
from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


_UpperCamelCase:					Dict     =logging.get_logger(__name__)  # pylint: disable=invalid-name





class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       UpperCamelCase_				 =							['''safety_checker''', '''feature_extractor''']



       def __init__(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			AutoencoderKL  ,      _lowerCAmelCase					:			CLIPTextModel  ,      _lowerCAmelCase					:			CLIPTokenizer  ,      _lowerCAmelCase					:			UNetaDConditionModel  ,      _lowerCAmelCase					:			Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]  ,      _lowerCAmelCase					:			StableDiffusionSafetyChecker  ,      _lowerCAmelCase					:			CLIPImageProcessor  ,      _lowerCAmelCase					:			bool = True  ,      )					->   Optional[int]:
         super().__init__()
         if hasattr(scheduler.config  ,      'steps_offset'       ) and scheduler.config.steps_offset != 1:
           _lowerCAmelCase						=		(
               F'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
               F''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
               'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
               ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
               ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
               ' file'
           )
           deprecate('steps_offset!=1'  ,      '1.0.0'  ,      _lowerCAmelCase  ,      standard_warn=_lowerCAmelCase       )
           _lowerCAmelCase						=		dict(scheduler.config       )
           _lowerCAmelCase						=		1
           _lowerCAmelCase						=		FrozenDict(_lowerCAmelCase       )

         self.register_modules(
             vae=_lowerCAmelCase  ,      text_encoder=_lowerCAmelCase  ,      tokenizer=_lowerCAmelCase  ,      unet=_lowerCAmelCase  ,      scheduler=_lowerCAmelCase  ,      safety_checker=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase  ,      )
         self.register_to_config(requires_safety_checker=_lowerCAmelCase       )



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   Dict[str, Any]:
         return {k: getattr(self  ,      _lowerCAmelCase       ) for k in self.config.keys() if not k.startswith('_'       )}



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple  ,      _lowerCAmelCase					:			Optional[Union[str, int]] = "auto"       )					->   str:
         if slice_size == "auto":
           # half the attention head size is usually a good trade-off between
           # speed and memory
           _lowerCAmelCase						=		self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]       )					->   Union[str, Any]:
         self.enable_attention_slicing(_lowerCAmelCase       )



       @torch.no_grad()
       def SCREAMING_SNAKE_CASE_				(     self					:			str  ,      _lowerCAmelCase					:			Union[str, List[str]]  ,      _lowerCAmelCase					:			Union[torch.FloatTensor, PIL.Image.Image]  ,      _lowerCAmelCase					:			Union[torch.FloatTensor, PIL.Image.Image]  ,      _lowerCAmelCase					:			float = 0.8  ,      _lowerCAmelCase					:			Optional[int] = 50  ,      _lowerCAmelCase					:			Optional[float] = 7.5  ,      _lowerCAmelCase					:			Optional[Union[str, List[str]]] = None  ,      _lowerCAmelCase					:			Optional[int] = 1  ,      _lowerCAmelCase					:			Optional[float] = 0.0  ,      _lowerCAmelCase					:			Optional[torch.Generator] = None  ,      _lowerCAmelCase					:			Optional[str] = "pil"  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,      _lowerCAmelCase					:			int = 1  ,      )					->   Union[str, Any]:
         # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
         return StableDiffusionInpaintPipelineLegacy(**self.components       )(
             prompt=_lowerCAmelCase  ,      image=_lowerCAmelCase  ,      mask_image=_lowerCAmelCase  ,      strength=_lowerCAmelCase  ,      num_inference_steps=_lowerCAmelCase  ,      guidance_scale=_lowerCAmelCase  ,      negative_prompt=_lowerCAmelCase  ,      num_images_per_prompt=_lowerCAmelCase  ,      eta=_lowerCAmelCase  ,      generator=_lowerCAmelCase  ,      output_type=_lowerCAmelCase  ,      return_dict=_lowerCAmelCase  ,      callback=_lowerCAmelCase  ,      )



       @torch.no_grad()
       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]  ,      _lowerCAmelCase					:			Union[str, List[str]]  ,      _lowerCAmelCase					:			Union[torch.FloatTensor, PIL.Image.Image]  ,      _lowerCAmelCase					:			float = 0.8  ,      _lowerCAmelCase					:			Optional[int] = 50  ,      _lowerCAmelCase					:			Optional[float] = 7.5  ,      _lowerCAmelCase					:			Optional[Union[str, List[str]]] = None  ,      _lowerCAmelCase					:			Optional[int] = 1  ,      _lowerCAmelCase					:			Optional[float] = 0.0  ,      _lowerCAmelCase					:			Optional[torch.Generator] = None  ,      _lowerCAmelCase					:			Optional[str] = "pil"  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,      _lowerCAmelCase					:			int = 1  ,      **_lowerCAmelCase					:			Dict  ,      )					->   List[str]:
         # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
         return StableDiffusionImgaImgPipeline(**self.components       )(
             prompt=_lowerCAmelCase  ,      image=_lowerCAmelCase  ,      strength=_lowerCAmelCase  ,      num_inference_steps=_lowerCAmelCase  ,      guidance_scale=_lowerCAmelCase  ,      negative_prompt=_lowerCAmelCase  ,      num_images_per_prompt=_lowerCAmelCase  ,      eta=_lowerCAmelCase  ,      generator=_lowerCAmelCase  ,      output_type=_lowerCAmelCase  ,      return_dict=_lowerCAmelCase  ,      callback=_lowerCAmelCase  ,      callback_steps=_lowerCAmelCase  ,      )






