


'''simple docstring'''

import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


a_						:       List[Any]      			=				"""true"""





def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		Dict=82      ,       UpperCAmelCase_		:		str=16	):
	set_seed(42	)
	lowerCamelCase_  				=						RegressionModel()
	lowerCamelCase_  				=						deepcopy(lowerCAmelCase_	)
	lowerCamelCase_  				=						RegressionDataset(length=lowerCAmelCase_	)
	lowerCamelCase_  				=						DataLoader(lowerCAmelCase_      ,       batch_size=lowerCAmelCase_	)
	model.to(accelerator.device	)
	lowerCamelCase_  				=						accelerator.prepare(lowerCAmelCase_      ,       lowerCAmelCase_	)
	return model, ddp_model, dataloader





def __snake_case      (   UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Dict=False	):
	lowerCamelCase_  				=						AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"	)
	lowerCamelCase_  				=						load_dataset("glue"      ,       "mrpc"      ,       split="validation"	)

	def tokenize_function(UpperCAmelCase_		:		Optional[int]	):
		lowerCamelCase_  				=						tokenizer(examples["sentence1"]      ,       examples["sentence2"]      ,       truncation=lowerCAmelCase_      ,       max_length=lowerCAmelCase_	)
		return outputs

	with accelerator.main_process_first():
		lowerCamelCase_  				=						dataset.map(
		    lowerCAmelCase_      ,       batched=lowerCAmelCase_      ,       remove_columns=["idx", "sentence1", "sentence2"]      ,       )

	lowerCamelCase_  				=						tokenized_datasets.rename_column("label"      ,       "labels"	)

	def collate_fn(UpperCAmelCase_		:		List[str]	):
		if use_longest:
			return tokenizer.pad(lowerCAmelCase_      ,       padding="longest"      ,       return_tensors="pt"	)
		return tokenizer.pad(lowerCAmelCase_      ,       padding="max_length"      ,       max_length=128      ,       return_tensors="pt"	)

	return DataLoader(lowerCAmelCase_      ,       shuffle=lowerCAmelCase_      ,       collate_fn=lowerCAmelCase_      ,       batch_size=16	)





def __snake_case      (   UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		Any	):
	lowerCamelCase_  				=						Accelerator(dispatch_batches=lowerCAmelCase_      ,       split_batches=lowerCAmelCase_	)
	lowerCamelCase_  				=						get_dataloader(lowerCAmelCase_      ,       not dispatch_batches	)
	lowerCamelCase_  				=						AutoModelForSequenceClassification.from_pretrained(
	    "hf-internal-testing/mrpc-bert-base-cased"      ,       return_dict=lowerCAmelCase_	)
	lowerCamelCase_  				=						accelerator.prepare(lowerCAmelCase_      ,       lowerCAmelCase_	)
	return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def __snake_case      (   UpperCAmelCase_		:		Dict      ,       UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Optional[int]	):
	lowerCamelCase_  				=						[]
	for batch in dataloader:
		lowerCamelCase_  				=						batch.values()
		with torch.no_grad():
			lowerCamelCase_  				=						model(lowerCAmelCase_	)
			lowerCamelCase_  				=						accelerator.gather_for_metrics((logit, target)	)
			logits_and_targets.append((logit, target)	)
	lowerCamelCase_  				=						[], []
	for logit, targ in logits_and_targets:
		logits.append(lowerCAmelCase_	)
		targs.append(lowerCAmelCase_	)
	lowerCamelCase_  				=						torch.cat(lowerCAmelCase_	), torch.cat(lowerCAmelCase_	)
	return logits, targs





def __snake_case      (   UpperCAmelCase_		:		Optional[Any]      ,       UpperCAmelCase_		:		Any=82      ,       UpperCAmelCase_		:		Optional[int]=False      ,       UpperCAmelCase_		:		Any=False      ,       UpperCAmelCase_		:		Tuple=16	):
	lowerCamelCase_  				=						get_basic_setup(lowerCAmelCase_      ,       lowerCAmelCase_      ,       lowerCAmelCase_	)
	lowerCamelCase_  				=						generate_predictions(lowerCAmelCase_      ,       lowerCAmelCase_      ,       lowerCAmelCase_	)
	assert (
	    len(lowerCAmelCase_	) == num_samples
	), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCAmelCase_	)}'''





def __snake_case      (   UpperCAmelCase_		:		Union[str, Any] = False      ,       UpperCAmelCase_		:		Any = False	):
	lowerCamelCase_  				=						evaluate.load("glue"      ,       "mrpc"	)
	lowerCamelCase_  				=						get_mrpc_setup(lowerCAmelCase_      ,       lowerCAmelCase_	)
	# First do baseline
	lowerCamelCase_  				=						setup["no"]
	model.to(lowerCAmelCase_	)
	model.eval()
	for batch in dataloader:
		batch.to(lowerCAmelCase_	)
		with torch.inference_mode():
			lowerCamelCase_  				=						model(**lowerCAmelCase_	)
		lowerCamelCase_  				=						outputs.logits.argmax(dim=-1	)
		metric.add_batch(predictions=lowerCAmelCase_      ,       references=batch["labels"]	)
	lowerCamelCase_  				=						metric.compute()

