

from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			lowerCamelCase__							=['vqvae']
			def __init__(	self							:       Dict					,					a							:       AutoencoderKL					,					a							:       UNetaDConditionModel					,					a							:       Mel					,					a							:       Union[DDIMScheduler, DDPMScheduler]					,					)							->				str:

				"""simple docstring"""

				super().__init__()
				self.register_modules(unet=a					,					scheduler=a					,					mel=a					,					vqvae=a		)
			def 							__UpperCamelCase       (	self							:       str		)							->				int:

				"""simple docstring"""

				return 50 if isinstance(self.scheduler					,					a		) else 1000
			@torch.no_grad()
			def __call__(	self							:       str					,					a							:       int = 1					,					a							:       str = None					,					a							:       np.ndarray = None					,					a							:       int = 0					,					a							:       int = 0					,					a							:       int = None					,					a							:       torch.Generator = None					,					a							:       float = 0					,					a							:       float = 0					,					a							:       torch.Generator = None					,					a							:       float = 0					,					a							:       torch.Tensor = None					,					a							:       torch.Tensor = None					,					a							:       Dict=True					,					)							->				Union[
			    Union[AudioPipelineOutput, ImagePipelineOutput],
			    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
			]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				steps or self.get_default_steps()
				self.scheduler.set_timesteps(a		)
				SCREAMING_SNAKE_CASE      :					str    =				step_generator or generator
				# For backwards compatibility
				if type(self.unet.config.sample_size		) == int:
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				(self.unet.config.sample_size, self.unet.config.sample_size)
				if noise is None:
					SCREAMING_SNAKE_CASE      :					Dict    =				randn_tensor(
					    (
					        batch_size,
					        self.unet.config.in_channels,
					        self.unet.config.sample_size[0],
					        self.unet.config.sample_size[1],
					    )					,					generator=a					,					device=self.device					,					)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				noise
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				None

				if audio_file is not None or raw_audio is not None:
					self.mel.load_audio(a					,					a		)
					SCREAMING_SNAKE_CASE      :					Dict    =				self.mel.audio_slice_to_image(a		)
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				np.frombuffer(input_image.tobytes()					,					dtype="uint8"		).reshape(
					    (input_image.height, input_image.width)		)
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				(input_image / 255) * 2 - 1
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				torch.tensor(input_image[np.newaxis, :, :]					,					dtype=torch.float		).to(self.device		)

					if self.vqvae is not None:
						SCREAMING_SNAKE_CASE      :					Dict    =				self.vqvae.encode(torch.unsqueeze(a					,					0		)		).latent_dist.sample(
						    generator=a		)[0]
						SCREAMING_SNAKE_CASE      :					Tuple    =				self.vqvae.config.scaling_factor * input_images

					if start_step > 0:
						SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.scheduler.add_noise(a					,					a					,					self.scheduler.timesteps[start_step - 1]		)

					SCREAMING_SNAKE_CASE      :					int    =				(
					    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
					)
					SCREAMING_SNAKE_CASE      :					int    =				int(mask_start_secs * pixels_per_second		)
					SCREAMING_SNAKE_CASE      :					int    =				int(mask_end_secs * pixels_per_second		)
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.scheduler.add_noise(a					,					a					,					torch.tensor(self.scheduler.timesteps[start_step:]		)		)

				for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]		)		):
					if isinstance(self.unet					,					a		):
						SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.unet(a					,					a					,					a		)["sample"]
					else:
						SCREAMING_SNAKE_CASE      :					List[Any]    =				self.unet(a					,					a		)["sample"]

					if isinstance(self.scheduler					,					a		):
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.scheduler.step(
						    model_output=a					,					timestep=a					,					sample=a					,					eta=a					,					generator=a					,					)["prev_sample"]
					else:
						SCREAMING_SNAKE_CASE      :					Dict    =				self.scheduler.step(
						    model_output=a					,					timestep=a					,					sample=a					,					generator=a					,					)["prev_sample"]

					if mask is not None:
						if mask_start > 0:
							SCREAMING_SNAKE_CASE      :					Optional[int]    =				mask[:, step, :, :mask_start]
						if mask_end > 0:
							SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				mask[:, step, :, -mask_end:]

				if self.vqvae is not None:
					# 0.18215 was scaling factor used in training to ensure unit variance
					SCREAMING_SNAKE_CASE      :					int    =				1 / self.vqvae.config.scaling_factor * images
					SCREAMING_SNAKE_CASE      :					List[str]    =				self.vqvae.decode(a		)["sample"]

				SCREAMING_SNAKE_CASE      :					Tuple    =				(images / 2 + 0.5).clamp(0					,					1		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				images.cpu().permute(0					,					2					,					3					,					1		).numpy()
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				(images * 255).round().astype("uint8"		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				list(
				    (Image.fromarray(_[:, :, 0]		) for _ in images)
				    if images.shape[3] == 1
				    else (Image.fromarray(a					,					mode="RGB"		).convert("L"		) for _ in images)		)

