
import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
							import torch
							from torch import nn

							from transformers import (
							    Adafactor,
							    AdamW,
							    get_constant_schedule,
							    get_constant_schedule_with_warmup,
							    get_cosine_schedule_with_warmup,
							    get_cosine_with_hard_restarts_schedule_with_warmup,
							    get_inverse_sqrt_schedule,
							    get_linear_schedule_with_warmup,
							    get_polynomial_decay_schedule_with_warmup,
							)







def        A__					(						lowerCamelCase     ,    lowerCamelCase=10						)  ->	List[str]:
				UpperCamelCase_:							Dict	=						[]
				for _ in range(lowerCamelCase						):
								lrs.append(scheduler.get_lr()[0]						)
								scheduler.step()
				return lrs







def        A__					(						lowerCamelCase     ,    lowerCamelCase=10						)  ->	Optional[int]:
				UpperCamelCase_:							Tuple	=						[]
				for step in range(lowerCamelCase						):
								lrs.append(scheduler.get_lr()[0]						)
								scheduler.step()
								if step == num_steps // 2:
												with tempfile.TemporaryDirectory() as tmpdirname:
																UpperCamelCase_:							List[str]	=						os.path.join(lowerCamelCase     ,    """schedule.bin"""						)
																torch.save(scheduler.state_dict()     ,    lowerCamelCase						)

																UpperCamelCase_:							int	=						torch.load(lowerCamelCase						)
																scheduler.load_state_dict(lowerCamelCase						)
				return lrs

@require_torch
class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       Tuple						,	snake_case_ :       Any						,	snake_case_ :       Optional[int]      ):
							self.assertEqual(len(snake_case_      )						,	len(snake_case_      )      )
							for a, b in zip(snake_case_						,	snake_case_      ):
											self.assertAlmostEqual(snake_case_						,	snake_case_						,	delta=snake_case_      )

			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Tuple	=						torch.tensor([0.1, -0.2, -0.1]						,	requires_grad=snake_case_      )
							UpperCamelCase_:							int	=						torch.tensor([0.4, 0.2, -0.5]      )
							UpperCamelCase_:							int	=						nn.MSELoss()
							# No warmup, constant schedule, no gradient clipping
							UpperCamelCase_:							Union[str, Any]	=						AdamW(params=[w]						,	lr=2e-1						,	weight_decay=0.0      )
							for _ in range(100      ):
											UpperCamelCase_:							List[Any]	=						criterion(snake_case_						,	snake_case_      )
											loss.backward()
											optimizer.step()
											w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
											w.grad.zero_()
							self.assertListAlmostEqual(w.tolist()						,	[0.4, 0.2, -0.5]						,	tol=1e-2      )




			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							Optional[int]	=						torch.tensor([0.1, -0.2, -0.1]						,	requires_grad=snake_case_      )
							UpperCamelCase_:							Tuple	=						torch.tensor([0.4, 0.2, -0.5]      )
							UpperCamelCase_:							Union[str, Any]	=						nn.MSELoss()
							# No warmup, constant schedule, no gradient clipping
							UpperCamelCase_:							List[Any]	=						Adafactor(
							    params=[w]						,	lr=1e-2						,	eps=(1e-30, 1e-3)						,	clip_threshold=1.0						,	decay_rate=-0.8						,	betaa=snake_case_						,	weight_decay=0.0						,	relative_step=snake_case_						,	scale_parameter=snake_case_						,	warmup_init=snake_case_						,	)
							for _ in range(1000      ):
											UpperCamelCase_:							Tuple	=						criterion(snake_case_						,	snake_case_      )
											loss.backward()
											optimizer.step()
											w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
											w.grad.zero_()
							self.assertListAlmostEqual(w.tolist()						,	[0.4, 0.2, -0.5]						,	tol=1e-2      )




@require_torch
class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						int					      = nn.Linear(50     ,       50  ) if is_torch_available() else None
			__UpperCamelCase      :						Union[str, Any]					      = AdamW(m.parameters()     ,       lr=10.0  ) if is_torch_available() else None
			__UpperCamelCase      :						Any					      = 10

			def       lowerCAmelCase__	(       self :       str						,	snake_case_ :       int						,	snake_case_ :       Optional[Any]						,	snake_case_ :       Tuple						,	snake_case_ :       Dict=None      ):
							self.assertEqual(len(snake_case_      )						,	len(snake_case_      )      )
							for a, b in zip(snake_case_						,	snake_case_      ):
											self.assertAlmostEqual(snake_case_						,	snake_case_						,	delta=snake_case_						,	msg=snake_case_      )




			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							str	=						{"""num_warmup_steps""": 2, """num_training_steps""": 10}
							# schedulers doct format
							# function: (sched_args_dict, expected_learning_rates)
							UpperCamelCase_:							List[str]	=						{
							    get_constant_schedule: ({}, [10.0] * self.num_steps),
							    get_constant_schedule_with_warmup: (
							        {"""num_warmup_steps""": 4},
							        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
							    ),
							    get_linear_schedule_with_warmup: (
							        {**common_kwargs},
							        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
							    ),
							    get_cosine_schedule_with_warmup: (
							        {**common_kwargs},
							        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
							    ),
							    get_cosine_with_hard_restarts_schedule_with_warmup: (
							        {**common_kwargs, """num_cycles""": 2},
							        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
							    ),
							    get_polynomial_decay_schedule_with_warmup: (
							        {**common_kwargs, """power""": 2.0, """lr_end""": 1e-7},
							        [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
							    ),
							    get_inverse_sqrt_schedule: (
							        {"""num_warmup_steps""": 2},
							        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
							    ),
							}

							for scheduler_func, data in scheds.items():
											UpperCamelCase_,   UpperCamelCase_:							Optional[int]	=						data

