



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


__snake_case    :		Optional[Any]  =logging.get_logger(__name__)  # pylint: disable=invalid-name



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,)					->      Tuple:

						"""simple docstring"""






						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 		lowerCAmelCase__				(self		,__lowerCamelCase = "auto"			)					->      Optional[int]:

						"""simple docstring"""






						if slice_size == "auto":
										lowerCAmelCase__					:    Tuple												=					self.unet.config.attention_head_dim // 2
						self.unet.set_attention_slice(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						self.enable_attention_slicing(__lowerCamelCase			)



		@torch.no_grad()
		def __call__(self		,__lowerCamelCase		,__lowerCamelCase=1_60_00		,__lowerCamelCase = 5_12		,__lowerCamelCase = 5_12		,__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		,)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					self.speech_processor.feature_extractor(
						    __lowerCamelCase		,return_tensors='''pt'''		,sampling_rate=__lowerCamelCase			).input_features.to(self.device			)
						lowerCAmelCase__					:    List[str]												=					self.speech_model.generate(__lowerCamelCase		,max_length=48_00_00			)

						lowerCAmelCase__					:    str												=					self.speech_processor.tokenizer.batch_decode(__lowerCamelCase		,skip_special_tokens=__lowerCamelCase		,normalize=__lowerCamelCase			)[
						    0
						]

						if isinstance(__lowerCamelCase		,__lowerCamelCase			):
										lowerCAmelCase__					:    Optional[Any]												=					1
						elif isinstance(__lowerCamelCase		,__lowerCamelCase			):
										lowerCAmelCase__					:    Tuple												=					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
						lowerCAmelCase__					:    Any												=					self.tokenizer(
						    __lowerCamelCase		,padding='''max_length'''		,max_length=self.tokenizer.model_max_length		,return_tensors='''pt'''		,)
						lowerCAmelCase__					:    Dict												=					text_inputs.input_ids

						if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
										lowerCAmelCase__					:    Dict												=					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}"""			)
										lowerCAmelCase__					:    str												=					text_input_ids[:, : self.tokenizer.model_max_length]
						lowerCAmelCase__					:    Any												=					self.text_encoder(text_input_ids.to(self.device			)			)[0]

						# duplicate text embeddings for each generation per prompt, using mps friendly method
						lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__					:    str												=					text_embeddings.shape
						lowerCAmelCase__					:    Optional[Any]												=					text_embeddings.repeat(1		,__lowerCamelCase		,1			)
						lowerCAmelCase__					:    Any												=					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.
						lowerCAmelCase__					:    List[str]												=					guidance_scale > 1.0
						# get unconditional embeddings for classifier free guidance
						if do_classifier_free_guidance:
										lowerCAmelCase__					:    List[str]
										if negative_prompt is None:
														lowerCAmelCase__					:    Optional[int]												=					[''''''] * 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			):
														lowerCAmelCase__					:    Dict												=					[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:
														lowerCAmelCase__					:    int												=					negative_prompt

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

										# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
										lowerCAmelCase__					:    str												=					uncond_embeddings.shape[1]
										lowerCAmelCase__					:    Optional[int]												=					uncond_embeddings.repeat(1		,__lowerCamelCase		,1			)
										lowerCAmelCase__					:    List[Any]												=					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
										lowerCAmelCase__					:    Dict												=					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`.
						lowerCAmelCase__					:    Any												=					(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
						lowerCAmelCase__					:    Optional[int]												=					text_embeddings.dtype
						if latents is None:
										if self.device.type == "mps":
														# randn does not exist on mps
														lowerCAmelCase__					:    Any												=					torch.randn(__lowerCamelCase		,generator=__lowerCamelCase		,device='''cpu'''		,dtype=__lowerCamelCase			).to(
														    self.device			)
										else:
														lowerCAmelCase__					:    Optional[Any]												=					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}"""			)
										lowerCAmelCase__					:    List[Any]												=					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
						lowerCAmelCase__					:    Optional[int]												=					self.scheduler.timesteps.to(self.device			)

						# scale the initial noise by the standard deviation required by the scheduler
						lowerCAmelCase__					:    Optional[Any]												=					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]
						lowerCAmelCase__					:    Any												=					'''eta''' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
						lowerCAmelCase__					:    int												=					{}
						if accepts_eta:
										lowerCAmelCase__					:    List[Any]												=					eta

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

										# predict the noise residual
										lowerCAmelCase__					:    str												=					self.unet(__lowerCamelCase		,__lowerCamelCase		,encoder_hidden_states=__lowerCamelCase			).sample

										# perform guidance
										if do_classifier_free_guidance:
														lowerCAmelCase__						,		lowerCAmelCase__					:    List[str]												=					noise_pred.chunk(2			)
														lowerCAmelCase__					:    Optional[Any]												=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

