





import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


a__: List[str]							   =						logging.get_logger(__name__)  # pylint: disable=invalid-name




class        SCREAMING_SNAKE_CASE__					(		__snake_case ):



 def __init__(				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,):
        super().__init__()

        if safety_checker is None:
               logger.warning(
                   f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                   ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
                   ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
                   ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
                   ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
                   ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'''					)

        self.register_modules(
            speech_model=__lowerCamelCase,speech_processor=__lowerCamelCase,vae=__lowerCamelCase,text_encoder=__lowerCamelCase,tokenizer=__lowerCamelCase,unet=__lowerCamelCase,scheduler=__lowerCamelCase,feature_extractor=__lowerCamelCase,)



 def 							UpperCamelCase     (				self,__lowerCamelCase = "auto"					):
        if slice_size == "auto":
               A__		   =		self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        self.enable_attention_slicing(__lowerCamelCase					)




 @torch.no_grad()
 def __call__(				self,__lowerCamelCase,__lowerCamelCase=1_6000,__lowerCamelCase = 512,__lowerCamelCase = 512,__lowerCamelCase = 50,__lowerCamelCase = 7.5,__lowerCamelCase = None,__lowerCamelCase = 1,__lowerCamelCase = 0.0,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = "pil",__lowerCamelCase = True,__lowerCamelCase = None,__lowerCamelCase = 1,**__lowerCamelCase,):
        A__		   =		self.speech_processor.feature_extractor(
            __lowerCamelCase,return_tensors='''pt''',sampling_rate=__lowerCamelCase					).input_features.to(self.device					)
        A__		   =		self.speech_model.generate(__lowerCamelCase,max_length=48_0000					)

        A__		   =		self.speech_processor.tokenizer.batch_decode(__lowerCamelCase,skip_special_tokens=__lowerCamelCase,normalize=__lowerCamelCase					)[
            0
        ]

        if isinstance(__lowerCamelCase,__lowerCamelCase					):
               A__		   =		1
        elif isinstance(__lowerCamelCase,__lowerCamelCase					):
               A__		   =		len(__lowerCamelCase					)
        else:
               raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(__lowerCamelCase					)}"					)

        if height % 8 != 0 or width % 8 != 0:
               raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}."					)

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(__lowerCamelCase,__lowerCamelCase					) or callback_steps <= 0)
        ):
               raise ValueError(
                   f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                   f" {type(__lowerCamelCase					)}."					)

        # get prompt text embeddings
        A__		   =		self.tokenizer(
            __lowerCamelCase,padding='''max_length''',max_length=self.tokenizer.model_max_length,return_tensors='''pt''',)
        A__		   =		text_inputs.input_ids

        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
               A__		   =		self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]					)
               logger.warning(
                   '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                   f" {self.tokenizer.model_max_length} tokens: {removed_text}"					)
               A__		   =		text_input_ids[:, : self.tokenizer.model_max_length]
        A__		   =		self.text_encoder(text_input_ids.to(self.device					)					)[0]

        # duplicate text embeddings for each generation per prompt, using mps friendly method
        A__		   =		text_embeddings.shape
        A__		   =		text_embeddings.repeat(1,__lowerCamelCase,1					)
        A__		   =		text_embeddings.view(bs_embed * num_images_per_prompt,__lowerCamelCase,-1					)

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        A__		   =		guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
               A__		   =		42
               if negative_prompt is None:
                      A__		   =		[""""""] * batch_size
               elif type(__lowerCamelCase					) is not type(__lowerCamelCase					):
                      raise TypeError(
                          f"`negative_prompt` should be the same type to `prompt`, but got {type(__lowerCamelCase					)} !="
                          f" {type(__lowerCamelCase					)}."					)
               elif isinstance(__lowerCamelCase,__lowerCamelCase					):
                      A__		   =		[negative_prompt]
               elif batch_size != len(__lowerCamelCase					):
                      raise ValueError(
                          f"`negative_prompt`: {negative_prompt} has batch size {len(__lowerCamelCase					)}, but `prompt`:"
                          f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                          ''' the batch size of `prompt`.'''					)
               else:
                      A__		   =		negative_prompt

               A__		   =		text_input_ids.shape[-1]
               A__		   =		self.tokenizer(
                   __lowerCamelCase,padding='''max_length''',max_length=__lowerCamelCase,truncation=__lowerCamelCase,return_tensors='''pt''',)
               A__		   =		self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]

               # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
               A__		   =		uncond_embeddings.shape[1]
               A__		   =		uncond_embeddings.repeat(1,__lowerCamelCase,1					)
               A__		   =		uncond_embeddings.view(batch_size * num_images_per_prompt,__lowerCamelCase,-1					)

