



"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


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

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					def __init__(       self							:  List[Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict     )->					Optional[int]:
								super().__init__()
								self.register_modules(unet=lowerCamelCase,  scheduler=lowerCamelCase     )




					@torch.no_grad()
					def __call__(       self							:  Optional[int],  lowerCamelCase							:  int = 1,  lowerCamelCase							:  int = 100,  lowerCamelCase							:  Optional[Union[torch.Generator, List[torch.Generator]]] = None,  lowerCamelCase							:  Optional[float] = None,  lowerCamelCase							:  bool = True,  )->					Union[AudioPipelineOutput, Tuple]:

								if audio_length_in_s is None:
											lowerCamelCase__		:		int			=self.unet.config.sample_size / self.unet.config.sample_rate

								lowerCamelCase__		:		List[str]			=audio_length_in_s * self.unet.config.sample_rate

								lowerCamelCase__		:		Any			=2 ** len(self.unet.up_blocks     )
								if sample_size < 3 * down_scale_factor:
											raise ValueError(
											    F'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
											    F''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''     )

								lowerCamelCase__		:		Optional[int]			=int(lowerCamelCase     )
								if sample_size % down_scale_factor != 0:
											lowerCamelCase__		:		Tuple			=(
											    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
											) * down_scale_factor
											logger.info(
											    F'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
											    F''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
											    ''' process.'''     )
								lowerCamelCase__		:		int			=int(lowerCamelCase     )

								lowerCamelCase__		:		str			=next(iter(self.unet.parameters()     )     ).dtype
								lowerCamelCase__		:		Union[str, Any]			=(batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(lowerCamelCase,  lowerCamelCase     ) and len(lowerCamelCase     ) != batch_size:
											raise ValueError(
											    F'''You have passed a list of generators of length {len(lowerCamelCase     )}, but requested an effective batch'''
											    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''     )

								lowerCamelCase__		:		str			=randn_tensor(lowerCamelCase,  generator=lowerCamelCase,  device=self.device,  dtype=lowerCamelCase     )

								# set step values
								self.scheduler.set_timesteps(lowerCamelCase,  device=audio.device     )
								lowerCamelCase__		:		Any			=self.scheduler.timesteps.to(lowerCamelCase     )

								for t in self.progress_bar(self.scheduler.timesteps     ):
											# 1. predict noise model_output
											lowerCamelCase__		:		str			=self.unet(lowerCamelCase,  lowerCamelCase     ).sample

											# 2. compute previous image: x_t -> t_t-1
											lowerCamelCase__		:		Dict			=self.scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase     ).prev_sample

								lowerCamelCase__		:		Optional[int]			=audio.clamp(-1,  1     ).float().cpu().numpy()

								lowerCamelCase__		:		List[Any]			=audio[:, :, :original_sample_size]

								if not return_dict:
											return (audio,)

								return AudioPipelineOutput(audios=lowerCamelCase     )









"""simple docstring"""






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      snake_case__   (		__lowerCamelCase				:    Union[str, Any]							,		__lowerCamelCase				:    Tuple							,		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    Tuple							,		__lowerCamelCase				:    Tuple=True							,		__lowerCamelCase				:    Dict="pt"	):
			"""simple docstring"""
			lowerCamelCase__		:		str			={'''add_prefix_space''': True} if isinstance(__lowerCamelCase							,		__lowerCamelCase	) and not line.startswith(''' '''	) else {}
			lowerCamelCase__		:		int			=padding_side
			return tokenizer(
			    [line]							,		max_length=__lowerCamelCase							,		padding='''max_length''' if pad_to_max_length else None							,		truncation=__lowerCamelCase							,		return_tensors=__lowerCamelCase							,		add_special_tokens=__lowerCamelCase							,		**__lowerCamelCase							,		)






def      snake_case__   (		__lowerCamelCase				:    str							,		__lowerCamelCase				:    List[Any]							,		__lowerCamelCase				:    Optional[int]=None							,		):
			"""simple docstring"""
			lowerCamelCase__		:		Any			=input_ids.ne(__lowerCamelCase	).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        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





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



					def __len__(       self							:  Dict     )->					Optional[int]:
								return len(self.src_lens     )



					def __getitem__(       self							:  List[str],  lowerCamelCase							:  Optional[int]     )->					Dict[str, torch.Tensor]:
								lowerCamelCase__		:		List[Any]			=index + 1  # linecache starts at 1
								lowerCamelCase__		:		Optional[int]			=self.prefix + linecache.getline(str(self.src_file     ),  lowerCamelCase     ).rstrip('''\n'''     )
								lowerCamelCase__		:		Optional[Any]			=linecache.getline(str(self.tgt_file     ),  lowerCamelCase     ).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,  lowerCamelCase     ):
											source_line += self.tokenizer.eos_token
											tgt_line += self.tokenizer.eos_token

