_lowerCAmelCase			)

		__lowercase             =  replicate(_lowerCAmelCase			)
		__lowercase             =  shard(_lowerCAmelCase			)

		__lowercase             =  jax.random.PRNGKey(0			)
		__lowercase             =  jax.random.split(_lowerCAmelCase       ,				jax.device_count()			)

		__lowercase             =  sd_pipe(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				num_inference_steps=25       ,				jit=_lowerCAmelCase			)[0]
		assert images.shape == (jax.device_count(), 1, 768, 768, 3)

		__lowercase             =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
		__lowercase             =  images[0, 253:256, 253:256, -1]

		__lowercase             =  jnp.asarray(jax.device_get(image_slice.flatten()			)			)
		__lowercase             =  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      :							str			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  """stabilityai/stable-diffusion-2"""
		__lowercase       ,						__lowercase             =  FlaxDPMSolverMultistepScheduler.from_pretrained(_lowerCAmelCase       ,				subfolder="""scheduler"""			)
		__lowercase       ,						__lowercase             =  FlaxStableDiffusionPipeline.from_pretrained(
		    _lowerCAmelCase       ,				scheduler=_lowerCAmelCase       ,				revision="""bf16"""       ,				dtype=jnp.bfloataa       ,				)
		__lowercase             =  scheduler_params

		__lowercase             =  """A painting of a squirrel eating a burger"""
		__lowercase             =  jax.device_count()
		__lowercase             =  num_samples * [prompt]
		__lowercase             =  sd_pipe.prepare_inputs(_lowerCAmelCase			)

		__lowercase             =  replicate(_lowerCAmelCase			)
		__lowercase             =  shard(_lowerCAmelCase			)

		__lowercase             =  jax.random.PRNGKey(0			)
		__lowercase             =  jax.random.split(_lowerCAmelCase       ,				jax.device_count()			)

		__lowercase             =  sd_pipe(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				num_inference_steps=25       ,				jit=_lowerCAmelCase			)[0]
		assert images.shape == (jax.device_count(), 1, 768, 768, 3)

		__lowercase             =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
		__lowercase             =  images[0, 253:256, 253:256, -1]

		__lowercase             =  jnp.asarray(jax.device_get(image_slice.flatten()			)			)
		__lowercase             =  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

