





import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase=True,       __lowerCamelCase="pt"	):
     _SCREAMING_SNAKE_CASE       :	Any						    =							{"add_prefix_space": True} if isinstance(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE	) and not line.startswith(" "	) else {}
     _SCREAMING_SNAKE_CASE       :	Dict						    =							padding_side
     return tokenizer(
         [line],       max_length=_SCREAMING_SNAKE_CASE,       padding="max_length" if pad_to_max_length else None,       truncation=_SCREAMING_SNAKE_CASE,       return_tensors=_SCREAMING_SNAKE_CASE,       add_special_tokens=_SCREAMING_SNAKE_CASE,       **_SCREAMING_SNAKE_CASE,       )






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase=None,       ):
     _SCREAMING_SNAKE_CASE       :	str						    =							input_ids.ne(_SCREAMING_SNAKE_CASE	).any(dim=0	)
     if attention_mask is None:
          return input_ids[:, keep_column_mask]
     else:
          return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])





class 			lowerCAmelCase__( snake_case__    ):






       '''simple docstring'''


       def __init__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase="train"    ,						__lowerCamelCase=None    ,						__lowerCamelCase=None    ,						__lowerCamelCase=None    ,						__lowerCamelCase=""    ,						)			->     Tuple:
            super().__init__()
            _SCREAMING_SNAKE_CASE       :	str						    =							Path(UpperCAmelCase_							).joinpath(type_path + ".source"							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							Path(UpperCAmelCase_							).joinpath(type_path + ".target"							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.get_char_lens(self.src_file							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							max_source_length
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							max_target_length
            assert min(self.src_lens							) > 0, F"""found empty line in {self.src_file}"""
            _SCREAMING_SNAKE_CASE       :	Dict						    =							tokenizer
            _SCREAMING_SNAKE_CASE       :	Any						    =							prefix
            if n_obs is not None:
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.src_lens[:n_obs]
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							src_lang
            _SCREAMING_SNAKE_CASE       :	int						    =							tgt_lang




       def __len__(      self							)			->     List[Any]:
            return len(self.src_lens							)




       def __getitem__(      self    ,						__lowerCamelCase							)			->     Dict[str, torch.Tensor]:
            _SCREAMING_SNAKE_CASE       :	int						    =							index + 1  # linecache starts at 1
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.prefix + linecache.getline(str(self.src_file							)    ,						UpperCAmelCase_							).rstrip("\n"							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							linecache.getline(str(self.tgt_file							)    ,						UpperCAmelCase_							).rstrip("\n"							)
            assert source_line, F"""empty source line for index {index}"""
            assert tgt_line, F"""empty tgt line for index {index}"""

            # Need to add eos token manually for T5
            if isinstance(self.tokenizer    ,						UpperCAmelCase_							):
                 source_line += self.tokenizer.eos_token
                 tgt_line += self.tokenizer.eos_token

            # Pad source and target to the right
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							(
                self.tokenizer.question_encoder if isinstance(self.tokenizer    ,						UpperCAmelCase_							) else self.tokenizer
            )
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.tokenizer.generator if isinstance(self.tokenizer    ,						UpperCAmelCase_							) else self.tokenizer

            _SCREAMING_SNAKE_CASE       :	int						    =							encode_line(UpperCAmelCase_    ,						UpperCAmelCase_    ,						self.max_source_length    ,						"right"							)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							encode_line(UpperCAmelCase_    ,						UpperCAmelCase_    ,						self.max_target_length    ,						"right"							)

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							source_inputs["input_ids"].squeeze()
            _SCREAMING_SNAKE_CASE       :	str						    =							target_inputs["input_ids"].squeeze()
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							source_inputs["attention_mask"].squeeze()
            return {
                "input_ids": source_ids,
                "attention_mask": src_mask,
                "decoder_input_ids": target_ids,
            }




       @staticmethod
       def     UpperCamelCase_			(      __lowerCamelCase							)			->     List[str]:
            return [len(UpperCAmelCase_							) for x in Path(UpperCAmelCase_							).open().readlines()]







