
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


lowercase__          ='true'




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Union[str, Any]=8_2  ,       lowerCAmelCase__							:    Dict=1_6  ):
	set_seed(4_2  )
	__a			:  Dict		    =  RegressionModel()
	__a			:  Dict		    =  deepcopy(lowercase__  )
	__a			:  Tuple		    =  RegressionDataset(length=lowercase__  )
	__a			:  Optional[Any]		    =  DataLoader(lowercase__  ,       batch_size=lowercase__  )
	model.to(accelerator.device  )
	__a			:  Union[str, Any]		    =  accelerator.prepare(lowercase__  ,       lowercase__  )
	return model, ddp_model, dataloader




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    str=False  ):
	__a			:  List[str]		    =  AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''  )
	__a			:  List[Any]		    =  load_dataset('''glue'''  ,       '''mrpc'''  ,       split='''validation'''  )

	def tokenize_function(lowerCAmelCase__							:    List[Any]  ):
		__a			:  Tuple		    =  tokenizer(examples['''sentence1''']  ,       examples['''sentence2''']  ,       truncation=lowercase__  ,       max_length=lowercase__  )
		return outputs

	with accelerator.main_process_first():
		__a			:  List[str]		    =  dataset.map(
		    lowercase__  ,       batched=lowercase__  ,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']  ,       )

	__a			:  Any		    =  tokenized_datasets.rename_column('''label'''  ,       '''labels'''  )

	def collate_fn(lowerCAmelCase__							:    int  ):
		if use_longest:
			return tokenizer.pad(lowercase__  ,       padding='''longest'''  ,       return_tensors='''pt'''  )
		return tokenizer.pad(lowercase__  ,       padding='''max_length'''  ,       max_length=1_2_8  ,       return_tensors='''pt'''  )

	return DataLoader(lowercase__  ,       shuffle=lowercase__  ,       collate_fn=lowercase__  ,       batch_size=1_6  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    str  ):
	__a			:  Dict		    =  Accelerator(dispatch_batches=lowercase__  ,       split_batches=lowercase__  )
	__a			:  List[Any]		    =  get_dataloader(lowercase__  ,       not dispatch_batches  )
	__a			:  str		    =  AutoModelForSequenceClassification.from_pretrained(
	    '''hf-internal-testing/mrpc-bert-base-cased'''  ,       return_dict=lowercase__  )
	__a			:  int		    =  accelerator.prepare(lowercase__  ,       lowercase__  )
	return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[Any]  ,       lowerCAmelCase__							:    Dict  ,       lowerCAmelCase__							:    Tuple  ):
	__a			:  Dict		    =  []
	for batch in dataloader:
		__a			:  str		    =  batch.values()
		with torch.no_grad():
			__a			:  List[Any]		    =  model(lowercase__  )
			__a			:  Dict		    =  accelerator.gather_for_metrics((logit, target)  )
			logits_and_targets.append((logit, target)  )
	__a			:  Any		    =  [], []
	for logit, targ in logits_and_targets:
		logits.append(lowercase__  )
		targs.append(lowercase__  )
	__a			:  str		    =  torch.cat(lowercase__  ), torch.cat(lowercase__  )
	return logits, targs




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    int=8_2  ,       lowerCAmelCase__							:    List[str]=False  ,       lowerCAmelCase__							:    Any=False  ,       lowerCAmelCase__							:    Optional[Any]=1_6  ):
	__a			:  List[str]		    =  get_basic_setup(lowercase__  ,       lowercase__  ,       lowercase__  )
	__a			:  Any		    =  generate_predictions(lowercase__  ,       lowercase__  ,       lowercase__  )
	assert (
	    len(lowercase__  ) == num_samples
	), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowercase__  )}"




def        __UpperCamelCase					(    lowerCAmelCase__							:    Dict = False  ,       lowerCAmelCase__							:    Optional[Any] = False  ):
	__a			:  Tuple		    =  evaluate.load('''glue'''  ,       '''mrpc'''  )
	__a			:  Any		    =  get_mrpc_setup(lowercase__  ,       lowercase__  )
	# First do baseline
	__a			:  Any		    =  setup['no']
	model.to(lowercase__  )
	model.eval()
	for batch in dataloader:
		batch.to(lowercase__  )
		with torch.inference_mode():
			__a			:  List[str]		    =  model(**lowercase__  )
		__a			:  str		    =  outputs.logits.argmax(dim=-1  )
		metric.add_batch(predictions=lowercase__  ,       references=batch['''labels''']  )
	__a			:  List[Any]		    =  metric.compute()

