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

from accelerate.utils.dataclasses import DistributedType







class     lowercase		:




							def __init__(    self       ,A__=2       ,A__=3       ,A__=6_4       ,A__=None):
												lowercase						     =						np.random.default_rng(A__)
												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):
												return self.length





							def __getitem__(    self       ,A__):
												return {"x": self.x[i], "y": self.y[i]}







class     lowercase		( torch.nn.Module		):




							def __init__(    self       ,A__=0       ,A__=0       ,A__=False):
												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       ,A__=None):
												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     lowercase		( torch.nn.Module		):




							def __init__(    self       ,A__=0       ,A__=0       ,A__=False):
												super().__init__()
												lowercase						     =						torch.nn.Parameter(torch.tensor(A__).float())
												lowercase						     =						torch.nn.Parameter(torch.tensor(A__).float())
												lowercase						     =						True





							def 			A__				(    self       ,A__=None):
												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 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__ = 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=lowerCAmelCase__		)
					lowercase						     =						datasets['''train'''].unique('''label'''		)

					lowercase						     =						{v: i for i, v in enumerate(lowerCAmelCase__		)}

					def tokenize_function(lowerCAmelCase__		):
										# max_length=None => use the model max length (it's actually the default)
										lowercase						     =						tokenizer(
										    examples['''sentence1''']			,      examples['''sentence2''']			,      truncation=lowerCAmelCase__			,      max_length=lowerCAmelCase__			,      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(
					    lowerCAmelCase__			,      batched=lowerCAmelCase__			,      remove_columns=['''sentence1''', '''sentence2''', '''label''']			,      )

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

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

					return train_dataloader, eval_dataloader






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__		:Any			  =   {
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Tuple			  =   ["DistilBertTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Union[str, Any]			  =   [
							    "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "DistilBertForMaskedLM",
							    "DistilBertForMultipleChoice",
							    "DistilBertForQuestionAnswering",
							    "DistilBertForSequenceClassification",
							    "DistilBertForTokenClassification",
							    "DistilBertModel",
							    "DistilBertPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Optional[int]			  =   [
							    "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFDistilBertForMaskedLM",
							    "TFDistilBertForMultipleChoice",
							    "TFDistilBertForQuestionAnswering",
							    "TFDistilBertForSequenceClassification",
							    "TFDistilBertForTokenClassification",
							    "TFDistilBertMainLayer",
							    "TFDistilBertModel",
							    "TFDistilBertPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Union[str, Any]			  =   [
							    "FlaxDistilBertForMaskedLM",
							    "FlaxDistilBertForMultipleChoice",
							    "FlaxDistilBertForQuestionAnswering",
							    "FlaxDistilBertForSequenceClassification",
							    "FlaxDistilBertForTokenClassification",
							    "FlaxDistilBertModel",
							    "FlaxDistilBertPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_distilbert import (
							    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DistilBertConfig,
							    DistilBertOnnxConfig,
							)
							from .tokenization_distilbert import DistilBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_distilbert_fast import DistilBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_distilbert import (
														    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DistilBertForMaskedLM,
														    DistilBertForMultipleChoice,
														    DistilBertForQuestionAnswering,
														    DistilBertForSequenceClassification,
														    DistilBertForTokenClassification,
														    DistilBertModel,
														    DistilBertPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_distilbert import (
														    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDistilBertForMaskedLM,
														    TFDistilBertForMultipleChoice,
														    TFDistilBertForQuestionAnswering,
														    TFDistilBertForSequenceClassification,
														    TFDistilBertForTokenClassification,
														    TFDistilBertMainLayer,
														    TFDistilBertModel,
														    TFDistilBertPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_distilbert import (
														    FlaxDistilBertForMaskedLM,
														    FlaxDistilBertForMultipleChoice,
														    FlaxDistilBertForQuestionAnswering,
														    FlaxDistilBertForSequenceClassification,
														    FlaxDistilBertForTokenClassification,
														    FlaxDistilBertModel,
														    FlaxDistilBertPreTrainedModel,
														)

else:
							import sys

							lowercase__		:List[str]			  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




