'''simple docstring'''




import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = (IPNDMScheduler,)
			UpperCamelCase__       = (('''num_inference_steps''', 50),)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    **lowercase_       :						Dict					):
										lowercase_      :    Tuple       		= {"""num_train_timesteps""": 1000}
										config.update(**lowercase_					)
										return config







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						Optional[Any]=0       ,    **lowercase_       :						Any					):
										lowercase_      :    Union[str, Any]       		= dict(self.forward_default_kwargs					)
										lowercase_      :    List[Any]       		= kwargs.pop("""num_inference_steps"""       ,    lowercase_					)
										lowercase_      :    Any       		= self.dummy_sample
										lowercase_      :    Optional[Any]       		= 0.1 * sample
										lowercase_      :    Tuple       		= [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
																	lowercase_      :    Optional[Any]       		= self.get_scheduler_config(**lowercase_					)
																	lowercase_      :    List[str]       		= scheduler_class(**lowercase_					)
																	scheduler.set_timesteps(lowercase_					)
																	# copy over dummy past residuals
																	lowercase_      :    Optional[int]       		= dummy_past_residuals[:]

																	if time_step is None:
																								lowercase_      :    List[str]       		= scheduler.timesteps[len(scheduler.timesteps					) // 2]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(lowercase_					)
																								lowercase_      :    Any       		= scheduler_class.from_pretrained(lowercase_					)
																								new_scheduler.set_timesteps(lowercase_					)
																								# copy over dummy past residuals
																								lowercase_      :    Dict       		= dummy_past_residuals[:]

																	lowercase_      :    List[str]       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample
																	lowercase_      :    Any       		= new_scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample

																	assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"

																	lowercase_      :    str       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample
																	lowercase_      :    Dict       		= new_scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample

																	assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										pass







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    lowercase_       :						int=0       ,    **lowercase_       :						Any					):
										lowercase_      :    Any       		= dict(self.forward_default_kwargs					)
										lowercase_      :    Dict       		= kwargs.pop("""num_inference_steps"""       ,    lowercase_					)
										lowercase_      :    Tuple       		= self.dummy_sample
										lowercase_      :    Optional[Any]       		= 0.1 * sample
										lowercase_      :    str       		= [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
																	lowercase_      :    Any       		= self.get_scheduler_config()
																	lowercase_      :    Any       		= scheduler_class(**lowercase_					)
																	scheduler.set_timesteps(lowercase_					)

																	# copy over dummy past residuals (must be after setting timesteps)
																	lowercase_      :    Any       		= dummy_past_residuals[:]

																	if time_step is None:
																								lowercase_      :    int       		= scheduler.timesteps[len(scheduler.timesteps					) // 2]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(lowercase_					)
																								lowercase_      :    List[Any]       		= scheduler_class.from_pretrained(lowercase_					)
																								# copy over dummy past residuals
																								new_scheduler.set_timesteps(lowercase_					)

																								# copy over dummy past residual (must be after setting timesteps)
																								lowercase_      :    Union[str, Any]       		= dummy_past_residuals[:]

																	lowercase_      :    int       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample
																	lowercase_      :    Dict       		= new_scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample

																	assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"

																	lowercase_      :    Union[str, Any]       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample
																	lowercase_      :    Optional[Any]       		= new_scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample

																	assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    **lowercase_       :						List[Any]					):
										lowercase_      :    str       		= self.scheduler_classes[0]
										lowercase_      :    str       		= self.get_scheduler_config(**lowercase_					)
										lowercase_      :    Dict       		= scheduler_class(**lowercase_					)

										lowercase_      :    List[Any]       		= 10
										lowercase_      :    int       		= self.dummy_model()
										lowercase_      :    Any       		= self.dummy_sample_deter
										scheduler.set_timesteps(lowercase_					)

										for i, t in enumerate(scheduler.timesteps					):
																	lowercase_      :    str       		= model(lowercase_       ,    lowercase_					)
																	lowercase_      :    str       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_					).prev_sample

										for i, t in enumerate(scheduler.timesteps					):
																	lowercase_      :    Dict       		= model(lowercase_       ,    lowercase_					)
																	lowercase_      :    Dict       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_					).prev_sample

										return sample







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										lowercase_      :    Optional[int]       		= dict(self.forward_default_kwargs					)

										lowercase_      :    Union[str, Any]       		= kwargs.pop("""num_inference_steps"""       ,    lowercase_					)

										for scheduler_class in self.scheduler_classes:
																	lowercase_      :    Optional[int]       		= self.get_scheduler_config()
																	lowercase_      :    Tuple       		= scheduler_class(**lowercase_					)

																	lowercase_      :    List[str]       		= self.dummy_sample
																	lowercase_      :    Dict       		= 0.1 * sample

																	if num_inference_steps is not None and hasattr(lowercase_       ,    """set_timesteps"""					):
																								scheduler.set_timesteps(lowercase_					)
																	elif num_inference_steps is not None and not hasattr(lowercase_       ,    """set_timesteps"""					):
																								lowercase_      :    List[Any]       		= num_inference_steps