       @torch.no_grad()
       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			Union[str, List[str]]  ,      _lowerCAmelCase					:			int = 512  ,      _lowerCAmelCase					:			int = 512  ,      _lowerCAmelCase					:			int = 50  ,      _lowerCAmelCase					:			float = 7.5  ,      _lowerCAmelCase					:			Optional[Union[str, List[str]]] = None  ,      _lowerCAmelCase					:			Optional[int] = 1  ,      _lowerCAmelCase					:			float = 0.0  ,      _lowerCAmelCase					:			Optional[torch.Generator] = None  ,      _lowerCAmelCase					:			Optional[torch.FloatTensor] = None  ,      _lowerCAmelCase					:			Optional[str] = "pil"  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,      _lowerCAmelCase					:			int = 1  ,      )					->   Any:
         # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
         return StableDiffusionPipeline(**self.components       )(
             prompt=_lowerCAmelCase  ,      height=_lowerCAmelCase  ,      width=_lowerCAmelCase  ,      num_inference_steps=_lowerCAmelCase  ,      guidance_scale=_lowerCAmelCase  ,      negative_prompt=_lowerCAmelCase  ,      num_images_per_prompt=_lowerCAmelCase  ,      eta=_lowerCAmelCase  ,      generator=_lowerCAmelCase  ,      latents=_lowerCAmelCase  ,      output_type=_lowerCAmelCase  ,      return_dict=_lowerCAmelCase  ,      callback=_lowerCAmelCase  ,      callback_steps=_lowerCAmelCase  ,      )


import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
       from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


_UpperCamelCase:					str     =get_tests_dir('fixtures/test_sentencepiece.model')





@require_torch
@require_torchaudio
@require_sentencepiece
class 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Any:
         _lowerCAmelCase						=		tempfile.mkdtemp()

         _lowerCAmelCase						=		['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
         _lowerCAmelCase						=		dict(zip(_lowerCAmelCase  ,      range(len(_lowerCAmelCase       )       )       )       )
         _lowerCAmelCase						=		Path(self.tmpdirname       )
         save_json(_lowerCAmelCase  ,      save_dir / VOCAB_FILES_NAMES['vocab_file']       )
         if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
           copyfile(_lowerCAmelCase  ,      save_dir / VOCAB_FILES_NAMES['spm_file']       )

         _lowerCAmelCase						=		SpeechaTextTokenizer.from_pretrained(self.tmpdirname       )
         tokenizer.save_pretrained(self.tmpdirname       )

         _lowerCAmelCase						=		{
             'feature_size': 24,
             'num_mel_bins': 24,
             'padding_value': 0.0,
             'sampling_rate': 1_6000,
             'return_attention_mask': False,
             'do_normalize': True,
         }
         save_json(_lowerCAmelCase  ,      save_dir / FEATURE_EXTRACTOR_NAME       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      **_lowerCAmelCase					:			Optional[Any]       )					->   List[str]:
         return SpeechaTextTokenizer.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      **_lowerCAmelCase					:			Dict       )					->   Tuple:
         return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   int:
         shutil.rmtree(self.tmpdirname       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   Any:
         _lowerCAmelCase						=		self.get_tokenizer()
         _lowerCAmelCase						=		self.get_feature_extractor()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         processor.save_pretrained(self.tmpdirname       )
         _lowerCAmelCase						=		SpeechaTextProcessor.from_pretrained(self.tmpdirname       )

         self.assertEqual(processor.tokenizer.get_vocab()  ,      tokenizer.get_vocab()       )
         self.assertIsInstance(processor.tokenizer  ,      _lowerCAmelCase       )

         self.assertEqual(processor.feature_extractor.to_json_string()  ,      feature_extractor.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   int:
         _lowerCAmelCase						=		SpeechaTextProcessor(
             tokenizer=self.get_tokenizer()  ,      feature_extractor=self.get_feature_extractor()       )
         processor.save_pretrained(self.tmpdirname       )

         _lowerCAmelCase						=		self.get_tokenizer(bos_token='(BOS)'  ,      eos_token='(EOS)'       )
         _lowerCAmelCase						=		self.get_feature_extractor(do_normalize=_lowerCAmelCase  ,      padding_value=1.0       )

         _lowerCAmelCase						=		SpeechaTextProcessor.from_pretrained(
             self.tmpdirname  ,      bos_token='(BOS)'  ,      eos_token='(EOS)'  ,      do_normalize=_lowerCAmelCase  ,      padding_value=1.0       )

         self.assertEqual(processor.tokenizer.get_vocab()  ,      tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer  ,      _lowerCAmelCase       )

         self.assertEqual(processor.feature_extractor.to_json_string()  ,      feature_extractor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		floats_list((3, 1000)       )

         _lowerCAmelCase						=		feature_extractor(_lowerCAmelCase  ,      return_tensors='np'       )
         _lowerCAmelCase						=		processor(_lowerCAmelCase  ,      return_tensors='np'       )

         for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum()  ,      input_processor[key].sum()  ,      delta=1e-2       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Any:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		'This is a test string'

         _lowerCAmelCase						=		processor(text=_lowerCAmelCase       )

         _lowerCAmelCase						=		tokenizer(_lowerCAmelCase       )

         for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]  ,      encoded_processor[key]       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   Tuple:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         _lowerCAmelCase						=		processor.batch_decode(_lowerCAmelCase       )
         _lowerCAmelCase						=		tokenizer.batch_decode(_lowerCAmelCase       )

         self.assertListEqual(_lowerCAmelCase  ,      _lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         self.assertListEqual(
             processor.model_input_names  ,      feature_extractor.model_input_names  ,      msg='`processor` and `feature_extractor` model input names do not match'  ,      )
