


"""simple docstring"""
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				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=10				)    ->				Optional[int]:
							snake_case_           =		[]
							for _ in range(_SCREAMING_SNAKE_CASE				):
														lrs.append(scheduler.get_lr()[0]				)
														scheduler.step()
							return lrs







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

																												snake_case_           =		torch.load(_SCREAMING_SNAKE_CASE				)
																												scheduler.load_state_dict(_SCREAMING_SNAKE_CASE				)
							return lrs





@require_torch
class      __A     (unittest.TestCase):





							'''simple docstring'''

							def 		lowerCAmelCase       (				self							:      Optional[Any]		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      str       )					->Optional[Any]:



														"""simple docstring"""



														self.assertEqual(len(UpperCAmelCase_       )		,       len(UpperCAmelCase_       )       )
														for a, b in zip(UpperCAmelCase_		,       UpperCAmelCase_       ):
																					self.assertAlmostEqual(UpperCAmelCase_		,       UpperCAmelCase_		,       delta=UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		torch.tensor([0.1, -0.2, -0.1]		,       requires_grad=UpperCAmelCase_       )
														snake_case_           =		torch.tensor([0.4, 0.2, -0.5]       )
														snake_case_           =		nn.MSELoss()
														# No warmup, constant schedule, no gradient clipping
														snake_case_           =		AdamW(params=[w]		,       lr=2E-1		,       weight_decay=0.0       )
														for _ in range(100       ):
																					snake_case_           =		criterion(UpperCAmelCase_		,       UpperCAmelCase_       )
																					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							:      Dict       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		torch.tensor([0.1, -0.2, -0.1]		,       requires_grad=UpperCAmelCase_       )
														snake_case_           =		torch.tensor([0.4, 0.2, -0.5]       )
														snake_case_           =		nn.MSELoss()
														# No warmup, constant schedule, no gradient clipping
														snake_case_           =		Adafactor(
														    params=[w]		,       lr=1E-2		,       eps=(1E-30, 1E-3)		,       clip_threshold=1.0		,       decay_rate=-0.8		,       betaa=UpperCAmelCase_		,       weight_decay=0.0		,       relative_step=UpperCAmelCase_		,       scale_parameter=UpperCAmelCase_		,       warmup_init=UpperCAmelCase_		,       )
														for _ in range(1_000       ):
																					snake_case_           =		criterion(UpperCAmelCase_		,       UpperCAmelCase_       )
																					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      __A     (unittest.TestCase):





							'''simple docstring'''

							__lowercase:		int							      =    nn.Linear(50						,		50) if is_torch_available() else None
							__lowercase:		Tuple							      =    AdamW(m.parameters()						,		lr=1_0.0) if is_torch_available() else None
							__lowercase:		int							      =    10



							def 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      Any=None       )					->Union[str, Any]:



														"""simple docstring"""



														self.assertEqual(len(UpperCAmelCase_       )		,       len(UpperCAmelCase_       )       )
														for a, b in zip(UpperCAmelCase_		,       UpperCAmelCase_       ):
																					self.assertAlmostEqual(UpperCAmelCase_		,       UpperCAmelCase_		,       delta=UpperCAmelCase_		,       msg=UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Any:



														"""simple docstring"""



														snake_case_           =		{"""num_warmup_steps""": 2, """num_training_steps""": 10}
														# schedulers doct format
														# function: (sched_args_dict, expected_learning_rates)
														snake_case_           =		{
														    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():
																					snake_case_       ,						snake_case_           =		data

																					snake_case_           =		scheduler_func(self.optimizer		,       **UpperCAmelCase_       )
																					self.assertEqual(len([scheduler.get_lr()[0]]       )		,       1       )
																					snake_case_           =		unwrap_schedule(UpperCAmelCase_		,       self.num_steps       )
																					self.assertListAlmostEqual(
																					    UpperCAmelCase_		,       UpperCAmelCase_		,       tol=1E-2		,       msg=F"""failed for {scheduler_func} in normal scheduler"""		,       )

																					snake_case_           =		scheduler_func(self.optimizer		,       **UpperCAmelCase_       )
																					if scheduler_func.__name__ != "get_constant_schedule":
																												LambdaScheduleWrapper.wrap_scheduler(UpperCAmelCase_       )  # wrap to test picklability of the schedule
																					snake_case_           =		unwrap_and_save_reload_schedule(UpperCAmelCase_		,       self.num_steps       )
																					self.assertListEqual(UpperCAmelCase_		,       UpperCAmelCase_		,       msg=F"""failed for {scheduler_func} in save and reload"""       )





class      __A     :





