





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    __A       (       unittest.TestCase					):

			'''simple docstring'''







			def 						__lowerCamelCase							(       self				):



				'''simple docstring'''







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





			def 						__lowerCamelCase							(       self				):



				'''simple docstring'''







				lowerCamelCase__														=						load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/sd2-inpaint/init_image.png'''				)
				lowerCamelCase__														=						load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''				)

				lowerCamelCase__														=						"xvjiarui/stable-diffusion-2-inpainting"
				lowerCamelCase__														=						FlaxStableDiffusionInpaintPipeline.from_pretrained(lowerCAmelCase_			,    safety_checker=lowerCAmelCase_				)

				lowerCamelCase__														=						"Face of a yellow cat, high resolution, sitting on a park bench"

				lowerCamelCase__														=						jax.random.PRNGKey(0				)
				lowerCamelCase__														=						5_0

				lowerCamelCase__														=						jax.device_count()
				lowerCamelCase__														=						num_samples * [prompt]
				lowerCamelCase__														=						num_samples * [init_image]
				lowerCamelCase__														=						num_samples * [mask_image]
				lowerCamelCase__														=						pipeline.prepare_inputs(lowerCAmelCase_			,    lowerCAmelCase_			,    lowerCAmelCase_				)

				# shard inputs and rng
				lowerCamelCase__														=						replicate(lowerCAmelCase_				)
				lowerCamelCase__														=						jax.random.split(lowerCAmelCase_			,    jax.device_count()				)
				lowerCamelCase__														=						shard(lowerCAmelCase_				)
				lowerCamelCase__														=						shard(lowerCAmelCase_				)
				lowerCamelCase__														=						shard(lowerCAmelCase_				)

				lowerCamelCase__														=						pipeline(
				    lowerCAmelCase_			,    lowerCAmelCase_			,    lowerCAmelCase_			,    lowerCAmelCase_			,    lowerCAmelCase_			,    lowerCAmelCase_			,    jit=lowerCAmelCase_				)

				lowerCamelCase__														=						output.images.reshape(lowerCAmelCase_			,    5_1_2			,    5_1_2			,    3				)

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

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











"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_camembert import CamembertTokenizer
else:
			lowerCamelCase_							      = None


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase_							      = {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase_							      = {
    '''camembert-base''': 512,
}

lowerCamelCase_							      = '''▁'''







class      UpperCamelCase_			(__A	):
	__magic_name__       =							VOCAB_FILES_NAMES
	__magic_name__       =							PRETRAINED_VOCAB_FILES_MAP
	__magic_name__       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__       =							['''input_ids''', '''attention_mask''']
	__magic_name__       =							CamembertTokenizer





	def __init__( self   :					Tuple      ,      lowerCAmelCase_   :					List[str]=None      ,      lowerCAmelCase_   :					Optional[int]=None      ,      lowerCAmelCase_   :					Optional[int]="<s>"      ,      lowerCAmelCase_   :					Dict="</s>"      ,      lowerCAmelCase_   :					Any="</s>"      ,      lowerCAmelCase_   :					Union[str, Any]="<s>"      ,      lowerCAmelCase_   :					int="<unk>"      ,      lowerCAmelCase_   :					Union[str, Any]="<pad>"      ,      lowerCAmelCase_   :					Union[str, Any]="<mask>"      ,      lowerCAmelCase_   :					Any=["<s>NOTUSED", "</s>NOTUSED"]      ,      **lowerCAmelCase_   :					Any      ,      )		->     Union[str, Any]:
				# Mask token behave like a normal word, i.e. include the space before it
				UpperCAmelCase_	:					Dict     	=   AddedToken(lowerCAmelCase_      ,      lstrip=lowerCAmelCase_      ,      rstrip=lowerCAmelCase_		) if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		) else mask_token

				super().__init__(
				    lowerCAmelCase_      ,      tokenizer_file=lowerCAmelCase_      ,      bos_token=lowerCAmelCase_      ,      eos_token=lowerCAmelCase_      ,      sep_token=lowerCAmelCase_      ,      cls_token=lowerCAmelCase_      ,      unk_token=lowerCAmelCase_      ,      pad_token=lowerCAmelCase_      ,      mask_token=lowerCAmelCase_      ,      additional_special_tokens=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )

				UpperCAmelCase_	:					str     	=   vocab_file
				UpperCAmelCase_	:					Optional[Any]     	=   False if not self.vocab_file else True





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     List[int]:

				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCAmelCase_	:					Tuple     	=   [self.cls_token_id]
				UpperCAmelCase_	:					Any     	=   [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     List[int]:
				UpperCAmelCase_	:					List[Any]     	=   [self.sep_token_id]
				UpperCAmelCase_	:					Optional[int]     	=   [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 + sep + token_ids_a + sep		) * [0]





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Optional[str] = None		)		->     Tuple[str]:
				if not self.can_save_slow_tokenizer:
							raise ValueError(
							    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
							    "tokenizer."		)

				if not os.path.isdir(lowerCAmelCase_		):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
							return
				UpperCAmelCase_	:					Dict     	=   os.path.join(
				    lowerCAmelCase_      ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

				if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCAmelCase_		):
							copyfile(self.vocab_file      ,      lowerCAmelCase_		)

				return (out_vocab_file,)



