





'''simple docstring'''

import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


__lowerCAmelCase				:      str						      =logging.get_logger(__name__)


__lowerCAmelCase				:      str						      ={"vocab_file": "vocab.json", "merges_file": "merges.txt"}

__lowerCAmelCase				:      Union[str, Any]						      ={
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
}

__lowerCAmelCase				:      Optional[Any]						      ={
    "allenai/longformer-base-4096": 4096,
    "allenai/longformer-large-4096": 4096,
    "allenai/longformer-large-4096-finetuned-triviaqa": 4096,
    "allenai/longformer-base-4096-extra.pos.embd.only": 4096,
    "allenai/longformer-large-4096-extra.pos.embd.only": 4096,
}





@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 		UpperCamelCase							(				):
    A__							=     (
        list(range(ord("!"			)		,			ord("~"			) + 1			)			) + list(range(ord("¡"			)		,			ord("¬"			) + 1			)			) + list(range(ord("®"			)		,			ord("ÿ"			) + 1			)			)
    )
    A__							=     bs[:]
    A__							=     0
    for b in range(2**8			):
        if b not in bs:
            bs.append(_lowerCamelCase			)
            cs.append(2**8 + n			)
            n += 1
    A__							=     [chr(_lowerCamelCase			) for n in cs]
    return dict(zip(_lowerCamelCase		,			_lowerCamelCase			)			)





def 		UpperCamelCase							(				_lowerCamelCase   :   Dict			):
    A__							=     set()
    A__							=     word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)			)
        A__							=     char
    return pairs






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							VOCAB_FILES_NAMES
 __lowercase  						=							PRETRAINED_VOCAB_FILES_MAP
 __lowercase  						=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase  						=							["""input_ids""", """attention_mask"""]
 def __init__(							self :int		,			lowercase_ :List[Any]		,			lowercase_ :int		,			lowercase_ :Union[str, Any]="replace"		,			lowercase_ :List[str]="<s>"		,			lowercase_ :str="</s>"		,			lowercase_ :Optional[int]="</s>"		,			lowercase_ :str="<s>"		,			lowercase_ :str="<unk>"		,			lowercase_ :Optional[int]="<pad>"		,			lowercase_ :Any="<mask>"		,			lowercase_ :Optional[Any]=False		,			**lowercase_ :Tuple		,			)->				List[str]:
     A__							=     AddedToken(lowercase_		,			lstrip=lowercase_		,			rstrip=lowercase_				) if isinstance(lowercase_		,			lowercase_				) else bos_token
     A__							=     AddedToken(lowercase_		,			lstrip=lowercase_		,			rstrip=lowercase_				) if isinstance(lowercase_		,			lowercase_				) else eos_token
     A__							=     AddedToken(lowercase_		,			lstrip=lowercase_		,			rstrip=lowercase_				) if isinstance(lowercase_		,			lowercase_				) else sep_token
     A__							=     AddedToken(lowercase_		,			lstrip=lowercase_		,			rstrip=lowercase_				) if isinstance(lowercase_		,			lowercase_				) else cls_token
     A__							=     AddedToken(lowercase_		,			lstrip=lowercase_		,			rstrip=lowercase_				) if isinstance(lowercase_		,			lowercase_				) else unk_token
     A__							=     AddedToken(lowercase_		,			lstrip=lowercase_		,			rstrip=lowercase_				) if isinstance(lowercase_		,			lowercase_				) else pad_token

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

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

     with open(lowercase_		,			encoding="utf-8"				) as vocab_handle:
         A__							=     json.load(lowercase_				)
     A__							=     {v: k for k, v in self.encoder.items()}
     A__							=     errors  # how to handle errors in decoding
     A__							=     bytes_to_unicode()
     A__							=     {v: k for k, v in self.byte_encoder.items()}
     with open(lowercase_		,			encoding="utf-8"				) as merges_handle:
         A__							=     merges_handle.read().split("\n"				)[1:-1]
     A__							=     [tuple(merge.split()				) for merge in bpe_merges]
     A__							=     dict(zip(lowercase_		,			range(len(lowercase_				)				)				)				)
     A__							=     {}
     A__							=     add_prefix_space