							'''simple docstring'''

							def __init__(				self							:      str		,       UpperCAmelCase_							:      List[str]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		fn



							def __call__(				self							:      Optional[Any]		,       *UpperCAmelCase_							:      Dict		,       **UpperCAmelCase_							:      int       )					->str:



														"""simple docstring"""



														return self.fn(*UpperCAmelCase_		,       **UpperCAmelCase_       )



							@classmethod
							def 		lowerCAmelCase       (				self							:      Optional[Any]		,       UpperCAmelCase_							:      Dict       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		list(map(self		,       scheduler.lr_lambdas       )       )







"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging


__SCREAMING_SNAKE_CASE							:    str		=    logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:    str		=    {
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}





class      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		Any							      =    """mctct"""



							def __init__(				self							:      Dict		,       UpperCAmelCase_							:      List[Any]=8_065		,       UpperCAmelCase_							:      Tuple=1_536		,       UpperCAmelCase_							:      Optional[Any]=36		,       UpperCAmelCase_							:      int=6_144		,       UpperCAmelCase_							:      Dict=4		,       UpperCAmelCase_							:      Any=384		,       UpperCAmelCase_							:      List[str]=920		,       UpperCAmelCase_							:      Any=1E-5		,       UpperCAmelCase_							:      Any=0.3		,       UpperCAmelCase_							:      Tuple="relu"		,       UpperCAmelCase_							:      Union[str, Any]=0.02		,       UpperCAmelCase_							:      Dict=0.3		,       UpperCAmelCase_							:      str=0.3		,       UpperCAmelCase_							:      Any=1		,       UpperCAmelCase_							:      Any=0		,       UpperCAmelCase_							:      int=2		,       UpperCAmelCase_							:      str=1		,       UpperCAmelCase_							:      Tuple=0.3		,       UpperCAmelCase_							:      Optional[Any]=1		,       UpperCAmelCase_							:      Optional[Any]=(7,)		,       UpperCAmelCase_							:      Optional[Any]=(3,)		,       UpperCAmelCase_							:      List[str]=80		,       UpperCAmelCase_							:      Tuple=1		,       UpperCAmelCase_							:      List[Any]=None		,       UpperCAmelCase_							:      List[str]="sum"		,       UpperCAmelCase_							:      Union[str, Any]=False		,       **UpperCAmelCase_							:      Any		,       )					->Dict:



														"""simple docstring"""



														super().__init__(**UpperCAmelCase_		,       pad_token_id=UpperCAmelCase_		,       bos_token_id=UpperCAmelCase_		,       eos_token_id=UpperCAmelCase_       )
														snake_case_           =		vocab_size
														snake_case_           =		hidden_size
														snake_case_           =		num_hidden_layers
														snake_case_           =		intermediate_size
														snake_case_           =		num_attention_heads
														snake_case_           =		attention_head_dim
														snake_case_           =		max_position_embeddings
														snake_case_           =		layer_norm_eps
														snake_case_           =		layerdrop
														snake_case_           =		hidden_act
														snake_case_           =		initializer_range
														snake_case_           =		hidden_dropout_prob
														snake_case_           =		attention_probs_dropout_prob
														snake_case_           =		pad_token_id
														snake_case_           =		bos_token_id
														snake_case_           =		eos_token_id
														snake_case_           =		conv_glu_dim
														snake_case_           =		conv_dropout
														snake_case_           =		num_conv_layers
														snake_case_           =		input_feat_per_channel
														snake_case_           =		input_channels
														snake_case_           =		conv_channels
														snake_case_           =		ctc_loss_reduction
														snake_case_           =		ctc_zero_infinity

														# prevents config testing fail with exporting to json
														snake_case_           =		list(UpperCAmelCase_       )
														snake_case_           =		list(UpperCAmelCase_       )

														if len(self.conv_kernel       ) != self.num_conv_layers:
																					raise ValueError(
																					    """Configuration for convolutional module is incorrect. """
																					    """It is required that `len(config.conv_kernel)` == `config.num_conv_layers` """
																					    F"""but is `len(config.conv_kernel) = {len(self.conv_kernel       )}`, """
																					    F"""`config.num_conv_layers = {self.num_conv_layers}`."""       )



