





'''simple docstring'''



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    __SCREAMING_SNAKE_CASE						(    __SCREAMING_SNAKE_CASE      ):



     '''simple docstring'''






     UpperCAmelCase__							:    str     	= ['''vqvae''']

     def __init__(       self    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , ):
       super().__init__()
       self.register_modules(unet=UpperCamelCase__    , scheduler=UpperCamelCase__    , mel=UpperCamelCase__    , vqvae=UpperCamelCase__						)

     def 						UpperCamelCase(       self						):
       return 50 if isinstance(self.scheduler    , UpperCamelCase__						) else 1_000

     @torch.no_grad()
     def __call__(       self    , lowerCamelCase = 1    , lowerCamelCase = None    , lowerCamelCase = None    , lowerCamelCase = 0    , lowerCamelCase = 0    , lowerCamelCase = None    , lowerCamelCase = None    , lowerCamelCase = 0    , lowerCamelCase = 0    , lowerCamelCase = None    , lowerCamelCase = 0    , lowerCamelCase = None    , lowerCamelCase = None    , lowerCamelCase=True    , ):
       _snake_case  		=					steps or self.get_default_steps()
       self.scheduler.set_timesteps(UpperCamelCase__						)
       _snake_case  		=					step_generator or generator
       # For backwards compatibility
       if type(self.unet.config.sample_size						) == int:
         _snake_case  		=					(self.unet.config.sample_size, self.unet.config.sample_size)
       if noise is None:
         _snake_case  		=					randn_tensor(
             (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size[0],
                 self.unet.config.sample_size[1],
             )    , generator=UpperCamelCase__    , device=self.device    , )
       _snake_case  		=					noise
       _snake_case  		=					None

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

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

         if start_step > 0:
           _snake_case  		=					self.scheduler.add_noise(UpperCamelCase__    , UpperCamelCase__    , self.scheduler.timesteps[start_step - 1]						)

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

       for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]						)						):
         if isinstance(self.unet    , UpperCamelCase__						):
           _snake_case  		=					self.unet(UpperCamelCase__    , UpperCamelCase__    , UpperCamelCase__						)['''sample''']
         else:
           _snake_case  		=					self.unet(UpperCamelCase__    , UpperCamelCase__						)['''sample''']

         if isinstance(self.scheduler    , UpperCamelCase__						):
           _snake_case  		=					self.scheduler.step(
               model_output=UpperCamelCase__    , timestep=UpperCamelCase__    , sample=UpperCamelCase__    , eta=UpperCamelCase__    , generator=UpperCamelCase__    , )['''prev_sample''']
         else:
           _snake_case  		=					self.scheduler.step(
               model_output=UpperCamelCase__    , timestep=UpperCamelCase__    , sample=UpperCamelCase__    , generator=UpperCamelCase__    , )['''prev_sample''']

         if mask is not None:
           if mask_start > 0:
             _snake_case  		=					mask[:, step, :, :mask_start]
           if mask_end > 0:
             _snake_case  		=					mask[:, step, :, -mask_end:]

       if self.vqvae is not None:
         # 0.18215 was scaling factor used in training to ensure unit variance
         _snake_case  		=					1 / self.vqvae.config.scaling_factor * images
         _snake_case  		=					self.vqvae.decode(UpperCamelCase__						)['''sample''']

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

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

       return BaseOutput(**AudioPipelineOutput(np.array(UpperCamelCase__						)[:, np.newaxis, :]						)    , **ImagePipelineOutput(UpperCamelCase__						)						)

     @torch.no_grad()
     def 						UpperCamelCase(       self    , lowerCamelCase    , lowerCamelCase = 50						):
       assert isinstance(self.scheduler    , UpperCamelCase__						)
       self.scheduler.set_timesteps(UpperCamelCase__						)
       _snake_case  		=					np.array(
           [np.frombuffer(image.tobytes()    , dtype="uint8"						).reshape((1, image.height, image.width)						) for image in images]						)
       _snake_case  		=					(sample / 255) * 2 - 1
       _snake_case  		=					torch.Tensor(UpperCamelCase__						).to(self.device						)

