





from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)





@dataclass
class A_					(			A_   ):
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 42


try:
       if not (is_transformers_available() and is_torch_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
       from .pipeline_cycle_diffusion import CycleDiffusionPipeline
       from .pipeline_stable_diffusion import StableDiffusionPipeline
       from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
       from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
       from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
       from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
       from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
       from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
       from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
       from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
       from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
       from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
       from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
       from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
       from .pipeline_stable_unclip import StableUnCLIPPipeline
       from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
       from .safety_checker import StableDiffusionSafetyChecker
       from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
       if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
       from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
       if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.26.0')):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ...utils.dummy_torch_and_transformers_objects import (
           StableDiffusionDepthaImgPipeline,
           StableDiffusionDiffEditPipeline,
           StableDiffusionPixaPixZeroPipeline,
       )
else:
       from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
       from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
       from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
       if not (
           is_torch_available()
           and is_transformers_available()
           and is_k_diffusion_available()
           and is_k_diffusion_version('>=', '0.0.12')
       ):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
       from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
       if not (is_transformers_available() and is_onnx_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ...utils.dummy_onnx_objects import *  # noqa F403
else:
       from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
       from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
       from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
       from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
       from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
       import flax





       @flax.struct.dataclass
       class A_					(			A_   ):
          lowerCAmelCase__        = 42
          lowerCAmelCase__        = 42





       from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
       from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
       from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
       from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
       from .safety_checker_flax import FlaxStableDiffusionSafetyChecker









import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class     UpperCAmelCase__       (					A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Tuple      =  TransfoXLTokenizer
    UpperCAmelCase__						:		str      =  False
    UpperCAmelCase__						:		Tuple      =  False






    def _a					(			self    )  ->       Union[str, Any]:
         super().setUp()

         __UpperCamelCase    							=[
             '<unk>',
             '[CLS]',
             '[SEP]',
             'want',
             'unwanted',
             'wa',
             'un',
             'running',
             ',',
             'low',
             'l',
         ]
         __UpperCamelCase    							=os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['vocab_file']    )
         with open(self.vocab_file					,   'w'					,   encoding='utf-8'    ) as vocab_writer:
              vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]    )    )






    def _a					(			self					,   **A_    )  ->       Optional[int]:
         __UpperCamelCase    							=True
         return TransfoXLTokenizer.from_pretrained(self.tmpdirname					,   **A_    )






    def _a					(			self					,   A_    )  ->       Tuple:
         __UpperCamelCase    							='<unk> UNwanted , running'
         __UpperCamelCase    							='<unk> unwanted, running'
         return input_text, output_text






    def _a					(			self    )  ->       str:
         __UpperCamelCase    							=TransfoXLTokenizer(vocab_file=self.vocab_file					,   lower_case=A_    )

         __UpperCamelCase    							=tokenizer.tokenize('<unk> UNwanted , running'    )
         self.assertListEqual(A_					,   ['<unk>', 'unwanted', ',', 'running']    )

         self.assertListEqual(tokenizer.convert_tokens_to_ids(A_    )					,   [0, 4, 8, 7]    )






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=TransfoXLTokenizer(lower_case=A_    )

         self.assertListEqual(
             tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )					,   ['hello', '!', 'how', 'are', 'you', '?']    )






    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=TransfoXLTokenizer(lower_case=A_    )

         self.assertListEqual(
             tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )					,   ['HeLLo', '!', 'how', 'Are', 'yoU', '?']    )






    def _a					(			self    )  ->       int:
         __UpperCamelCase    							=TransfoXLTokenizer(lower_case=A_    )
         __UpperCamelCase    							='Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
         __UpperCamelCase    							=[
             'Hello',
             '(',
             'bracket',
             ')',
             'and',
             'side',
             '@-@',
             'scrolled',
             '[',
             'and',
             ']',
             'Henry',
             '\'s',
             '$',
             '5',
             '@,@',
             '000',
             'with',
             '3',
             '@.@',
             '34',
             'm',
             '.',
             'What',
             '\'s',
             'up',
             '!',
             '?',
         ]

         self.assertListEqual(tokenizer.tokenize(A_    )					,   A_    )

         self.assertEqual(tokenizer.convert_tokens_to_string(A_    )					,   A_    )






    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=self.get_tokenizer()
         __UpperCamelCase    							=len(A_    )

         tokenizer.add_tokens(['new1', 'new2']    )
         tokenizer.move_added_token('new1'					,   1    )

         # Check that moved token is not copied (duplicate)
         self.assertEqual(len(A_    )					,   original_len + 2    )
         # Check that token is moved to specified id
         self.assertEqual(tokenizer.encode('new1'    )					,   [1]    )
         self.assertEqual(tokenizer.decode([1]    )					,   'new1'    )