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

								lowerCamelCase__		:		Optional[int]			=encode_line(lowerCamelCase,  lowerCamelCase,  self.max_source_length,  '''right'''     )
								lowerCamelCase__		:		str			=encode_line(lowerCamelCase,  lowerCamelCase,  self.max_target_length,  '''right'''     )

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



					@staticmethod
					def 		snake_case       (       lowerCamelCase							:  Union[str, Any]     )->					Optional[int]:
								return [len(lowerCamelCase     ) for x in Path(lowerCamelCase     ).open().readlines()]




					def 		snake_case       (       self							:  str,  lowerCamelCase							:  str     )->					Dict[str, torch.Tensor]:
								lowerCamelCase__		:		List[Any]			=torch.stack([x['''input_ids'''] for x in batch]     )
								lowerCamelCase__		:		int			=torch.stack([x['''attention_mask'''] for x in batch]     )
								lowerCamelCase__		:		Union[str, Any]			=torch.stack([x['''decoder_input_ids'''] for x in batch]     )
								lowerCamelCase__		:		str			=(
								    self.tokenizer.generator.pad_token_id
								    if isinstance(self.tokenizer,  lowerCamelCase     )
								    else self.tokenizer.pad_token_id
								)
								lowerCamelCase__		:		List[str]			=(
								    self.tokenizer.question_encoder.pad_token_id
								    if isinstance(self.tokenizer,  lowerCamelCase     )
								    else self.tokenizer.pad_token_id
								)
								lowerCamelCase__		:		Optional[int]			=trim_batch(lowerCamelCase,  lowerCamelCase     )
								lowerCamelCase__	,   lowerCamelCase__		:		Any			=trim_batch(lowerCamelCase,  lowerCamelCase,  attention_mask=lowerCamelCase     )
								lowerCamelCase__		:		List[str]			={
								    '''input_ids''': source_ids,
								    '''attention_mask''': source_mask,
								    '''decoder_input_ids''': y,
								}
								return batch





_lowercase : Any      						=						getLogger(__name__)






def      snake_case__   (		__lowerCamelCase				:    List[List]	):
			"""simple docstring"""
			return list(itertools.chain.from_iterable(__lowerCamelCase	)	)






def      snake_case__   (		__lowerCamelCase				:    str	):
			"""simple docstring"""
			lowerCamelCase__		:		Dict			=get_git_info()
			save_json(__lowerCamelCase							,		os.path.join(__lowerCamelCase							,		'''git_log.json'''	)	)






def      snake_case__   (		__lowerCamelCase				:    Union[str, Any]							,		__lowerCamelCase				:    Optional[int]							,		__lowerCamelCase				:    Dict=4							,		**__lowerCamelCase				:    int	):
			"""simple docstring"""
			with open(__lowerCamelCase							,		'''w'''	) as f:
						json.dump(__lowerCamelCase							,		__lowerCamelCase							,		indent=__lowerCamelCase							,		**__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			with open(__lowerCamelCase	) as f:
						return json.load(__lowerCamelCase	)






def      snake_case__   (		):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]			=git.Repo(search_parent_directories=__lowerCamelCase	)
			lowerCamelCase__		:		Union[str, Any]			={
			    '''repo_id''': str(__lowerCamelCase	),
			    '''repo_sha''': str(repo.head.object.hexsha	),
			    '''repo_branch''': str(repo.active_branch	),
			    '''hostname''': str(socket.gethostname()	),
			}
			return repo_infos






def      snake_case__   (		__lowerCamelCase				:    Callable							,		__lowerCamelCase				:    Iterable	):
			"""simple docstring"""
			return list(map(__lowerCamelCase							,		__lowerCamelCase	)	)






def      snake_case__   (		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			with open(__lowerCamelCase							,		'''wb'''	) as f:
						return pickle.dump(__lowerCamelCase							,		__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    str	):
			"""simple docstring"""

			def remove_articles(__lowerCamelCase				:    List[Any]	):
						return re.sub(R'''\b(a|an|the)\b'''							,		''' '''							,		__lowerCamelCase	)

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

			def remove_punc(__lowerCamelCase				:    Optional[Any]	):
						lowerCamelCase__		:		Tuple			=set(string.punctuation	)
						return "".join(ch for ch in text if ch not in exclude	)

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

			return white_space_fix(remove_articles(remove_punc(lower(__lowerCamelCase	)	)	)	)






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






def      snake_case__   (		__lowerCamelCase				:    Dict							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			return normalize_answer(__lowerCamelCase	) == normalize_answer(__lowerCamelCase	)






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






def      snake_case__   (		__lowerCamelCase				:    List[str]	):
			"""simple docstring"""
			return model_prefix.startswith('''rag'''	)






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




