
"""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 			snake_case  (				__lowercase ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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







							@torch.no_grad()
							def __call__(self							,							SCREAMING_SNAKE_CASE_ = 1							,							SCREAMING_SNAKE_CASE_ = 1_00							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_ = True							,							):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_    			=			audio_length_in_s * self.unet.config.sample_rate

									SCREAMING_SNAKE_CASE_    			=			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}.'     )

									SCREAMING_SNAKE_CASE_    			=			int(SCREAMING_SNAKE_CASE_     )
									if sample_size % down_scale_factor != 0:
											SCREAMING_SNAKE_CASE_    			=			(
											    (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.'''     )
									SCREAMING_SNAKE_CASE_    			=			int(SCREAMING_SNAKE_CASE_     )

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

									SCREAMING_SNAKE_CASE_    			=			randn_tensor(SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_							,							device=self.device							,							dtype=SCREAMING_SNAKE_CASE_     )

									# set step values
									self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_							,							device=audio.device     )
									SCREAMING_SNAKE_CASE_    			=			self.scheduler.timesteps.to(SCREAMING_SNAKE_CASE_     )

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

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

									SCREAMING_SNAKE_CASE_    			=			audio.clamp(-1							,							1     ).float().cpu().numpy()

									SCREAMING_SNAKE_CASE_    			=			audio[:, :, :original_sample_size]

									if not return_dict:
											return (audio,)

									return AudioPipelineOutput(audios=SCREAMING_SNAKE_CASE_     )

"""simple docstring"""




import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 			snake_case  (				__lowercase							,      unittest.TestCase ):
							UpperCAmelCase__      =   None
							UpperCAmelCase__      =   BloomTokenizerFast
							UpperCAmelCase__      =   BloomTokenizerFast
							UpperCAmelCase__      =   True
							UpperCAmelCase__      =   False
							UpperCAmelCase__      =   '''tokenizer_file'''
							UpperCAmelCase__      =   {'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}

							def _lowercase       (self     ):

									"""simple docstring"""







									super().setUp()
									SCREAMING_SNAKE_CASE_    			=			BloomTokenizerFast.from_pretrained('''bigscience/tokenizer'''     )
									tokenizer.save_pretrained(self.tmpdirname     )

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									kwargs.update(self.special_tokens_map     )
									return BloomTokenizerFast.from_pretrained(self.tmpdirname							,							**SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.get_rust_tokenizer()

									SCREAMING_SNAKE_CASE_    			=			['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
									SCREAMING_SNAKE_CASE_    			=			[[21_75, 2_37_14, 7_31_73, 14_42_52, 2], [77, 13_26_19, 34_78, 3_68, 10_95_86, 3_54_33, 2]]

									SCREAMING_SNAKE_CASE_    			=			tokenizer.batch_encode_plus(SCREAMING_SNAKE_CASE_     )['''input_ids''']
									self.assertListEqual(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     )
									self.assertListEqual(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_=6     ):

									"""simple docstring"""







									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'     ):
													SCREAMING_SNAKE_CASE_    			=			self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )
													# tokenizer_r.pad_token = None # Hotfixing padding = None
													# Simple input
													SCREAMING_SNAKE_CASE_    			=			'''This is a simple input'''
													SCREAMING_SNAKE_CASE_    			=			['''This is a simple input 1''', '''This is a simple input 2''']
													SCREAMING_SNAKE_CASE_    			=			('''This is a simple input''', '''This is a pair''')
													SCREAMING_SNAKE_CASE_    			=			[
													    ('''This is a simple input 1''', '''This is a simple input 2'''),
													    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
													]

													# Simple input tests
													try:
															tokenizer_r.encode(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )
															tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )

															tokenizer_r.batch_encode_plus(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )
															tokenizer_r.encode(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )
															tokenizer_r.batch_encode_plus(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )
													except ValueError:
															self.fail('''Bloom Tokenizer should be able to deal with padding'''     )

													SCREAMING_SNAKE_CASE_    			=			None  # Hotfixing padding = None
													self.assertRaises(SCREAMING_SNAKE_CASE_							,							tokenizer_r.encode							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''     )

													# Simple input
													self.assertRaises(SCREAMING_SNAKE_CASE_							,							tokenizer_r.encode_plus							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''     )

													# Simple input
													self.assertRaises(
													    SCREAMING_SNAKE_CASE_							,							tokenizer_r.batch_encode_plus							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''							,							)

													# Pair input
													self.assertRaises(SCREAMING_SNAKE_CASE_							,							tokenizer_r.encode							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''     )

													# Pair input
													self.assertRaises(SCREAMING_SNAKE_CASE_							,							tokenizer_r.encode_plus							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''     )

													# Pair input
													self.assertRaises(
													    SCREAMING_SNAKE_CASE_							,							tokenizer_r.batch_encode_plus							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''							,							)

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.get_rust_tokenizer()
									SCREAMING_SNAKE_CASE_    			=			load_dataset('''xnli'''							,							'''all_languages'''							,							split='''test'''							,							streaming=SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			next(iter(SCREAMING_SNAKE_CASE_     )     )['''premise''']  # pick up one data
									SCREAMING_SNAKE_CASE_    			=			list(sample_data.values()     )

									SCREAMING_SNAKE_CASE_    			=			list(map(tokenizer.encode							,							SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			[tokenizer.decode(SCREAMING_SNAKE_CASE_							,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_     ) for x in output_tokens]
									self.assertListEqual(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )







							def _lowercase       (self     ):

									"""simple docstring"""







									self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map     )							,							1     )
									self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()     )[0]     )							,							1     )