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

from accelerate.utils.dataclasses import DistributedType







class 			__A:




				def __init__(     self		,     _snake_case=2		,     _snake_case=3		,     _snake_case=64		,     _snake_case=None				)    ->      Union[str, Any]:


								'''simple docstring'''




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




				def __len__(     self				)    ->      Tuple:


								'''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 			__A( torch.nn.Module			):




				def __init__(     self		,     _snake_case=0		,     _snake_case=0		,     _snake_case=False				)    ->      Any:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=None				)    ->      Tuple:


								'''simple docstring'''




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







class 			__A( torch.nn.Module			):




				def __init__(     self		,     _snake_case=0		,     _snake_case=0		,     _snake_case=False				)    ->      Dict:


								'''simple docstring'''




								super().__init__()
								__a										=    torch.nn.Parameter(torch.tensor(_snake_case				).float()				)
								__a										=    torch.nn.Parameter(torch.tensor(_snake_case				).float()				)
								__a										=    True




				def 				SCREAMING_SNAKE_CASE_		(     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}"""				)
												__a										=    False
								return x * self.a + self.b



def     __lowerCAmelCase			(			a__						,    a__ = 16  )       ->  List[str]:
				from datasets import load_dataset
				from transformers import AutoTokenizer

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

				__a										=    {v: i for i, v in enumerate(a__  )}

				def tokenize_function(a__  ):
								# max_length=None => use the model max length (it's actually the default)
								__a										=    tokenizer(
								    examples['''sentence1''']						,    examples['''sentence2''']						,    truncation=a__						,    max_length=a__						,    padding='''max_length'''  )
								if "label" in examples:
												__a										=    [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
				__a										=    datasets.map(
				    a__						,    batched=a__						,    remove_columns=['''sentence1''', '''sentence2''', '''label''']						,    )

				def collate_fn(a__  ):
								# 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(a__						,    padding='''max_length'''						,    max_length=128						,    return_tensors='''pt'''  )
								return tokenizer.pad(a__						,    padding='''longest'''						,    return_tensors='''pt'''  )

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

				return train_dataloader, eval_dataloader

from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)
