


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



class 	__lowerCamelCase			(_a						):







       def __init__(			self: Any,A_: WhisperForConditionalGeneration,A_: WhisperProcessor,A_: AutoencoderKL,A_: CLIPTextModel,A_: CLIPTokenizer,A_: UNetaDConditionModel,A_: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],A_: StableDiffusionSafetyChecker,A_: CLIPImageProcessor,):






            '''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=A_,speech_processor=A_,vae=A_,text_encoder=A_,tokenizer=A_,unet=A_,scheduler=A_,feature_extractor=A_,)







       def 		snake_case_	(			self: int,A_: Optional[Union[str, int]] = "auto"					):






            '''simple docstring'''




            if slice_size == "auto":
                 __UpperCamelCase =			self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(A_					)







       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            self.enable_attention_slicing(A_					)







       @torch.no_grad()
       def __call__(			self: List[str],A_: List[Any],A_: int=1_6000,A_: int = 512,A_: int = 512,A_: int = 50,A_: float = 7.5,A_: Optional[Union[str, List[str]]] = None,A_: Optional[int] = 1,A_: float = 0.0,A_: Optional[torch.Generator] = None,A_: Optional[torch.FloatTensor] = None,A_: Optional[str] = "pil",A_: bool = True,A_: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,A_: int = 1,**A_: str,):






            '''simple docstring'''




            __UpperCamelCase =			self.speech_processor.feature_extractor(
                A_,return_tensors='pt',sampling_rate=A_					).input_features.to(self.device					)
            __UpperCamelCase =			self.speech_model.generate(A_,max_length=48_0000					)

            __UpperCamelCase =			self.speech_processor.tokenizer.batch_decode(A_,skip_special_tokens=A_,normalize=A_					)[
                0
            ]

            if isinstance(A_,A_					):
                 __UpperCamelCase =			1
            elif isinstance(A_,A_					):
                 __UpperCamelCase =			len(A_					)
            else:
                 raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(A_					)}'''					)

            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(A_,A_					) or callback_steps <= 0)
            ):
                 raise ValueError(
                     F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                     F''' {type(A_					)}.'''					)

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

            if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                 __UpperCamelCase =			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}'''					)
                 __UpperCamelCase =			text_input_ids[:, : self.tokenizer.model_max_length]
            __UpperCamelCase =			self.text_encoder(text_input_ids.to(self.device					)					)[0]

            # duplicate text embeddings for each generation per prompt, using mps friendly method
            __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			text_embeddings.shape
            __UpperCamelCase =			text_embeddings.repeat(1,A_,1					)
            __UpperCamelCase =			text_embeddings.view(bs_embed * num_images_per_prompt,A_,-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.
            __UpperCamelCase =			guidance_scale > 1.0
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                 __UpperCamelCase =			42
                 if negative_prompt is None:
                      __UpperCamelCase =			[''] * batch_size
                 elif type(A_					) is not type(A_					):
                      raise TypeError(
                          F'''`negative_prompt` should be the same type to `prompt`, but got {type(A_					)} !='''
                          F''' {type(A_					)}.'''					)
                 elif isinstance(A_,A_					):
                      __UpperCamelCase =			[negative_prompt]
                 elif batch_size != len(A_					):
                      raise ValueError(
                          F'''`negative_prompt`: {negative_prompt} has batch size {len(A_					)}, but `prompt`:'''
                          F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                          ' the batch size of `prompt`.'					)
                 else:
                      __UpperCamelCase =			negative_prompt

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

                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 __UpperCamelCase =			uncond_embeddings.shape[1]
                 __UpperCamelCase =			uncond_embeddings.repeat(1,A_,1					)
                 __UpperCamelCase =			uncond_embeddings.view(batch_size * num_images_per_prompt,A_,-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
                 __UpperCamelCase =			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`.
            __UpperCamelCase =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
            __UpperCamelCase =			text_embeddings.dtype
            if latents is None:
                 if self.device.type == "mps":
                      # randn does not exist on mps
                      __UpperCamelCase =			torch.randn(A_,generator=A_,device='cpu',dtype=A_					).to(
                          self.device					)
                 else:
                      __UpperCamelCase =			torch.randn(A_,generator=A_,device=self.device,dtype=A_					)
            else:
                 if latents.shape != latents_shape:
                      raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''					)
                 __UpperCamelCase =			latents.to(self.device					)