       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     Dict[str, torch.Tensor]:
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							torch.stack([x["input_ids"] for x in batch]							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							torch.stack([x["attention_mask"] for x in batch]							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							torch.stack([x["decoder_input_ids"] for x in batch]							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							(
                self.tokenizer.generator.pad_token_id
                if isinstance(self.tokenizer    ,						UpperCAmelCase_							)
                else self.tokenizer.pad_token_id
            )
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							(
                self.tokenizer.question_encoder.pad_token_id
                if isinstance(self.tokenizer    ,						UpperCAmelCase_							)
                else self.tokenizer.pad_token_id
            )
            _SCREAMING_SNAKE_CASE       :	Any						    =							trim_batch(UpperCAmelCase_    ,						UpperCAmelCase_							)
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	List[Any]						    =							trim_batch(UpperCAmelCase_    ,						UpperCAmelCase_    ,						attention_mask=UpperCAmelCase_							)
            _SCREAMING_SNAKE_CASE       :	str						    =							{
                "input_ids": source_ids,
                "attention_mask": source_mask,
                "decoder_input_ids": y,
            }
            return batch
UpperCamelCase__      =getLogger(__name__)






def     lowerCamelCase__			(__lowerCamelCase	):
     return list(itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE	)	)






def     lowerCamelCase__			(__lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							get_git_info()
     save_json(_SCREAMING_SNAKE_CASE,       os.path.join(_SCREAMING_SNAKE_CASE,       "git_log.json"	)	)






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase=4,       **__lowerCamelCase	):
     with open(_SCREAMING_SNAKE_CASE,       "w"	) as f:
          json.dump(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE,       indent=_SCREAMING_SNAKE_CASE,       **_SCREAMING_SNAKE_CASE	)






def     lowerCamelCase__			(__lowerCamelCase	):
     with open(_SCREAMING_SNAKE_CASE	) as f:
          return json.load(_SCREAMING_SNAKE_CASE	)






def     lowerCamelCase__			():
     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							git.Repo(search_parent_directories=_SCREAMING_SNAKE_CASE	)
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							{
         "repo_id": str(_SCREAMING_SNAKE_CASE	),
         "repo_sha": str(repo.head.object.hexsha	),
         "repo_branch": str(repo.active_branch	),
         "hostname": str(socket.gethostname()	),
     }
     return repo_infos






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase	):
     return list(map(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE	)	)






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase	):
     with open(_SCREAMING_SNAKE_CASE,       "wb"	) as f:
          return pickle.dump(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE	)






def     lowerCamelCase__			(__lowerCamelCase	):

     def remove_articles(__lowerCamelCase	):
          return re.sub(R"\b(a|an|the)\b",       " ",       _SCREAMING_SNAKE_CASE	)

     def white_space_fix(__lowerCamelCase	):
          return " ".join(text.split()	)

     def remove_punc(__lowerCamelCase	):
          _SCREAMING_SNAKE_CASE       :	Tuple						    =							set(string.punctuation	)
          return "".join(ch for ch in text if ch not in exclude	)

     def lower(__lowerCamelCase	):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE	)	)	)	)






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Dict						    =							normalize_answer(_SCREAMING_SNAKE_CASE	).split()
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							normalize_answer(_SCREAMING_SNAKE_CASE	).split()
     _SCREAMING_SNAKE_CASE       :	int						    =							Counter(_SCREAMING_SNAKE_CASE	) & Counter(_SCREAMING_SNAKE_CASE	)
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							sum(common.values()	)
     if num_same == 0:
          return 0
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							1.0 * num_same / len(_SCREAMING_SNAKE_CASE	)
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							1.0 * num_same / len(_SCREAMING_SNAKE_CASE	)
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							(2 * precision * recall) / (precision + recall)
     return fa






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase	):
     return normalize_answer(_SCREAMING_SNAKE_CASE	) == normalize_answer(_SCREAMING_SNAKE_CASE	)






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase	):
     assert len(_SCREAMING_SNAKE_CASE	) == len(_SCREAMING_SNAKE_CASE	)
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							0
     for hypo, pred in zip(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE	):
          em += exact_match_score(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE	)
     if len(_SCREAMING_SNAKE_CASE	) > 0:
          em /= len(_SCREAMING_SNAKE_CASE	)
     return {"em": em}






def     lowerCamelCase__			(__lowerCamelCase	):
     return model_prefix.startswith("rag"	)






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Any						    =							{p: p for p in extra_params}
     # T5 models don't have `dropout` param, they have `dropout_rate` instead
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							"dropout_rate"
     for p in extra_params:
          if getattr(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE	):
               if not hasattr(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE	) and not hasattr(_SCREAMING_SNAKE_CASE,       equivalent_param[p]	):
                    logger.info("config doesn't have a `{}` attribute".format(_SCREAMING_SNAKE_CASE	)	)
                    delattr(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE	)
                    continue
               _SCREAMING_SNAKE_CASE       :	str						    =							p if hasattr(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE	) else equivalent_param[p]
               setattr(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE,       getattr(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE	)	)
               delattr(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE	)
     return hparams, config






