


from typing import List

import numpy as np


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   {key: len(_UpperCamelCase  ) for key, value in gen_kwargs.items() if isinstance(_UpperCamelCase       ,			_UpperCamelCase  )}
			if len(set(lists_lengths.values()  )  ) > 1:
						raise RuntimeError(
						    (
						        '''Sharding is ambiguous for this dataset: '''
						        + '''we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'''
						        + '''\n'''.join(F'\t- key {key} has length {length}' for key, length in lists_lengths.items()  )
						        + '''\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '''
						        + '''and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'''
						    )  )
			__lowercase 					=   max(lists_lengths.values()       ,			default=0  )
			return max(1       ,			_UpperCamelCase  )


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   []
			for group_idx in range(_UpperCamelCase  ):
						__lowercase 					=   num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
						if num_shards_to_add == 0:
									break
						__lowercase 					=   shards_indices_per_group[-1].stop if shards_indices_per_group else 0
						__lowercase 					=   range(_UpperCamelCase       ,			start + num_shards_to_add  )
						shards_indices_per_group.append(_UpperCamelCase  )
			return shards_indices_per_group


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   _number_of_shards_in_gen_kwargs(_UpperCamelCase  )
			if num_shards == 1:
						return [dict(_UpperCamelCase  )]
			else:
						__lowercase 					=   _distribute_shards(num_shards=_UpperCamelCase       ,			max_num_jobs=_UpperCamelCase  )
						return [
						    {
						        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
						        if isinstance(_UpperCamelCase       ,			_UpperCamelCase  )
						        else value
						        for key, value in gen_kwargs.items()
						    }
						    for group_idx in range(len(_UpperCamelCase  )  )
						]


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			return {
			    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
			    if isinstance(gen_kwargs_list[0][key]       ,			_UpperCamelCase  )
			    else gen_kwargs_list[0][key]
			    for key in gen_kwargs_list[0]
			}





def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   {len(_UpperCamelCase  ) for value in gen_kwargs.values() if isinstance(_UpperCamelCase       ,			_UpperCamelCase  )}
			__lowercase 					=   {}
			for size in list_sizes:
						__lowercase 					=   list(range(_UpperCamelCase  )  )
						rng.shuffle(indices_per_size[size]  )
			# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
			__lowercase 					=   dict(_UpperCamelCase  )
			for key, value in shuffled_kwargs.items():
						if isinstance(_UpperCamelCase       ,			_UpperCamelCase  ):
									__lowercase 					=   [value[i] for i in indices_per_size[len(_UpperCamelCase  )]]
			return shuffled_kwargs






import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType
class 	lowerCamelCase_       :



					'''simple docstring'''







					def __init__(				self ,       snake_case_=2 ,       snake_case_=3 ,       snake_case_=6_4 ,       snake_case_=None						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   np.random.default_rng(snake_case_						)
								__lowercase 					=   length
								__lowercase 					=   rng.normal(size=(length,)						).astype(np.floataa						)
								__lowercase 					=   a * self.x + b + rng.normal(scale=0.1 ,       size=(length,)						).astype(np.floataa						)






					def __len__(				self						)   ->			Dict:







								'''simple docstring'''

								return self.length






					def __getitem__(				self ,       snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

								return {"x": self.x[i], "y": self.y[i]}




class 	lowerCamelCase_       (				torch.nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_=0 ,       snake_case_=0 ,       snake_case_=False						)   ->			Optional[Any]:







								'''simple docstring'''

								super().__init__()
								__lowercase 					=   torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
								__lowercase 					=   torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
								__lowercase 					=   True






					def A      (				self ,       snake_case_=None						)   ->			List[Any]:







								'''simple docstring'''

								if self.first_batch:
											print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'						)
											__lowercase 					=   False
								return x * self.a[0] + self.b[0]




class 	lowerCamelCase_       (				torch.nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_=0 ,       snake_case_=0 ,       snake_case_=False						)   ->			List[str]:







								'''simple docstring'''

								super().__init__()
								__lowercase 					=   torch.nn.Parameter(torch.tensor(snake_case_						).float()						)
								__lowercase 					=   torch.nn.Parameter(torch.tensor(snake_case_						).float()						)
								__lowercase 					=   True






					def A      (				self ,       snake_case_=None						)   ->			str:







								'''simple docstring'''

								if self.first_batch:
											print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'						)
											__lowercase 					=   False
								return x * self.a + self.b




def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase = 16  ):


			'''simple docstring'''







			from datasets import load_dataset
			from transformers import AutoTokenizer

			__lowercase 					=   AutoTokenizer.from_pretrained('''bert-base-cased'''  )
			__lowercase 					=   {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
			__lowercase 					=   load_dataset('''csv'''       ,			data_files=_UpperCamelCase  )
			__lowercase 					=   datasets['''train'''].unique('''label'''  )

			__lowercase 					=   {v: i for i, v in enumerate(_UpperCamelCase  )}

			def tokenize_function(_UpperCamelCase  ):
						# max_length=None => use the model max length (it's actually the default)
						__lowercase 					=   tokenizer(
						    examples['''sentence1''']       ,			examples['''sentence2''']       ,			truncation=_UpperCamelCase       ,			max_length=_UpperCamelCase       ,			padding='''max_length'''  )
						if "label" in examples:
									__lowercase 					=   [label_to_id[l] for l in examples['''label''']]
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			__lowercase 					=   datasets.map(
			    _UpperCamelCase       ,			batched=_UpperCamelCase       ,			remove_columns=['''sentence1''', '''sentence2''', '''label''']       ,			)

			def collate_fn(_UpperCamelCase  ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(_UpperCamelCase       ,			padding='''max_length'''       ,			max_length=1_28       ,			return_tensors='''pt'''  )
						return tokenizer.pad(_UpperCamelCase       ,			padding='''longest'''       ,			return_tensors='''pt'''  )

			# Instantiate dataloaders.
			__lowercase 					=   DataLoader(tokenized_datasets['''train''']       ,			shuffle=_UpperCamelCase       ,			collate_fn=_UpperCamelCase       ,			batch_size=2  )
			__lowercase 					=   DataLoader(tokenized_datasets['''validation''']       ,			shuffle=_UpperCamelCase       ,			collate_fn=_UpperCamelCase       ,			batch_size=1  )

			return train_dataloader, eval_dataloader