               # For classifier free guidance, we need to do two forward passes.
               # Here we concatenate the unconditional and text embeddings into a single batch
               # to avoid doing two forward passes
               A__		   =		torch.cat([uncond_embeddings, text_embeddings]					)

        # get the initial random noise unless the user supplied it

        # Unlike in other pipelines, latents need to be generated in the target device
        # for 1-to-1 results reproducibility with the CompVis implementation.
        # However this currently doesn't work in `mps`.
        A__		   =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
        A__		   =		text_embeddings.dtype
        if latents is None:
               if self.device.type == "mps":
                      # randn does not exist on mps
                      A__		   =		torch.randn(__lowerCamelCase,generator=__lowerCamelCase,device='''cpu''',dtype=__lowerCamelCase					).to(
                          self.device					)
               else:
                      A__		   =		torch.randn(__lowerCamelCase,generator=__lowerCamelCase,device=self.device,dtype=__lowerCamelCase					)
        else:
               if latents.shape != latents_shape:
                      raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"					)
               A__		   =		latents.to(self.device					)

        # set timesteps
        self.scheduler.set_timesteps(__lowerCamelCase					)

        # Some schedulers like PNDM have timesteps as arrays
        # It's more optimized to move all timesteps to correct device beforehand
        A__		   =		self.scheduler.timesteps.to(self.device					)

        # scale the initial noise by the standard deviation required by the scheduler
        A__		   =		latents * self.scheduler.init_noise_sigma

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        A__		   =		"""eta""" in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
        A__		   =		{}
        if accepts_eta:
               A__		   =		eta

        for i, t in enumerate(self.progress_bar(__lowerCamelCase					)					):
               # expand the latents if we are doing classifier free guidance
               A__		   =		torch.cat([latents] * 2					) if do_classifier_free_guidance else latents
               A__		   =		self.scheduler.scale_model_input(__lowerCamelCase,__lowerCamelCase					)

               # predict the noise residual
               A__		   =		self.unet(__lowerCamelCase,__lowerCamelCase,encoder_hidden_states=__lowerCamelCase					).sample

               # perform guidance
               if do_classifier_free_guidance:
                      A__		   =		noise_pred.chunk(2					)
                      A__		   =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

               # compute the previous noisy sample x_t -> x_t-1
               A__		   =		self.scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase					).prev_sample

               # call the callback, if provided
               if callback is not None and i % callback_steps == 0:
                      callback(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					)

        A__		   =		1 / 0.18215 * latents
        A__		   =		self.vae.decode(__lowerCamelCase					).sample

        A__		   =		(image / 2 + 0.5).clamp(0,1					)

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        A__		   =		image.cpu().permute(0,2,3,1					).float().numpy()

        if output_type == "pil":
               A__		   =		self.numpy_to_pil(__lowerCamelCase					)

        if not return_dict:
               return image

        return StableDiffusionPipelineOutput(images=__lowerCamelCase,nsfw_content_detected=__lowerCamelCase					)













"""simple docstring"""







import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase            =	get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
class      UpperCamelCase		(__snake_case						,						unittest.TestCase     ):
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		XLMProphetNetTokenizer
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		False
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		True






						def      __snake_case      (		self  :Optional[int]				)      ->List[str]:
										super().setUp()

										# We have a SentencePiece fixture for testing
										lowercase    :      List[str]       = XLMProphetNetTokenizer(__magic_name__			, keep_accents=__magic_name__				)
										tokenizer.save_pretrained(self.tmpdirname				)






						def      __snake_case      (		self  :int				)      ->Any:
										lowercase    :      Dict       = """[PAD]"""
										lowercase    :      Tuple       = 0

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(__magic_name__				)			, __magic_name__				)
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(__magic_name__				)			, __magic_name__				)






						def      __snake_case      (		self  :Any				)      ->Union[str, Any]:
										lowercase    :      Optional[int]       = list(self.get_tokenizer().get_vocab().keys()				)

										self.assertEqual(vocab_keys[0]			, """[PAD]"""				)
										self.assertEqual(vocab_keys[1]			, """[CLS]"""				)
										self.assertEqual(vocab_keys[-1]			, """j"""				)
										self.assertEqual(len(__magic_name__				)			, 1_012				)






						def      __snake_case      (		self  :int				)      ->int:
										self.assertEqual(self.get_tokenizer().vocab_size			, 1_012				)






						def      __snake_case      (		self  :Optional[Any]				)      ->List[Any]:
										lowercase    :      Optional[Any]       = XLMProphetNetTokenizer(__magic_name__			, keep_accents=__magic_name__				)