											UpperCamelCase_:							int	=						scheduler_func(self.optimizer						,	**snake_case_      )
											self.assertEqual(len([scheduler.get_lr()[0]]      )						,	1      )
											UpperCamelCase_:							Any	=						unwrap_schedule(snake_case_						,	self.num_steps      )
											self.assertListAlmostEqual(
											    snake_case_						,	snake_case_						,	tol=1e-2						,	msg=f'''failed for {scheduler_func} in normal scheduler'''						,	)

											UpperCamelCase_:							Optional[int]	=						scheduler_func(self.optimizer						,	**snake_case_      )
											if scheduler_func.__name__ != "get_constant_schedule":
															LambdaScheduleWrapper.wrap_scheduler(snake_case_      )  # wrap to test picklability of the schedule
											UpperCamelCase_:							Union[str, Any]	=						unwrap_and_save_reload_schedule(snake_case_						,	self.num_steps      )
											self.assertListEqual(snake_case_						,	snake_case_						,	msg=f'''failed for {scheduler_func} in save and reload'''      )




class        _UpperCamelCase				:






			'''simple docstring'''

			def __init__(       self :       Dict						,	snake_case_ :       Optional[int]      ):
							UpperCamelCase_:							List[str]	=						fn

			def __call__(       self :       Union[str, Any]						,	*snake_case_ :       str						,	**snake_case_ :       Any      ):
							return self.fn(*snake_case_						,	**snake_case_      )




			@classmethod
			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       Optional[int]      ):
							UpperCamelCase_:							Any	=						list(map(self						,	scheduler.lr_lambdas      )      )








import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Union[str, Any]	=						"""laion/clap-htsat-unfused"""
							UpperCamelCase_:							List[str]	=						tempfile.mkdtemp()

			def       lowerCAmelCase__	(       self :       Tuple						,	**snake_case_ :       Optional[Any]      ):
							return RobertaTokenizer.from_pretrained(self.checkpoint						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       str						,	**snake_case_ :       Any      ):
							return ClapFeatureExtractor.from_pretrained(self.checkpoint						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							shutil.rmtree(self.tmpdirname      )

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Union[str, Any]	=						self.get_tokenizer()
							UpperCamelCase_:							int	=						self.get_feature_extractor()

							UpperCamelCase_:							List[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							processor.save_pretrained(self.tmpdirname      )
							UpperCamelCase_:							Any	=						ClapProcessor.from_pretrained(self.tmpdirname      )

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

							self.assertEqual(processor.feature_extractor.to_json_string()						,	feature_extractor.to_json_string()      )
							self.assertIsInstance(processor.feature_extractor						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Any	=						ClapProcessor(tokenizer=self.get_tokenizer()						,	feature_extractor=self.get_feature_extractor()      )
							processor.save_pretrained(self.tmpdirname      )

							UpperCamelCase_:							Optional[int]	=						self.get_tokenizer(bos_token="""(BOS)"""						,	eos_token="""(EOS)"""      )
							UpperCamelCase_:							Dict	=						self.get_feature_extractor(do_normalize=snake_case_						,	padding_value=1.0      )

							UpperCamelCase_:							List[str]	=						ClapProcessor.from_pretrained(
							    self.tmpdirname						,	bos_token="""(BOS)"""						,	eos_token="""(EOS)"""						,	do_normalize=snake_case_						,	padding_value=1.0      )

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

							self.assertEqual(processor.feature_extractor.to_json_string()						,	feature_extractor_add_kwargs.to_json_string()      )
							self.assertIsInstance(processor.feature_extractor						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							int	=						self.get_feature_extractor()
							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer()

							UpperCamelCase_:							Dict	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							Optional[Any]	=						floats_list((3, 1000)      )

							UpperCamelCase_:							List[str]	=						feature_extractor(snake_case_						,	return_tensors="""np"""      )
							UpperCamelCase_:							int	=						processor(audios=snake_case_						,	return_tensors="""np"""      )

							for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()						,	input_processor[key].sum()						,	delta=1e-2      )

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							List[Any]	=						self.get_feature_extractor()
							UpperCamelCase_:							List[str]	=						self.get_tokenizer()

							UpperCamelCase_:							List[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							Dict	=						"""This is a test string"""

							UpperCamelCase_:							Tuple	=						processor(text=snake_case_      )

							UpperCamelCase_:							Optional[int]	=						tokenizer(snake_case_      )

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

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							List[str]	=						self.get_feature_extractor()
							UpperCamelCase_:							Any	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							str	=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							UpperCamelCase_:							Tuple	=						processor.batch_decode(snake_case_      )
							UpperCamelCase_:							str	=						tokenizer.batch_decode(snake_case_      )

							self.assertListEqual(snake_case_						,	snake_case_      )




			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Any	=						self.get_feature_extractor()
							UpperCamelCase_:							str	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							self.assertListEqual(
							    processor.model_input_names[2:]						,	feature_extractor.model_input_names						,	msg="""`processor` and `feature_extractor` model input names do not match"""						,	)






