


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

from accelerate.utils.dataclasses import DistributedType

class 		__a				:
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=3      ,   _SCREAMING_SNAKE_CASE=64      ,   _SCREAMING_SNAKE_CASE=None						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      np.random.default_rng(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      length
									_UpperCAmelCase					=      rng.normal(size=(length,)						).astype(np.floataa						)
									_UpperCAmelCase					=      a * self.x + b + rng.normal(scale=0.1      ,   size=(length,)						).astype(np.floataa						)
		def __len__(      self						)			->	List[Any]:




									"""simple docstring"""





									return self.length






		def __getitem__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




									"""simple docstring"""





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



class 		__a				(    torch.nn.Module      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=False						)			->	str:




									"""simple docstring"""





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






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE=None						)			->	int:




									"""simple docstring"""





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



class 		__a				(    torch.nn.Module      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=False						)			->	Any:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=      torch.nn.Parameter(torch.tensor(_SCREAMING_SNAKE_CASE						).float()						)
									_UpperCAmelCase					=      torch.nn.Parameter(torch.tensor(_SCREAMING_SNAKE_CASE						).float()						)
									_UpperCAmelCase					=      True






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE=None						)			->	List[str]:




									"""simple docstring"""





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



def 	lowerCAmelCase__			(      a__:		int      ,  a__:		int = 1_6	)  ->		List[Any]:


							'''simple docstring'''





							from datasets import load_dataset
							from transformers import AutoTokenizer

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

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

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

							def collate_fn(a__:		Union[str, Any]	):
														# 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=1_2_8      ,  return_tensors='pt'	)
														return tokenizer.pad(a__      ,  padding='longest'      ,  return_tensors='pt'	)

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

							return train_dataloader, eval_dataloader




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available

@dataclass
class 		__a				(    UpperCAmelCase      ):
		_a     :  Union[List[np.ndarray], torch.FloatTensor]


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
		from .pipeline_text_to_video_synth import TextToVideoSDPipeline
		from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
		from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
