




"""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_fnet import FNetTokenizer
else:
       a					=			None

a					=			logging.get_logger(__name__)
a					=			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

a					=			{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

a					=			{
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

a					=			'▁'





class 				lowercase_       (     __lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase    :     str        =	VOCAB_FILES_NAMES
 UpperCAmelCase    :     str        =	PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase    :     int        =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase    :     Tuple        =	['''input_ids''', '''token_type_ids''']
 UpperCAmelCase    :     Dict        =	FNetTokenizer

 def __init__( self       :       Any    ,							_UpperCAmelCase       :       int=None    ,							_UpperCAmelCase       :       Union[str, Any]=None    ,							_UpperCAmelCase       :       Optional[int]=False    ,							_UpperCAmelCase       :       int=True    ,							_UpperCAmelCase       :       List[Any]=True    ,							_UpperCAmelCase       :       List[Any]="<unk>"    ,							_UpperCAmelCase       :       str="[SEP]"    ,							_UpperCAmelCase       :       str="<pad>"    ,							_UpperCAmelCase       :       int="[CLS]"    ,							_UpperCAmelCase       :       List[str]="[MASK]"    ,							**_UpperCAmelCase       :       Optional[Any]    ,							):
       # Mask token behave like a normal word, i.e. include the space before it and
       # is included in the raw text, there should be a match in a non-normalized sentence.
       _A					   =   (
           AddedToken(A__    ,							lstrip=A__    ,							rstrip=A__    ,							normalized=A__		)
           if isinstance(A__    ,							A__		)
           else mask_token
       )

       super().__init__(
           A__    ,							tokenizer_file=A__    ,							do_lower_case=A__    ,							remove_space=A__    ,							keep_accents=A__    ,							unk_token=A__    ,							sep_token=A__    ,							pad_token=A__    ,							cls_token=A__    ,							mask_token=A__    ,							**A__    ,							)

       _A					   =   do_lower_case
       _A					   =   remove_space
       _A					   =   keep_accents
       _A					   =   vocab_file
       _A					   =   False if not self.vocab_file else True

 def 			lowerCAmelCase_							( self       :       Dict    ,							_UpperCAmelCase       :       Union[str, Any]    ,							_UpperCAmelCase       :       Tuple = None		):
       _A					   =   [self.sep_token_id]
       _A					   =   [self.cls_token_id]
       if token_ids_a is None:
             return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep

 def 			lowerCAmelCase_							( self       :       Tuple    ,							_UpperCAmelCase       :       Any    ,							_UpperCAmelCase       :       Tuple = None		):
       _A					   =   [self.sep_token_id]
       _A					   =   [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 			lowerCAmelCase_							( self       :       Union[str, Any]    ,							_UpperCAmelCase       :       Optional[Any]    ,							_UpperCAmelCase       :       str = None		):
       if not os.path.isdir(A__		):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
             return
       _A					   =   os.path.join(
           A__    ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

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

       return (out_vocab_file,)




import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class     _a						(unittest.TestCase				):


					'''simple docstring'''






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    int        =		UNetaDModel(
						    block_out_channels=(32, 64)    ,       layers_per_block=2    ,       sample_size=32    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,       up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,       )
						return model






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    Dict        =		VQModel(
						    block_out_channels=[32, 64]    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,       latent_channels=3    ,       )
						return model






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    int        =		CLIPTextConfig(
						    bos_token_id=0    ,       eos_token_id=2    ,       hidden_size=32    ,       intermediate_size=37    ,       layer_norm_eps=1e-05    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       pad_token_id=1    ,       vocab_size=1000    ,       )
						return CLIPTextModel(A__			)






					def   __A			( self			):
						A__							:    int        =		self.dummy_uncond_unet
						A__							:    Optional[int]        =		DDIMScheduler()
						A__							:    List[Any]        =		self.dummy_vq_model

						A__							:    Optional[int]        =		LDMPipeline(unet=A__    ,       vqvae=A__    ,       scheduler=A__			)
						ldm.to(A__			)
						ldm.set_progress_bar_config(disable=A__			)

						A__							:    Tuple        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=2    ,       output_type="""numpy"""			).images

						A__							:    Optional[int]        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=2    ,       output_type="""numpy"""    ,       return_dict=A__			)[0]

						A__							:    str        =		image[0, -3:, -3:, -1]
						A__							:    Tuple        =		image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						A__							:    Dict        =		np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]			)
						A__							:    List[Any]        =		1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice			).max() < tolerance
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < tolerance



@slow
@require_torch
class     _a						(unittest.TestCase				):


					'''simple docstring'''






					def   __A			( self			):
						A__							:    Optional[Any]        =		LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""			)
						ldm.to(A__			)
						ldm.set_progress_bar_config(disable=A__			)

						A__							:    Tuple        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=5    ,       output_type="""numpy"""			).images

						A__							:    int        =		image[0, -3:, -3:, -1]

						assert image.shape == (1, 256, 256, 3)
						A__							:    Any        =		np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]			)
						A__							:    int        =		1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice			).max() < tolerance



