

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''': 5_12,
}

lowerCAmelCase_       	=	'''▁'''





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   int									=				VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE     :   int									=				PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE     :   List[Any]									=				["input_ids", "attention_mask"]
			SCREAMING_SNAKE_CASE     :   Any									=				CamembertTokenizer







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

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

									snake_case_   =	vocab_file
									snake_case_   =	False if not self.vocab_file else True







			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							)      ->List[int]:

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







			def 				snake_case__(     self :				Union[str, Any]							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							)      ->List[int]:
									snake_case_   =	[self.sep_token_id]
									snake_case_   =	[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 				snake_case__(     self :				List[str]							,   _UpperCamelCase :				str							,   _UpperCamelCase :				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(_UpperCamelCase							):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
															return
									snake_case_   =	os.path.join(
									    _UpperCamelCase							,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

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

									return (out_vocab_file,)


import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
					import jax
					import jax.numpy as jnp





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







			'''simple docstring'''

			def 				snake_case__(     self :				Any							,   _UpperCamelCase :				Any							,   _UpperCamelCase :				Tuple							)      ->List[Any]:
									return f'''gaussian_noise_s={seed}_shape={'_'.join([str(_UpperCamelCase							) for s in shape]							)}.npy'''







			def 				snake_case__(     self :				Any							)      ->List[str]:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				Union[str, Any]=0							,   _UpperCamelCase :				int=(4, 4, 6_4, 6_4)							,   _UpperCamelCase :				Optional[int]=False							)      ->Tuple:
									snake_case_   =	jnp.bfloataa if fpaa else jnp.floataa
									snake_case_   =	jnp.array(load_hf_numpy(self.get_file_format(_UpperCamelCase							,   _UpperCamelCase							)							)							,   dtype=_UpperCamelCase							)
									return image







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				Optional[Any]=False							,   _UpperCamelCase :				Optional[int]="CompVis/stable-diffusion-v1-4"							)      ->Optional[Any]:
									snake_case_   =	jnp.bfloataa if fpaa else jnp.floataa
									snake_case_   =	'''bf16''' if fpaa else None

									snake_case_,    snake_case_   =	FlaxUNetaDConditionModel.from_pretrained(
									    _UpperCamelCase							,   subfolder='''unet'''							,   dtype=_UpperCamelCase							,   revision=_UpperCamelCase							)
									return model, params







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				List[Any]=0							,   _UpperCamelCase :				Tuple=(4, 7_7, 7_6_8)							,   _UpperCamelCase :				List[Any]=False							)      ->int:
									snake_case_   =	jnp.bfloataa if fpaa else jnp.floataa
									snake_case_   =	jnp.array(load_hf_numpy(self.get_file_format(_UpperCamelCase							,   _UpperCamelCase							)							)							,   dtype=_UpperCamelCase							)
									return hidden_states







			@parameterized.expand(
			    [
			        # fmt: off
			        [8_3, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
			        [1_7, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
			        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
			        [3, 1_0_0_0, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
			        # fmt: on
			    ]							)
			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				List[Any]							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				Optional[int]							)      ->Union[str, Any]:
									snake_case_,    snake_case_   =	self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_latents(_UpperCamelCase							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_encoder_hidden_states(_UpperCamelCase							,   fpaa=_UpperCamelCase							)

									snake_case_   =	model.apply(
									    {'''params''': params}							,   _UpperCamelCase							,   jnp.array(_UpperCamelCase							,   dtype=jnp.intaa							)							,   encoder_hidden_states=_UpperCamelCase							,   ).sample

									assert sample.shape == latents.shape

									snake_case_   =	jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)							,   dtype=jnp.floataa							)
									snake_case_   =	jnp.array(_UpperCamelCase							,   dtype=jnp.floataa							)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
									assert jnp.allclose(_UpperCamelCase							,   _UpperCamelCase							,   atol=1e-2							)







			@parameterized.expand(
			    [
			        # fmt: off
			        [8_3, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
			        [1_7, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
			        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
			        [3, 1_0_0_0, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
			        # fmt: on
			    ]							)
			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				Optional[Any]							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				str							)      ->Dict:
									snake_case_,    snake_case_   =	self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_latents(_UpperCamelCase							,   shape=(4, 4, 9_6, 9_6)							,   fpaa=_UpperCamelCase							)
									snake_case_   =	self.get_encoder_hidden_states(_UpperCamelCase							,   shape=(4, 7_7, 1_0_2_4)							,   fpaa=_UpperCamelCase							)

									snake_case_   =	model.apply(
									    {'''params''': params}							,   _UpperCamelCase							,   jnp.array(_UpperCamelCase							,   dtype=jnp.intaa							)							,   encoder_hidden_states=_UpperCamelCase							,   ).sample

									assert sample.shape == latents.shape

									snake_case_   =	jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)							,   dtype=jnp.floataa							)
									snake_case_   =	jnp.array(_UpperCamelCase							,   dtype=jnp.floataa							)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
									assert jnp.allclose(_UpperCamelCase							,   _UpperCamelCase							,   atol=1e-2							)