       for t in self.progress_bar(torch.flip(self.scheduler.timesteps    , (0,)						)						):
         _snake_case  		=					t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
         _snake_case  		=					self.scheduler.alphas_cumprod[t]
         _snake_case  		=					(
             self.scheduler.alphas_cumprod[prev_timestep]
             if prev_timestep >= 0
             else self.scheduler.final_alpha_cumprod
         )
         _snake_case  		=					1 - alpha_prod_t
         _snake_case  		=					self.unet(UpperCamelCase__    , UpperCamelCase__						)['''sample''']
         _snake_case  		=					(1 - alpha_prod_t_prev) ** 0.5 * model_output
         _snake_case  		=					(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
         _snake_case  		=					sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

       return sample






     @staticmethod
     def 						UpperCamelCase(       lowerCamelCase    , lowerCamelCase    , lowerCamelCase						):
       _snake_case  		=					acos(torch.dot(torch.flatten(UpperCamelCase__						)    , torch.flatten(UpperCamelCase__						)						) / torch.norm(UpperCamelCase__						) / torch.norm(UpperCamelCase__						)						)
       return sin((1 - alpha) * theta						) * xa / sin(UpperCamelCase__						) + sin(alpha * theta						) * xa / sin(UpperCamelCase__						)




'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

lowercase__						       =      logging.get_logger(__name__)


lowercase__						       =      {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase__						       =      {
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

lowercase__						       =      {"facebook/blenderbot-3B": 1_2_8}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def    _UpperCamelCase						(       )  ->							Optional[Any]:


		'''simple docstring'''


		snake_case  :       Dict     			=						(
		    list(range(ord('''!'''			) ,       ord('''~'''			) + 1			)			) + list(range(ord('''¡'''			) ,       ord('''¬'''			) + 1			)			) + list(range(ord('''®'''			) ,       ord('''ÿ'''			) + 1			)			)
		)
		snake_case  :       int     			=						bs[:]
		snake_case  :       Any     			=						0
		for b in range(2**8			):
				if b not in bs:
						bs.append(SCREAMING_SNAKE_CASE__			)
						cs.append(2**8 + n			)
						n += 1
		snake_case  :       Tuple     			=						[chr(SCREAMING_SNAKE_CASE__			) for n in cs]
		return dict(zip(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							Any:


		'''simple docstring'''


		snake_case  :       List[str]     			=						set()
		snake_case  :       str     			=						word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)			)
				snake_case  :       Dict     			=						char
		return pairs






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						VOCAB_FILES_NAMES
		lowerCamelCase     							=						PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase     							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase     							=						["""input_ids""", """attention_mask"""]



		def __init__(      self				:			Tuple  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			List[Any]  ,	UpperCamelCase__				:			str="replace"  ,	UpperCamelCase__				:			int="<s>"  ,	UpperCamelCase__				:			Optional[int]="</s>"  ,	UpperCamelCase__				:			int="</s>"  ,	UpperCamelCase__				:			Optional[int]="<s>"  ,	UpperCamelCase__				:			Any="<unk>"  ,	UpperCamelCase__				:			List[Any]="<pad>"  ,	UpperCamelCase__				:			Optional[int]="<mask>"  ,	UpperCamelCase__				:			Optional[Any]=False  ,	**UpperCamelCase__				:			Dict  ,	)						->			str:






				"""simple docstring"""







				snake_case  :       str     			=						AddedToken(UpperCamelCase__  ,	lstrip=UpperCamelCase__  ,	rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__  ,	UpperCamelCase__      ) else bos_token
				snake_case  :       Any     			=						AddedToken(UpperCamelCase__  ,	lstrip=UpperCamelCase__  ,	rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__  ,	UpperCamelCase__      ) else eos_token
				snake_case  :       List[str]     			=						AddedToken(UpperCamelCase__  ,	lstrip=UpperCamelCase__  ,	rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__  ,	UpperCamelCase__      ) else sep_token
				snake_case  :       Optional[Any]     			=						AddedToken(UpperCamelCase__  ,	lstrip=UpperCamelCase__  ,	rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__  ,	UpperCamelCase__      ) else cls_token
				snake_case  :       int     			=						AddedToken(UpperCamelCase__  ,	lstrip=UpperCamelCase__  ,	rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__  ,	UpperCamelCase__      ) else unk_token
				snake_case  :       str     			=						AddedToken(UpperCamelCase__  ,	lstrip=UpperCamelCase__  ,	rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__  ,	UpperCamelCase__      ) else pad_token

