
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler


@dataclass
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							0
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							3.0


class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											self.assertDictEqual(MockClass().to_kwargs()						,				{}     )
											self.assertDictEqual(MockClass(a=2     ).to_kwargs()						,				{'a': 2}     )
											self.assertDictEqual(MockClass(a=2						,				b=A_     ).to_kwargs()						,				{'a': 2, 'b': True}     )
											self.assertDictEqual(MockClass(a=2						,				c=2.25     ).to_kwargs()						,				{'a': 2, 'c': 2.25}     )







				@require_cuda
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     GradScalerKwargs(init_scale=1024						,				growth_factor=2     )
											AcceleratorState._reset_state()
											UpperCamelCase   			=     Accelerator(mixed_precision='fp16'						,				kwargs_handlers=[scaler_handler]     )
											print(accelerator.use_fpaa     )
											UpperCamelCase   			=     accelerator.scaler

											# Check the kwargs have been applied
											self.assertEqual(scaler._init_scale						,				1_024.0     )
											self.assertEqual(scaler._growth_factor						,				2.0     )

											# Check the other values are at the default
											self.assertEqual(scaler._backoff_factor						,				0.5     )
											self.assertEqual(scaler._growth_interval						,				2000     )
											self.assertEqual(scaler._enabled						,				A_     )







				@require_multi_gpu
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__     )]
											execute_subprocess_async(A_						,				env=os.environ.copy()     )


if __name__ == "__main__":
						lowerCAmelCase					:    Tuple           = DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
						lowerCAmelCase					:    List[str]           = Accelerator(kwargs_handlers=[ddp_scaler])
						lowerCAmelCase					:    List[Any]           = torch.nn.Linear(1_00, 2_00)
						lowerCAmelCase					:    int           = accelerator.prepare(model)

						# Check the values changed in kwargs
						lowerCAmelCase					:    Dict           = ''
						lowerCAmelCase					:    Dict           = model.bucket_bytes_cap // (10_24 * 10_24)
						if observed_bucket_cap_map != 15:
												error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
						if model.find_unused_parameters is not True:
												error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

						# Check the values of the defaults
						if model.dim != 0:
												error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
						if model.broadcast_buffers is not True:
												error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
						if model.gradient_as_bucket_view is not False:
												error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

						# Raise error at the end to make sure we don't stop at the first failure.
						if len(error_msg) > 0:
												raise ValueError(error_msg)






'''simple docstring'''
import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase					:    int           = logging.get_logger(__name__)

lowerCAmelCase					:    Tuple           = {
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""unispeech-sat"""







				def __init__(       self						,				A_=32						,				A_=768						,				A_=12						,				A_=12						,				A_=3072						,				A_="gelu"						,				A_=0.1						,				A_=0.1						,				A_=0.1						,				A_=0.0						,				A_=0.0						,				A_=0.1						,				A_=0.1						,				A_=0.02						,				A_=1e-5						,				A_="group"						,				A_="gelu"						,				A_=(512, 512, 512, 512, 512, 512, 512)						,				A_=(5, 2, 2, 2, 2, 2, 2)						,				A_=(10, 3, 3, 3, 3, 2, 2)						,				A_=False						,				A_=128						,				A_=16						,				A_=False						,				A_=True						,				A_=0.05						,				A_=10						,				A_=2						,				A_=0.0						,				A_=10						,				A_=0						,				A_=320						,				A_=2						,				A_=0.1						,				A_=100						,				A_=256						,				A_=256						,				A_=0.1						,				A_="mean"						,				A_=False						,				A_=False						,				A_=256						,				A_=(512, 512, 512, 512, 1500)						,				A_=(5, 3, 3, 1, 1)						,				A_=(1, 2, 3, 1, 1)						,				A_=512						,				A_=0						,				A_=1						,				A_=2						,				A_=504						,				**A_						,				)->		Tuple:







											'''simple docstring'''






											super().__init__(**A_						,				pad_token_id=A_						,				bos_token_id=A_						,				eos_token_id=A_     )
											UpperCamelCase   			=     hidden_size
											UpperCamelCase   			=     feat_extract_norm
											UpperCamelCase   			=     feat_extract_activation
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     conv_bias
											UpperCamelCase   			=     num_conv_pos_embeddings
											UpperCamelCase   			=     num_conv_pos_embedding_groups
											UpperCamelCase   			=     len(self.conv_dim     )
											UpperCamelCase   			=     num_hidden_layers
											UpperCamelCase   			=     intermediate_size
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     num_attention_heads
											UpperCamelCase   			=     hidden_dropout
											UpperCamelCase   			=     attention_dropout
											UpperCamelCase   			=     activation_dropout
											UpperCamelCase   			=     feat_proj_dropout
											UpperCamelCase   			=     final_dropout
											UpperCamelCase   			=     layerdrop
											UpperCamelCase   			=     layer_norm_eps
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     num_clusters
											UpperCamelCase   			=     do_stable_layer_norm
											UpperCamelCase   			=     use_weighted_layer_sum

											if (
											    (len(self.conv_stride     ) != self.num_feat_extract_layers)
											    or (len(self.conv_kernel     ) != self.num_feat_extract_layers)
											    or (len(self.conv_dim     ) != self.num_feat_extract_layers)
											):
																		raise ValueError(
																		    'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
																		    ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
																		    F''' {len(self.conv_dim     )}`, `len(config.conv_stride) = {len(self.conv_stride     )}`,'''
																		    F''' `len(config.conv_kernel) = {len(self.conv_kernel     )}`.'''     )

											# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
											UpperCamelCase   			=     apply_spec_augment
											UpperCamelCase   			=     mask_time_prob
											UpperCamelCase   			=     mask_time_length
											UpperCamelCase   			=     mask_time_min_masks
											UpperCamelCase   			=     mask_feature_prob
											UpperCamelCase   			=     mask_feature_length
											UpperCamelCase   			=     mask_feature_min_masks

											# parameters for pretraining with codevector quantized representations
											UpperCamelCase   			=     num_codevectors_per_group
											UpperCamelCase   			=     num_codevector_groups
											UpperCamelCase   			=     contrastive_logits_temperature
											UpperCamelCase   			=     feat_quantizer_dropout
											UpperCamelCase   			=     num_negatives
											UpperCamelCase   			=     codevector_dim
											UpperCamelCase   			=     proj_codevector_dim
											UpperCamelCase   			=     diversity_loss_weight

											# ctc loss
											UpperCamelCase   			=     ctc_loss_reduction
											UpperCamelCase   			=     ctc_zero_infinity

											# SequenceClassification-specific parameter. Feel free to ignore for other classes.
											UpperCamelCase   			=     classifier_proj_size

											# XVector-specific parameters. Feel free to ignore for other classes.
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     xvector_output_dim







				@property
				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											return functools.reduce(operator.mul						,				self.conv_stride						,				1     )