										lowercase    :      Union[str, Any]       = tokenizer.tokenize("""This is a test"""				)
										self.assertListEqual(__magic_name__			, ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(__magic_name__				)			, [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]			, )

										lowercase    :      Optional[Any]       = tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
										self.assertListEqual(
										    __magic_name__			, [
										        SPIECE_UNDERLINE + """I""",
										        SPIECE_UNDERLINE + """was""",
										        SPIECE_UNDERLINE + """b""",
										        """or""",
										        """n""",
										        SPIECE_UNDERLINE + """in""",
										        SPIECE_UNDERLINE + """""",
										        """9""",
										        """2""",
										        """0""",
										        """0""",
										        """0""",
										        """,""",
										        SPIECE_UNDERLINE + """and""",
										        SPIECE_UNDERLINE + """this""",
										        SPIECE_UNDERLINE + """is""",
										        SPIECE_UNDERLINE + """f""",
										        """al""",
										        """s""",
										        """é""",
										        """.""",
										    ]			, )
										lowercase    :      Optional[int]       = tokenizer.convert_tokens_to_ids(__magic_name__				)
										self.assertListEqual(
										    __magic_name__			, [
										        value + tokenizer.fairseq_offset
										        for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
										    ]			, )

										lowercase    :      Union[str, Any]       = tokenizer.convert_ids_to_tokens(__magic_name__				)
										self.assertListEqual(
										    __magic_name__			, [
										        SPIECE_UNDERLINE + """I""",
										        SPIECE_UNDERLINE + """was""",
										        SPIECE_UNDERLINE + """b""",
										        """or""",
										        """n""",
										        SPIECE_UNDERLINE + """in""",
										        SPIECE_UNDERLINE + """""",
										        """[UNK]""",
										        """2""",
										        """0""",
										        """0""",
										        """0""",
										        """,""",
										        SPIECE_UNDERLINE + """and""",
										        SPIECE_UNDERLINE + """this""",
										        SPIECE_UNDERLINE + """is""",
										        SPIECE_UNDERLINE + """f""",
										        """al""",
										        """s""",
										        """[UNK]""",
										        """.""",
										    ]			, )






						@cached_property
						def      __snake_case      (		self  :List[Any]				)      ->str:
										return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""				)






						@slow
						def      __snake_case      (		self  :int				)      ->Optional[Any]:
										lowercase    :      str       = """Hello World!"""
										lowercase    :      Optional[Any]       = [35_389, 6_672, 49, 2]
										self.assertListEqual(__magic_name__			, self.big_tokenizer.encode(__magic_name__				)				)






						@slow
						def      __snake_case      (		self  :Optional[int]				)      ->Union[str, Any]:
										# fmt: off
										lowercase    :      Optional[int]       = {"""input_ids""": [[11_073, 82_783, 18, 26, 82_783, 549, 51_540, 248, 17_209, 1_301, 217, 20, 215_186, 1_325, 147, 17_209, 1_301, 217, 20, 56_370, 53, 122_020, 20, 16_477, 27, 87_355, 4_548, 20, 4_728, 78_392, 17, 159_969, 18, 26, 24_491, 629, 15, 538, 22_704, 5_439, 15, 2_788, 24_491, 9_885, 15, 43_534, 605, 15, 814, 18_403, 33_200, 29, 15, 43_534, 24_458, 12_410, 111, 24_966, 83_669, 9_637, 144_068, 26, 850, 22_346, 27, 147, 24_966, 83_669, 83_490, 26, 39_113, 735, 27, 689, 656, 2_800, 1_339, 4_600, 53, 122_020, 115_785, 34, 816, 1_339, 46_887, 18, 147, 53_905, 1_951, 42_238, 41_170, 17_732, 834, 436, 15, 27_523, 98_733, 217, 147, 5_542, 4_981, 930, 17_347, 16, 2], [20_091, 629, 94, 82_786, 58, 490, 20, 1_528, 84, 53_905, 344, 80_592, 110_128, 18_822, 5_267, 1_306, 62, 152_537, 308, 7_997, 401, 124_427, 549, 35_442, 225, 109, 15_055, 25_748, 147, 7_119, 43_712, 34, 767, 135_366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63_784, 119_466, 17, 147_808, 88_214, 18, 656, 81, 32, 3_296, 10_280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
										# fmt: on

										self.tokenizer_integration_test_util(
										    expected_encoding=__magic_name__			, model_name="""microsoft/xprophetnet-large-wiki100-cased"""			, revision="""1acad1643ddd54a44df6a1b797ada8373685d90e"""			, )