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     A__							=     re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"				)
 @property
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				List[str]:
     return len(self.encoder				)
 def 	UpperCAmelCase_				(							self :Dict				)->				int:
     return dict(self.encoder		,			**self.added_tokens_encoder				)
 def 	UpperCAmelCase_				(							self :Optional[int]		,			lowercase_ :str				)->				Optional[int]:
     if token in self.cache:
         return self.cache[token]
     A__							=     tuple(lowercase_				)
     A__							=     get_pairs(lowercase_				)

     if not pairs:
         return token

     while True:
         A__							=     min(lowercase_		,			key=lambda lowercase_				: self.bpe_ranks.get(lowercase_		,			float("inf"				)				)				)
         if bigram not in self.bpe_ranks:
             break
         A__,      A__							=     bigram
         A__							=     []
         A__							=     0
         while i < len(lowercase_				):
             try:
                 A__							=     word.index(lowercase_		,			lowercase_				)
             except ValueError:
                 new_word.extend(word[i:]				)
                 break
             else:
                 new_word.extend(word[i:j]				)
                 A__							=     j

             if word[i] == first and i < len(lowercase_				) - 1 and word[i + 1] == second:
                 new_word.append(first + second				)
                 i += 2
             else:
                 new_word.append(word[i]				)
                 i += 1
         A__							=     tuple(lowercase_				)
         A__							=     new_word
         if len(lowercase_				) == 1:
             break
         else:
             A__							=     get_pairs(lowercase_				)
     A__							=     " ".join(lowercase_				)
     A__							=     word
     return word
 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :Union[str, Any]				)->				Dict:
     A__							=     []
     for token in re.findall(self.pat		,			lowercase_				):
         A__							=     "".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(lowercase_				).split(" "				)				)
     return bpe_tokens
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :Dict				)->				Optional[int]:
     return self.encoder.get(lowercase_		,			self.encoder.get(self.unk_token				)				)
 def 	UpperCAmelCase_				(							self :List[str]		,			lowercase_ :Optional[Any]				)->				Union[str, Any]:
     return self.decoder.get(lowercase_				)
 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :Dict				)->				Union[str, Any]:
     A__							=     "".join(lowercase_				)
     A__							=     bytearray([self.byte_decoder[c] for c in text]				).decode("utf-8"		,			errors=self.errors				)
     return text
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :str		,			lowercase_ :Optional[str] = None				)->				Tuple[str]:
     if not os.path.isdir(lowercase_				):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory"				)
         return
     A__							=     os.path.join(
         lowercase_		,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)
     A__							=     os.path.join(
         lowercase_		,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]				)

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

     A__							=     0
     with open(lowercase_		,			"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 lowercase_				: 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!"				)
                 A__							=     token_index
             writer.write(" ".join(lowercase_				) + "\n"				)
             index += 1

     return vocab_file, merge_file
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :List[int]		,			lowercase_ :Optional[List[int]] = None				)->				List[int]:
     if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     A__							=     [self.cls_token_id]
     A__							=     [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep
 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :List[int]		,			lowercase_ :Optional[List[int]] = None		,			lowercase_ :bool = False				)->				List[int]:
     if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=lowercase_		,			token_ids_a=lowercase_		,			already_has_special_tokens=lowercase_				)

     if token_ids_a is None:
         return [1] + ([0] * len(lowercase_				)) + [1]
     return [1] + ([0] * len(lowercase_				)) + [1, 1] + ([0] * len(lowercase_				)) + [1]
 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :List[int]		,			lowercase_ :Optional[List[int]] = None				)->				List[int]:
     A__							=     [self.sep_token_id]
     A__							=     [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 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :Dict		,			lowercase_ :List[Any]=False		,			**lowercase_ :Dict				)->				Dict:
     A__							=     kwargs.pop("add_prefix_space"		,			self.add_prefix_space				)
     if (is_split_into_words or add_prefix_space) and (len(lowercase_				) > 0 and not text[0].isspace()):
         A__							=     " " + text
     return (text, kwargs)










