
import os
import sys
import unittest


_A   =     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
_A   =     os.path.join(git_repo_path, "src", "transformers")

_A   =     "\n{0} = None\n"

_A   =     "\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


_A   =     "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"





class 	_lowerCAmelCase					(	unittest.TestCase       ):




				def      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	find_backend("    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"     )
							self.assertIsNone(_UpperCamelCase     )

							lowerCAmelCase_						=	find_backend("    if not is_tokenizers_available():"     )
							self.assertEqual(_UpperCamelCase				,				"tokenizers"     )

							lowerCAmelCase_						=	find_backend("    if not is_tensorflow_text_available():"     )
							self.assertEqual(_UpperCamelCase				,				"tensorflow_text"     )

							lowerCAmelCase_						=	find_backend("    if not (is_sentencepiece_available() and is_tokenizers_available()):"     )
							self.assertEqual(_UpperCamelCase				,				"sentencepiece_and_tokenizers"     )

							lowerCAmelCase_						=	find_backend(
							    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"     )
							self.assertEqual(_UpperCamelCase				,				"sentencepiece_and_tensorflow_text"     )

							lowerCAmelCase_						=	find_backend(
							    "    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"     )
							self.assertEqual(_UpperCamelCase				,				"sentencepiece_and_tokenizers_and_vision"     )




				def      __a						(					self     )				->      Union[str, Any]:
							lowerCAmelCase_						=	read_init()
							# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
							self.assertIn("torch"				,				_UpperCamelCase     )
							self.assertIn("tensorflow_text"				,				_UpperCamelCase     )
							self.assertIn("sentencepiece_and_tokenizers"				,				_UpperCamelCase     )

							# Likewise, we can't assert on the exact content of a key
							self.assertIn("BertModel"				,				objects["torch"]     )
							self.assertIn("TFBertModel"				,				objects["tf"]     )
							self.assertIn("FlaxBertModel"				,				objects["flax"]     )
							self.assertIn("BertModel"				,				objects["torch"]     )
							self.assertIn("TFBertTokenizer"				,				objects["tensorflow_text"]     )
							self.assertIn("convert_slow_tokenizer"				,				objects["sentencepiece_and_tokenizers"]     )




				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	create_dummy_object("CONSTANT"				,				"'torch'"     )
							self.assertEqual(_UpperCamelCase				,				"\nCONSTANT = None\n"     )

							lowerCAmelCase_						=	create_dummy_object("function"				,				"'torch'"     )
							self.assertEqual(
							    _UpperCamelCase				,				"\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"     )

							lowerCAmelCase_						=	"\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n"
							lowerCAmelCase_						=	create_dummy_object("FakeClass"				,				"'torch'"     )
							self.assertEqual(_UpperCamelCase				,				_UpperCamelCase     )




				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	"# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n"
							lowerCAmelCase_						=	create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}     )
							self.assertEqual(dummy_files["torch"]				,				_UpperCamelCase     )





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

from accelerate.utils.dataclasses import DistributedType





class 	_lowerCAmelCase					:




				def __init__(					self				,				_UpperCamelCase=2				,				_UpperCamelCase=3				,				_UpperCamelCase=64				,				_UpperCamelCase=None     )				->      Optional[Any]:
							lowerCAmelCase_						=	np.random.default_rng(_UpperCamelCase     )
							lowerCAmelCase_						=	length
							lowerCAmelCase_						=	rng.normal(size=(length,)     ).astype(np.floataa     )
							lowerCAmelCase_						=	a * self.x + b + rng.normal(scale=0.1				,				size=(length,)     ).astype(np.floataa     )




				def __len__(					self     )				->      int:
							return self.length




				def __getitem__(					self				,				_UpperCamelCase     )				->      Dict:
							return {"x": self.x[i], "y": self.y[i]}





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=False     )				->      List[Any]:
							super().__init__()
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase_						=	True




				def      __a						(					self				,				_UpperCamelCase=None     )				->      Any:
							if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
										lowerCAmelCase_						=	False
							return x * self.a[0] + self.b[0]





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=False     )				->      Optional[int]:
							super().__init__()
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	True




				def      __a						(					self				,				_UpperCamelCase=None     )				->      int:
							if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
										lowerCAmelCase_						=	False
							return x * self.a + self.b






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[Any]    ,							__lowerCAmelCase  :  int = 16				):




			"""simple docstring"""


			from datasets import load_dataset
			from transformers import AutoTokenizer

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

			lowerCAmelCase_						=	{v: i for i, v in enumerate(__lowerCAmelCase				)}

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

			def collate_fn(__lowerCAmelCase  :  Optional[int]				):
						# 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.
			lowerCAmelCase_						=	DataLoader(tokenized_datasets["train"]    ,							shuffle=__lowerCAmelCase    ,							collate_fn=__lowerCAmelCase    ,							batch_size=2				)
			lowerCAmelCase_						=	DataLoader(tokenized_datasets["validation"]    ,							shuffle=__lowerCAmelCase    ,							collate_fn=__lowerCAmelCase    ,							batch_size=1				)

			return train_dataloader, eval_dataloader