										# compute the previous noisy sample x_t -> x_t-1
										lowerCAmelCase__					:    Optional[int]												=					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			)

						lowerCAmelCase__					:    int												=					1 / 0.1_8215 * latents
						lowerCAmelCase__					:    Tuple												=					self.vae.decode(__lowerCamelCase			).sample

						lowerCAmelCase__					:    int												=					(image / 2 + 0.5).clamp(0		,1			)

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

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

						if not return_dict:
										return image

						return StableDiffusionPipelineOutput(images=__lowerCamelCase		,nsfw_content_detected=__lowerCamelCase			)






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


__snake_case    :		int  =get_tests_dir('fixtures/test_sentencepiece.model')



@require_sentencepiece
class 	lowerCamelCase__      (							lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =XLMProphetNetTokenizer
		snake_case_     =False
		snake_case_     =True



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						super().setUp()

						# We have a SentencePiece fixture for testing
						lowerCAmelCase__					:    int												=					XLMProphetNetTokenizer(__lowerCamelCase		,keep_accents=__lowerCamelCase			)
						tokenizer.save_pretrained(self.tmpdirname			)



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					'''[PAD]'''
						lowerCAmelCase__					:    Tuple												=					0

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



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					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(__lowerCamelCase			)		,10_12			)



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						self.assertEqual(self.get_tokenizer().vocab_size		,10_12			)



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					XLMProphetNetTokenizer(__lowerCamelCase		,keep_accents=__lowerCamelCase			)

						lowerCAmelCase__					:    Tuple												=					tokenizer.tokenize('''This is a test'''			)
						self.assertListEqual(__lowerCamelCase		,['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']			)

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(__lowerCamelCase			)		,[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]		,)

						lowerCAmelCase__					:    Dict												=					tokenizer.tokenize('''I was born in 92000, and this is falsé.'''			)
						self.assertListEqual(
						    __lowerCamelCase		,[
						        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''',
						        '''.''',
						    ]		,)
						lowerCAmelCase__					:    Optional[Any]												=					tokenizer.convert_tokens_to_ids(__lowerCamelCase			)
						self.assertListEqual(
						    __lowerCamelCase		,[
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, -9, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, -9, 4]
						    ]		,)

						lowerCAmelCase__					:    Optional[Any]												=					tokenizer.convert_ids_to_tokens(__lowerCamelCase			)
						self.assertListEqual(
						    __lowerCamelCase		,[
						        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 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''			)



		@slow
		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					'''Hello World!'''
						lowerCAmelCase__					:    str												=					[3_53_89, 66_72, 49, 2]
						self.assertListEqual(__lowerCamelCase		,self.big_tokenizer.encode(__lowerCamelCase			)			)



		@slow
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					{'''input_ids''': [[1_10_73, 8_27_83, 18, 26, 8_27_83, 5_49, 5_15_40, 2_48, 1_72_09, 13_01, 2_17, 20, 21_51_86, 13_25, 1_47, 1_72_09, 13_01, 2_17, 20, 5_63_70, 53, 12_20_20, 20, 1_64_77, 27, 8_73_55, 45_48, 20, 47_28, 7_83_92, 17, 15_99_69, 18, 26, 2_44_91, 6_29, 15, 5_38, 2_27_04, 54_39, 15, 27_88, 2_44_91, 98_85, 15, 4_35_34, 6_05, 15, 8_14, 1_84_03, 3_32_00, 29, 15, 4_35_34, 2_44_58, 1_24_10, 1_11, 2_49_66, 8_36_69, 96_37, 14_40_68, 26, 8_50, 2_23_46, 27, 1_47, 2_49_66, 8_36_69, 8_34_90, 26, 3_91_13, 7_35, 27, 6_89, 6_56, 28_00, 13_39, 46_00, 53, 12_20_20, 11_57_85, 34, 8_16, 13_39, 4_68_87, 18, 1_47, 5_39_05, 19_51, 4_22_38, 4_11_70, 1_77_32, 8_34, 4_36, 15, 2_75_23, 9_87_33, 2_17, 1_47, 55_42, 49_81, 9_30, 1_73_47, 16, 2], [2_00_91, 6_29, 94, 8_27_86, 58, 4_90, 20, 15_28, 84, 5_39_05, 3_44, 8_05_92, 11_01_28, 1_88_22, 52_67, 13_06, 62, 15_25_37, 3_08, 79_97, 4_01, 12_44_27, 5_49, 3_54_42, 2_25, 1_09, 1_50_55, 2_57_48, 1_47, 71_19, 4_37_12, 34, 7_67, 13_53_66, 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], [5_92, 6_37_84, 11_94_66, 17, 14_78_08, 8_82_14, 18, 6_56, 81, 32, 32_96, 1_02_80, 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=__lowerCamelCase		,model_name='''microsoft/xprophetnet-large-wiki100-cased'''		,revision='''1acad1643ddd54a44df6a1b797ada8373685d90e'''		,)