'''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   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""vqvae"""]
 def __init__(							self :Dict		,			lowercase_ :AutoencoderKL		,			lowercase_ :UNetaDConditionModel		,			lowercase_ :Mel		,			lowercase_ :Union[DDIMScheduler, DDPMScheduler]		,			)->				Optional[Any]:
     super().__init__()
     self.register_modules(unet=lowercase_		,			scheduler=lowercase_		,			mel=lowercase_		,			vqvae=lowercase_				)
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				int:
     return 50 if isinstance(self.scheduler		,			lowercase_				) else 10_00
 @torch.no_grad()
 def __call__(							self :Optional[Any]		,			lowercase_ :int = 1		,			lowercase_ :str = None		,			lowercase_ :np.ndarray = None		,			lowercase_ :int = 0		,			lowercase_ :int = 0		,			lowercase_ :int = None		,			lowercase_ :torch.Generator = None		,			lowercase_ :float = 0		,			lowercase_ :float = 0		,			lowercase_ :torch.Generator = None		,			lowercase_ :float = 0		,			lowercase_ :torch.Tensor = None		,			lowercase_ :torch.Tensor = None		,			lowercase_ :Tuple=True		,			)->				Union[
     Union[AudioPipelineOutput, ImagePipelineOutput],
     Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
 ]:
     A__							=     steps or self.get_default_steps()
     self.scheduler.set_timesteps(lowercase_				)
     A__							=     step_generator or generator
     # For backwards compatibility
     if type(self.unet.config.sample_size				) == int:
         A__							=     (self.unet.config.sample_size, self.unet.config.sample_size)
     if noise is None:
         A__							=     randn_tensor(
             (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size[0],
                 self.unet.config.sample_size[1],
             )		,			generator=lowercase_		,			device=self.device		,			)
     A__							=     noise
     A__							=     None

     if audio_file is not None or raw_audio is not None:
         self.mel.load_audio(lowercase_		,			lowercase_				)
         A__							=     self.mel.audio_slice_to_image(lowercase_				)
         A__							=     np.frombuffer(input_image.tobytes()		,			dtype="uint8"				).reshape(
             (input_image.height, input_image.width)				)
         A__							=     (input_image / 2_55) * 2 - 1
         A__							=     torch.tensor(input_image[np.newaxis, :, :]		,			dtype=torch.float				).to(self.device				)

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

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

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

     for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]				)				):
         if isinstance(self.unet		,			lowercase_				):
             A__							=     self.unet(lowercase_		,			lowercase_		,			lowercase_				)["sample"]
         else:
             A__							=     self.unet(lowercase_		,			lowercase_				)["sample"]

         if isinstance(self.scheduler		,			lowercase_				):
             A__							=     self.scheduler.step(
                 model_output=lowercase_		,			timestep=lowercase_		,			sample=lowercase_		,			eta=lowercase_		,			generator=lowercase_		,			)["prev_sample"]
         else:
             A__							=     self.scheduler.step(
                 model_output=lowercase_		,			timestep=lowercase_		,			sample=lowercase_		,			generator=lowercase_		,			)["prev_sample"]

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

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

     A__							=     (images / 2 + 0.5).clamp(0		,			1				)
     A__							=     images.cpu().permute(0		,			2		,			3		,			1				).numpy()
     A__							=     (images * 2_55).round().astype("uint8"				)
     A__							=     list(
         (Image.fromarray(_[:, :, 0]				) for _ in images)
         if images.shape[3] == 1
         else (Image.fromarray(lowercase_		,			mode="RGB"				).convert("L"				) for _ in images)				)

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

     return BaseOutput(**AudioPipelineOutput(np.array(lowercase_				)[:, np.newaxis, :]				)		,			**ImagePipelineOutput(lowercase_				)				)
 @torch.no_grad()
 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :List[Image.Image]		,			lowercase_ :int = 50				)->				np.ndarray:
     assert isinstance(self.scheduler		,			lowercase_				)
     self.scheduler.set_timesteps(lowercase_				)
     A__							=     np.array(
         [np.frombuffer(image.tobytes()		,			dtype="uint8"				).reshape((1, image.height, image.width)				) for image in images]				)
     A__							=     (sample / 2_55) * 2 - 1
     A__							=     torch.Tensor(lowercase_				).to(self.device				)

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

     return sample



 @staticmethod
 def 	UpperCAmelCase_				(							lowercase_ :torch.Tensor		,			lowercase_ :torch.Tensor		,			lowercase_ :float				)->				torch.Tensor:
     A__							=     acos(torch.dot(torch.flatten(lowercase_				)		,			torch.flatten(lowercase_				)				) / torch.norm(lowercase_				) / torch.norm(lowercase_				)				)
     return sin((1 - alpha) * theta				) * xa / sin(lowercase_				) + sin(alpha * theta				) * xa / sin(lowercase_				)



