



"""simple docstring"""






import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_rjieba
@require_tokenizers
class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_		,   unittest.TestCase   ):


					'''simple docstring'''





					_a   			=							RoFormerTokenizer
					_a   			=							RoFormerTokenizerFast
					_a   			=							True
					_a   			=							True



					def 		snake_case       (       self							:  Union[str, Any]     )->					List[str]:
								super().setUp()



					def 		snake_case       (       self							:  Optional[int],  **lowerCamelCase							:  str     )->					Dict:
								return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''',  **lowerCamelCase     )



					def 		snake_case       (       self							:  Optional[Any],  **lowerCamelCase							:  Any     )->					Optional[Any]:
								return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''',  **lowerCamelCase     )



					def 		snake_case       (       self							:  int     )->					Union[str, Any]:
								lowerCamelCase__		:		List[Any]			='''永和服装饰品有限公司,今天天气非常好'''
								lowerCamelCase__		:		int			='''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
								return input_text, output_text



					def 		snake_case       (       self							:  Tuple     )->					Dict:
								lowerCamelCase__		:		Optional[Any]			=self.get_tokenizer()
								lowerCamelCase__	,   lowerCamelCase__		:		List[str]			=self.get_chinese_input_output_texts()
								lowerCamelCase__		:		Optional[Any]			=tokenizer.tokenize(lowerCamelCase     )

								self.assertListEqual(lowerCamelCase,  output_text.split()     )

								lowerCamelCase__		:		List[str]			=tokens + [tokenizer.unk_token]
								lowerCamelCase__		:		str			=[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase     ),  lowerCamelCase     )



					def 		snake_case       (       self							:  Optional[int]     )->					Optional[int]:
								lowerCamelCase__		:		Union[str, Any]			=self.get_rust_tokenizer()
								lowerCamelCase__	,   lowerCamelCase__		:		List[Any]			=self.get_chinese_input_output_texts()
								lowerCamelCase__		:		Tuple			=tokenizer.tokenize(lowerCamelCase     )
								self.assertListEqual(lowerCamelCase,  output_text.split()     )
								lowerCamelCase__		:		str			=tokens + [tokenizer.unk_token]
								lowerCamelCase__		:		int			=[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase     ),  lowerCamelCase     )



					def 		snake_case       (       self							:  Tuple     )->					Optional[int]:
								pass



					def 		snake_case       (       self							:  Dict     )->					Optional[int]:
								pass




					def 		snake_case       (       self							:  List[str]     )->					Optional[int]:
								pass









"""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     )




