


'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

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


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






class 		lowerCAmelCase__	(	UpperCAmelCase__		):




			def __init__(						self     :   Union[str, Any]		,     lowerCamelCase__     :   Tuple		,     lowerCamelCase__     :   Union[str, Any]       )      ->Optional[int]:



					'''simple docstring'''







					super().__init__()
					self.register_modules(unet=lowerCamelCase__		,     scheduler=lowerCamelCase__       )






			@torch.no_grad()
			def __call__(						self     :   Optional[int]		,     lowerCamelCase__     :   int = 1		,     lowerCamelCase__     :   int = 1_00		,     lowerCamelCase__     :   Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     lowerCamelCase__     :   Optional[float] = None		,     lowerCamelCase__     :   bool = True		,     )      ->Union[AudioPipelineOutput, Tuple]:



					'''simple docstring'''







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

					_UpperCAmelCase						:				int	    =					audio_length_in_s * self.unet.config.sample_rate

					_UpperCAmelCase						:				List[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}."""       )

					_UpperCAmelCase						:				Union[str, Any]	    =					int(lowerCamelCase__       )
					if sample_size % down_scale_factor != 0:
							_UpperCAmelCase						:				Union[str, Any]	    =					(
							    (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."       )
					_UpperCAmelCase						:				List[str]	    =					int(lowerCamelCase__       )

					_UpperCAmelCase						:				str	    =					next(iter(self.unet.parameters()       )       ).dtype
					_UpperCAmelCase						:				List[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."""       )

					_UpperCAmelCase						:				Any	    =					randn_tensor(lowerCamelCase__		,     generator=lowerCamelCase__		,     device=self.device		,     dtype=lowerCamelCase__       )

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

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

							# 2. compute previous image: x_t -> t_t-1
							_UpperCAmelCase						:				Optional[int]	    =					self.scheduler.step(lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__       ).prev_sample

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

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

					if not return_dict:
							return (audio,)

					return AudioPipelineOutput(audios=lowerCamelCase__       )







'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


lowerCamelCase__					=   logging.get_logger(__name__)

lowerCamelCase__					=   {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase__					=   {
    'vocab_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
        ),
        'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
        ),
        'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
        ),
        'bert-base-multilingual-cased': (
            'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-cased': (
            'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
        ),
    },
}

lowerCamelCase__					=   {
    'bert-base-uncased': 512,
    'bert-large-uncased': 512,
    'bert-base-cased': 512,
    'bert-large-cased': 512,
    'bert-base-multilingual-uncased': 512,
    'bert-base-multilingual-cased': 512,
    'bert-base-chinese': 512,
    'bert-base-german-cased': 512,
    'bert-large-uncased-whole-word-masking': 512,
    'bert-large-cased-whole-word-masking': 512,
    'bert-large-uncased-whole-word-masking-finetuned-squad': 512,
    'bert-large-cased-whole-word-masking-finetuned-squad': 512,
    'bert-base-cased-finetuned-mrpc': 512,
    'bert-base-german-dbmdz-cased': 512,
    'bert-base-german-dbmdz-uncased': 512,
    'TurkuNLP/bert-base-finnish-cased-v1': 512,
    'TurkuNLP/bert-base-finnish-uncased-v1': 512,
    'wietsedv/bert-base-dutch-cased': 512,
}

lowerCamelCase__					=   {
    'bert-base-uncased': {'do_lower_case': True},
    'bert-large-uncased': {'do_lower_case': True},
    'bert-base-cased': {'do_lower_case': False},
    'bert-large-cased': {'do_lower_case': False},
    'bert-base-multilingual-uncased': {'do_lower_case': True},
    'bert-base-multilingual-cased': {'do_lower_case': False},
    'bert-base-chinese': {'do_lower_case': False},
    'bert-base-german-cased': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
    'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
    'bert-base-german-dbmdz-cased': {'do_lower_case': False},
    'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
    'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
    'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
    'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Union[str, Any]				      =   VOCAB_FILES_NAMES
			lowerCAmelCase       :		Dict				      =   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase       :		Tuple				      =   PRETRAINED_INIT_CONFIGURATION
			lowerCAmelCase       :		Optional[int]				      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase       :		Any				      =   BertTokenizer




			def __init__(						self     :   int		,     lowerCamelCase__     :   Optional[Any]=None		,     lowerCamelCase__     :   Optional[int]=None		,     lowerCamelCase__     :   str=True		,     lowerCamelCase__     :   Tuple="[UNK]"		,     lowerCamelCase__     :   str="[SEP]"		,     lowerCamelCase__     :   Optional[Any]="[PAD]"		,     lowerCamelCase__     :   List[str]="[CLS]"		,     lowerCamelCase__     :   Union[str, Any]="[MASK]"		,     lowerCamelCase__     :   str=True		,     lowerCamelCase__     :   Dict=None		,     **lowerCamelCase__     :   Union[str, Any]		,     )      ->Tuple:



					'''simple docstring'''







					super().__init__(
					    lowerCamelCase__		,     tokenizer_file=lowerCamelCase__		,     do_lower_case=lowerCamelCase__		,     unk_token=lowerCamelCase__		,     sep_token=lowerCamelCase__		,     pad_token=lowerCamelCase__		,     cls_token=lowerCamelCase__		,     mask_token=lowerCamelCase__		,     tokenize_chinese_chars=lowerCamelCase__		,     strip_accents=lowerCamelCase__		,     **lowerCamelCase__		,     )

					_UpperCAmelCase						:				Optional[Any]	    =					json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
					if (
					    normalizer_state.get("lowercase"		,     lowerCamelCase__       ) != do_lower_case
					    or normalizer_state.get("strip_accents"		,     lowerCamelCase__       ) != strip_accents
					    or normalizer_state.get("handle_chinese_chars"		,     lowerCamelCase__       ) != tokenize_chinese_chars
					):
							_UpperCAmelCase						:				str	    =					getattr(lowerCamelCase__		,     normalizer_state.pop("type"       )       )
							_UpperCAmelCase						:				Optional[Any]	    =					do_lower_case
							_UpperCAmelCase						:				Any	    =					strip_accents
							_UpperCAmelCase						:				List[Any]	    =					tokenize_chinese_chars
							_UpperCAmelCase						:				int	    =					normalizer_class(**lowerCamelCase__       )

					_UpperCAmelCase						:				Union[str, Any]	    =					do_lower_case




			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   List[str]		,     lowerCamelCase__     :   str=None       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

					if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

					return output




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



					'''simple docstring'''







					_UpperCAmelCase						:				Tuple	    =					[self.sep_token_id]
					_UpperCAmelCase						:				int	    =					[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       ) * [0] + len(token_ids_a + sep       ) * [1]






			def 							lowerCAmelCase__			(						self     :   List[Any]		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Optional[str] = None       )      ->Tuple[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self._tokenizer.model.save(lowerCamelCase__		,     name=lowerCamelCase__       )
					return tuple(lowerCamelCase__       )