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 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    ['vqvae']




       def __init__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						)			->     Optional[Any]:
            super().__init__()
            self.register_modules(unet=__lowerCamelCase    ,						scheduler=__lowerCamelCase    ,						mel=__lowerCamelCase    ,						vqvae=__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     int:
            return 5_0 if isinstance(self.scheduler    ,						__lowerCamelCase							) else 1_0_0_0




       @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    ,						)			->     Union[
           Union[AudioPipelineOutput, ImagePipelineOutput],
           Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
       ]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							steps or self.get_default_steps()
            self.scheduler.set_timesteps(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							step_generator or generator
            # For backwards compatibility
            if type(self.unet.config.sample_size							) == int:
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							(self.unet.config.sample_size, self.unet.config.sample_size)
            if noise is None:
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							randn_tensor(
                     (
                         batch_size,
                         self.unet.config.in_channels,
                         self.unet.config.sample_size[0],
                         self.unet.config.sample_size[1],
                     )    ,						generator=__lowerCamelCase    ,						device=self.device    ,						)
            _SCREAMING_SNAKE_CASE       :	Any						    =							noise
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							None

            if audio_file is not None or raw_audio is not None:
                 self.mel.load_audio(__lowerCamelCase    ,						__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.mel.audio_slice_to_image(__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	str						    =							np.frombuffer(input_image.tobytes()    ,						dtype="uint8"							).reshape(
                     (input_image.height, input_image.width)							)
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							(input_image / 2_5_5) * 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       :	Optional[int]						    =							self.vqvae.encode(torch.unsqueeze(__lowerCamelCase    ,						0							)							).latent_dist.sample(
                          generator=__lowerCamelCase							)[0]
                      _SCREAMING_SNAKE_CASE       :	Dict						    =							self.vqvae.config.scaling_factor * input_images

                 if start_step > 0:
                      _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.scheduler.add_noise(__lowerCamelCase    ,						__lowerCamelCase    ,						self.scheduler.timesteps[start_step - 1]							)

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

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

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

                 if mask is not None:
                      if mask_start > 0:
                           _SCREAMING_SNAKE_CASE       :	Tuple						    =							mask[:, step, :, :mask_start]
                      if mask_end > 0:
                           _SCREAMING_SNAKE_CASE       :	Optional[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       :	Union[str, Any]						    =							1 / self.vqvae.config.scaling_factor * images
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.vqvae.decode(__lowerCamelCase							)["sample"]

            _SCREAMING_SNAKE_CASE       :	Tuple						    =							(images / 2 + 0.5).clamp(0    ,						1							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							images.cpu().permute(0    ,						2    ,						3    ,						1							).numpy()
            _SCREAMING_SNAKE_CASE       :	int						    =							(images * 2_5_5).round().astype("uint8"							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							list(
                (Image.fromarray(_[:, :, 0]							) for _ in images)
                if images.shape[3] == 1
                else (Image.fromarray(__lowerCamelCase    ,						mode="RGB"							).convert("L"							) for _ in images)							)

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

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




       @torch.no_grad()
       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = 5_0							)			->     np.ndarray:
            assert isinstance(self.scheduler    ,						__lowerCamelCase							)
            self.scheduler.set_timesteps(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							np.array(
                [np.frombuffer(image.tobytes()    ,						dtype="uint8"							).reshape((1, image.height, image.width)							) for image in images]							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							(sample / 2_5_5) * 2 - 1
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							torch.Tensor(__lowerCamelCase							).to(self.device							)

            for t in self.progress_bar(torch.flip(self.scheduler.timesteps    ,						(0,)							)							):
                 _SCREAMING_SNAKE_CASE       :	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       :	List[str]						    =							(
                     self.scheduler.alphas_cumprod[prev_timestep]
                     if prev_timestep >= 0
                     else self.scheduler.final_alpha_cumprod
                 )
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							1 - alpha_prod_t
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.unet(__lowerCamelCase    ,						__lowerCamelCase							)["sample"]
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							(1 - alpha_prod_t_prev) ** 0.5 * model_output
                 _SCREAMING_SNAKE_CASE       :	Any						    =							(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

            return sample







       @staticmethod
       def     UpperCamelCase_			(      __lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							)			->     torch.Tensor:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							acos(torch.dot(torch.flatten(__lowerCamelCase							)    ,						torch.flatten(__lowerCamelCase							)							) / torch.norm(__lowerCamelCase							) / torch.norm(__lowerCamelCase							)							)
            return sin((1 - alpha) * theta							) * xa / sin(__lowerCamelCase							) + sin(alpha * theta							) * xa / sin(__lowerCamelCase							)