				SCREAMING_SNAKE_CASE      :					int    =				[self.mel.image_to_audio(a		) for _ in images]
				if not return_dict:
					return images, (self.mel.get_sample_rate(), audios)

				return BaseOutput(**AudioPipelineOutput(np.array(a		)[:, np.newaxis, :]		)					,					**ImagePipelineOutput(a		)		)
			@torch.no_grad()
			def 							__UpperCamelCase       (	self							:       List[str]					,					a							:       List[Image.Image]					,					a							:       int = 50		)							->				np.ndarray:

				"""simple docstring"""

				assert isinstance(self.scheduler					,					a		)
				self.scheduler.set_timesteps(a		)
				SCREAMING_SNAKE_CASE      :					str    =				np.array(
				    [np.frombuffer(image.tobytes()					,					dtype="uint8"		).reshape((1, image.height, image.width)		) for image in images]		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				(sample / 255) * 2 - 1
				SCREAMING_SNAKE_CASE      :					Dict    =				torch.Tensor(a		).to(self.device		)

				for t in self.progress_bar(torch.flip(self.scheduler.timesteps					,					(0,)		)		):
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
					SCREAMING_SNAKE_CASE      :					int    =				self.scheduler.alphas_cumprod[t]
					SCREAMING_SNAKE_CASE      :					Any    =				(
					    self.scheduler.alphas_cumprod[prev_timestep]
					    if prev_timestep >= 0
					    else self.scheduler.final_alpha_cumprod
					)
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				1 - alpha_prod_t
					SCREAMING_SNAKE_CASE      :					Dict    =				self.unet(a					,					a		)["sample"]
					SCREAMING_SNAKE_CASE      :					Dict    =				(1 - alpha_prod_t_prev) ** 0.5 * model_output
					SCREAMING_SNAKE_CASE      :					List[str]    =				(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

				return sample






			@staticmethod
			def 							__UpperCamelCase       (	a							:       torch.Tensor					,					a							:       torch.Tensor					,					a							:       float		)							->				torch.Tensor:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Tuple    =				acos(torch.dot(torch.flatten(a		)					,					torch.flatten(a		)		) / torch.norm(a		) / torch.norm(a		)		)
				return sin((1 - alpha) * theta		) * xa / sin(a		) + sin(alpha * theta		) * xa / sin(a		)

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

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
		import tensorflow as tf

if is_torch_available():
		import torch


lowercase  :					List[str]													=       logging.get_logger(__name__)




class  lowerCamelCase__							:




	'''simple docstring'''




	def __init__(					self		:str     ,   a		:str = None     ,   a		:uuid.UUID = None     ,   a		:Tuple=None     ,   a		:Optional[Any]=None						)		-> str:
								if not conversation_id:
															__UpperCamelCase	:   Dict			   =					uuid.uuida()
								if past_user_inputs is None:
															__UpperCamelCase	:   List[Any]			   =					[]
								if generated_responses is None:
															__UpperCamelCase	:   Any			   =					[]

								__UpperCamelCase	:   uuid.UUID			   =					conversation_id
								__UpperCamelCase	:   List[str]			   =					past_user_inputs
								__UpperCamelCase	:   List[str]			   =					generated_responses
								__UpperCamelCase	:   Optional[str]			   =					text







	def __eq__(					self		:Optional[int]     ,   a		:Optional[int]						)		-> Union[str, Any]:
								if not isinstance(a     ,   a						):
															return False
								if self.uuid == other.uuid:
															return True
								return (
								    self.new_user_input == other.new_user_input
								    and self.past_user_inputs == other.past_user_inputs
								    and self.generated_responses == other.generated_responses
								)







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:str     ,   a		:bool = False						)		-> str:
								if self.new_user_input:
															if overwrite:
																						logger.warning(
																						    f'User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '
																						    f'with: "{text}".'						)
																						__UpperCamelCase	:   Any			   =					text
															else:
																						logger.warning(
																						    f'User input added while unprocessed input was existing: "{self.new_user_input}" new input '
																						    f'ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'						)
								else:
															__UpperCamelCase	:   int			   =					text







	def    _lowerCamelCase				(					self		:List[str]						)		-> int:
								if self.new_user_input:
															self.past_user_inputs.append(self.new_user_input						)
								__UpperCamelCase	:   Dict			   =					None







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:str						)		-> Optional[int]:
								self.generated_responses.append(a						)







	def    _lowerCamelCase				(					self		:int						)		-> Optional[Any]:
								for user_input, generated_response in zip(self.past_user_inputs     ,   self.generated_responses						):
															yield True, user_input
															yield False, generated_response
								if self.new_user_input:
															yield True, self.new_user_input







	def __repr__(					self		:List[str]						)		-> List[Any]:
								__UpperCamelCase	:   Any			   =					f'Conversation id: {self.uuid} \n'
								for is_user, text in self.iter_texts():
															__UpperCamelCase	:   str			   =					"user" if is_user else "bot"
															output += f'{name} >> {text} \n'
								return output




@add_end_docstrings(
    __lowercase     ,						R'\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    '     ,						)
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	def __init__(					self		:Tuple     ,   *a		:Tuple     ,   **a		:List[str]						)		-> Tuple:
								super().__init__(*a     ,   **a						)
								if self.tokenizer.pad_token_id is None:
															__UpperCamelCase	:   int			   =					self.tokenizer.eos_token