																	# copy over dummy past residuals (must be done after set_timesteps)
																	lowercase_      :    List[Any]       		= [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
																	lowercase_      :    Tuple       		= dummy_past_residuals[:]

																	lowercase_      :    int       		= scheduler.timesteps[5]
																	lowercase_      :    List[Any]       		= scheduler.timesteps[6]

																	lowercase_      :    str       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample
																	lowercase_      :    List[Any]       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample

																	self.assertEqual(output_a.shape       ,    sample.shape					)
																	self.assertEqual(output_a.shape       ,    output_a.shape					)

																	lowercase_      :    Dict       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample
																	lowercase_      :    List[Any]       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					).prev_sample

																	self.assertEqual(output_a.shape       ,    sample.shape					)
																	self.assertEqual(output_a.shape       ,    output_a.shape					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										for timesteps in [100, 1000]:
																	self.check_over_configs(num_train_timesteps=lowercase_       ,    time_step=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										for t, num_inference_steps in zip([1, 5, 10]       ,    [10, 50, 100]					):
																	self.check_over_forward(num_inference_steps=lowercase_       ,    time_step=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										lowercase_      :    Optional[Any]       		= self.full_loop()
										lowercase_      :    str       		= torch.mean(torch.abs(lowercase_					)					)

										assert abs(result_mean.item() - 2540529					) < 10






'''simple docstring'''




import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow





@require_torch
class 					__magic_name__		(					unittest.TestCase):







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    List[Any]       		= """ylacombe/bark-small"""
										lowercase_      :    List[str]       		= tempfile.mkdtemp()
										lowercase_      :    Tuple       		= """en_speaker_1"""
										lowercase_      :    Union[str, Any]       		= """This is a test string"""
										lowercase_      :    int       		= """speaker_embeddings_path.json"""
										lowercase_      :    Any       		= """speaker_embeddings"""







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    **lowercase_       :						Optional[int]					):
										return AutoTokenizer.from_pretrained(self.checkpoint       ,    **lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										shutil.rmtree(self.tmpdirname					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										lowercase_      :    Any       		= self.get_tokenizer()

										lowercase_      :    Optional[Any]       		= BarkProcessor(tokenizer=lowercase_					)

										processor.save_pretrained(self.tmpdirname					)
										lowercase_      :    Union[str, Any]       		= BarkProcessor.from_pretrained(self.tmpdirname					)

										self.assertEqual(processor.tokenizer.get_vocab()       ,    tokenizer.get_vocab()					)







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    Any       		= BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint       ,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,    )
										processor.save_pretrained(
										    self.tmpdirname       ,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,    speaker_embeddings_directory=self.speaker_embeddings_directory       ,    )

										lowercase_      :    List[str]       		= self.get_tokenizer(bos_token="""(BOS)"""       ,    eos_token="""(EOS)"""					)

										lowercase_      :    Optional[Any]       		= BarkProcessor.from_pretrained(
										    self.tmpdirname       ,    self.speaker_embeddings_dict_path       ,    bos_token="""(BOS)"""       ,    eos_token="""(EOS)"""       ,    )

										self.assertEqual(processor.tokenizer.get_vocab()       ,    tokenizer_add_kwargs.get_vocab()					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										lowercase_      :    Optional[int]       		= BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint       ,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,    )

										lowercase_      :    Optional[int]       		= 35
										lowercase_      :    int       		= 2
										lowercase_      :    Union[str, Any]       		= 8

										lowercase_      :    Union[str, Any]       		= {
										    """semantic_prompt""": np.ones(lowercase_					),
										    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)					),
										    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)					),
										}

										# test providing already loaded voice_preset
										lowercase_      :    str       		= processor(text=self.input_string       ,    voice_preset=lowercase_					)

										lowercase_      :    Dict       		= inputs["""history_prompt"""]
										for key in voice_preset:
																	self.assertListEqual(voice_preset[key].tolist()       ,    processed_voice_preset.get(lowercase_       ,    np.array([]					)					).tolist()					)

										# test loading voice preset from npz file
										lowercase_      :    Any       		= os.path.join(self.tmpdirname       ,    """file.npz"""					)
										np.savez(lowercase_       ,    **lowercase_					)
										lowercase_      :    Optional[Any]       		= processor(text=self.input_string       ,    voice_preset=lowercase_					)
										lowercase_      :    List[Any]       		= inputs["""history_prompt"""]

										for key in voice_preset:
																	self.assertListEqual(voice_preset[key].tolist()       ,    processed_voice_preset.get(lowercase_       ,    np.array([]					)					).tolist()					)

										# test loading voice preset from the hub
										lowercase_      :    Union[str, Any]       		= processor(text=self.input_string       ,    voice_preset=self.voice_preset					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										lowercase_      :    List[str]       		= self.get_tokenizer()

										lowercase_      :    int       		= BarkProcessor(tokenizer=lowercase_					)

										lowercase_      :    Any       		= processor(text=self.input_string					)

										lowercase_      :    List[str]       		= tokenizer(
										    self.input_string       ,    padding="""max_length"""       ,    max_length=256       ,    add_special_tokens=lowercase_       ,    return_attention_mask=lowercase_       ,    return_token_type_ids=lowercase_       ,    )

										for key in encoded_tok.keys():
																	self.assertListEqual(encoded_tok[key]       ,    encoded_processor[key].squeeze().tolist()					)