				# Mask token behave like a normal word, i.e. include the space before it
				snake_case  :       str     			=						AddedToken(UpperCamelCase__  ,	lstrip=UpperCamelCase__  ,	rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__  ,	UpperCamelCase__      ) else mask_token

				super().__init__(
				    errors=UpperCamelCase__  ,	bos_token=UpperCamelCase__  ,	eos_token=UpperCamelCase__  ,	unk_token=UpperCamelCase__  ,	sep_token=UpperCamelCase__  ,	cls_token=UpperCamelCase__  ,	pad_token=UpperCamelCase__  ,	mask_token=UpperCamelCase__  ,	add_prefix_space=UpperCamelCase__  ,	**UpperCamelCase__  ,	)

				with open(UpperCamelCase__  ,	encoding='''utf-8'''      ) as vocab_handle:
						snake_case  :       Union[str, Any]     			=						json.load(UpperCamelCase__      )
				snake_case  :       Dict     			=						{v: k for k, v in self.encoder.items()}
				snake_case  :       Optional[int]     			=						errors  # how to handle errors in decoding
				snake_case  :       List[Any]     			=						bytes_to_unicode()
				snake_case  :       Dict     			=						{v: k for k, v in self.byte_encoder.items()}
				with open(UpperCamelCase__  ,	encoding='''utf-8'''      ) as merges_handle:
						snake_case  :       Optional[Any]     			=						merges_handle.read().split('''\n'''      )[1:-1]
				snake_case  :       Optional[int]     			=						[tuple(merge.split()      ) for merge in bpe_merges]
				snake_case  :       Optional[int]     			=						dict(zip(UpperCamelCase__  ,	range(len(UpperCamelCase__      )      )      )      )
				snake_case  :       int     			=						{}
				snake_case  :       Any     			=						add_prefix_space

				# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
				snake_case  :       Any     			=						re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''      )



		@property
		# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Any:






				"""simple docstring"""







				return len(self.encoder      )



		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Optional[int]:






				"""simple docstring"""







				return dict(self.encoder  ,	**self.added_tokens_encoder      )



		def       lowerCAmelCase				(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			Tuple      )						->			Dict:






				"""simple docstring"""







				if token in self.cache:
						return self.cache[token]
				snake_case  :       str     			=						tuple(UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						get_pairs(UpperCamelCase__      )

				if not pairs:
						return token

				while True:
						snake_case  :       Tuple     			=						min(UpperCamelCase__  ,	key=lambda UpperCamelCase__      : self.bpe_ranks.get(UpperCamelCase__  ,	float('''inf'''      )      )      )
						if bigram not in self.bpe_ranks:
								break
						snake_case			,snake_case  :       List[str]     			=						bigram
						snake_case  :       int     			=						[]
						snake_case  :       int     			=						0
						while i < len(UpperCamelCase__      ):
								try:
										snake_case  :       Optional[int]     			=						word.index(UpperCamelCase__  ,	UpperCamelCase__      )
								except ValueError:
										new_word.extend(word[i:]      )
										break
								else:
										new_word.extend(word[i:j]      )
										snake_case  :       Optional[int]     			=						j

								if word[i] == first and i < len(UpperCamelCase__      ) - 1 and word[i + 1] == second:
										new_word.append(first + second      )
										i += 2
								else:
										new_word.append(word[i]      )
										i += 1
						snake_case  :       str     			=						tuple(UpperCamelCase__      )
						snake_case  :       Tuple     			=						new_word
						if len(UpperCamelCase__      ) == 1:
								break
						else:
								snake_case  :       List[str]     			=						get_pairs(UpperCamelCase__      )
				snake_case  :       str     			=						''' '''.join(UpperCamelCase__      )
				snake_case  :       int     			=						word
				return word



		def       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			List[Any]      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       str     			=						[]
				for token in re.findall(self.pat  ,	UpperCamelCase__      ):
						snake_case  :       Optional[int]     			=						''''''.join(
						    self.byte_encoder[b] for b in token.encode('''utf-8'''      )      )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
						bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase__      ).split(''' '''      )      )
				return bpe_tokens



		def       lowerCAmelCase				(      self				:			int  ,	UpperCamelCase__				:			Any      )						->			Any:






				"""simple docstring"""







				return self.encoder.get(UpperCamelCase__  ,	self.encoder.get(self.unk_token      )      )



		def       lowerCAmelCase				(      self				:			Any  ,	UpperCamelCase__				:			List[Any]      )						->			int:






				"""simple docstring"""







				return self.decoder.get(UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			int      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						''''''.join(UpperCamelCase__      )
				snake_case  :       int     			=						bytearray([self.byte_decoder[c] for c in text]      ).decode('''utf-8'''  ,	errors=self.errors      )
				return text



		def       lowerCAmelCase				(      self				:			int  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			Optional[str] = None      )						->			Tuple[str]:






				"""simple docstring"""







				if not os.path.isdir(UpperCamelCase__      ):
						logger.error(f'Vocabulary path ({save_directory}) should be a directory'      )
						return
				snake_case  :       int     			=						os.path.join(
				    UpperCamelCase__  ,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
				snake_case  :       List[Any]     			=						os.path.join(
				    UpperCamelCase__  ,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']      )

				with open(UpperCamelCase__  ,	'''w'''  ,	encoding='''utf-8'''      ) as f:
						f.write(json.dumps(self.encoder  ,	indent=2  ,	sort_keys=UpperCamelCase__  ,	ensure_ascii=UpperCamelCase__      ) + '''\n'''      )

				snake_case  :       str     			=						0
				with open(UpperCamelCase__  ,	'''w'''  ,	encoding='''utf-8'''      ) as writer:
						writer.write('''#version: 0.2\n'''      )
						for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,	key=lambda UpperCamelCase__      : kv[1]      ):
								if index != token_index:
										logger.warning(
										    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
										    ''' Please check that the tokenizer is not corrupted!'''      )
										snake_case  :       List[str]     			=						token_index
								writer.write(''' '''.join(UpperCamelCase__      ) + '''\n'''      )
								index += 1

				return vocab_file, merge_file



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			List[int]  ,	UpperCamelCase__				:			Optional[List[int]] = None  ,	UpperCamelCase__				:			bool = False      )						->			List[int]:






				"""simple docstring"""







				if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=UpperCamelCase__  ,	token_ids_a=UpperCamelCase__  ,	already_has_special_tokens=UpperCamelCase__      )

				if token_ids_a is None:
						return [1] + ([0] * len(UpperCamelCase__      )) + [1]
				return [1] + ([0] * len(UpperCamelCase__      )) + [1, 1] + ([0] * len(UpperCamelCase__      )) + [1]



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			List[int]  ,	UpperCamelCase__				:			Optional[List[int]] = None      )						->			List[int]:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						[self.sep_token_id]
				snake_case  :       Tuple     			=						[self.cls_token_id]

				if token_ids_a is None:
						return len(cls + token_ids_a + sep      ) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			Dict  ,	UpperCamelCase__				:			Any=False  ,	**UpperCamelCase__				:			List[Any]      )						->			int:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						kwargs.pop('''add_prefix_space'''  ,	self.add_prefix_space      )
				if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__      ) > 0 and not text[0].isspace()):
						snake_case  :       Dict     			=						''' ''' + text
				return (text, kwargs)



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			List[int]  ,	UpperCamelCase__				:			Optional[List[int]] = None      )						->			str:






				"""simple docstring"""







				return token_ids_a + [self.eos_token_id]



		def       lowerCAmelCase				(      self				:			str  ,	UpperCamelCase__				:			"Conversation"      )						->			List[int]:






				"""simple docstring"""







				snake_case  :       Any     			=						[]
				for is_user, text in conversation.iter_texts():
						if is_user:
								# We need to space prefix as it's being done within blenderbot
								inputs.append(''' ''' + text      )
						else:
								# Generated responses should contain them already.
								inputs.append(UpperCamelCase__      )

				snake_case  :       List[str]     			=						'''  '''.join(UpperCamelCase__      )
				snake_case  :       Tuple     			=						self.encode(UpperCamelCase__      )
				if len(UpperCamelCase__      ) > self.model_max_length:
						snake_case  :       List[Any]     			=						input_ids[-self.model_max_length :]
						logger.warning(f'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'      )
				return input_ids