            # set timesteps
            self.scheduler.set_timesteps(A_					)

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

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

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

                 # predict the noise residual
                 __UpperCamelCase =			self.unet(A_,A_,encoder_hidden_states=A_					).sample

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

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

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

            __UpperCamelCase =			1 / 0.1_8_2_1_5 * latents
            __UpperCamelCase =			self.vae.decode(A_					).sample

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

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

            if output_type == "pil":
                 __UpperCamelCase =			self.numpy_to_pil(A_					)

            if not return_dict:
                 return image

            return StableDiffusionPipelineOutput(images=A_,nsfw_content_detected=A_					)






from typing import Any



class 	__lowerCamelCase			:







       def __init__(			self: int,A_: Any					):






            '''simple docstring'''




            __UpperCamelCase =			data
            __UpperCamelCase =			None







       def __repr__(			self: Any					):






            '''simple docstring'''




            return F'''Node({self.data})'''



class 	__lowerCamelCase			:







       def __init__(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			None







       def __iter__(			self: int					):






            '''simple docstring'''




            __UpperCamelCase =			self.head
            while node:
                 yield node.data
                 __UpperCamelCase =			node.next







       def __len__(			self: List[str]					):






            '''simple docstring'''




            return sum(1 for _ in self					)







       def __repr__(			self: Any					):






            '''simple docstring'''




            return "->".join([str(A_					) for item in self]					)







       def __getitem__(			self: int,A_: int					):






            '''simple docstring'''




            if not 0 <= index < len(self					):
                 raise ValueError('list index out of range.'					)
            for i, node in enumerate(self					):
                 if i == index:
                      return node
            return None







       def __setitem__(			self: int,A_: int,A_: Any					):






            '''simple docstring'''




            if not 0 <= index < len(self					):
                 raise ValueError('list index out of range.'					)
            __UpperCamelCase =			self.head
            for _ in range(A_					):
                 __UpperCamelCase =			current.next
            __UpperCamelCase =			data







       def 		snake_case_	(			self: Union[str, Any],A_: Any					):






            '''simple docstring'''




            self.insert_nth(len(self					),A_					)







       def 		snake_case_	(			self: List[Any],A_: Any					):






            '''simple docstring'''




            self.insert_nth(0,A_					)







       def 		snake_case_	(			self: Optional[Any],A_: int,A_: Any					):






            '''simple docstring'''




            if not 0 <= index <= len(self					):
                 raise IndexError('list index out of range'					)
            __UpperCamelCase =			Node(A_					)
            if self.head is None:
                 __UpperCamelCase =			new_node
            elif index == 0:
                 __UpperCamelCase =			self.head  # link new_node to head
                 __UpperCamelCase =			new_node
            else:
                 __UpperCamelCase =			self.head
                 for _ in range(index - 1					):
                      __UpperCamelCase =			temp.next
                 __UpperCamelCase =			temp.next
                 __UpperCamelCase =			new_node







       def 		snake_case_	(			self: str					):  # print every node data






            '''simple docstring'''




            print(self					)







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            return self.delete_nth(0					)







       def 		snake_case_	(			self: str					):  # delete from tail






            '''simple docstring'''




            return self.delete_nth(len(self					) - 1					)







       def 		snake_case_	(			self: Any,A_: int = 0					):






            '''simple docstring'''




            if not 0 <= index <= len(self					) - 1:  # test if index is valid
                 raise IndexError('List index out of range.'					)
            __UpperCamelCase =			self.head  # default first node
            if index == 0:
                 __UpperCamelCase =			self.head.next
            else:
                 __UpperCamelCase =			self.head
                 for _ in range(index - 1					):
                      __UpperCamelCase =			temp.next
                 __UpperCamelCase =			temp.next
                 __UpperCamelCase =			temp.next.next
            return delete_node.data







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            return self.head is None







       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            __UpperCamelCase =			None
            __UpperCamelCase =			self.head

            while current:
                 # Store the current node's next node.
                 __UpperCamelCase =			current.next
                 # Make the current node's next point backwards
                 __UpperCamelCase =			prev
                 # Make the previous node be the current node
                 __UpperCamelCase =			current
                 # Make the current node the next node (to progress iteration)
                 __UpperCamelCase =			next_node
            # Return prev in order to put the head at the end
            __UpperCamelCase =			prev






def 		_A		(		)					-> None:


     """simple docstring"""


     __UpperCamelCase =			LinkedList()
     assert linked_list.is_empty() is True
     assert str(_lowercase ) == ""

     try:
          linked_list.delete_head()
          raise AssertionError  # This should not happen.
     except IndexError:
          assert True  # This should happen.

     try:
          linked_list.delete_tail()
          raise AssertionError  # This should not happen.
     except IndexError:
          assert True  # This should happen.

     for i in range(10 ):
          assert len(_lowercase ) == i
          linked_list.insert_nth(_lowercase					,  i + 1 )
     assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(1					,  11 ) )

     linked_list.insert_head(0 )
     linked_list.insert_tail(11 )
     assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(0					,  12 ) )

     assert linked_list.delete_head() == 0
     assert linked_list.delete_nth(9 ) == 10
     assert linked_list.delete_tail() == 11
     assert len(_lowercase ) == 9
     assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(1					,  10 ) )

     assert all(linked_list[i] == i + 1 for i in range(0					,  9 ) ) is True

     for i in range(0					,  9 ):
          __UpperCamelCase =			-i
     assert all(linked_list[i] == -i for i in range(0					,  9 ) ) is True

     linked_list.reverse()
     assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(-8					,  1 ) )






def 		_A		(		)					-> None:


     """simple docstring"""


     __UpperCamelCase =			[
         -9,
         1_00,
         Node(77_34_51_12 ),
         'dlrow olleH',
         7,
         55_55,
         0,
         -1_92.5_55_55,
         'Hello, world!',
         77.9,
         Node(10 ),
         None,
         None,
         12.20,
     ]
     __UpperCamelCase =			LinkedList()

     for i in test_input:
          linked_list.insert_tail(_lowercase )

     # Check if it's empty or not
     assert linked_list.is_empty() is False
     assert (
         str(_lowercase ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
         "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
     )

     # Delete the head
     __UpperCamelCase =			linked_list.delete_head()
     assert result == -9
     assert (
         str(_lowercase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
         "Hello, world!->77.9->Node(10)->None->None->12.2"
     )

     # Delete the tail
     __UpperCamelCase =			linked_list.delete_tail()
     assert result == 12.2
     assert (
         str(_lowercase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
         "Hello, world!->77.9->Node(10)->None->None"
     )

     # Delete a node in specific location in linked list
     __UpperCamelCase =			linked_list.delete_nth(10 )
     assert result is None
     assert (
         str(_lowercase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
         "Hello, world!->77.9->Node(10)->None"
     )

     # Add a Node instance to its head
     linked_list.insert_head(Node('Hello again, world!' ) )
     assert (
         str(_lowercase )
         == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
         "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
     )

     # Add None to its tail
     linked_list.insert_tail(_lowercase )
     assert (
         str(_lowercase )
         == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
         "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
     )

     # Reverse the linked list
     linked_list.reverse()
     assert (
         str(_lowercase )
         == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
         "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
     )






def 		_A		(		)					-> List[str]:


     """simple docstring"""


     from doctest import testmod

     testmod()

     __UpperCamelCase =			LinkedList()
     linked_list.insert_head(input('Inserting 1st at head ' ).strip() )
     linked_list.insert_head(input('Inserting 2nd at head ' ).strip() )
     print('\nPrint list:' )
     linked_list.print_list()
     linked_list.insert_tail(input('\nInserting 1st at tail ' ).strip() )
     linked_list.insert_tail(input('Inserting 2nd at tail ' ).strip() )
     print('\nPrint list:' )
     linked_list.print_list()
     print('\nDelete head' )
     linked_list.delete_head()
     print('Delete tail' )
     linked_list.delete_tail()
     print('\nPrint list:' )
     linked_list.print_list()
     print('\nReverse linked list' )
     linked_list.reverse()
     print('\nPrint list:' )
     linked_list.print_list()
     print('\nString representation of linked list:' )
     print(_lowercase )
     print('\nReading/changing Node data using indexing:' )
     print(f'''Element at Position 1: {linked_list[1]}''' )
     __UpperCamelCase =			input('Enter New Value: ' ).strip()
     print('New list:' )
     print(_lowercase )
     print(f'''length of linked_list is : {len(_lowercase )}''' )


if __name__ == "__main__":
    main()