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:List[Any]=None     ,   a		:str=None     ,   a		:int=None     ,   **a		:str						)		-> List[str]:
								__UpperCamelCase	:   List[str]			   =					{}
								__UpperCamelCase	:   List[str]			   =					{}
								__UpperCamelCase	:   str			   =					{}

								if min_length_for_response is not None:
															__UpperCamelCase	:   Optional[Any]			   =					min_length_for_response
								if minimum_tokens is not None:
															__UpperCamelCase	:   List[str]			   =					minimum_tokens

								if "max_length" in generate_kwargs:
															__UpperCamelCase	:   List[Any]			   =					generate_kwargs["max_length"]
															# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
								if clean_up_tokenization_spaces is not None:
															__UpperCamelCase	:   List[Any]			   =					clean_up_tokenization_spaces

								if generate_kwargs:
															forward_params.update(a						)
								return preprocess_params, forward_params, postprocess_params







	def __call__(					self		:Dict     ,   a		:Union[Conversation, List[Conversation]]     ,   a		:List[Any]=0     ,   **a		:Any						)		-> Union[str, Any]:
								__UpperCamelCase	:   Optional[int]			   =					super().__call__(a     ,   num_workers=a     ,   **a						)
								if isinstance(a     ,   a						) and len(a						) == 1:
															return outputs[0]
								return outputs







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:Conversation     ,   a		:Dict=3_2						)		-> Dict[str, Any]:
								if not isinstance(a     ,   a						):
															raise ValueError("ConversationalPipeline, expects Conversation as inputs"						)
								if conversation.new_user_input is None:
															raise ValueError(
															    f'Conversation with UUID {type(conversation.uuid						)} does not contain new user input to process. '
															    "Add user inputs with the conversation's `add_user_input` method"						)
								if hasattr(self.tokenizer     ,   "_build_conversation_input_ids"						):
															__UpperCamelCase	:   str			   =					self.tokenizer._build_conversation_input_ids(a						)
								else:
															# If the tokenizer cannot handle conversations, we default to only the old version
															__UpperCamelCase	:   Optional[Any]			   =					self._legacy_parse_and_tokenize(a						)

								if self.framework == "pt":
															__UpperCamelCase	:   Dict			   =					torch.LongTensor([input_ids]						)
								elif self.framework == "tf":
															__UpperCamelCase	:   Any			   =					tf.constant([input_ids]						)
								return {"input_ids": input_ids, "conversation": conversation}







	def    _lowerCamelCase				(					self		:Any     ,   a		:List[Any]     ,   a		:Optional[Any]=1_0     ,   **a		:Tuple						)		-> List[str]:
								__UpperCamelCase	:   Union[str, Any]			   =					generate_kwargs.get("max_length"     ,   self.model.config.max_length						)

								__UpperCamelCase	:   Dict			   =					model_inputs["input_ids"].shape[1]
								if max_length - minimum_tokens < n:
															logger.warning(f'Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'						)
															__UpperCamelCase	:   Dict			   =					max_length - minimum_tokens
															__UpperCamelCase	:   Optional[int]			   =					model_inputs["input_ids"][:, -trim:]
															if "attention_mask" in model_inputs:
																						__UpperCamelCase	:   Dict			   =					model_inputs["attention_mask"][:, -trim:]
								__UpperCamelCase	:   List[str]			   =					model_inputs.pop("conversation"						)
								__UpperCamelCase	:   Optional[int]			   =					max_length
								__UpperCamelCase	:   str			   =					self.model.generate(**a     ,   **a						)
								if self.model.config.is_encoder_decoder:
															__UpperCamelCase	:   List[str]			   =					1
								else:
															__UpperCamelCase	:   Optional[int]			   =					n
								return {"output_ids": output_ids[:, start_position:], "conversation": conversation}







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:str     ,   a		:Optional[int]=True						)		-> Union[str, Any]:
								__UpperCamelCase	:   List[str]			   =					model_outputs["output_ids"]
								__UpperCamelCase	:   Any			   =					self.tokenizer.decode(
								    output_ids[0]     ,   skip_special_tokens=a     ,   clean_up_tokenization_spaces=a     ,   )
								__UpperCamelCase	:   int			   =					model_outputs["conversation"]
								conversation.mark_processed()
								conversation.append_response(a						)
								return conversation







	def    _lowerCamelCase				(					self		:str     ,   a		:Conversation						)		-> Dict:
								__UpperCamelCase	:   int			   =					self.tokenizer.eos_token_id
								__UpperCamelCase	:   Any			   =					[]
								for is_user, text in conversation.iter_texts():
															if eos_token_id is not None:
																						input_ids.extend(self.tokenizer.encode(a     ,   add_special_tokens=a						) + [eos_token_id]						)
															else:
																						input_ids.extend(self.tokenizer.encode(a     ,   add_special_tokens=a						)						)

								if len(a						) > self.tokenizer.model_max_length:
															__UpperCamelCase	:   Union[str, Any]			   =					input_ids[-self.tokenizer.model_max_length :]
								return input_ids