






"""simple docstring"""



import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase	:	Dict				=      logging.get_logger(__name__)

_lowerCAmelCase	:	Any				=      {"""vocab_file""": """vocab.json"""}

_lowerCAmelCase	:	List[str]				=      {
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

_lowerCAmelCase	:	Union[str, Any]				=      {"""mgp-str""": 27}




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    =VOCAB_FILES_NAMES
   SCREAMING_SNAKE_CASE_    =PRETRAINED_VOCAB_FILES_MAP
   SCREAMING_SNAKE_CASE_    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   def __init__(		self      :	Optional[Any]							,			snake_case__      :	List[Any]							,			snake_case__      :	Dict="[GO]"							,			snake_case__      :	List[Any]="[GO]"							,			snake_case__      :	str="[s]"							,			snake_case__      :	Tuple="[GO]"							,			**snake_case__      :	int							):


        '''simple docstring'''
        super().__init__(
            unk_token=snake_case__							,			bos_token=snake_case__							,			eos_token=snake_case__							,			pad_token=snake_case__							,			**snake_case__							,			)

        with open(snake_case__							,			encoding="utf-8"							) as vocab_handle:
             UpperCAmelCase__						:    Dict							=							json.load(snake_case__							)
        UpperCAmelCase__						:    Dict							=							{v: k for k, v in self.vocab.items()}
   @property
   def       __a							(		self      :	Optional[int]							):


        '''simple docstring'''
        return len(self.vocab							)
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        return dict(self.vocab							,			**self.added_tokens_encoder							)
   def       __a							(		self      :	Optional[int]							,			snake_case__      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							[]
        for s in text:
             char_tokens.extend(snake_case__							)
        return char_tokens
   def       __a							(		self      :	int							,			snake_case__      :	int							):


        '''simple docstring'''
        return self.vocab.get(snake_case__							,			self.vocab.get(self.unk_token							)							)
   def       __a							(		self      :	int							,			snake_case__      :	Dict							):


        '''simple docstring'''
        return self.decoder.get(snake_case__							)




   def       __a							(		self      :	Tuple							,			snake_case__      :	str							,			snake_case__      :	Optional[str] = None							):


        '''simple docstring'''
        if not os.path.isdir(snake_case__							):
             logger.error("Vocabulary path ({}) should be a directory".format(snake_case__							)							)
             return
        UpperCAmelCase__						:    Union[str, Any]							=							os.path.join(
            snake_case__							,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

        with open(snake_case__							,			"w"							,			encoding="utf-8"							) as f:
             f.write(json.dumps(self.vocab							,			indent=2							,			sort_keys=snake_case__							,			ensure_ascii=snake_case__							) + "\n"							)

        return (vocab_file,)










"""simple docstring"""



import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, 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      :	Optional[int]							):


        '''simple docstring'''
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()




   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-inpaint/init_image.png"							)
        UpperCAmelCase__						:    Any							=							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"							)

        UpperCAmelCase__						:    str							=							"xvjiarui/stable-diffusion-2-inpainting"
        UpperCAmelCase__							,				UpperCAmelCase__						:    List[Any]							=							FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case__							,			safety_checker=snake_case__							)

        UpperCAmelCase__						:    List[str]							=							"Face of a yellow cat, high resolution, sitting on a park bench"

        UpperCAmelCase__						:    List[str]							=							jax.random.PRNGKey(0							)
        UpperCAmelCase__						:    Optional[int]							=							5_0

        UpperCAmelCase__						:    List[Any]							=							jax.device_count()
        UpperCAmelCase__						:    int							=							num_samples * [prompt]
        UpperCAmelCase__						:    str							=							num_samples * [init_image]
        UpperCAmelCase__						:    Optional[Any]							=							num_samples * [mask_image]
        UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							pipeline.prepare_inputs(snake_case__							,			snake_case__							,			snake_case__							)

        # shard inputs and rng
        UpperCAmelCase__						:    Dict							=							replicate(snake_case__							)
        UpperCAmelCase__						:    Dict							=							jax.random.split(snake_case__							,			jax.device_count()							)
        UpperCAmelCase__						:    List[str]							=							shard(snake_case__							)
        UpperCAmelCase__						:    str							=							shard(snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							shard(snake_case__							)

        UpperCAmelCase__						:    Dict							=							pipeline(
            snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			jit=snake_case__							)

        UpperCAmelCase__						:    List[str]							=							output.images.reshape(snake_case__							,			5_1_2							,			5_1_2							,			3							)

        UpperCAmelCase__						:    Tuple							=							images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        UpperCAmelCase__						:    str							=							jnp.asarray(jax.device_get(image_slice.flatten()							)							)
        UpperCAmelCase__						:    int							=							jnp.array(
            [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084]							)
        print(f'output_slice: {output_slice}'							)
        assert jnp.abs(output_slice - expected_slice							).max() < 1e-2


