
import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class        lowerCAmelCase__      (   __lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: Tuple	=		BioGptTokenizer
						UpperCamelCase_							: Dict	=		False

						def  A_				(			self				)					->      int:


								'''simple docstring'''

								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								_UpperCamelCase						    =	[
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """w</w>""",
								    """r</w>""",
								    """t</w>""",
								    """lo""",
								    """low""",
								    """er</w>""",
								    """low</w>""",
								    """lowest</w>""",
								    """newer</w>""",
								    """wider</w>""",
								    """<unk>""",
								]
								_UpperCamelCase						    =	dict(zip(a       ,							range(len(a				)				)				)				)
								_UpperCamelCase						    =	["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

								_UpperCamelCase						    =	os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES["""vocab_file"""]				)
								_UpperCamelCase						    =	os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES["""merges_file"""]				)
								with open(self.vocab_file       ,							"""w"""				) as fp:
										fp.write(json.dumps(a				)				)
								with open(self.merges_file       ,							"""w"""				) as fp:
										fp.write("""\n""".join(a				)				)

						def  A_				(			self       ,							a				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	"""lower newer"""
								_UpperCamelCase						    =	"""lower newer"""
								return input_text, output_text

						def  A_				(			self				)					->      Union[str, Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	BioGptTokenizer(self.vocab_file       ,							self.merges_file				)

								_UpperCamelCase						    =	"""lower"""
								_UpperCamelCase						    =	["""low""", """er</w>"""]
								_UpperCamelCase						    =	tokenizer.tokenize(a				)
								self.assertListEqual(a       ,							a				)

								_UpperCamelCase						    =	tokens + ["""<unk>"""]
								_UpperCamelCase						    =	[14, 15, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(a				)       ,							a				)







						@slow
						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	BioGptTokenizer.from_pretrained("""microsoft/biogpt"""				)

								_UpperCamelCase						    =	tokenizer.encode("""sequence builders"""       ,							add_special_tokens=a				)
								_UpperCamelCase						    =	tokenizer.encode("""multi-sequence build"""       ,							add_special_tokens=a				)

								_UpperCamelCase						    =	tokenizer.build_inputs_with_special_tokens(a				)
								_UpperCamelCase						    =	tokenizer.build_inputs_with_special_tokens(a       ,							a				)

								self.assertTrue(encoded_sentence == [2] + text				)
								self.assertTrue(encoded_pair == [2] + text + [2] + text_a				)





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__      (   __lowercase	):

						def __init__(			self       ,							a       ,							a				)					->      Any:


								'''simple docstring'''

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







						@torch.no_grad()
						def __call__(			self       ,							a = 1       ,							a = 1_00       ,							a = None       ,							a = None       ,							a = True       ,							)					->      Union[AudioPipelineOutput, Tuple]:


								'''simple docstring'''

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

								_UpperCamelCase						    =	audio_length_in_s * self.unet.config.sample_rate

								_UpperCamelCase						    =	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						    =	int(a				)
								if sample_size % down_scale_factor != 0:
										_UpperCamelCase						    =	(
										    (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						    =	int(a				)

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

								_UpperCamelCase						    =	randn_tensor(a       ,							generator=a       ,							device=self.device       ,							dtype=a				)

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

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

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

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

								_UpperCamelCase						    =	audio[:, :, :original_sample_size]

								if not return_dict:
										return (audio,)

								return AudioPipelineOutput(audios=a				)



