


import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__lowerCAmelCase	: Union[str, Any]  				=False
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				pass




@nightly
@require_torch_gpu
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				def 		__magic_name__(       self			:Any	)		->			Dict:
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


				def 		__magic_name__(       self			:Tuple	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''			,			torch_dtype=torch.floataa	)
										pipe.to(lowercase_	)
										pipe.set_progress_bar_config(disable=lowercase_	)

										__SCREAMING_SNAKE_CASE						:     List[str]					=		load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''	)

										__SCREAMING_SNAKE_CASE						:     List[str]					=		torch.manual_seed(0	)
										__SCREAMING_SNAKE_CASE						:     int					=		pipe.dual_guided(
										    prompt='''first prompt'''			,			image=lowercase_			,			text_to_image_strength=0.75			,			generator=lowercase_			,			guidance_scale=7.5			,			num_inference_steps=2			,			output_type='''numpy'''			,			).images

										with tempfile.TemporaryDirectory() as tmpdirname:
																pipe.save_pretrained(lowercase_	)
																__SCREAMING_SNAKE_CASE						:     Tuple					=		VersatileDiffusionPipeline.from_pretrained(lowercase_			,			torch_dtype=torch.floataa	)
										pipe.to(lowercase_	)
										pipe.set_progress_bar_config(disable=lowercase_	)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		generator.manual_seed(0	)
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		pipe.dual_guided(
										    prompt='''first prompt'''			,			image=lowercase_			,			text_to_image_strength=0.75			,			generator=lowercase_			,			guidance_scale=7.5			,			num_inference_steps=2			,			output_type='''numpy'''			,			).images

										assert np.abs(image - new_image	).sum() < 1E-5, "Models don't have the same forward pass"



				def 		__magic_name__(       self			:Union[str, Any]	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''			,			torch_dtype=torch.floataa	)
										pipe.to(lowercase_	)
										pipe.set_progress_bar_config(disable=lowercase_	)

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		"cyberpunk 2077"
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''	)
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		torch.manual_seed(0	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		pipe.dual_guided(
										    prompt=lowercase_			,			image=lowercase_			,			text_to_image_strength=0.75			,			generator=lowercase_			,			guidance_scale=7.5			,			num_inference_steps=50			,			output_type='''numpy'''			,			).images

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		image[0, 253:256, 253:256, -1]

										assert image.shape == (1, 512, 512, 3)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1

										__SCREAMING_SNAKE_CASE						:     Any					=		"A painting of a squirrel eating a burger "
										__SCREAMING_SNAKE_CASE						:     Dict					=		torch.manual_seed(0	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		pipe.text_to_image(
										    prompt=lowercase_			,			generator=lowercase_			,			guidance_scale=7.5			,			num_inference_steps=50			,			output_type='''numpy'''	).images

										__SCREAMING_SNAKE_CASE						:     List[str]					=		image[0, 253:256, 253:256, -1]

										assert image.shape == (1, 512, 512, 3)
										__SCREAMING_SNAKE_CASE						:     int					=		np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1

										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		pipe.image_variation(lowercase_			,			generator=lowercase_			,			output_type='''numpy'''	).images

										__SCREAMING_SNAKE_CASE						:     Tuple					=		image[0, 253:256, 253:256, -1]

										assert image.shape == (1, 512, 512, 3)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1


"""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 A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						List[str]       =		VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__			:						Tuple       =		PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__			:						Any       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		["""input_ids""", """token_type_ids"""]
  SCREAMING_SNAKE_CASE__			:						Tuple       =		FNetTokenizer







  def __init__(		self				,   lowercase_=None				,   lowercase_=None				,   lowercase_=False				,   lowercase_=True				,   lowercase_=True				,   lowercase_="<unk>"				,   lowercase_="[SEP]"				,   lowercase_="<pad>"				,   lowercase_="[CLS]"				,   lowercase_="[MASK]"				,   **lowercase_				,   ):

     """simple docstring"""
     # 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.
     UpperCAmelCase_					:					int			     =    (
         AddedToken(lowercase_				,   lstrip=lowercase_				,   rstrip=lowercase_				,   normalized=lowercase_  )
         if isinstance(lowercase_				,   lowercase_  )
         else mask_token
     )

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

     UpperCAmelCase_					:					Any			     =    do_lower_case
     UpperCAmelCase_					:					Tuple			     =    remove_space
     UpperCAmelCase_					:					str			     =    keep_accents
     UpperCAmelCase_					:					Any			     =    vocab_file
     UpperCAmelCase_					:					List[Any]			     =    False if not self.vocab_file else True







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    [self.sep_token_id]
     UpperCAmelCase_					:					Any			     =    [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        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    [self.sep_token_id]
     UpperCAmelCase_					:					Any			     =    [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        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     if not os.path.isdir(lowercase_  ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
        return
     UpperCAmelCase_					:					List[str]			     =    os.path.join(
         lowercase_				,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

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

     return (out_vocab_file,)