	# Then do distributed
	__a			:  Dict		    =  setup['ddp']
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			__a			:  Any		    =  model(**lowercase__  )
		__a			:  Optional[Any]		    =  outputs.logits.argmax(dim=-1  )
		__a			:  Any		    =  batch['labels']
		__a			:  Optional[int]		    =  accelerator.gather_for_metrics((preds, references)  )
		metric.add_batch(predictions=lowercase__  ,       references=lowercase__  )
	__a			:  str		    =  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        __UpperCamelCase					(    ):
	__a			:  List[Any]		    =  Accelerator(split_batches=lowercase__  ,       dispatch_batches=lowercase__  )
	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(lowercase__  ,       lowercase__  )
				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]:
			__a			:  List[str]		    =  Accelerator(split_batches=lowercase__  ,       dispatch_batches=lowercase__  )
			if accelerator.is_local_main_process:
				print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"  )
			test_torch_metrics(lowercase__  ,       9_9  )
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('''**Test last batch is not dropped when perfectly divisible**'''  )
	__a			:  Optional[int]		    =  Accelerator()
	test_torch_metrics(lowercase__  ,       5_1_2  )
	accelerator.state._reset_state()




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
						main()


from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


lowercase__          =logging.get_logger(__name__)

lowercase__          ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__          ={
    'vocab_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
    },
    'merges_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
    },
}

lowercase__          ={'allegro/herbert-base-cased': 514}
lowercase__          ={}



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE				:							Dict     	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				HerbertTokenizer
   def __init__(self			: str ,    snake_case_			: Optional[Any]=None ,    snake_case_			: Tuple=None ,    snake_case_			: Dict=None ,    snake_case_			: List[str]="<s>" ,    snake_case_			: Any="<unk>" ,    snake_case_			: Optional[int]="<pad>" ,    snake_case_			: Optional[Any]="<mask>" ,    snake_case_			: List[Any]="</s>" ,    **snake_case_			: Optional[int] ,    ):
    super().__init__(
        snake_case_ ,    snake_case_ ,    tokenizer_file=snake_case_ ,    cls_token=snake_case_ ,    unk_token=snake_case_ ,    pad_token=snake_case_ ,    mask_token=snake_case_ ,    sep_token=snake_case_ ,    **snake_case_ ,    )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  int		    =  [self.cls_token_id]
    __a			:  Union[str, Any]		    =  [self.sep_token_id]
    if token_ids_a is None:
     return cls + token_ids_a + sep

    return cls + token_ids_a + sep + token_ids_a + sep
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None ,    snake_case_			: bool = False       ):
    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=snake_case_ ,    token_ids_a=snake_case_ ,    already_has_special_tokens=snake_case_       )

    if token_ids_a is None:
     return [1] + ([0] * len(snake_case_       )) + [1]
    return [1] + ([0] * len(snake_case_       )) + [1] + ([0] * len(snake_case_       )) + [1]
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  Optional[int]		    =  [self.sep_token_id]
    __a			:  int		    =  [self.cls_token_id]

    if token_ids_a is None:
     return len(cls + token_ids_a + sep       ) * [0]
    return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]







   def 				lowerCAmelCase      (self			: int ,    snake_case_			: str ,    snake_case_			: Optional[str] = None       ):
    __a			:  Any		    =  self._tokenizer.model.save(snake_case_ ,    name=snake_case_       )
    return tuple(snake_case_       )