	# Then do distributed
	lowerCamelCase_  				=						setup["ddp"]
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			lowerCamelCase_  				=						model(**lowerCAmelCase_	)
		lowerCamelCase_  				=						outputs.logits.argmax(dim=-1	)
		lowerCamelCase_  				=						batch["labels"]
		lowerCamelCase_  				=						accelerator.gather_for_metrics((preds, references)	)
		metric.add_batch(predictions=lowerCAmelCase_      ,       references=lowerCAmelCase_	)
	lowerCamelCase_  				=						metric.compute()

	for key in "accuracy f1".split():
		assert math.isclose(
		    baseline[key]      ,       distributed[key]	), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''





def __snake_case      (   ):
	lowerCamelCase_  				=						Accelerator(split_batches=lowerCAmelCase_      ,       dispatch_batches=lowerCAmelCase_	)
	if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_warning()
	else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	# These are a bit slower so they should only be ran on the GPU or TPU
	if torch.cuda.is_available() or is_tpu_available():
		if accelerator.is_local_main_process:
			print("**Testing gather_for_metrics**"	)
		for split_batches in [True, False]:
			for dispatch_batches in [True, False]:
				if accelerator.is_local_main_process:
					print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'''	)
				test_mrpc(lowerCAmelCase_      ,       lowerCAmelCase_	)
				accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print("**Test torch metrics**"	)
	for split_batches in [True, False]:
		for dispatch_batches in [True, False]:
			lowerCamelCase_  				=						Accelerator(split_batches=lowerCAmelCase_      ,       dispatch_batches=lowerCAmelCase_	)
			if accelerator.is_local_main_process:
				print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''	)
			test_torch_metrics(lowerCAmelCase_      ,       99	)
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print("**Test last batch is not dropped when perfectly divisible**"	)
	lowerCamelCase_  				=						Accelerator()
	test_torch_metrics(lowerCAmelCase_      ,       512	)
	accelerator.state._reset_state()





def __snake_case      (   UpperCAmelCase_		:		Optional[int]	):
	main()


if __name__ == "__main__":
					main()







'''simple docstring'''

import functools
import operator

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


a_						:       List[str]      			=				logging.get_logger(__name__)

a_						:       Dict      			=				{
    """microsoft/unispeech-large-1500h-cv""": (
        """https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"""
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						"unispeech"






  def __init__(					self    ,						UpperCamelCase=32    ,						UpperCamelCase=768    ,						UpperCamelCase=12    ,						UpperCamelCase=12    ,						UpperCamelCase=3072    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.02    ,						UpperCamelCase=1e-5    ,						UpperCamelCase="group"    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=(512, 512, 512, 512, 512, 512, 512)    ,						UpperCamelCase=(5, 2, 2, 2, 2, 2, 2)    ,						UpperCamelCase=(10, 3, 3, 3, 3, 2, 2)    ,						UpperCamelCase=False    ,						UpperCamelCase=128    ,						UpperCamelCase=16    ,						UpperCamelCase=False    ,						UpperCamelCase=True    ,						UpperCamelCase=0.05    ,						UpperCamelCase=10    ,						UpperCamelCase=2    ,						UpperCamelCase=0.0    ,						UpperCamelCase=10    ,						UpperCamelCase=0    ,						UpperCamelCase=320    ,						UpperCamelCase=2    ,						UpperCamelCase=0.1    ,						UpperCamelCase=100    ,						UpperCamelCase=256    ,						UpperCamelCase=256    ,						UpperCamelCase=0.1    ,						UpperCamelCase="mean"    ,						UpperCamelCase=False    ,						UpperCamelCase=False    ,						UpperCamelCase=256    ,						UpperCamelCase=80    ,						UpperCamelCase=0    ,						UpperCamelCase=1    ,						UpperCamelCase=2    ,						UpperCamelCase=0.5    ,						**UpperCamelCase    ,						):







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

   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
   lowerCamelCase_  				=						apply_spec_augment
   lowerCamelCase_  				=						mask_time_prob
   lowerCamelCase_  				=						mask_time_length
   lowerCamelCase_  				=						mask_time_min_masks
   lowerCamelCase_  				=						mask_feature_prob
   lowerCamelCase_  				=						mask_feature_length
   lowerCamelCase_  				=						mask_feature_min_masks

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

   # ctc loss
   lowerCamelCase_  				=						ctc_loss_reduction
   lowerCamelCase_  				=						ctc_zero_infinity

   # pretraining loss
   lowerCamelCase_  				=						replace_prob







  @property
  def 						snake_case	(					self  ):







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



